librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 12:31:00	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 12:28:53	card stringlengths 11 1.01M
SHENMU007/neunit_BASE_V12.5	SHENMU007	2023-07-12T23:39:19Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-12T20:39:15Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
ayanban011/vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split	ayanban011	2023-07-12T23:33:16Z	165	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-12T18:58:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0411 - Accuracy: 0.8333 - Brier Loss: 0.3084 - Nll: 1.3568 - F1 Micro: 0.8333 - F1 Macro: 0.8183 - Ece: 0.1563 - Aurc: 0.0847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.99 \| 43 \| 0.7544 \| 0.7960 \| 0.3088 \| 1.3391 \| 0.7960 \| 0.7715 \| 0.1991 \| 0.0817 \| \| No log \| 2.0 \| 87 \| 0.7158 \| 0.8218 \| 0.2920 \| 1.1888 \| 0.8218 \| 0.7941 \| 0.1863 \| 0.0741 \| \| No log \| 2.98 \| 130 \| 0.7144 \| 0.7989 \| 0.2932 \| 1.2958 \| 0.7989 \| 0.7701 \| 0.1628 \| 0.0749 \| \| No log \| 3.99 \| 174 \| 0.6762 \| 0.8305 \| 0.2749 \| 1.1916 \| 0.8305 \| 0.8076 \| 0.1844 \| 0.0678 \| \| No log \| 4.98 \| 217 \| 0.6710 \| 0.8362 \| 0.2745 \| 1.0739 \| 0.8362 \| 0.8076 \| 0.1696 \| 0.0664 \| \| No log \| 5.99 \| 261 \| 0.6532 \| 0.8362 \| 0.2675 \| 1.0011 \| 0.8362 \| 0.8115 \| 0.1750 \| 0.0602 \| \| No log \| 6.98 \| 304 \| 0.6404 \| 0.8362 \| 0.2635 \| 1.0072 \| 0.8362 \| 0.8106 \| 0.1714 \| 0.0633 \| \| No log \| 7.99 \| 348 \| 0.6635 \| 0.8218 \| 0.2707 \| 1.0903 \| 0.8218 \| 0.8030 \| 0.1513 \| 0.0770 \| \| No log \| 9.0 \| 392 \| 0.6167 \| 0.8420 \| 0.2534 \| 1.0176 \| 0.8420 \| 0.8259 \| 0.1613 \| 0.0796 \| \| No log \| 9.99 \| 435 \| 0.6496 \| 0.8276 \| 0.2703 \| 0.9646 \| 0.8276 \| 0.8085 \| 0.1643 \| 0.0588 \| \| No log \| 11.0 \| 479 \| 0.6091 \| 0.8506 \| 0.2467 \| 1.1036 \| 0.8506 \| 0.8308 \| 0.1483 \| 0.0650 \| \| 0.4309 \| 11.98 \| 522 \| 0.6075 \| 0.8420 \| 0.2483 \| 0.9144 \| 0.8420 \| 0.8246 \| 0.1391 \| 0.0519 \| \| 0.4309 \| 12.99 \| 566 \| 0.6164 \| 0.8276 \| 0.2576 \| 0.9703 \| 0.8276 \| 0.8092 \| 0.1467 \| 0.0645 \| \| 0.4309 \| 13.98 \| 609 \| 0.5893 \| 0.8592 \| 0.2347 \| 1.1493 \| 0.8592 \| 0.8483 \| 0.1347 \| 0.0715 \| \| 0.4309 \| 14.99 \| 653 \| 0.6123 \| 0.8477 \| 0.2485 \| 1.1889 \| 0.8477 \| 0.8232 \| 0.1587 \| 0.0764 \| \| 0.4309 \| 16.0 \| 697 \| 0.6352 \| 0.8420 \| 0.2615 \| 1.1999 \| 0.8420 \| 0.8403 \| 0.1368 \| 0.0668 \| \| 0.4309 \| 16.99 \| 740 \| 0.6329 \| 0.8333 \| 0.2625 \| 1.1748 \| 0.8333 \| 0.8249 \| 0.1267 \| 0.0744 \| \| 0.4309 \| 18.0 \| 784 \| 0.6350 \| 0.8448 \| 0.2590 \| 1.2154 \| 0.8448 \| 0.8386 \| 0.1423 \| 0.0688 \| \| 0.4309 \| 18.98 \| 827 \| 0.5892 \| 0.8592 \| 0.2383 \| 1.1001 \| 0.8592 \| 0.8515 \| 0.1293 \| 0.0630 \| \| 0.4309 \| 19.99 \| 871 \| 0.5981 \| 0.8477 \| 0.2476 \| 1.0104 \| 0.8477 \| 0.8375 \| 0.1345 \| 0.0630 \| \| 0.4309 \| 20.98 \| 914 \| 0.6484 \| 0.8420 \| 0.2642 \| 1.3553 \| 0.8420 \| 0.8292 \| 0.1490 \| 0.0770 \| \| 0.4309 \| 21.99 \| 958 \| 0.6298 \| 0.8305 \| 0.2657 \| 1.1220 \| 0.8305 \| 0.8208 \| 0.1292 \| 0.0670 \| \| 0.1285 \| 22.98 \| 1001 \| 0.6325 \| 0.8391 \| 0.2633 \| 1.2549 \| 0.8391 \| 0.8362 \| 0.1328 \| 0.0708 \| \| 0.1285 \| 23.99 \| 1045 \| 0.6032 \| 0.8534 \| 0.2486 \| 1.1258 \| 0.8534 \| 0.8444 \| 0.1229 \| 0.0706 \| \| 0.1285 \| 25.0 \| 1089 \| 0.6080 \| 0.8534 \| 0.2460 \| 1.2033 \| 0.8534 \| 0.8414 \| 0.1257 \| 0.0755 \| \| 0.1285 \| 25.99 \| 1132 \| 0.6321 \| 0.8391 \| 0.2667 \| 1.2242 \| 0.8391 \| 0.8355 \| 0.1349 \| 0.0697 \| \| 0.1285 \| 27.0 \| 1176 \| 0.6325 \| 0.8592 \| 0.2522 \| 1.2029 \| 0.8592 \| 0.8493 \| 0.1278 \| 0.0778 \| \| 0.1285 \| 27.98 \| 1219 \| 0.6585 \| 0.8534 \| 0.2546 \| 1.3669 \| 0.8534 \| 0.8378 \| 0.1368 \| 0.0890 \| \| 0.1285 \| 28.99 \| 1263 \| 0.6302 \| 0.8563 \| 0.2517 \| 1.2419 \| 0.8563 \| 0.8508 \| 0.1294 \| 0.0751 \| \| 0.1285 \| 29.98 \| 1306 \| 0.6663 \| 0.8477 \| 0.2637 \| 1.4132 \| 0.8477 \| 0.8339 \| 0.1399 \| 0.0828 \| \| 0.1285 \| 30.99 \| 1350 \| 0.7063 \| 0.8362 \| 0.2799 \| 1.4323 \| 0.8362 \| 0.8330 \| 0.1441 \| 0.0863 \| \| 0.1285 \| 32.0 \| 1394 \| 0.6564 \| 0.8506 \| 0.2570 \| 1.1583 \| 0.8506 \| 0.8417 \| 0.1358 \| 0.0847 \| \| 0.1285 \| 32.99 \| 1437 \| 0.6738 \| 0.8477 \| 0.2647 \| 1.3855 \| 0.8477 \| 0.8398 \| 0.1305 \| 0.0775 \| \| 0.1285 \| 34.0 \| 1481 \| 0.6528 \| 0.8563 \| 0.2559 \| 1.2601 \| 0.8563 \| 0.8462 \| 0.1310 \| 0.0789 \| \| 0.0385 \| 34.98 \| 1524 \| 0.6534 \| 0.8563 \| 0.2537 \| 1.2931 \| 0.8563 \| 0.8461 \| 0.1241 \| 0.0773 \| \| 0.0385 \| 35.99 \| 1568 \| 0.6541 \| 0.8534 \| 0.2525 \| 1.2589 \| 0.8534 \| 0.8449 \| 0.1315 \| 0.0833 \| \| 0.0385 \| 36.98 \| 1611 \| 0.6769 \| 0.8592 \| 0.2545 \| 1.4351 \| 0.8592 \| 0.8492 \| 0.1242 \| 0.0792 \| \| 0.0385 \| 37.99 \| 1655 \| 0.6824 \| 0.8592 \| 0.2576 \| 1.2241 \| 0.8592 \| 0.8472 \| 0.1327 \| 0.0810 \| \| 0.0385 \| 38.98 \| 1698 \| 0.6843 \| 0.8563 \| 0.2589 \| 1.3394 \| 0.8563 \| 0.8450 \| 0.1311 \| 0.0802 \| \| 0.0385 \| 39.99 \| 1742 \| 0.6964 \| 0.8506 \| 0.2630 \| 1.2625 \| 0.8506 \| 0.8405 \| 0.1310 \| 0.0789 \| \| 0.0385 \| 41.0 \| 1786 \| 0.7051 \| 0.8534 \| 0.2671 \| 1.3296 \| 0.8534 \| 0.8434 \| 0.1353 \| 0.0794 \| \| 0.0385 \| 41.99 \| 1829 \| 0.7006 \| 0.8506 \| 0.2645 \| 1.2965 \| 0.8506 \| 0.8400 \| 0.1373 \| 0.0796 \| \| 0.0385 \| 43.0 \| 1873 \| 0.7054 \| 0.8563 \| 0.2646 \| 1.2973 \| 0.8563 \| 0.8450 \| 0.1313 \| 0.0790 \| \| 0.0385 \| 43.98 \| 1916 \| 0.7143 \| 0.8506 \| 0.2673 \| 1.2640 \| 0.8506 \| 0.8399 \| 0.1359 \| 0.0803 \| \| 0.0385 \| 44.99 \| 1960 \| 0.7168 \| 0.8534 \| 0.2665 \| 1.3058 \| 0.8534 \| 0.8429 \| 0.1389 \| 0.0820 \| \| 0.0206 \| 45.98 \| 2003 \| 0.7204 \| 0.8506 \| 0.2669 \| 1.3009 \| 0.8506 \| 0.8384 \| 0.1336 \| 0.0805 \| \| 0.0206 \| 46.99 \| 2047 \| 0.7265 \| 0.8534 \| 0.2683 \| 1.2633 \| 0.8534 \| 0.8415 \| 0.1319 \| 0.0806 \| \| 0.0206 \| 48.0 \| 2091 \| 0.7311 \| 0.8506 \| 0.2695 \| 1.2725 \| 0.8506 \| 0.8396 \| 0.1372 \| 0.0811 \| \| 0.0206 \| 48.99 \| 2134 \| 0.7384 \| 0.8477 \| 0.2729 \| 1.3385 \| 0.8477 \| 0.8364 \| 0.1387 \| 0.0807 \| \| 0.0206 \| 50.0 \| 2178 \| 0.7383 \| 0.8534 \| 0.2695 \| 1.1951 \| 0.8534 \| 0.8406 \| 0.1344 \| 0.0827 \| \| 0.0206 \| 50.98 \| 2221 \| 0.7440 \| 0.8506 \| 0.2740 \| 1.3360 \| 0.8506 \| 0.8394 \| 0.1418 \| 0.0812 \| \| 0.0206 \| 51.99 \| 2265 \| 0.7455 \| 0.8506 \| 0.2727 \| 1.2704 \| 0.8506 \| 0.8388 \| 0.1351 \| 0.0816 \| \| 0.0206 \| 52.98 \| 2308 \| 0.7474 \| 0.8506 \| 0.2708 \| 1.2622 \| 0.8506 \| 0.8384 \| 0.1334 \| 0.0823 \| \| 0.0206 \| 53.99 \| 2352 \| 0.7581 \| 0.8477 \| 0.2750 \| 1.3446 \| 0.8477 \| 0.8374 \| 0.1406 \| 0.0826 \| \| 0.0206 \| 54.98 \| 2395 \| 0.7571 \| 0.8477 \| 0.2751 \| 1.3703 \| 0.8477 \| 0.8363 \| 0.1378 \| 0.0814 \| \| 0.0206 \| 55.99 \| 2439 \| 0.7618 \| 0.8477 \| 0.2752 \| 1.3702 \| 0.8477 \| 0.8363 \| 0.1363 \| 0.0827 \| \| 0.0206 \| 57.0 \| 2483 \| 0.7638 \| 0.8477 \| 0.2749 \| 1.3774 \| 0.8477 \| 0.8363 \| 0.1394 \| 0.0819 \| \| 0.0135 \| 57.99 \| 2526 \| 0.7693 \| 0.8477 \| 0.2760 \| 1.3370 \| 0.8477 \| 0.8363 \| 0.1378 \| 0.0824 \| \| 0.0135 \| 59.0 \| 2570 \| 0.7724 \| 0.8448 \| 0.2779 \| 1.3710 \| 0.8448 \| 0.8344 \| 0.1431 \| 0.0823 \| \| 0.0135 \| 59.98 \| 2613 \| 0.7780 \| 0.8477 \| 0.2784 \| 1.3328 \| 0.8477 \| 0.8363 \| 0.1463 \| 0.0828 \| \| 0.0135 \| 60.99 \| 2657 \| 0.7818 \| 0.8477 \| 0.2795 \| 1.3289 \| 0.8477 \| 0.8363 \| 0.1466 \| 0.0828 \| \| 0.0135 \| 61.98 \| 2700 \| 0.7847 \| 0.8420 \| 0.2805 \| 1.3308 \| 0.8420 \| 0.8308 \| 0.1418 \| 0.0830 \| \| 0.0135 \| 62.99 \| 2744 \| 0.7851 \| 0.8448 \| 0.2782 \| 1.3650 \| 0.8448 \| 0.8344 \| 0.1411 \| 0.0834 \| \| 0.0135 \| 64.0 \| 2788 \| 0.7925 \| 0.8420 \| 0.2829 \| 1.4383 \| 0.8420 \| 0.8319 \| 0.1425 \| 0.0821 \| \| 0.0135 \| 64.99 \| 2831 \| 0.7959 \| 0.8448 \| 0.2826 \| 1.4130 \| 0.8448 \| 0.8353 \| 0.1431 \| 0.0826 \| \| 0.0135 \| 66.0 \| 2875 \| 0.7989 \| 0.8420 \| 0.2821 \| 1.4040 \| 0.8420 \| 0.8285 \| 0.1446 \| 0.0833 \| \| 0.0135 \| 66.98 \| 2918 \| 0.7996 \| 0.8477 \| 0.2807 \| 1.3296 \| 0.8477 \| 0.8363 \| 0.1464 \| 0.0837 \| \| 0.0135 \| 67.99 \| 2962 \| 0.8042 \| 0.8448 \| 0.2824 \| 1.3637 \| 0.8448 \| 0.8344 \| 0.1434 \| 0.0837 \| \| 0.0097 \| 68.98 \| 3005 \| 0.8095 \| 0.8391 \| 0.2845 \| 1.3635 \| 0.8391 \| 0.8275 \| 0.1468 \| 0.0835 \| \| 0.0097 \| 69.99 \| 3049 \| 0.8073 \| 0.8448 \| 0.2824 \| 1.3640 \| 0.8448 \| 0.8344 \| 0.1413 \| 0.0833 \| \| 0.0097 \| 70.98 \| 3092 \| 0.8140 \| 0.8477 \| 0.2834 \| 1.3617 \| 0.8477 \| 0.8363 \| 0.1444 \| 0.0837 \| \| 0.0097 \| 71.99 \| 3136 \| 0.8152 \| 0.8420 \| 0.2842 \| 1.4009 \| 0.8420 \| 0.8277 \| 0.1439 \| 0.0840 \| \| 0.0097 \| 73.0 \| 3180 \| 0.8163 \| 0.8391 \| 0.2858 \| 1.4029 \| 0.8391 \| 0.8246 \| 0.1482 \| 0.0836 \| \| 0.0097 \| 73.99 \| 3223 \| 0.8192 \| 0.8391 \| 0.2844 \| 1.3644 \| 0.8391 \| 0.8240 \| 0.1475 \| 0.0843 \| \| 0.0097 \| 75.0 \| 3267 \| 0.8225 \| 0.8448 \| 0.2836 \| 1.3593 \| 0.8448 \| 0.8344 \| 0.1473 \| 0.0847 \| \| 0.0097 \| 75.98 \| 3310 \| 0.8267 \| 0.8362 \| 0.2859 \| 1.3642 \| 0.8362 \| 0.8207 \| 0.1473 \| 0.0840 \| \| 0.0097 \| 76.99 \| 3354 \| 0.8275 \| 0.8391 \| 0.2847 \| 1.3618 \| 0.8391 \| 0.8240 \| 0.1450 \| 0.0849 \| \| 0.0097 \| 77.98 \| 3397 \| 0.8325 \| 0.8362 \| 0.2879 \| 1.3686 \| 0.8362 \| 0.8207 \| 0.1491 \| 0.0843 \| \| 0.0097 \| 78.99 \| 3441 \| 0.8389 \| 0.8448 \| 0.2885 \| 1.3629 \| 0.8448 \| 0.8329 \| 0.1504 \| 0.0833 \| \| 0.0097 \| 80.0 \| 3485 \| 0.8420 \| 0.8420 \| 0.2887 \| 1.3610 \| 0.8420 \| 0.8261 \| 0.1458 \| 0.0837 \| \| 0.0073 \| 80.99 \| 3528 \| 0.8452 \| 0.8362 \| 0.2900 \| 1.4064 \| 0.8362 \| 0.8221 \| 0.1488 \| 0.0833 \| \| 0.0073 \| 82.0 \| 3572 \| 0.8492 \| 0.8362 \| 0.2898 \| 1.4076 \| 0.8362 \| 0.8221 \| 0.1500 \| 0.0837 \| \| 0.0073 \| 82.98 \| 3615 \| 0.8478 \| 0.8362 \| 0.2895 \| 1.3609 \| 0.8362 \| 0.8207 \| 0.1485 \| 0.0847 \| \| 0.0073 \| 83.99 \| 3659 \| 0.8483 \| 0.8391 \| 0.2880 \| 1.3622 \| 0.8391 \| 0.8243 \| 0.1480 \| 0.0842 \| \| 0.0073 \| 84.98 \| 3702 \| 0.8534 \| 0.8420 \| 0.2892 \| 1.3609 \| 0.8420 \| 0.8261 \| 0.1468 \| 0.0843 \| \| 0.0073 \| 85.99 \| 3746 \| 0.8547 \| 0.8333 \| 0.2898 \| 1.4028 \| 0.8333 \| 0.8186 \| 0.1513 \| 0.0846 \| \| 0.0073 \| 86.98 \| 3789 \| 0.8618 \| 0.8391 \| 0.2906 \| 1.3597 \| 0.8391 \| 0.8243 \| 0.1445 \| 0.0846 \| \| 0.0073 \| 87.99 \| 3833 \| 0.8594 \| 0.8420 \| 0.2885 \| 1.3265 \| 0.8420 \| 0.8311 \| 0.1462 \| 0.0848 \| \| 0.0073 \| 89.0 \| 3877 \| 0.8669 \| 0.8391 \| 0.2911 \| 1.3592 \| 0.8391 \| 0.8243 \| 0.1471 \| 0.0843 \| \| 0.0073 \| 89.99 \| 3920 \| 0.8664 \| 0.8391 \| 0.2901 \| 1.3597 \| 0.8391 \| 0.8243 \| 0.1468 \| 0.0852 \| \| 0.0073 \| 91.0 \| 3964 \| 0.8678 \| 0.8420 \| 0.2905 \| 1.3253 \| 0.8420 \| 0.8296 \| 0.1462 \| 0.0854 \| \| 0.0057 \| 91.98 \| 4007 \| 0.8719 \| 0.8391 \| 0.2909 \| 1.3585 \| 0.8391 \| 0.8243 \| 0.1475 \| 0.0853 \| \| 0.0057 \| 92.99 \| 4051 \| 0.8768 \| 0.8391 \| 0.2930 \| 1.3595 \| 0.8391 \| 0.8243 \| 0.1493 \| 0.0852 \| \| 0.0057 \| 93.98 \| 4094 \| 0.8785 \| 0.8333 \| 0.2928 \| 1.4034 \| 0.8333 \| 0.8203 \| 0.1529 \| 0.0849 \| \| 0.0057 \| 94.99 \| 4138 \| 0.8859 \| 0.8333 \| 0.2942 \| 1.3684 \| 0.8333 \| 0.8183 \| 0.1543 \| 0.0844 \| \| 0.0057 \| 96.0 \| 4182 \| 0.8839 \| 0.8362 \| 0.2937 \| 1.3597 \| 0.8362 \| 0.8221 \| 0.1497 \| 0.0852 \| \| 0.0057 \| 96.99 \| 4225 \| 0.8864 \| 0.8333 \| 0.2940 \| 1.4012 \| 0.8333 \| 0.8203 \| 0.1532 \| 0.0850 \| \| 0.0057 \| 98.0 \| 4269 \| 0.8879 \| 0.8362 \| 0.2941 \| 1.3607 \| 0.8362 \| 0.8221 \| 0.1504 \| 0.0849 \| \| 0.0057 \| 98.98 \| 4312 \| 0.8921 \| 0.8333 \| 0.2954 \| 1.3609 \| 0.8333 \| 0.8183 \| 0.1521 \| 0.0851 \| \| 0.0057 \| 99.99 \| 4356 \| 0.8949 \| 0.8391 \| 0.2945 \| 1.3575 \| 0.8391 \| 0.8243 \| 0.1491 \| 0.0854 \| \| 0.0057 \| 100.98 \| 4399 \| 0.8945 \| 0.8362 \| 0.2945 \| 1.3591 \| 0.8362 \| 0.8221 \| 0.1500 \| 0.0856 \| \| 0.0057 \| 101.99 \| 4443 \| 0.8985 \| 0.8333 \| 0.2944 \| 1.3599 \| 0.8333 \| 0.8183 \| 0.1530 \| 0.0854 \| \| 0.0057 \| 102.98 \| 4486 \| 0.8987 \| 0.8391 \| 0.2951 \| 1.3586 \| 0.8391 \| 0.8246 \| 0.1499 \| 0.0850 \| \| 0.0045 \| 103.99 \| 4530 \| 0.9025 \| 0.8362 \| 0.2957 \| 1.3592 \| 0.8362 \| 0.8221 \| 0.1510 \| 0.0857 \| \| 0.0045 \| 105.0 \| 4574 \| 0.9082 \| 0.8305 \| 0.2972 \| 1.3625 \| 0.8305 \| 0.8165 \| 0.1568 \| 0.0852 \| \| 0.0045 \| 105.99 \| 4617 \| 0.9087 \| 0.8362 \| 0.2958 \| 1.3579 \| 0.8362 \| 0.8221 \| 0.1505 \| 0.0858 \| \| 0.0045 \| 107.0 \| 4661 \| 0.9105 \| 0.8305 \| 0.2977 \| 1.3619 \| 0.8305 \| 0.8165 \| 0.1561 \| 0.0844 \| \| 0.0045 \| 107.98 \| 4704 \| 0.9136 \| 0.8305 \| 0.2978 \| 1.3994 \| 0.8305 \| 0.8165 \| 0.1559 \| 0.0851 \| \| 0.0045 \| 108.99 \| 4748 \| 0.9148 \| 0.8391 \| 0.2968 \| 1.3573 \| 0.8391 \| 0.8243 \| 0.1504 \| 0.0856 \| \| 0.0045 \| 109.98 \| 4791 \| 0.9188 \| 0.8333 \| 0.2974 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1532 \| 0.0850 \| \| 0.0045 \| 110.99 \| 4835 \| 0.9164 \| 0.8362 \| 0.2959 \| 1.3595 \| 0.8362 \| 0.8221 \| 0.1507 \| 0.0857 \| \| 0.0045 \| 112.0 \| 4879 \| 0.9221 \| 0.8333 \| 0.2977 \| 1.3573 \| 0.8333 \| 0.8183 \| 0.1550 \| 0.0857 \| \| 0.0045 \| 112.99 \| 4922 \| 0.9256 \| 0.8305 \| 0.2990 \| 1.3599 \| 0.8305 \| 0.8165 \| 0.1574 \| 0.0852 \| \| 0.0045 \| 114.0 \| 4966 \| 0.9284 \| 0.8305 \| 0.2994 \| 1.3610 \| 0.8305 \| 0.8165 \| 0.1572 \| 0.0848 \| \| 0.0037 \| 114.98 \| 5009 \| 0.9312 \| 0.8333 \| 0.2998 \| 1.3565 \| 0.8333 \| 0.8183 \| 0.1537 \| 0.0857 \| \| 0.0037 \| 115.99 \| 5053 \| 0.9322 \| 0.8333 \| 0.2995 \| 1.3583 \| 0.8333 \| 0.8183 \| 0.1543 \| 0.0852 \| \| 0.0037 \| 116.98 \| 5096 \| 0.9385 \| 0.8305 \| 0.3007 \| 1.3593 \| 0.8305 \| 0.8165 \| 0.1577 \| 0.0852 \| \| 0.0037 \| 117.99 \| 5140 \| 0.9386 \| 0.8305 \| 0.3009 \| 1.4329 \| 0.8305 \| 0.8165 \| 0.1582 \| 0.0851 \| \| 0.0037 \| 118.98 \| 5183 \| 0.9386 \| 0.8333 \| 0.2996 \| 1.3570 \| 0.8333 \| 0.8183 \| 0.1542 \| 0.0855 \| \| 0.0037 \| 119.99 \| 5227 \| 0.9406 \| 0.8333 \| 0.2995 \| 1.3554 \| 0.8333 \| 0.8183 \| 0.1540 \| 0.0848 \| \| 0.0037 \| 121.0 \| 5271 \| 0.9442 \| 0.8305 \| 0.3006 \| 1.3589 \| 0.8305 \| 0.8165 \| 0.1570 \| 0.0849 \| \| 0.0037 \| 121.99 \| 5314 \| 0.9435 \| 0.8333 \| 0.3000 \| 1.3551 \| 0.8333 \| 0.8183 \| 0.1546 \| 0.0855 \| \| 0.0037 \| 123.0 \| 5358 \| 0.9456 \| 0.8333 \| 0.2996 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1544 \| 0.0848 \| \| 0.0037 \| 123.98 \| 5401 \| 0.9490 \| 0.8333 \| 0.3008 \| 1.3561 \| 0.8333 \| 0.8183 \| 0.1547 \| 0.0850 \| \| 0.0037 \| 124.99 \| 5445 \| 0.9500 \| 0.8333 \| 0.3011 \| 1.3592 \| 0.8333 \| 0.8183 \| 0.1551 \| 0.0846 \| \| 0.0037 \| 125.98 \| 5488 \| 0.9513 \| 0.8333 \| 0.3003 \| 1.3549 \| 0.8333 \| 0.8183 \| 0.1544 \| 0.0845 \| \| 0.0031 \| 126.99 \| 5532 \| 0.9575 \| 0.8305 \| 0.3024 \| 1.3580 \| 0.8305 \| 0.8165 \| 0.1581 \| 0.0849 \| \| 0.0031 \| 128.0 \| 5576 \| 0.9593 \| 0.8305 \| 0.3025 \| 1.4028 \| 0.8305 \| 0.8165 \| 0.1591 \| 0.0851 \| \| 0.0031 \| 128.99 \| 5619 \| 0.9594 \| 0.8305 \| 0.3021 \| 1.3619 \| 0.8305 \| 0.8165 \| 0.1579 \| 0.0849 \| \| 0.0031 \| 130.0 \| 5663 \| 0.9628 \| 0.8305 \| 0.3025 \| 1.3589 \| 0.8305 \| 0.8165 \| 0.1587 \| 0.0847 \| \| 0.0031 \| 130.98 \| 5706 \| 0.9652 \| 0.8305 \| 0.3031 \| 1.3599 \| 0.8305 \| 0.8165 \| 0.1593 \| 0.0844 \| \| 0.0031 \| 131.99 \| 5750 \| 0.9646 \| 0.8362 \| 0.3005 \| 1.3353 \| 0.8362 \| 0.8205 \| 0.1520 \| 0.0851 \| \| 0.0031 \| 132.98 \| 5793 \| 0.9658 \| 0.8333 \| 0.3021 \| 1.3562 \| 0.8333 \| 0.8183 \| 0.1555 \| 0.0849 \| \| 0.0031 \| 133.99 \| 5837 \| 0.9698 \| 0.8333 \| 0.3023 \| 1.3545 \| 0.8333 \| 0.8183 \| 0.1554 \| 0.0845 \| \| 0.0031 \| 134.98 \| 5880 \| 0.9716 \| 0.8333 \| 0.3032 \| 1.3559 \| 0.8333 \| 0.8183 \| 0.1555 \| 0.0852 \| \| 0.0031 \| 135.99 \| 5924 \| 0.9736 \| 0.8305 \| 0.3037 \| 1.3624 \| 0.8305 \| 0.8165 \| 0.1584 \| 0.0849 \| \| 0.0031 \| 137.0 \| 5968 \| 0.9760 \| 0.8333 \| 0.3039 \| 1.3575 \| 0.8333 \| 0.8183 \| 0.1551 \| 0.0845 \| \| 0.0026 \| 137.99 \| 6011 \| 0.9789 \| 0.8305 \| 0.3041 \| 1.3569 \| 0.8305 \| 0.8165 \| 0.1592 \| 0.0848 \| \| 0.0026 \| 139.0 \| 6055 \| 0.9801 \| 0.8305 \| 0.3040 \| 1.3574 \| 0.8305 \| 0.8165 \| 0.1598 \| 0.0854 \| \| 0.0026 \| 139.98 \| 6098 \| 0.9806 \| 0.8333 \| 0.3035 \| 1.3552 \| 0.8333 \| 0.8183 \| 0.1557 \| 0.0852 \| \| 0.0026 \| 140.99 \| 6142 \| 0.9835 \| 0.8333 \| 0.3041 \| 1.3574 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0846 \| \| 0.0026 \| 141.98 \| 6185 \| 0.9838 \| 0.8333 \| 0.3037 \| 1.3549 \| 0.8333 \| 0.8183 \| 0.1557 \| 0.0849 \| \| 0.0026 \| 142.99 \| 6229 \| 0.9872 \| 0.8333 \| 0.3044 \| 1.3544 \| 0.8333 \| 0.8183 \| 0.1557 \| 0.0851 \| \| 0.0026 \| 144.0 \| 6273 \| 0.9900 \| 0.8305 \| 0.3056 \| 1.3654 \| 0.8305 \| 0.8165 \| 0.1597 \| 0.0847 \| \| 0.0026 \| 144.99 \| 6316 \| 0.9907 \| 0.8333 \| 0.3049 \| 1.3551 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0854 \| \| 0.0026 \| 146.0 \| 6360 \| 0.9896 \| 0.8333 \| 0.3044 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0843 \| \| 0.0026 \| 146.98 \| 6403 \| 0.9938 \| 0.8333 \| 0.3053 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0844 \| \| 0.0026 \| 147.99 \| 6447 \| 0.9962 \| 0.8305 \| 0.3056 \| 1.3615 \| 0.8305 \| 0.8165 \| 0.1594 \| 0.0844 \| \| 0.0026 \| 148.98 \| 6490 \| 0.9954 \| 0.8305 \| 0.3051 \| 1.3601 \| 0.8305 \| 0.8165 \| 0.1590 \| 0.0847 \| \| 0.0022 \| 149.99 \| 6534 \| 0.9961 \| 0.8333 \| 0.3043 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1554 \| 0.0847 \| \| 0.0022 \| 150.98 \| 6577 \| 1.0026 \| 0.8333 \| 0.3059 \| 1.3555 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0853 \| \| 0.0022 \| 151.99 \| 6621 \| 1.0004 \| 0.8333 \| 0.3049 \| 1.3544 \| 0.8333 \| 0.8183 \| 0.1566 \| 0.0847 \| \| 0.0022 \| 153.0 \| 6665 \| 1.0024 \| 0.8305 \| 0.3058 \| 1.3606 \| 0.8305 \| 0.8165 \| 0.1595 \| 0.0846 \| \| 0.0022 \| 153.99 \| 6708 \| 1.0054 \| 0.8305 \| 0.3064 \| 1.3598 \| 0.8305 \| 0.8165 \| 0.1591 \| 0.0848 \| \| 0.0022 \| 155.0 \| 6752 \| 1.0053 \| 0.8333 \| 0.3054 \| 1.3548 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0845 \| \| 0.0022 \| 155.98 \| 6795 \| 1.0068 \| 0.8333 \| 0.3053 \| 1.3548 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0846 \| \| 0.0022 \| 156.99 \| 6839 \| 1.0076 \| 0.8333 \| 0.3055 \| 1.3551 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0844 \| \| 0.0022 \| 157.98 \| 6882 \| 1.0105 \| 0.8333 \| 0.3059 \| 1.3546 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0845 \| \| 0.0022 \| 158.99 \| 6926 \| 1.0114 \| 0.8333 \| 0.3061 \| 1.3555 \| 0.8333 \| 0.8183 \| 0.1559 \| 0.0851 \| \| 0.0022 \| 160.0 \| 6970 \| 1.0108 \| 0.8333 \| 0.3061 \| 1.3586 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0848 \| \| 0.002 \| 160.99 \| 7013 \| 1.0129 \| 0.8333 \| 0.3064 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1560 \| 0.0845 \| \| 0.002 \| 162.0 \| 7057 \| 1.0141 \| 0.8333 \| 0.3060 \| 1.3542 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0845 \| \| 0.002 \| 162.98 \| 7100 \| 1.0150 \| 0.8333 \| 0.3063 \| 1.3555 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0847 \| \| 0.002 \| 163.99 \| 7144 \| 1.0181 \| 0.8305 \| 0.3071 \| 1.3616 \| 0.8305 \| 0.8165 \| 0.1587 \| 0.0847 \| \| 0.002 \| 164.98 \| 7187 \| 1.0197 \| 0.8305 \| 0.3073 \| 1.3610 \| 0.8305 \| 0.8165 \| 0.1585 \| 0.0847 \| \| 0.002 \| 165.99 \| 7231 \| 1.0203 \| 0.8333 \| 0.3071 \| 1.3566 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0846 \| \| 0.002 \| 166.98 \| 7274 \| 1.0214 \| 0.8333 \| 0.3070 \| 1.3561 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0845 \| \| 0.002 \| 167.99 \| 7318 \| 1.0211 \| 0.8333 \| 0.3067 \| 1.3558 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0846 \| \| 0.002 \| 169.0 \| 7362 \| 1.0255 \| 0.8305 \| 0.3077 \| 1.3564 \| 0.8305 \| 0.8165 \| 0.1592 \| 0.0846 \| \| 0.002 \| 169.99 \| 7405 \| 1.0238 \| 0.8333 \| 0.3066 \| 1.3535 \| 0.8333 \| 0.8183 \| 0.1567 \| 0.0844 \| \| 0.002 \| 171.0 \| 7449 \| 1.0258 \| 0.8333 \| 0.3075 \| 1.3580 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.002 \| 171.98 \| 7492 \| 1.0260 \| 0.8333 \| 0.3073 \| 1.3594 \| 0.8333 \| 0.8183 \| 0.1559 \| 0.0846 \| \| 0.0018 \| 172.99 \| 7536 \| 1.0281 \| 0.8305 \| 0.3077 \| 1.3584 \| 0.8305 \| 0.8165 \| 0.1586 \| 0.0847 \| \| 0.0018 \| 173.98 \| 7579 \| 1.0274 \| 0.8333 \| 0.3073 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1560 \| 0.0851 \| \| 0.0018 \| 174.99 \| 7623 \| 1.0323 \| 0.8305 \| 0.3082 \| 1.3577 \| 0.8305 \| 0.8165 \| 0.1596 \| 0.0848 \| \| 0.0018 \| 176.0 \| 7667 \| 1.0303 \| 0.8333 \| 0.3076 \| 1.3579 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0846 \| \| 0.0018 \| 176.99 \| 7710 \| 1.0325 \| 0.8333 \| 0.3081 \| 1.3567 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0845 \| \| 0.0018 \| 178.0 \| 7754 \| 1.0319 \| 0.8333 \| 0.3077 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1560 \| 0.0847 \| \| 0.0018 \| 178.98 \| 7797 \| 1.0340 \| 0.8333 \| 0.3081 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.0018 \| 179.99 \| 7841 \| 1.0331 \| 0.8333 \| 0.3072 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0018 \| 180.98 \| 7884 \| 1.0346 \| 0.8333 \| 0.3079 \| 1.3563 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0847 \| \| 0.0018 \| 181.99 \| 7928 \| 1.0344 \| 0.8333 \| 0.3079 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0847 \| \| 0.0018 \| 182.98 \| 7971 \| 1.0363 \| 0.8333 \| 0.3080 \| 1.3556 \| 0.8333 \| 0.8183 \| 0.1566 \| 0.0850 \| \| 0.0016 \| 183.99 \| 8015 \| 1.0368 \| 0.8333 \| 0.3080 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0847 \| \| 0.0016 \| 185.0 \| 8059 \| 1.0369 \| 0.8333 \| 0.3080 \| 1.3563 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.0016 \| 185.99 \| 8102 \| 1.0373 \| 0.8333 \| 0.3080 \| 1.3565 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0850 \| \| 0.0016 \| 187.0 \| 8146 \| 1.0377 \| 0.8333 \| 0.3080 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0846 \| \| 0.0016 \| 187.98 \| 8189 \| 1.0392 \| 0.8333 \| 0.3084 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0846 \| \| 0.0016 \| 188.99 \| 8233 \| 1.0391 \| 0.8333 \| 0.3082 \| 1.3564 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0848 \| \| 0.0016 \| 189.98 \| 8276 \| 1.0393 \| 0.8333 \| 0.3081 \| 1.3561 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.0016 \| 190.99 \| 8320 \| 1.0398 \| 0.8333 \| 0.3084 \| 1.3582 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0846 \| \| 0.0016 \| 192.0 \| 8364 \| 1.0405 \| 0.8333 \| 0.3083 \| 1.3558 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0016 \| 192.99 \| 8407 \| 1.0401 \| 0.8333 \| 0.3082 \| 1.3558 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0016 \| 194.0 \| 8451 \| 1.0407 \| 0.8333 \| 0.3083 \| 1.3564 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0016 \| 194.98 \| 8494 \| 1.0414 \| 0.8333 \| 0.3086 \| 1.3573 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0015 \| 195.99 \| 8538 \| 1.0410 \| 0.8333 \| 0.3084 \| 1.3567 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0848 \| \| 0.0015 \| 196.98 \| 8581 \| 1.0411 \| 0.8333 \| 0.3084 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0846 \| \| 0.0015 \| 197.42 \| 8600 \| 1.0411 \| 0.8333 \| 0.3084 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0847 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3
Isotonic/informal_to_formal	Isotonic	2023-07-12T22:55:28Z	111	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "style-transfer", "seq2seq", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-01T05:59:36Z	--- language: "en" tags: - style-transfer - text2text-generation - seq2seq inference: true --- # Formality Style Transfer ## Model description T5 Model for Formality Style Transfer. Trained on the GYAFC dataset. ## How to use PyTorch model available. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Isotonic/informal_to_formal") model = AutoModelForSeq2SeqLM.from_pretrained("Isotonic/informal_to_formal") sentence = "will you look into these two deals and let me know" text = "Make the following sentence Formal: " + sentence + " </s>" encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") outputs = model.generate( input_ids=input_ids, attention_mask=attention_masks, max_length=256, do_sample=True, top_k=120, top_p=0.95, early_stopping=True, num_return_sequences=5 ) for output in outputs: line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True) print(line) Output: "Would you look into the two deals in question, then let me know?" ```
komo-dono/rammatra_jp	komo-dono	2023-07-12T22:52:40Z	0	0	null	[ "region:us" ]	null	2023-07-12T22:51:26Z	--- license: openrail language: - ja tags: - music rammatra jp 500 epoch
KingShmeeky/KingshmeekyRVC	KingShmeeky	2023-07-12T22:43:21Z	0	0	null	[ "music", "en", "license:openrail", "region:us" ]	null	2023-07-12T22:30:27Z	--- license: openrail language: - en tags: - music ---
nolanaatama/nglshdbhtcvbrnnknckrbckrgnshnmpctrvcv2150pchmklgn	nolanaatama	2023-07-12T22:40:26Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-12T22:08:55Z	--- license: creativeml-openrail-m ---
lovelyxs/Pyramids	lovelyxs	2023-07-12T22:37:03Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-12T22:36:58Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: lovelyxs/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
komo-dono/collei_jp	komo-dono	2023-07-12T22:28:58Z	0	0	null	[ "region:us" ]	null	2023-07-12T22:27:51Z	--- license: openrail language: - ja tags: - music collei japanese 500 epoch
cworthingtonfujitsu/falcon-7b-instruct-jukebox	cworthingtonfujitsu	2023-07-12T21:58:59Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-12T21:58:36Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
lovelyxs/ppo-SnowballTarget	lovelyxs	2023-07-12T21:43:03Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-12T21:42:55Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: lovelyxs/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
whywynn/q-FrozenLake-v1-4x4-noSlippery	whywynn	2023-07-12T21:24:58Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T21:24:49Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="whywynn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
yanex0/cn-v1-1	yanex0	2023-07-12T21:20:20Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-07-12T21:14:00Z	--- license: openrail --- This is the model files for [ControlNet 1.1](https://github.com/lllyasviel/ControlNet-v1-1-nightly). This model card will be filled in a more detailed way after 1.1 is officially merged into ControlNet.
SrPrieto/ppo-LunarLander-v2	SrPrieto	2023-07-12T21:14:49Z	5	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T21:14:30Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.18 +/- 13.06 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
lovelyxs/Reinforce-Pixelcopter-PLE-v0	lovelyxs	2023-07-12T21:06:20Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T21:06:13Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.30 +/- 39.51 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
carbon225/byt5-abbreviations-pl	carbon225	2023-07-12T21:00:28Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "pl", "dataset:carbon225/poleval-abbreviation-disambiguation-wiki", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-09T21:40:24Z	--- datasets: - carbon225/poleval-abbreviation-disambiguation-wiki language: - pl widget: - text: "Kolejne 0,12 <mask>pkt. proc.</mask> wynika ze spadku popytu na polski eksport, a 0,08 z zaburzeń na rynku wewnętrznym" example_title: "Example 1" --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MarcoIPolo/distilbert-base-uncased-finetuned-emotion	MarcoIPolo	2023-07-12T20:50:49Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T16:05:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9245630401134893 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2193 - Accuracy: 0.9245 - F1: 0.9246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3295 \| 0.899 \| 0.8946 \| \| No log \| 2.0 \| 500 \| 0.2193 \| 0.9245 \| 0.9246 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
grace-pro/afriberta-small-finetuned-hausa-2e-4	grace-pro	2023-07-12T20:50:37Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-12T20:15:26Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-small-finetuned-hausa-2e-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # afriberta-small-finetuned-hausa-2e-4 This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2081 - Precision: 0.6383 - Recall: 0.4793 - F1: 0.5475 - Accuracy: 0.9589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1575 \| 1.0 \| 1312 \| 0.1439 \| 0.6452 \| 0.3971 \| 0.4917 \| 0.9569 \| \| 0.1201 \| 2.0 \| 2624 \| 0.1371 \| 0.6344 \| 0.4451 \| 0.5231 \| 0.9578 \| \| 0.0831 \| 3.0 \| 3936 \| 0.1544 \| 0.6444 \| 0.4727 \| 0.5454 \| 0.9591 \| \| 0.0523 \| 4.0 \| 5248 \| 0.1836 \| 0.6500 \| 0.4683 \| 0.5444 \| 0.9592 \| \| 0.0318 \| 5.0 \| 6560 \| 0.2081 \| 0.6383 \| 0.4793 \| 0.5475 \| 0.9589 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
foreverip/q-FrozenLake-v1-4x4-noSlippery	foreverip	2023-07-12T20:49:59Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T20:49:56Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="foreverip/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
kimnguyenwork/Taxi-v3	kimnguyenwork	2023-07-12T20:45:36Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T20:45:29Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="kimnguyenwork/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
1aurent/diy-ppo-LunarLander-v2	1aurent	2023-07-12T20:42:54Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T20:03:20Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -41.63 +/- 67.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'repo_id': '1aurent/diy-ppo-LunarLander-v2' 'exp_name': 'hugggingface_colab' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.0003 'num_envs': 32 'num_steps': 64 'anneal_lr': True 'gae': True 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'batch_size': 2048 'minibatch_size': 512} ```
kimnguyenwork/q-FrozenLake-v1	kimnguyenwork	2023-07-12T20:42:26Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T20:42:20Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="kimnguyenwork/q-FrozenLake-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
NasimB/gpt2-concat-guten-rarity-no-cut	NasimB	2023-07-12T20:33:38Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-12T18:48:47Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-rarity-no-cut results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-guten-rarity-no-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6869 \| 0.29 \| 500 \| 5.6385 \| \| 5.3235 \| 0.59 \| 1000 \| 5.2015 \| \| 4.9865 \| 0.88 \| 1500 \| 4.9498 \| \| 4.7068 \| 1.18 \| 2000 \| 4.8080 \| \| 4.5674 \| 1.47 \| 2500 \| 4.6941 \| \| 4.4601 \| 1.76 \| 3000 \| 4.5872 \| \| 4.3293 \| 2.06 \| 3500 \| 4.5155 \| \| 4.1497 \| 2.35 \| 4000 \| 4.4676 \| \| 4.1182 \| 2.64 \| 4500 \| 4.4072 \| \| 4.0826 \| 2.94 \| 5000 \| 4.3514 \| \| 3.8664 \| 3.23 \| 5500 \| 4.3488 \| \| 3.8272 \| 3.53 \| 6000 \| 4.3168 \| \| 3.8034 \| 3.82 \| 6500 \| 4.2843 \| \| 3.6795 \| 4.11 \| 7000 \| 4.2836 \| \| 3.5333 \| 4.41 \| 7500 \| 4.2764 \| \| 3.534 \| 4.7 \| 8000 \| 4.2603 \| \| 3.5182 \| 4.99 \| 8500 \| 4.2478 \| \| 3.3437 \| 5.29 \| 9000 \| 4.2620 \| \| 3.3384 \| 5.58 \| 9500 \| 4.2601 \| \| 3.3385 \| 5.88 \| 10000 \| 4.2595 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
SHENMU007/neunit_BASE_V12.4	SHENMU007	2023-07-12T20:26:54Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-12T17:42:58Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
VK246/IC_ver5b_coco_swin_gpt2_01pc_1e	VK246	2023-07-12T20:14:54Z	46	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-07-12T19:47:07Z	--- tags: - generated_from_trainer datasets: - coco metrics: - rouge - bleu model-index: - name: IC_ver5b_coco_swin_gpt2_01pc_1e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IC_ver5b_coco_swin_gpt2_01pc_1e This model is a fine-tuned version of [VK246/IC_ver5a_coco_swin_gpt2_05pc_1e](https://huggingface.co/VK246/IC_ver5a_coco_swin_gpt2_05pc_1e) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 1.1266 - Rouge1: 27.4772 - Rouge2: 5.9305 - Rougel: 25.1138 - Rougelsum: 25.1235 - Bleu: 2.437 - Gen Len: 11.1124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:------:\|:-------:\| \| 1.2093 \| 0.42 \| 25 \| 1.1552 \| 22.8898 \| 3.6353 \| 20.6781 \| 20.6737 \| 1.1554 \| 11.1124 \| \| 1.2149 \| 0.85 \| 50 \| 1.1358 \| 26.2857 \| 5.2765 \| 24.0266 \| 24.0308 \| 2.1954 \| 11.1124 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jd06/TwoSentenceHorrorModel	jd06	2023-07-12T20:14:37Z	211	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-11T20:51:49Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: TwoSentenceHorrorModel results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TwoSentenceHorrorModel This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3563 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 4.7786 \| \| No log \| 2.0 \| 2 \| 4.4930 \| \| No log \| 3.0 \| 3 \| 4.3563 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
odunola/transcriber-t5-v8	odunola	2023-07-12T20:08:13Z	114	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-06T12:24:29Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: transcriber-t5-v8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # transcriber-t5-v8 This model is a fine-tuned version of [odunola/transcriber-t5-v7](https://huggingface.co/odunola/transcriber-t5-v7) on the [odunola/transcriberv3](https://huggingface.co/datasets/odunola/transcriberv3) dataset. It achieves the following results on the evaluation set: - Loss: 0.1924 ## Model description This model, a fine-tuned T5, is designed to pinpoint and extract specific Bible scriptures' chapter and verse from a string of text. It's a major component of a broader project but is also versatile enough for your other applications. The model's learning is driven by a unique dataset, painstakingly compiled from transcripts of sermons on YouTube and various online platforms. This handpicked, curated data equips the model with a specialized understanding of religious discourse and biblical references. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2215 \| 0.23 \| 500 \| 0.2216 \| \| 0.1814 \| 0.47 \| 1000 \| 0.2197 \| \| 0.2011 \| 0.7 \| 1500 \| 0.2059 \| \| 0.2595 \| 0.94 \| 2000 \| 0.2009 \| \| 0.2412 \| 1.17 \| 2500 \| 0.2019 \| \| 0.1785 \| 1.41 \| 3000 \| 0.1970 \| \| 0.1962 \| 1.64 \| 3500 \| 0.1983 \| \| 0.2009 \| 1.88 \| 4000 \| 0.1963 \| \| 0.2013 \| 2.11 \| 4500 \| 0.1923 \| \| 0.2715 \| 2.35 \| 5000 \| 0.1929 \| \| 0.2488 \| 2.58 \| 5500 \| 0.1936 \| \| 0.1185 \| 2.81 \| 6000 \| 0.1924 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
newsrx/instructor-large	newsrx	2023-07-12T19:56:14Z	7	0	sentence-transformers	[ "sentence-transformers", "pytorch", "t5", "text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "prompt-retrieval", "text-reranking", "feature-extraction", "sentence-similarity", "transformers", "English", "Sentence Similarity", "natural_questions", "ms_marco", "fever", "hotpot_qa", "mteb", "en", "arxiv:2212.09741", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "region:us" ]	sentence-similarity	2023-07-12T19:56:14Z	--- pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - prompt-retrieval - text-reranking - sentence-transformers - feature-extraction - sentence-similarity - transformers - t5 - English - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb language: en inference: false license: apache-2.0 model-index: - name: INSTRUCTOR results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 88.13432835820896 - type: ap value: 59.298209334395665 - type: f1 value: 83.31769058643586 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.526375 - type: ap value: 88.16327709705504 - type: f1 value: 91.51095801287843 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.856 - type: f1 value: 45.41490917650942 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 31.223 - type: map_at_10 value: 47.947 - type: map_at_100 value: 48.742000000000004 - type: map_at_1000 value: 48.745 - type: map_at_3 value: 43.137 - type: map_at_5 value: 45.992 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 48.4 - type: mrr_at_100 value: 49.202 - type: mrr_at_1000 value: 49.205 - type: mrr_at_3 value: 43.551 - type: mrr_at_5 value: 46.467999999999996 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 57.045 - type: ndcg_at_100 value: 60.175 - type: ndcg_at_1000 value: 60.233000000000004 - type: ndcg_at_3 value: 47.171 - type: ndcg_at_5 value: 52.322 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 8.599 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.63 - type: precision_at_5 value: 14.282 - type: recall_at_1 value: 31.223 - type: recall_at_10 value: 85.989 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 58.89 - type: recall_at_5 value: 71.408 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 43.1621946393635 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.56417132407894 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.29539304390207 - type: mrr value: 76.44484017060196 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_spearman value: 84.38746499431112 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.51298701298701 - type: f1 value: 77.49041754069235 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.61848554098577 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 31.32623280148178 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.803000000000004 - type: map_at_10 value: 48.848 - type: map_at_100 value: 50.5 - type: map_at_1000 value: 50.602999999999994 - type: map_at_3 value: 45.111000000000004 - type: map_at_5 value: 47.202 - type: mrr_at_1 value: 44.635000000000005 - type: mrr_at_10 value: 55.593 - type: mrr_at_100 value: 56.169999999999995 - type: mrr_at_1000 value: 56.19499999999999 - type: mrr_at_3 value: 53.361999999999995 - type: mrr_at_5 value: 54.806999999999995 - type: ndcg_at_1 value: 44.635000000000005 - type: ndcg_at_10 value: 55.899 - type: ndcg_at_100 value: 60.958 - type: ndcg_at_1000 value: 62.302 - type: ndcg_at_3 value: 51.051 - type: ndcg_at_5 value: 53.351000000000006 - type: precision_at_1 value: 44.635000000000005 - type: precision_at_10 value: 10.786999999999999 - type: precision_at_100 value: 1.6580000000000001 - type: precision_at_1000 value: 0.213 - type: precision_at_3 value: 24.893 - type: precision_at_5 value: 17.740000000000002 - type: recall_at_1 value: 35.803000000000004 - type: recall_at_10 value: 68.657 - type: recall_at_100 value: 89.77199999999999 - type: recall_at_1000 value: 97.67 - type: recall_at_3 value: 54.066 - type: recall_at_5 value: 60.788 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.706 - type: map_at_10 value: 44.896 - type: map_at_100 value: 46.299 - type: map_at_1000 value: 46.44 - type: map_at_3 value: 41.721000000000004 - type: map_at_5 value: 43.486000000000004 - type: mrr_at_1 value: 41.592 - type: mrr_at_10 value: 50.529 - type: mrr_at_100 value: 51.22 - type: mrr_at_1000 value: 51.258 - type: mrr_at_3 value: 48.205999999999996 - type: mrr_at_5 value: 49.528 - type: ndcg_at_1 value: 41.592 - type: ndcg_at_10 value: 50.77199999999999 - type: ndcg_at_100 value: 55.383 - type: ndcg_at_1000 value: 57.288 - type: ndcg_at_3 value: 46.324 - type: ndcg_at_5 value: 48.346000000000004 - type: precision_at_1 value: 41.592 - type: precision_at_10 value: 9.516 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 22.399 - type: precision_at_5 value: 15.770999999999999 - type: recall_at_1 value: 33.706 - type: recall_at_10 value: 61.353 - type: recall_at_100 value: 80.182 - type: recall_at_1000 value: 91.896 - type: recall_at_3 value: 48.204 - type: recall_at_5 value: 53.89699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 44.424 - type: map_at_10 value: 57.169000000000004 - type: map_at_100 value: 58.202 - type: map_at_1000 value: 58.242000000000004 - type: map_at_3 value: 53.825 - type: map_at_5 value: 55.714 - type: mrr_at_1 value: 50.470000000000006 - type: mrr_at_10 value: 60.489000000000004 - type: mrr_at_100 value: 61.096 - type: mrr_at_1000 value: 61.112 - type: mrr_at_3 value: 58.192 - type: mrr_at_5 value: 59.611999999999995 - type: ndcg_at_1 value: 50.470000000000006 - type: ndcg_at_10 value: 63.071999999999996 - type: ndcg_at_100 value: 66.964 - type: ndcg_at_1000 value: 67.659 - type: ndcg_at_3 value: 57.74399999999999 - type: ndcg_at_5 value: 60.367000000000004 - type: precision_at_1 value: 50.470000000000006 - type: precision_at_10 value: 10.019 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.558999999999997 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 44.424 - type: recall_at_10 value: 77.02 - type: recall_at_100 value: 93.738 - type: recall_at_1000 value: 98.451 - type: recall_at_3 value: 62.888 - type: recall_at_5 value: 69.138 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.294 - type: map_at_10 value: 34.503 - type: map_at_100 value: 35.641 - type: map_at_1000 value: 35.724000000000004 - type: map_at_3 value: 31.753999999999998 - type: map_at_5 value: 33.190999999999995 - type: mrr_at_1 value: 28.362 - type: mrr_at_10 value: 36.53 - type: mrr_at_100 value: 37.541000000000004 - type: mrr_at_1000 value: 37.602000000000004 - type: mrr_at_3 value: 33.917 - type: mrr_at_5 value: 35.358000000000004 - type: ndcg_at_1 value: 28.362 - type: ndcg_at_10 value: 39.513999999999996 - type: ndcg_at_100 value: 44.815 - type: ndcg_at_1000 value: 46.839 - type: ndcg_at_3 value: 34.02 - type: ndcg_at_5 value: 36.522 - type: precision_at_1 value: 28.362 - type: precision_at_10 value: 6.101999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.161999999999999 - type: precision_at_5 value: 9.966 - type: recall_at_1 value: 26.294 - type: recall_at_10 value: 53.098 - type: recall_at_100 value: 76.877 - type: recall_at_1000 value: 91.834 - type: recall_at_3 value: 38.266 - type: recall_at_5 value: 44.287 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.407 - type: map_at_10 value: 25.185999999999996 - type: map_at_100 value: 26.533 - type: map_at_1000 value: 26.657999999999998 - type: map_at_3 value: 22.201999999999998 - type: map_at_5 value: 23.923 - type: mrr_at_1 value: 20.522000000000002 - type: mrr_at_10 value: 29.522 - type: mrr_at_100 value: 30.644 - type: mrr_at_1000 value: 30.713 - type: mrr_at_3 value: 26.679000000000002 - type: mrr_at_5 value: 28.483000000000004 - type: ndcg_at_1 value: 20.522000000000002 - type: ndcg_at_10 value: 30.656 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.675 - type: ndcg_at_3 value: 25.319000000000003 - type: ndcg_at_5 value: 27.992 - type: precision_at_1 value: 20.522000000000002 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 16.407 - type: recall_at_10 value: 43.164 - type: recall_at_100 value: 69.695 - type: recall_at_1000 value: 89.41900000000001 - type: recall_at_3 value: 28.634999999999998 - type: recall_at_5 value: 35.308 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.473 - type: map_at_10 value: 41.676 - type: map_at_100 value: 43.120999999999995 - type: map_at_1000 value: 43.230000000000004 - type: map_at_3 value: 38.306000000000004 - type: map_at_5 value: 40.355999999999995 - type: mrr_at_1 value: 37.536 - type: mrr_at_10 value: 47.643 - type: mrr_at_100 value: 48.508 - type: mrr_at_1000 value: 48.551 - type: mrr_at_3 value: 45.348 - type: mrr_at_5 value: 46.744 - type: ndcg_at_1 value: 37.536 - type: ndcg_at_10 value: 47.823 - type: ndcg_at_100 value: 53.395 - type: ndcg_at_1000 value: 55.271 - type: ndcg_at_3 value: 42.768 - type: ndcg_at_5 value: 45.373000000000005 - type: precision_at_1 value: 37.536 - type: precision_at_10 value: 8.681 - type: precision_at_100 value: 1.34 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.468 - type: precision_at_5 value: 14.495 - type: recall_at_1 value: 30.473 - type: recall_at_10 value: 60.092999999999996 - type: recall_at_100 value: 82.733 - type: recall_at_1000 value: 94.875 - type: recall_at_3 value: 45.734 - type: recall_at_5 value: 52.691 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.976000000000003 - type: map_at_10 value: 41.097 - type: map_at_100 value: 42.547000000000004 - type: map_at_1000 value: 42.659000000000006 - type: map_at_3 value: 37.251 - type: map_at_5 value: 39.493 - type: mrr_at_1 value: 37.557 - type: mrr_at_10 value: 46.605000000000004 - type: mrr_at_100 value: 47.487 - type: mrr_at_1000 value: 47.54 - type: mrr_at_3 value: 43.721 - type: mrr_at_5 value: 45.411 - type: ndcg_at_1 value: 37.557 - type: ndcg_at_10 value: 47.449000000000005 - type: ndcg_at_100 value: 53.052 - type: ndcg_at_1000 value: 55.010999999999996 - type: ndcg_at_3 value: 41.439 - type: ndcg_at_5 value: 44.292 - type: precision_at_1 value: 37.557 - type: precision_at_10 value: 8.847 - type: precision_at_100 value: 1.357 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 20.091 - type: precision_at_5 value: 14.384 - type: recall_at_1 value: 29.976000000000003 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 84.245 - type: recall_at_1000 value: 96.97200000000001 - type: recall_at_3 value: 43.794 - type: recall_at_5 value: 51.778999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.099166666666665 - type: map_at_10 value: 38.1365 - type: map_at_100 value: 39.44491666666667 - type: map_at_1000 value: 39.55858333333334 - type: map_at_3 value: 35.03641666666666 - type: map_at_5 value: 36.79833333333334 - type: mrr_at_1 value: 33.39966666666667 - type: mrr_at_10 value: 42.42583333333333 - type: mrr_at_100 value: 43.28575 - type: mrr_at_1000 value: 43.33741666666667 - type: mrr_at_3 value: 39.94975 - type: mrr_at_5 value: 41.41633333333334 - type: ndcg_at_1 value: 33.39966666666667 - type: ndcg_at_10 value: 43.81741666666667 - type: ndcg_at_100 value: 49.08166666666667 - type: ndcg_at_1000 value: 51.121166666666674 - type: ndcg_at_3 value: 38.73575 - type: ndcg_at_5 value: 41.18158333333333 - type: precision_at_1 value: 33.39966666666667 - type: precision_at_10 value: 7.738916666666667 - type: precision_at_100 value: 1.2265833333333331 - type: precision_at_1000 value: 0.15983333333333336 - type: precision_at_3 value: 17.967416666666665 - type: precision_at_5 value: 12.78675 - type: recall_at_1 value: 28.099166666666665 - type: recall_at_10 value: 56.27049999999999 - type: recall_at_100 value: 78.93291666666667 - type: recall_at_1000 value: 92.81608333333334 - type: recall_at_3 value: 42.09775 - type: recall_at_5 value: 48.42533333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.663 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.426 - type: map_at_1000 value: 31.519000000000002 - type: map_at_3 value: 28.069 - type: map_at_5 value: 29.256999999999998 - type: mrr_at_1 value: 26.687 - type: mrr_at_10 value: 33.107 - type: mrr_at_100 value: 34.055 - type: mrr_at_1000 value: 34.117999999999995 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.14 - type: ndcg_at_1 value: 26.687 - type: ndcg_at_10 value: 34.615 - type: ndcg_at_100 value: 39.776 - type: ndcg_at_1000 value: 42.05 - type: ndcg_at_3 value: 30.322 - type: ndcg_at_5 value: 32.157000000000004 - type: precision_at_1 value: 26.687 - type: precision_at_10 value: 5.491 - type: precision_at_100 value: 0.877 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.139000000000001 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.663 - type: recall_at_10 value: 45.035 - type: recall_at_100 value: 68.554 - type: recall_at_1000 value: 85.077 - type: recall_at_3 value: 32.982 - type: recall_at_5 value: 37.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.403 - type: map_at_10 value: 25.197000000000003 - type: map_at_100 value: 26.355 - type: map_at_1000 value: 26.487 - type: map_at_3 value: 22.733 - type: map_at_5 value: 24.114 - type: mrr_at_1 value: 21.37 - type: mrr_at_10 value: 29.091 - type: mrr_at_100 value: 30.018 - type: mrr_at_1000 value: 30.096 - type: mrr_at_3 value: 26.887 - type: mrr_at_5 value: 28.157 - type: ndcg_at_1 value: 21.37 - type: ndcg_at_10 value: 30.026000000000003 - type: ndcg_at_100 value: 35.416 - type: ndcg_at_1000 value: 38.45 - type: ndcg_at_3 value: 25.764 - type: ndcg_at_5 value: 27.742 - type: precision_at_1 value: 21.37 - type: precision_at_10 value: 5.609 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 12.423 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 17.403 - type: recall_at_10 value: 40.573 - type: recall_at_100 value: 64.818 - type: recall_at_1000 value: 86.53699999999999 - type: recall_at_3 value: 28.493000000000002 - type: recall_at_5 value: 33.660000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.639 - type: map_at_10 value: 38.951 - type: map_at_100 value: 40.238 - type: map_at_1000 value: 40.327 - type: map_at_3 value: 35.842 - type: map_at_5 value: 37.617 - type: mrr_at_1 value: 33.769 - type: mrr_at_10 value: 43.088 - type: mrr_at_100 value: 44.03 - type: mrr_at_1000 value: 44.072 - type: mrr_at_3 value: 40.656 - type: mrr_at_5 value: 42.138999999999996 - type: ndcg_at_1 value: 33.769 - type: ndcg_at_10 value: 44.676 - type: ndcg_at_100 value: 50.416000000000004 - type: ndcg_at_1000 value: 52.227999999999994 - type: ndcg_at_3 value: 39.494 - type: ndcg_at_5 value: 42.013 - type: precision_at_1 value: 33.769 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.221 - type: precision_at_5 value: 12.966 - type: recall_at_1 value: 28.639 - type: recall_at_10 value: 57.687999999999995 - type: recall_at_100 value: 82.541 - type: recall_at_1000 value: 94.896 - type: recall_at_3 value: 43.651 - type: recall_at_5 value: 49.925999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.57 - type: map_at_10 value: 40.004 - type: map_at_100 value: 41.75 - type: map_at_1000 value: 41.97 - type: map_at_3 value: 36.788 - type: map_at_5 value: 38.671 - type: mrr_at_1 value: 35.375 - type: mrr_at_10 value: 45.121 - type: mrr_at_100 value: 45.994 - type: mrr_at_1000 value: 46.04 - type: mrr_at_3 value: 42.227 - type: mrr_at_5 value: 43.995 - type: ndcg_at_1 value: 35.375 - type: ndcg_at_10 value: 46.392 - type: ndcg_at_100 value: 52.196 - type: ndcg_at_1000 value: 54.274 - type: ndcg_at_3 value: 41.163 - type: ndcg_at_5 value: 43.813 - type: precision_at_1 value: 35.375 - type: precision_at_10 value: 8.676 - type: precision_at_100 value: 1.678 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 19.104 - type: precision_at_5 value: 13.913 - type: recall_at_1 value: 29.57 - type: recall_at_10 value: 58.779 - type: recall_at_100 value: 83.337 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 44.005 - type: recall_at_5 value: 50.975 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.832 - type: map_at_10 value: 29.733999999999998 - type: map_at_100 value: 30.727 - type: map_at_1000 value: 30.843999999999998 - type: map_at_3 value: 26.834999999999997 - type: map_at_5 value: 28.555999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 31.791999999999998 - type: mrr_at_100 value: 32.666000000000004 - type: mrr_at_1000 value: 32.751999999999995 - type: mrr_at_3 value: 29.144 - type: mrr_at_5 value: 30.622 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.744 - type: ndcg_at_1000 value: 42.407000000000004 - type: ndcg_at_3 value: 29.421000000000003 - type: ndcg_at_5 value: 32.211 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 5.675 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.753999999999998 - type: precision_at_5 value: 9.353 - type: recall_at_1 value: 20.832 - type: recall_at_10 value: 48.795 - type: recall_at_100 value: 70.703 - type: recall_at_1000 value: 90.187 - type: recall_at_3 value: 34.455000000000005 - type: recall_at_5 value: 40.967 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.334 - type: map_at_10 value: 19.009999999999998 - type: map_at_100 value: 21.129 - type: map_at_1000 value: 21.328 - type: map_at_3 value: 15.152 - type: map_at_5 value: 17.084 - type: mrr_at_1 value: 23.453 - type: mrr_at_10 value: 36.099 - type: mrr_at_100 value: 37.069 - type: mrr_at_1000 value: 37.104 - type: mrr_at_3 value: 32.096000000000004 - type: mrr_at_5 value: 34.451 - type: ndcg_at_1 value: 23.453 - type: ndcg_at_10 value: 27.739000000000004 - type: ndcg_at_100 value: 35.836 - type: ndcg_at_1000 value: 39.242 - type: ndcg_at_3 value: 21.263 - type: ndcg_at_5 value: 23.677 - type: precision_at_1 value: 23.453 - type: precision_at_10 value: 9.199 - type: precision_at_100 value: 1.791 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.2 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 10.334 - type: recall_at_10 value: 35.177 - type: recall_at_100 value: 63.009 - type: recall_at_1000 value: 81.938 - type: recall_at_3 value: 19.914 - type: recall_at_5 value: 26.077 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.212 - type: map_at_10 value: 17.386 - type: map_at_100 value: 24.234 - type: map_at_1000 value: 25.724999999999998 - type: map_at_3 value: 12.727 - type: map_at_5 value: 14.785 - type: mrr_at_1 value: 59.25 - type: mrr_at_10 value: 68.687 - type: mrr_at_100 value: 69.133 - type: mrr_at_1000 value: 69.14099999999999 - type: mrr_at_3 value: 66.917 - type: mrr_at_5 value: 67.742 - type: ndcg_at_1 value: 48.625 - type: ndcg_at_10 value: 36.675999999999995 - type: ndcg_at_100 value: 41.543 - type: ndcg_at_1000 value: 49.241 - type: ndcg_at_3 value: 41.373 - type: ndcg_at_5 value: 38.707 - type: precision_at_1 value: 59.25 - type: precision_at_10 value: 28.525 - type: precision_at_100 value: 9.027000000000001 - type: precision_at_1000 value: 1.8339999999999999 - type: precision_at_3 value: 44.833 - type: precision_at_5 value: 37.35 - type: recall_at_1 value: 8.212 - type: recall_at_10 value: 23.188 - type: recall_at_100 value: 48.613 - type: recall_at_1000 value: 73.093 - type: recall_at_3 value: 14.419 - type: recall_at_5 value: 17.798 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.725 - type: f1 value: 46.50743309855908 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 55.086 - type: map_at_10 value: 66.914 - type: map_at_100 value: 67.321 - type: map_at_1000 value: 67.341 - type: map_at_3 value: 64.75800000000001 - type: map_at_5 value: 66.189 - type: mrr_at_1 value: 59.28600000000001 - type: mrr_at_10 value: 71.005 - type: mrr_at_100 value: 71.304 - type: mrr_at_1000 value: 71.313 - type: mrr_at_3 value: 69.037 - type: mrr_at_5 value: 70.35 - type: ndcg_at_1 value: 59.28600000000001 - type: ndcg_at_10 value: 72.695 - type: ndcg_at_100 value: 74.432 - type: ndcg_at_1000 value: 74.868 - type: ndcg_at_3 value: 68.72200000000001 - type: ndcg_at_5 value: 71.081 - type: precision_at_1 value: 59.28600000000001 - type: precision_at_10 value: 9.499 - type: precision_at_100 value: 1.052 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 27.503 - type: precision_at_5 value: 17.854999999999997 - type: recall_at_1 value: 55.086 - type: recall_at_10 value: 86.453 - type: recall_at_100 value: 94.028 - type: recall_at_1000 value: 97.052 - type: recall_at_3 value: 75.821 - type: recall_at_5 value: 81.6 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.262999999999998 - type: map_at_10 value: 37.488 - type: map_at_100 value: 39.498 - type: map_at_1000 value: 39.687 - type: map_at_3 value: 32.529 - type: map_at_5 value: 35.455 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.239000000000004 - type: mrr_at_100 value: 54.086 - type: mrr_at_1000 value: 54.122 - type: mrr_at_3 value: 51.235 - type: mrr_at_5 value: 52.415 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 45.446 - type: ndcg_at_100 value: 52.429 - type: ndcg_at_1000 value: 55.169000000000004 - type: ndcg_at_3 value: 41.882000000000005 - type: ndcg_at_5 value: 43.178 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.931999999999999 - type: precision_at_100 value: 2.025 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 28.652 - type: precision_at_5 value: 21.204 - type: recall_at_1 value: 22.262999999999998 - type: recall_at_10 value: 52.447 - type: recall_at_100 value: 78.045 - type: recall_at_1000 value: 94.419 - type: recall_at_3 value: 38.064 - type: recall_at_5 value: 44.769 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 32.519 - type: map_at_10 value: 45.831 - type: map_at_100 value: 46.815 - type: map_at_1000 value: 46.899 - type: map_at_3 value: 42.836 - type: map_at_5 value: 44.65 - type: mrr_at_1 value: 65.037 - type: mrr_at_10 value: 72.16 - type: mrr_at_100 value: 72.51100000000001 - type: mrr_at_1000 value: 72.53 - type: mrr_at_3 value: 70.682 - type: mrr_at_5 value: 71.54599999999999 - type: ndcg_at_1 value: 65.037 - type: ndcg_at_10 value: 55.17999999999999 - type: ndcg_at_100 value: 58.888 - type: ndcg_at_1000 value: 60.648 - type: ndcg_at_3 value: 50.501 - type: ndcg_at_5 value: 52.977 - type: precision_at_1 value: 65.037 - type: precision_at_10 value: 11.530999999999999 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 31.483 - type: precision_at_5 value: 20.845 - type: recall_at_1 value: 32.519 - type: recall_at_10 value: 57.657000000000004 - type: recall_at_100 value: 72.30199999999999 - type: recall_at_1000 value: 84.024 - type: recall_at_3 value: 47.225 - type: recall_at_5 value: 52.113 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.3168 - type: ap value: 83.80165516037135 - type: f1 value: 88.29942471066407 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.724999999999998 - type: map_at_10 value: 32.736 - type: map_at_100 value: 33.938 - type: map_at_1000 value: 33.991 - type: map_at_3 value: 28.788000000000004 - type: map_at_5 value: 31.016 - type: mrr_at_1 value: 21.361 - type: mrr_at_10 value: 33.323 - type: mrr_at_100 value: 34.471000000000004 - type: mrr_at_1000 value: 34.518 - type: mrr_at_3 value: 29.453000000000003 - type: mrr_at_5 value: 31.629 - type: ndcg_at_1 value: 21.361 - type: ndcg_at_10 value: 39.649 - type: ndcg_at_100 value: 45.481 - type: ndcg_at_1000 value: 46.775 - type: ndcg_at_3 value: 31.594 - type: ndcg_at_5 value: 35.543 - type: precision_at_1 value: 21.361 - type: precision_at_10 value: 6.3740000000000006 - type: precision_at_100 value: 0.931 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.514999999999999 - type: precision_at_5 value: 10.100000000000001 - type: recall_at_1 value: 20.724999999999998 - type: recall_at_10 value: 61.034 - type: recall_at_100 value: 88.062 - type: recall_at_1000 value: 97.86399999999999 - type: recall_at_3 value: 39.072 - type: recall_at_5 value: 48.53 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.8919288645691 - type: f1 value: 93.57059586398059 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.97993616051072 - type: f1 value: 48.244319183606535 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.90047074646941 - type: f1 value: 66.48999056063725 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.34566240753195 - type: f1 value: 73.54164154290658 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.21866934757011 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.000936217235534 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.68189362520352 - type: mrr value: 32.69603637784303 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.078 - type: map_at_10 value: 12.671 - type: map_at_100 value: 16.291 - type: map_at_1000 value: 17.855999999999998 - type: map_at_3 value: 9.610000000000001 - type: map_at_5 value: 11.152 - type: mrr_at_1 value: 43.963 - type: mrr_at_10 value: 53.173 - type: mrr_at_100 value: 53.718999999999994 - type: mrr_at_1000 value: 53.756 - type: mrr_at_3 value: 50.980000000000004 - type: mrr_at_5 value: 52.42 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.086 - type: ndcg_at_100 value: 32.545 - type: ndcg_at_1000 value: 41.144999999999996 - type: ndcg_at_3 value: 39.434999999999995 - type: ndcg_at_5 value: 37.888 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.014999999999997 - type: precision_at_100 value: 8.594 - type: precision_at_1000 value: 2.169 - type: precision_at_3 value: 37.049 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 6.078 - type: recall_at_10 value: 16.17 - type: recall_at_100 value: 34.512 - type: recall_at_1000 value: 65.447 - type: recall_at_3 value: 10.706 - type: recall_at_5 value: 13.158 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 27.378000000000004 - type: map_at_10 value: 42.178 - type: map_at_100 value: 43.32 - type: map_at_1000 value: 43.358000000000004 - type: map_at_3 value: 37.474000000000004 - type: map_at_5 value: 40.333000000000006 - type: mrr_at_1 value: 30.823 - type: mrr_at_10 value: 44.626 - type: mrr_at_100 value: 45.494 - type: mrr_at_1000 value: 45.519 - type: mrr_at_3 value: 40.585 - type: mrr_at_5 value: 43.146 - type: ndcg_at_1 value: 30.794 - type: ndcg_at_10 value: 50.099000000000004 - type: ndcg_at_100 value: 54.900999999999996 - type: ndcg_at_1000 value: 55.69499999999999 - type: ndcg_at_3 value: 41.238 - type: ndcg_at_5 value: 46.081 - type: precision_at_1 value: 30.794 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 18.926000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 27.378000000000004 - type: recall_at_10 value: 71.842 - type: recall_at_100 value: 92.565 - type: recall_at_1000 value: 98.402 - type: recall_at_3 value: 49.053999999999995 - type: recall_at_5 value: 60.207 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.557 - type: map_at_10 value: 84.729 - type: map_at_100 value: 85.369 - type: map_at_1000 value: 85.382 - type: map_at_3 value: 81.72 - type: map_at_5 value: 83.613 - type: mrr_at_1 value: 81.3 - type: mrr_at_10 value: 87.488 - type: mrr_at_100 value: 87.588 - type: mrr_at_1000 value: 87.589 - type: mrr_at_3 value: 86.53 - type: mrr_at_5 value: 87.18599999999999 - type: ndcg_at_1 value: 81.28999999999999 - type: ndcg_at_10 value: 88.442 - type: ndcg_at_100 value: 89.637 - type: ndcg_at_1000 value: 89.70700000000001 - type: ndcg_at_3 value: 85.55199999999999 - type: ndcg_at_5 value: 87.154 - type: precision_at_1 value: 81.28999999999999 - type: precision_at_10 value: 13.489999999999998 - type: precision_at_100 value: 1.54 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.708 - type: recall_at_1 value: 70.557 - type: recall_at_10 value: 95.645 - type: recall_at_100 value: 99.693 - type: recall_at_1000 value: 99.995 - type: recall_at_3 value: 87.359 - type: recall_at_5 value: 91.89699999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 63.65060114776209 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.63271250680617 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.263 - type: map_at_10 value: 10.801 - type: map_at_100 value: 12.888 - type: map_at_1000 value: 13.224 - type: map_at_3 value: 7.362 - type: map_at_5 value: 9.149000000000001 - type: mrr_at_1 value: 21 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.513 - type: mrr_at_1000 value: 32.58 - type: mrr_at_3 value: 28.116999999999997 - type: mrr_at_5 value: 29.976999999999997 - type: ndcg_at_1 value: 21 - type: ndcg_at_10 value: 18.551000000000002 - type: ndcg_at_100 value: 26.657999999999998 - type: ndcg_at_1000 value: 32.485 - type: ndcg_at_3 value: 16.834 - type: ndcg_at_5 value: 15.204999999999998 - type: precision_at_1 value: 21 - type: precision_at_10 value: 9.84 - type: precision_at_100 value: 2.16 - type: precision_at_1000 value: 0.35500000000000004 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 13.62 - type: recall_at_1 value: 4.263 - type: recall_at_10 value: 19.922 - type: recall_at_100 value: 43.808 - type: recall_at_1000 value: 72.14500000000001 - type: recall_at_3 value: 9.493 - type: recall_at_5 value: 13.767999999999999 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_spearman value: 81.27446313317233 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_spearman value: 76.27963301217527 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_spearman value: 88.18495048450949 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_spearman value: 81.91982338692046 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_spearman value: 89.00896818385291 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_spearman value: 85.48814644586132 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_spearman value: 90.30116926966582 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_spearman value: 67.74132963032342 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_spearman value: 86.87741355780479 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.0019012295875 - type: mrr value: 94.70267024188593 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 50.05 - type: map_at_10 value: 59.36 - type: map_at_100 value: 59.967999999999996 - type: map_at_1000 value: 60.023 - type: map_at_3 value: 56.515 - type: map_at_5 value: 58.272999999999996 - type: mrr_at_1 value: 53 - type: mrr_at_10 value: 61.102000000000004 - type: mrr_at_100 value: 61.476 - type: mrr_at_1000 value: 61.523 - type: mrr_at_3 value: 58.778 - type: mrr_at_5 value: 60.128 - type: ndcg_at_1 value: 53 - type: ndcg_at_10 value: 64.43100000000001 - type: ndcg_at_100 value: 66.73599999999999 - type: ndcg_at_1000 value: 68.027 - type: ndcg_at_3 value: 59.279 - type: ndcg_at_5 value: 61.888 - type: precision_at_1 value: 53 - type: precision_at_10 value: 8.767 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.444000000000003 - type: precision_at_5 value: 15.667 - type: recall_at_1 value: 50.05 - type: recall_at_10 value: 78.511 - type: recall_at_100 value: 88.5 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 64.117 - type: recall_at_5 value: 70.867 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72178217821782 - type: cos_sim_ap value: 93.0728601593541 - type: cos_sim_f1 value: 85.6727976766699 - type: cos_sim_precision value: 83.02063789868667 - type: cos_sim_recall value: 88.5 - type: dot_accuracy value: 99.72178217821782 - type: dot_ap value: 93.07287396168348 - type: dot_f1 value: 85.6727976766699 - type: dot_precision value: 83.02063789868667 - type: dot_recall value: 88.5 - type: euclidean_accuracy value: 99.72178217821782 - type: euclidean_ap value: 93.07285657982895 - type: euclidean_f1 value: 85.6727976766699 - type: euclidean_precision value: 83.02063789868667 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.72475247524753 - type: manhattan_ap value: 93.02792973059809 - type: manhattan_f1 value: 85.7727737973388 - type: manhattan_precision value: 87.84067085953879 - type: manhattan_recall value: 83.8 - type: max_accuracy value: 99.72475247524753 - type: max_ap value: 93.07287396168348 - type: max_f1 value: 85.7727737973388 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.77583615550819 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.151636938606956 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.16607939471187 - type: mrr value: 52.95172046091163 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.314646669495666 - type: cos_sim_spearman value: 31.83562491439455 - type: dot_pearson value: 31.314590842874157 - type: dot_spearman value: 31.83363065810437 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.3010000000000002 - type: map_at_100 value: 7.2139999999999995 - type: map_at_1000 value: 20.179 - type: map_at_3 value: 0.528 - type: map_at_5 value: 0.8019999999999999 - type: mrr_at_1 value: 72 - type: mrr_at_10 value: 83.39999999999999 - type: mrr_at_100 value: 83.39999999999999 - type: mrr_at_1000 value: 83.39999999999999 - type: mrr_at_3 value: 81.667 - type: mrr_at_5 value: 83.06700000000001 - type: ndcg_at_1 value: 66 - type: ndcg_at_10 value: 58.059000000000005 - type: ndcg_at_100 value: 44.316 - type: ndcg_at_1000 value: 43.147000000000006 - type: ndcg_at_3 value: 63.815999999999995 - type: ndcg_at_5 value: 63.005 - type: precision_at_1 value: 72 - type: precision_at_10 value: 61.4 - type: precision_at_100 value: 45.62 - type: precision_at_1000 value: 19.866 - type: precision_at_3 value: 70 - type: precision_at_5 value: 68.8 - type: recall_at_1 value: 0.198 - type: recall_at_10 value: 1.517 - type: recall_at_100 value: 10.587 - type: recall_at_1000 value: 41.233 - type: recall_at_3 value: 0.573 - type: recall_at_5 value: 0.907 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.894 - type: map_at_10 value: 8.488999999999999 - type: map_at_100 value: 14.445 - type: map_at_1000 value: 16.078 - type: map_at_3 value: 4.589 - type: map_at_5 value: 6.019 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 39.82 - type: mrr_at_100 value: 40.752 - type: mrr_at_1000 value: 40.771 - type: mrr_at_3 value: 34.354 - type: mrr_at_5 value: 37.721 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 21.563 - type: ndcg_at_100 value: 33.857 - type: ndcg_at_1000 value: 46.199 - type: ndcg_at_3 value: 22.296 - type: ndcg_at_5 value: 21.770999999999997 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.142999999999999 - type: precision_at_1000 value: 1.541 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 1.894 - type: recall_at_10 value: 14.931 - type: recall_at_100 value: 45.524 - type: recall_at_1000 value: 83.243 - type: recall_at_3 value: 5.712 - type: recall_at_5 value: 8.386000000000001 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.049 - type: ap value: 13.85116971310922 - type: f1 value: 54.37504302487686 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.1312959818902 - type: f1 value: 64.11413877009383 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 54.13103431861502 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.327889372355 - type: cos_sim_ap value: 77.42059895975699 - type: cos_sim_f1 value: 71.02706903250873 - type: cos_sim_precision value: 69.75324344950394 - type: cos_sim_recall value: 72.34828496042216 - type: dot_accuracy value: 87.327889372355 - type: dot_ap value: 77.4209479346677 - type: dot_f1 value: 71.02706903250873 - type: dot_precision value: 69.75324344950394 - type: dot_recall value: 72.34828496042216 - type: euclidean_accuracy value: 87.327889372355 - type: euclidean_ap value: 77.42096495861037 - type: euclidean_f1 value: 71.02706903250873 - type: euclidean_precision value: 69.75324344950394 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.31000774870358 - type: manhattan_ap value: 77.38930750711619 - type: manhattan_f1 value: 71.07935314027831 - type: manhattan_precision value: 67.70957726295677 - type: manhattan_recall value: 74.80211081794195 - type: max_accuracy value: 87.327889372355 - type: max_ap value: 77.42096495861037 - type: max_f1 value: 71.07935314027831 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.58939729110878 - type: cos_sim_ap value: 87.17594155025475 - type: cos_sim_f1 value: 79.21146953405018 - type: cos_sim_precision value: 76.8918527109307 - type: cos_sim_recall value: 81.67539267015707 - type: dot_accuracy value: 89.58939729110878 - type: dot_ap value: 87.17593963273593 - type: dot_f1 value: 79.21146953405018 - type: dot_precision value: 76.8918527109307 - type: dot_recall value: 81.67539267015707 - type: euclidean_accuracy value: 89.58939729110878 - type: euclidean_ap value: 87.17592466925834 - type: euclidean_f1 value: 79.21146953405018 - type: euclidean_precision value: 76.8918527109307 - type: euclidean_recall value: 81.67539267015707 - type: manhattan_accuracy value: 89.62626615438352 - type: manhattan_ap value: 87.16589873161546 - type: manhattan_f1 value: 79.25143598295348 - type: manhattan_precision value: 76.39494177323712 - type: manhattan_recall value: 82.32984293193716 - type: max_accuracy value: 89.62626615438352 - type: max_ap value: 87.17594155025475 - type: max_f1 value: 79.25143598295348 duplicated_from: hkunlp/instructor-large --- # hkunlp/instructor-large We introduce Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) *by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))! The model is easy to use with our customized* `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! ************************** Updates ************************** * 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance. * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out! ## Quick start <hr /> ## Installation ```bash pip install InstructorEmbedding ``` ## Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-large') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title:" embeddings = model.encode([[instruction,sentence]]) print(embeddings) ``` ## Use cases <hr /> ## Calculate embeddings for your customized texts If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:                           Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. ## Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with customized embeddings. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] embeddings_a = model.encode(sentences_a) embeddings_b = model.encode(sentences_b) similarities = cosine_similarity(embeddings_a,embeddings_b) print(similarities) ``` ## Information Retrieval You can also use customized embeddings for information retrieval. ```python import numpy as np from sklearn.metrics.pairwise import cosine_similarity query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loansâ€”and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] query_embeddings = model.encode(query) corpus_embeddings = model.encode(corpus) similarities = cosine_similarity(query_embeddings,corpus_embeddings) retrieved_doc_id = np.argmax(similarities) print(retrieved_doc_id) ``` ## Clustering Use customized embeddings for clustering texts in groups. ```python import sklearn.cluster sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] embeddings = model.encode(sentences) clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) clustering_model.fit(embeddings) cluster_assignment = clustering_model.labels_ print(cluster_assignment) ```
chh6/v0TaxiAttempt	chh6	2023-07-12T19:47:12Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T19:47:10Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: v0TaxiAttempt results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="chh6/v0TaxiAttempt", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
veluchs/dqn-SpaceInvadersNoFrameskip-v4-4	veluchs	2023-07-12T19:41:22Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T19:40:57Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 264.50 +/- 87.36 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga veluchs -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga veluchs -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga veluchs ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ImanAndrea/BART-eng-to-ro	ImanAndrea	2023-07-12T19:33:27Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:wmt16-en-ro-pre-processed", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-12T06:56:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16-en-ro-pre-processed model-index: - name: BART-eng-to-ro results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BART-eng-to-ro This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the wmt16-en-ro-pre-processed dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.10.1 - Tokenizers 0.13.3
vuiseng9/baseline-ft-mrpc-IRoberta-b-8bit	vuiseng9	2023-07-12T19:21:04Z	6	0	transformers	[ "transformers", "pytorch", "ibert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T18:39:16Z	--- language: - en tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: baseline-ft-mrpc-IRoberta-b-8bit results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8970588235294118 - name: F1 type: f1 value: 0.9257950530035336 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # baseline-ft-mrpc-IRoberta-b-8bit This model is a fine-tuned version of [vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized](https://huggingface.co/vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.3871 - Accuracy: 0.8971 - F1: 0.9258 - Combined Score: 0.9114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.0021 \| 1.0 \| 230 \| 0.4017 \| 0.8848 \| 0.9147 \| 0.8998 \| \| 0.0026 \| 2.0 \| 460 \| 0.4105 \| 0.8873 \| 0.9173 \| 0.9023 \| \| 0.0026 \| 3.0 \| 690 \| 0.3707 \| 0.8946 \| 0.9236 \| 0.9091 \| \| 0.0037 \| 4.0 \| 920 \| 0.3893 \| 0.8946 \| 0.9228 \| 0.9087 \| \| 1.324 \| 5.0 \| 1150 \| 0.3871 \| 0.8897 \| 0.9204 \| 0.9050 \| \| 0.0227 \| 6.0 \| 1380 \| 0.3951 \| 0.8897 \| 0.9201 \| 0.9049 \| \| 0.0081 \| 7.0 \| 1610 \| 0.3818 \| 0.8824 \| 0.9155 \| 0.8989 \| \| 0.0054 \| 8.0 \| 1840 \| 0.3902 \| 0.8873 \| 0.9181 \| 0.9027 \| \| 0.0383 \| 9.0 \| 2070 \| 0.3659 \| 0.8922 \| 0.9225 \| 0.9073 \| \| 0.3861 \| 10.0 \| 2300 \| 0.4260 \| 0.8652 \| 0.9030 \| 0.8841 \| \| 0.0028 \| 11.0 \| 2530 \| 0.3619 \| 0.8946 \| 0.9234 \| 0.9090 \| \| 0.0957 \| 12.0 \| 2760 \| 0.3871 \| 0.8971 \| 0.9258 \| 0.9114 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
4bit/WizardLM-13B-V1.1-GPTQ	4bit	2023-07-12T19:06:41Z	8	3	transformers	[ "transformers", "llama", "text-generation", "arxiv:2304.12244", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-07-12T19:03:12Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # WizardLM's WizardLM 13B V1.1 GPTQ These files are GPTQ model files for [WizardLM's WizardLM 13B V1.1](https://huggingface.co/WizardLM/WizardLM-13B-V1.1). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate). ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.1) ## Prompt template: Vicuna ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ``` ## Provided files Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. \| Branch \| Bits \| Group Size \| Act Order (desc_act) \| File Size \| ExLlama Compatible? \| Made With \| Description \| \| ------ \| ---- \| ---------- \| -------------------- \| --------- \| ------------------- \| --------- \| ----------- \| \| main \| 4 \| 128 \| False \| 7.45 GB \| True \| GPTQ-for-LLaMa \| Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. \| \| gptq-4bit-32g-actorder_True \| 4 \| 32 \| True \| 8.00 GB \| True \| AutoGPTQ \| 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. \| \| gptq-4bit-64g-actorder_True \| 4 \| 64 \| True \| 7.51 GB \| True \| AutoGPTQ \| 4-bit, with Act Order and group size. 64g uses less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. \| \| gptq-4bit-128g-actorder_True \| 4 \| 128 \| True \| 7.26 GB \| True \| AutoGPTQ \| 4-bit, with Act Order androup size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. \| \| gptq-8bit--1g-actorder_True \| 8 \| None \| True \| 13.36 GB \| False \| AutoGPTQ \| 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. \| \| gptq-8bit-128g-actorder_False \| 8 \| 128 \| False \| 13.65 GB \| False \| AutoGPTQ \| 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. \| ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/WizardLM-13B-V1.1-GPTQ:gptq-4bit-32g-actorder_True` - With Git, you can clone a branch with: ``` git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ` ``` - In Python Transformers code, the branch is the `revision` parameter; see below. ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/WizardLM-13B-V1.1-GPTQ`. - To download from a specific branch, enter for example `TheBloke/WizardLM-13B-V1.1-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done" 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `WizardLM-13B-V1.1-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! ## How to use this GPTQ model from Python code First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: `GITHUB_ACTIONS=true pip install auto-gptq` Then try the following example code: ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name_or_path = "TheBloke/WizardLM-13B-V1.1-GPTQ" model_basename = "wizardlm-13b-v1.1-GPTQ-4bit-128g.no-act.order" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename use_safetensors=True, trust_remote_code=True, device="cuda:0", use_triton=use_triton, quantize_config=None) """ To download from a specific branch, use the revision parameter, as in this example: model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, revision="gptq-4bit-32g-actorder_True", model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Compatibility The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork. ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz. Patreon special mentions: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: WizardLM's WizardLM 13B V1.1 This is the Full-Weight of WizardLM-13B V1.1 model. Repository: https://github.com/nlpxucan/WizardLM Twitter: https://twitter.com/WizardLM_AI/status/1677282955490918401 - 🔥🔥🔥 [7/7/2023] We released WizardLM V1.1 models. The WizardLM-13B-V1.1 is here ([Demo_13B-V1.1](https://e8a06366ccd1c4d1.gradio.app), [Demo_13B-V1.1_bak-1](https://59da107262a25764.gradio.app), [Demo_13B-V1.1_bak-2](https://dfc5113f66739c80.gradio.app), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.1)). WizardLM-7B-V1.1, WizardLM-30B-V1.1, and WizardLM-65B-V1.1 are coming soon. Please checkout the [Full Model Weights](https://huggingface.co/WizardLM) and [paper](https://arxiv.org/abs/2304.12244). - 🔥🔥🔥 [7/7/2023] The WizardLM-13B-V1.1 achieves 6.74 on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), 86.32% on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and 99.3% on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
ramymohamed/ppo-LunarLander-v2	ramymohamed	2023-07-12T19:05:33Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T19:05:12Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.71 +/- 24.27 name: mean_reward verified: false --- # ppo Agent playing LunarLander-v2 This is a trained model of a ppo agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
gmurillo/set-fit-goup-6-f	gmurillo	2023-07-12T19:00:20Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bart", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-12T18:59:06Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gmurillo/set-fit-goup-6-f This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-6-f") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
gmurillo/set-fit-goup-4-f	gmurillo	2023-07-12T18:57:16Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bart", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-12T18:55:50Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gmurillo/set-fit-goup-4-f This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-4-f") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
gmurillo/set-fit-goup-1-f	gmurillo	2023-07-12T18:51:50Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bart", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-12T18:50:32Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gmurillo/set-fit-goup-1-f This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-1-f") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
komo-dono/dashiegames	komo-dono	2023-07-12T18:44:13Z	0	0	null	[ "region:us" ]	null	2023-07-12T18:42:38Z	--- license: openrail language: - en tags: - music dashiegames 500 epoch
tyavika/LR1E4-BS16-Distilbert-QA-Pytorch-FULL	tyavika	2023-07-12T18:39:38Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-07T04:59:00Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Distilbert-QA-Pytorch-FULL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # LR1E4-BS16-Distilbert-QA-Pytorch-FULL This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4071 \| 1.0 \| 3290 \| 1.2792 \| \| 1.0123 \| 2.0 \| 6580 \| 1.2843 \| \| 0.6916 \| 3.0 \| 9870 \| 1.3888 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized	vuiseng9	2023-07-12T18:33:30Z	107	0	transformers	[ "transformers", "pytorch", "ibert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T18:24:52Z	--- language: - en tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: baseline-ft-mrpc-IRoberta-b-unquantized results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8995098039215687 - name: F1 type: f1 value: 0.9266547406082289 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # baseline-ft-mrpc-IRoberta-b-unquantized This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5354 - Accuracy: 0.8995 - F1: 0.9267 - Combined Score: 0.9131 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.1212 \| 1.0 \| 230 \| 0.3401 \| 0.8799 \| 0.9136 \| 0.8967 \| \| 0.0347 \| 2.0 \| 460 \| 0.3085 \| 0.8676 \| 0.9059 \| 0.8868 \| \| 0.0495 \| 3.0 \| 690 \| 0.3552 \| 0.8848 \| 0.9174 \| 0.9011 \| \| 0.0024 \| 4.0 \| 920 \| 0.4960 \| 0.8824 \| 0.9158 \| 0.8991 \| \| 0.0046 \| 5.0 \| 1150 \| 0.5354 \| 0.8995 \| 0.9267 \| 0.9131 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Taki3d/CrackDetectionLowRes	Taki3d	2023-07-12T18:24:18Z	202	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-11T22:00:17Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: CrackDetectionLowRes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9940476190476191 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CrackDetectionLowRes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9940 - Loss: 0.0183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:--------:\|:---------------:\| \| 0.0126 \| 1.0 \| 992 \| 0.9879 \| 0.0344 \| \| 0.0788 \| 2.0 \| 1904 \| 0.9933 \| 0.0220 \| \| 0.1336 \| 3.0 \| 2856 \| 0.9933 \| 0.0222 \| \| 0.0066 \| 4.0 \| 3808 \| 0.9933 \| 0.0190 \| \| 0.0528 \| 5.0 \| 4760 \| 0.9940 \| 0.0183 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3
hsultanbey/autocomplete_trainer	hsultanbey	2023-07-12T18:23:42Z	143	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-12T18:22:39Z	--- license: mit tags: - generated_from_trainer model-index: - name: autocomplete_trainer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # autocomplete_trainer This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
wenbowen/BundleSDF	wenbowen	2023-07-12T18:21:31Z	0	0	null	[ "arxiv:2303.14158", "region:us" ]	null	2023-07-12T18:19:41Z	Paper https://arxiv.org/abs/2303.14158 Project page https://bundlesdf.github.io/
asrimanth/person-thumbs-up-lora	asrimanth	2023-07-12T18:19:11Z	2	3	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-12T18:18:41Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - asrimanth/person-thumbs-up-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
Danish-summarisation/DanSumT5-pilot	Danish-summarisation	2023-07-12T18:12:28Z	122	2	transformers	[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "summarization", "da", "arxiv:1804.11283", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-07-05T10:06:53Z	--- language: - da tags: - summarization widget: - text: "De strejkende SAS-piloter melder sig nu klar til gøre en undtagelse fra strejken for at hente strandede chartergæster hjem fra flere ferieområder. Undtagelsen skal gælde nogle uger frem, men piloterne vil under ingen omstændigheder have nye gæster med sig ned til de samme destinationer. Det skriver SAS Pilot Group i en pressemeddelelse. - Vi forstår, at det er uundgåeligt, at vores passagerer bliver ramt af strejken. Men vi piloter er altid fokuseret på at opføre os ansvarligt med passagersikkerheden som højeste prioritet, siger Martin Lindgren, der er formand for SAS Pilot Group i Norden. Men for at hjælpe strandede gæster kræver de strejkende piloter samtidig, at SAS' trækker sin lockout af piloterne tilbage. Samtidig ser SAS Pilot Group det som en forudsætning, at SAS ikke får hjælp fra andre flyselskaber til at flyve nye passagerer til de samme destinationer, som piloterne tilbyder at flyve gæster hjem fra, skriver fagforeningen." example_title: "Example 1" - text: "Mere end 21.000 krigsforbrydelser. Så mange efterforsker de ukrainske myndigheder lige nu ifølge den ukrainske rigsadvokat, Iryna Venediktova. Hun oplyser til britiske BBC, at der bliver anmeldt mellem 200 og 300 nye sager om dagen. Forbrydelserne er ifølge Venediktova svære at efterforske, fordi det kan være vanskeligt at komme frem til de relevante områder og mennesker. Men hun understreger overfor BBC, at russiske soldater, der har dræbt, tortureret eller voldtaget civile, bør forstå, at det kun er et spørgsmål om tid, før de alle vil komme for retten. Rusland er blevet anklaget for en lang række krigsforbrydelser, siden landet invaderede Ukraine den 24. februar, men afviser alle anklager." example_title: "Example 2" - text: "Det nye studie Cognitive Science på Aarhus Universitet, som i år havde Østjyllands højeste adgangskrav på 11,7 i karaktergennemsnit, udklækker det første hold bachelorer til sommer. Men når de skal læse videre på kandidaten må de til udlandet, hvis ikke de vil skifte til et andet fag. Aarhus Universitet kan nemlig ikke nå at oprette en kandidat i Cognitive Science til næste sommer, hvor det første hold bachelorer er færdige. Det rammer blandt andre Julie Sohn, der startede på uddannelsen i sommeren 2015, og derfor kun mangler et år, før hun er bachelor. - Jeg synes, at det er ærgerligt, at vi som nye studerende på et populært studie ikke kan tage en kandidat i Danmark, siger hun. Bacheloruddannelsen i Cognitive Science blev oprettet af Aarhus Universitet i 2015, og uddannelsen kombinerer viden om menneskelig adfærd med avanceret statistik. Da der endnu ikke er oprettet en kandidatuddannelse indenfor dette område, har Julie Sohn i stedet mulighed for at læse en kandidatgrad i for eksempel informationsvidenskab. Hun vil dog hellere fortsætte på Cognitive Science, og derfor overvejer hun nu at læse videre i udlandet. - Det ser ud til, at det er den eneste mulighed, hvis man gerne vil læse videre på noget, der faktisk passer ind til vores studie, siger hun. Nye regler giver forsinkelse På Aarhus Universitet havde man håbet på at have kandidatuddannelsen klar, når det første hold bachelorer bliver færdige til sommer. Arbejdet er dog blevet forsinket, fordi der er kommet nye regler for, hvornår man må oprette en uddannelse, fortæller Niels Lehmann, prodekan på fakultetet Arts, som Cognitive Science hører under. Det er nogle meget dygtige studerende, der kommer ind på uddannelsen, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast. NIELS LEHMANN, PRODEKAN, AARHUS UNIVERSITET Tidligere skulle Danmarks Akkrediteringsinstitution se alle nye uddannelser efter i sømmene for at sikre, at kvaliteten var i orden. Nu skal uddannelsesinstitutionerne selv stå for det kvalitetstjek. Men det tjek har Aarhus Universitet endnu ikke fået grønt lys til selv at udføre, fortæller prodekanen. - Vi ville meget gerne have kunnet nå at få et udbud på kandidaten i gang i 2018, men så længe man er under institutionsakkreditering, så kan man ikke ansøge om nye uddannelser, siger han. Det er endnu usikkert, hvornår Aarhus Universitet kan oprette kandidaten i Cognitive Science. Hvis de får alle de nødvendige godkendelser, kan den tidligst være klar i 2019. Prodekan Niels Lehmann frygter, at Danmark kommer til at miste nogle af landets skarpeste studerende, hvis de er nødt til at rejse til udlandet for at gøre deres uddannelse færdig. - Det er nogle meget, meget dygtige studerende, der kommer ind på denne uddannelse, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast, siger han. Hos Danmarks Akkrediteringsinstitution forstår man godt, at universitets ansatte og studenrede ærgrer sig. - Jeg kan godt forstå, at Aarhus Universitet ærgrer sig over, at det trækker ud, og at der går noget tid, før man får mulighed for at oprette nye uddannelser, og at man ikke har fået den genvej til at oprette nye uddannelser, som ville være fuldt med, hvis man havde opnået en positiv institutionsakkreditering, siger kommunikationsansvarlig Daniel Sebastian Larsen. I år var Cognitive Science i Aarhus den uddannelse i Danmark, der havde det fjerde højeste karakterkrav - det højeste var 'AP Graduate in Marketing Management' på Erhvervsakademi Sjælland med et krav på 12,3." example_title: "Example 3" --- # mT5-base fine-tuned for News article Summarisation ✏️🧾 [Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for summarisation downstream task. # Model summary This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019). # References Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283 Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831
arstep/q-FrozenLake-v1-4x4-noSlippery	arstep	2023-07-12T18:12:13Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T18:12:10Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="arstep/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ayanban011/vit-base_tobacco_wr_0.01_wd_0.2	ayanban011	2023-07-12T18:09:56Z	165	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-12T15:22:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_tobacco_wr_0.01_wd_0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_wr_0.01_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9831 - Accuracy: 0.755 - Brier Loss: 0.3936 - Nll: 1.4572 - F1 Micro: 0.755 - F1 Macro: 0.7245 - Ece: 0.2101 - Aurc: 0.1059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7384 \| 0.815 \| 0.3056 \| 1.1863 \| 0.815 \| 0.8021 \| 0.2211 \| 0.0789 \| \| No log \| 2.0 \| 25 \| 0.7504 \| 0.795 \| 0.3109 \| 1.2594 \| 0.795 \| 0.7691 \| 0.2235 \| 0.0789 \| \| No log \| 2.96 \| 37 \| 0.7415 \| 0.765 \| 0.3109 \| 1.0445 \| 0.765 \| 0.7422 \| 0.2323 \| 0.0762 \| \| No log \| 4.0 \| 50 \| 0.7222 \| 0.805 \| 0.2989 \| 1.3200 \| 0.805 \| 0.7951 \| 0.2028 \| 0.0682 \| \| No log \| 4.96 \| 62 \| 0.7160 \| 0.815 \| 0.2989 \| 1.2307 \| 0.815 \| 0.8067 \| 0.2381 \| 0.0688 \| \| No log \| 6.0 \| 75 \| 0.7149 \| 0.825 \| 0.2950 \| 1.3097 \| 0.825 \| 0.8127 \| 0.2110 \| 0.0809 \| \| No log \| 6.96 \| 87 \| 0.7157 \| 0.815 \| 0.2986 \| 1.2310 \| 0.815 \| 0.8007 \| 0.2195 \| 0.0779 \| \| No log \| 8.0 \| 100 \| 0.7245 \| 0.81 \| 0.3020 \| 1.3030 \| 0.81 \| 0.7949 \| 0.1997 \| 0.0834 \| \| No log \| 8.96 \| 112 \| 0.7111 \| 0.815 \| 0.2972 \| 1.2310 \| 0.815 \| 0.8009 \| 0.2090 \| 0.0793 \| \| No log \| 10.0 \| 125 \| 0.7344 \| 0.81 \| 0.3087 \| 1.2974 \| 0.81 \| 0.7956 \| 0.2240 \| 0.0913 \| \| No log \| 10.96 \| 137 \| 0.7264 \| 0.81 \| 0.3057 \| 1.3216 \| 0.81 \| 0.7907 \| 0.2074 \| 0.0879 \| \| No log \| 12.0 \| 150 \| 0.7301 \| 0.805 \| 0.3056 \| 1.2448 \| 0.805 \| 0.7919 \| 0.2112 \| 0.0932 \| \| No log \| 12.96 \| 162 \| 0.7224 \| 0.805 \| 0.3020 \| 1.2950 \| 0.805 \| 0.7899 \| 0.1930 \| 0.0915 \| \| No log \| 14.0 \| 175 \| 0.7235 \| 0.81 \| 0.3045 \| 1.2965 \| 0.81 \| 0.7956 \| 0.2025 \| 0.0902 \| \| No log \| 14.96 \| 187 \| 0.7239 \| 0.81 \| 0.3039 \| 1.2970 \| 0.81 \| 0.7956 \| 0.2156 \| 0.0906 \| \| No log \| 16.0 \| 200 \| 0.7293 \| 0.81 \| 0.3060 \| 1.4336 \| 0.81 \| 0.7924 \| 0.2029 \| 0.0955 \| \| No log \| 16.96 \| 212 \| 0.7253 \| 0.805 \| 0.3044 \| 1.2984 \| 0.805 \| 0.7853 \| 0.1946 \| 0.0911 \| \| No log \| 18.0 \| 225 \| 0.7293 \| 0.805 \| 0.3076 \| 1.1852 \| 0.805 \| 0.7861 \| 0.2015 \| 0.0916 \| \| No log \| 18.96 \| 237 \| 0.7328 \| 0.8 \| 0.3050 \| 1.3844 \| 0.8000 \| 0.7836 \| 0.1900 \| 0.0986 \| \| No log \| 20.0 \| 250 \| 0.7263 \| 0.8 \| 0.3058 \| 1.2943 \| 0.8000 \| 0.7754 \| 0.2003 \| 0.0900 \| \| No log \| 20.96 \| 262 \| 0.7370 \| 0.805 \| 0.3102 \| 1.3612 \| 0.805 \| 0.7818 \| 0.1998 \| 0.0939 \| \| No log \| 22.0 \| 275 \| 0.7412 \| 0.795 \| 0.3118 \| 1.3673 \| 0.795 \| 0.7714 \| 0.1952 \| 0.0957 \| \| No log \| 22.96 \| 287 \| 0.7326 \| 0.795 \| 0.3077 \| 1.3572 \| 0.795 \| 0.7666 \| 0.1935 \| 0.0925 \| \| No log \| 24.0 \| 300 \| 0.7308 \| 0.805 \| 0.3076 \| 1.4265 \| 0.805 \| 0.7818 \| 0.1968 \| 0.0906 \| \| No log \| 24.96 \| 312 \| 0.7424 \| 0.8 \| 0.3122 \| 1.3666 \| 0.8000 \| 0.7774 \| 0.1853 \| 0.0960 \| \| No log \| 26.0 \| 325 \| 0.7383 \| 0.8 \| 0.3097 \| 1.3644 \| 0.8000 \| 0.7774 \| 0.1911 \| 0.0952 \| \| No log \| 26.96 \| 337 \| 0.7468 \| 0.805 \| 0.3143 \| 1.4295 \| 0.805 \| 0.7829 \| 0.1952 \| 0.0971 \| \| No log \| 28.0 \| 350 \| 0.7484 \| 0.795 \| 0.3135 \| 1.4327 \| 0.795 \| 0.7668 \| 0.1906 \| 0.0989 \| \| No log \| 28.96 \| 362 \| 0.7459 \| 0.8 \| 0.3135 \| 1.3624 \| 0.8000 \| 0.7704 \| 0.1945 \| 0.0944 \| \| No log \| 30.0 \| 375 \| 0.7513 \| 0.8 \| 0.3160 \| 1.4282 \| 0.8000 \| 0.7698 \| 0.1991 \| 0.0953 \| \| No log \| 30.96 \| 387 \| 0.7535 \| 0.795 \| 0.3168 \| 1.4336 \| 0.795 \| 0.7656 \| 0.1799 \| 0.0980 \| \| No log \| 32.0 \| 400 \| 0.7540 \| 0.8 \| 0.3171 \| 1.4469 \| 0.8000 \| 0.7774 \| 0.1943 \| 0.0959 \| \| No log \| 32.96 \| 412 \| 0.7566 \| 0.8 \| 0.3180 \| 1.3772 \| 0.8000 \| 0.7704 \| 0.1809 \| 0.0972 \| \| No log \| 34.0 \| 425 \| 0.7641 \| 0.795 \| 0.3220 \| 1.3878 \| 0.795 \| 0.7691 \| 0.1937 \| 0.1021 \| \| No log \| 34.96 \| 437 \| 0.7602 \| 0.795 \| 0.3192 \| 1.3748 \| 0.795 \| 0.7656 \| 0.1908 \| 0.0973 \| \| No log \| 36.0 \| 450 \| 0.7577 \| 0.8 \| 0.3186 \| 1.4397 \| 0.8000 \| 0.7704 \| 0.1858 \| 0.0951 \| \| No log \| 36.96 \| 462 \| 0.7724 \| 0.795 \| 0.3233 \| 1.3800 \| 0.795 \| 0.7656 \| 0.1796 \| 0.1002 \| \| No log \| 38.0 \| 475 \| 0.7675 \| 0.795 \| 0.3223 \| 1.3659 \| 0.795 \| 0.7654 \| 0.1862 \| 0.0976 \| \| No log \| 38.96 \| 487 \| 0.7772 \| 0.79 \| 0.3261 \| 1.4406 \| 0.79 \| 0.7633 \| 0.1877 \| 0.1062 \| \| 0.0949 \| 40.0 \| 500 \| 0.7631 \| 0.795 \| 0.3211 \| 1.4302 \| 0.795 \| 0.7654 \| 0.1896 \| 0.0953 \| \| 0.0949 \| 40.96 \| 512 \| 0.7878 \| 0.79 \| 0.3304 \| 1.5689 \| 0.79 \| 0.7631 \| 0.1770 \| 0.1014 \| \| 0.0949 \| 42.0 \| 525 \| 0.7639 \| 0.815 \| 0.3198 \| 1.5524 \| 0.815 \| 0.7879 \| 0.1890 \| 0.0938 \| \| 0.0949 \| 42.96 \| 537 \| 0.7967 \| 0.785 \| 0.3350 \| 1.4516 \| 0.785 \| 0.7606 \| 0.1809 \| 0.1040 \| \| 0.0949 \| 44.0 \| 550 \| 0.7735 \| 0.81 \| 0.3237 \| 1.4915 \| 0.81 \| 0.7784 \| 0.2079 \| 0.0963 \| \| 0.0949 \| 44.96 \| 562 \| 0.7859 \| 0.79 \| 0.3300 \| 1.5076 \| 0.79 \| 0.7594 \| 0.1851 \| 0.0991 \| \| 0.0949 \| 46.0 \| 575 \| 0.7917 \| 0.79 \| 0.3323 \| 1.5062 \| 0.79 \| 0.7631 \| 0.1944 \| 0.1015 \| \| 0.0949 \| 46.96 \| 587 \| 0.7879 \| 0.795 \| 0.3310 \| 1.4304 \| 0.795 \| 0.7656 \| 0.1761 \| 0.0987 \| \| 0.0949 \| 48.0 \| 600 \| 0.7917 \| 0.79 \| 0.3320 \| 1.4382 \| 0.79 \| 0.7629 \| 0.1828 \| 0.0994 \| \| 0.0949 \| 48.96 \| 612 \| 0.7950 \| 0.79 \| 0.3333 \| 1.4929 \| 0.79 \| 0.7549 \| 0.1811 \| 0.0995 \| \| 0.0949 \| 50.0 \| 625 \| 0.8005 \| 0.79 \| 0.3363 \| 1.4351 \| 0.79 \| 0.7631 \| 0.1953 \| 0.1009 \| \| 0.0949 \| 50.96 \| 637 \| 0.8019 \| 0.79 \| 0.3363 \| 1.4891 \| 0.79 \| 0.7631 \| 0.1837 \| 0.0998 \| \| 0.0949 \| 52.0 \| 650 \| 0.8021 \| 0.79 \| 0.3354 \| 1.4899 \| 0.79 \| 0.7586 \| 0.1990 \| 0.0994 \| \| 0.0949 \| 52.96 \| 662 \| 0.8012 \| 0.795 \| 0.3346 \| 1.4913 \| 0.795 \| 0.7648 \| 0.1878 \| 0.0984 \| \| 0.0949 \| 54.0 \| 675 \| 0.8078 \| 0.785 \| 0.3388 \| 1.4920 \| 0.785 \| 0.7559 \| 0.1938 \| 0.1000 \| \| 0.0949 \| 54.96 \| 687 \| 0.8092 \| 0.785 \| 0.3391 \| 1.5003 \| 0.785 \| 0.7561 \| 0.1878 \| 0.1004 \| \| 0.0949 \| 56.0 \| 700 \| 0.8095 \| 0.795 \| 0.3378 \| 1.4914 \| 0.795 \| 0.7634 \| 0.1879 \| 0.0994 \| \| 0.0949 \| 56.96 \| 712 \| 0.8124 \| 0.785 \| 0.3396 \| 1.4926 \| 0.785 \| 0.7555 \| 0.1996 \| 0.1005 \| \| 0.0949 \| 58.0 \| 725 \| 0.8120 \| 0.78 \| 0.3391 \| 1.4405 \| 0.78 \| 0.7469 \| 0.1924 \| 0.1005 \| \| 0.0949 \| 58.96 \| 737 \| 0.8185 \| 0.785 \| 0.3406 \| 1.5007 \| 0.785 \| 0.7523 \| 0.1910 \| 0.1013 \| \| 0.0949 \| 60.0 \| 750 \| 0.8182 \| 0.785 \| 0.3421 \| 1.4371 \| 0.785 \| 0.7555 \| 0.1727 \| 0.1010 \| \| 0.0949 \| 60.96 \| 762 \| 0.8224 \| 0.78 \| 0.3443 \| 1.4404 \| 0.78 \| 0.7475 \| 0.1941 \| 0.1019 \| \| 0.0949 \| 62.0 \| 775 \| 0.8267 \| 0.78 \| 0.3463 \| 1.4995 \| 0.78 \| 0.7535 \| 0.1927 \| 0.1016 \| \| 0.0949 \| 62.96 \| 787 \| 0.8252 \| 0.775 \| 0.3447 \| 1.4965 \| 0.775 \| 0.7465 \| 0.1798 \| 0.1016 \| \| 0.0949 \| 64.0 \| 800 \| 0.8286 \| 0.78 \| 0.3446 \| 1.4987 \| 0.78 \| 0.7475 \| 0.1911 \| 0.1014 \| \| 0.0949 \| 64.96 \| 812 \| 0.8308 \| 0.78 \| 0.3467 \| 1.4438 \| 0.78 \| 0.7469 \| 0.1866 \| 0.1019 \| \| 0.0949 \| 66.0 \| 825 \| 0.8346 \| 0.775 \| 0.3476 \| 1.4592 \| 0.775 \| 0.7444 \| 0.1878 \| 0.1034 \| \| 0.0949 \| 66.96 \| 837 \| 0.8348 \| 0.78 \| 0.3484 \| 1.4423 \| 0.78 \| 0.7515 \| 0.1821 \| 0.1021 \| \| 0.0949 \| 68.0 \| 850 \| 0.8376 \| 0.78 \| 0.3481 \| 1.4970 \| 0.78 \| 0.7469 \| 0.1922 \| 0.1015 \| \| 0.0949 \| 68.96 \| 862 \| 0.8379 \| 0.78 \| 0.3497 \| 1.4388 \| 0.78 \| 0.7515 \| 0.1850 \| 0.1018 \| \| 0.0949 \| 70.0 \| 875 \| 0.8452 \| 0.78 \| 0.3512 \| 1.4974 \| 0.78 \| 0.7525 \| 0.1828 \| 0.1036 \| \| 0.0949 \| 70.96 \| 887 \| 0.8437 \| 0.78 \| 0.3511 \| 1.4519 \| 0.78 \| 0.7469 \| 0.2076 \| 0.1032 \| \| 0.0949 \| 72.0 \| 900 \| 0.8485 \| 0.775 \| 0.3535 \| 1.5163 \| 0.775 \| 0.7444 \| 0.1863 \| 0.1033 \| \| 0.0949 \| 72.96 \| 912 \| 0.8516 \| 0.77 \| 0.3555 \| 1.5015 \| 0.7700 \| 0.7404 \| 0.1868 \| 0.1030 \| \| 0.0949 \| 74.0 \| 925 \| 0.8507 \| 0.77 \| 0.3541 \| 1.4407 \| 0.7700 \| 0.7404 \| 0.1996 \| 0.1026 \| \| 0.0949 \| 74.96 \| 937 \| 0.8508 \| 0.77 \| 0.3540 \| 1.4424 \| 0.7700 \| 0.7400 \| 0.1996 \| 0.1025 \| \| 0.0949 \| 76.0 \| 950 \| 0.8559 \| 0.77 \| 0.3558 \| 1.4487 \| 0.7700 \| 0.7400 \| 0.2001 \| 0.1031 \| \| 0.0949 \| 76.96 \| 962 \| 0.8564 \| 0.77 \| 0.3568 \| 1.4389 \| 0.7700 \| 0.7400 \| 0.1804 \| 0.1024 \| \| 0.0949 \| 78.0 \| 975 \| 0.8611 \| 0.775 \| 0.3569 \| 1.4966 \| 0.775 \| 0.7440 \| 0.1831 \| 0.1031 \| \| 0.0949 \| 78.96 \| 987 \| 0.8573 \| 0.77 \| 0.3557 \| 1.4946 \| 0.7700 \| 0.7400 \| 0.1933 \| 0.1013 \| \| 0.0366 \| 80.0 \| 1000 \| 0.8640 \| 0.775 \| 0.3587 \| 1.4998 \| 0.775 \| 0.7440 \| 0.1871 \| 0.1032 \| \| 0.0366 \| 80.96 \| 1012 \| 0.8640 \| 0.77 \| 0.3585 \| 1.4983 \| 0.7700 \| 0.7400 \| 0.1921 \| 0.1032 \| \| 0.0366 \| 82.0 \| 1025 \| 0.8696 \| 0.77 \| 0.3608 \| 1.5032 \| 0.7700 \| 0.7400 \| 0.2033 \| 0.1036 \| \| 0.0366 \| 82.96 \| 1037 \| 0.8702 \| 0.77 \| 0.3613 \| 1.4987 \| 0.7700 \| 0.7400 \| 0.2022 \| 0.1029 \| \| 0.0366 \| 84.0 \| 1050 \| 0.8686 \| 0.77 \| 0.3597 \| 1.4446 \| 0.7700 \| 0.7400 \| 0.1887 \| 0.1028 \| \| 0.0366 \| 84.96 \| 1062 \| 0.8700 \| 0.77 \| 0.3607 \| 1.4365 \| 0.7700 \| 0.7400 \| 0.1900 \| 0.1025 \| \| 0.0366 \| 86.0 \| 1075 \| 0.8756 \| 0.765 \| 0.3621 \| 1.5009 \| 0.765 \| 0.7308 \| 0.1983 \| 0.1040 \| \| 0.0366 \| 86.96 \| 1087 \| 0.8768 \| 0.76 \| 0.3623 \| 1.5035 \| 0.76 \| 0.7282 \| 0.1956 \| 0.1040 \| \| 0.0366 \| 88.0 \| 1100 \| 0.8762 \| 0.765 \| 0.3618 \| 1.4409 \| 0.765 \| 0.7308 \| 0.1957 \| 0.1033 \| \| 0.0366 \| 88.96 \| 1112 \| 0.8777 \| 0.765 \| 0.3629 \| 1.4427 \| 0.765 \| 0.7308 \| 0.1974 \| 0.1036 \| \| 0.0366 \| 90.0 \| 1125 \| 0.8854 \| 0.76 \| 0.3661 \| 1.4681 \| 0.76 \| 0.7282 \| 0.2023 \| 0.1049 \| \| 0.0366 \| 90.96 \| 1137 \| 0.8867 \| 0.76 \| 0.3660 \| 1.5057 \| 0.76 \| 0.7282 \| 0.1995 \| 0.1049 \| \| 0.0366 \| 92.0 \| 1150 \| 0.8849 \| 0.765 \| 0.3648 \| 1.5011 \| 0.765 \| 0.7308 \| 0.1953 \| 0.1039 \| \| 0.0366 \| 92.96 \| 1162 \| 0.8898 \| 0.76 \| 0.3670 \| 1.5077 \| 0.76 \| 0.7282 \| 0.2083 \| 0.1045 \| \| 0.0366 \| 94.0 \| 1175 \| 0.8891 \| 0.765 \| 0.3662 \| 1.4520 \| 0.765 \| 0.7308 \| 0.2091 \| 0.1040 \| \| 0.0366 \| 94.96 \| 1187 \| 0.8910 \| 0.755 \| 0.3679 \| 1.4460 \| 0.755 \| 0.7247 \| 0.2039 \| 0.1043 \| \| 0.0366 \| 96.0 \| 1200 \| 0.8935 \| 0.76 \| 0.3684 \| 1.4435 \| 0.76 \| 0.7273 \| 0.1929 \| 0.1041 \| \| 0.0366 \| 96.96 \| 1212 \| 0.8964 \| 0.755 \| 0.3689 \| 1.4526 \| 0.755 \| 0.7247 \| 0.1980 \| 0.1048 \| \| 0.0366 \| 98.0 \| 1225 \| 0.8979 \| 0.755 \| 0.3701 \| 1.4507 \| 0.755 \| 0.7247 \| 0.2017 \| 0.1045 \| \| 0.0366 \| 98.96 \| 1237 \| 0.8965 \| 0.755 \| 0.3692 \| 1.4474 \| 0.755 \| 0.7247 \| 0.2057 \| 0.1044 \| \| 0.0366 \| 100.0 \| 1250 \| 0.9019 \| 0.755 \| 0.3716 \| 1.4526 \| 0.755 \| 0.7247 \| 0.2051 \| 0.1047 \| \| 0.0366 \| 100.96 \| 1262 \| 0.8994 \| 0.755 \| 0.3694 \| 1.4485 \| 0.755 \| 0.7247 \| 0.1979 \| 0.1041 \| \| 0.0366 \| 102.0 \| 1275 \| 0.9023 \| 0.755 \| 0.3715 \| 1.4465 \| 0.755 \| 0.7247 \| 0.2140 \| 0.1044 \| \| 0.0366 \| 102.96 \| 1287 \| 0.9048 \| 0.755 \| 0.3720 \| 1.4472 \| 0.755 \| 0.7247 \| 0.2066 \| 0.1045 \| \| 0.0366 \| 104.0 \| 1300 \| 0.9060 \| 0.755 \| 0.3719 \| 1.4565 \| 0.755 \| 0.7247 \| 0.2003 \| 0.1044 \| \| 0.0366 \| 104.96 \| 1312 \| 0.9105 \| 0.755 \| 0.3735 \| 1.4625 \| 0.755 \| 0.7247 \| 0.2094 \| 0.1053 \| \| 0.0366 \| 106.0 \| 1325 \| 0.9099 \| 0.76 \| 0.3738 \| 1.4463 \| 0.76 \| 0.7273 \| 0.2050 \| 0.1044 \| \| 0.0366 \| 106.96 \| 1337 \| 0.9111 \| 0.755 \| 0.3751 \| 1.4486 \| 0.755 \| 0.7247 \| 0.2173 \| 0.1041 \| \| 0.0366 \| 108.0 \| 1350 \| 0.9149 \| 0.755 \| 0.3745 \| 1.5081 \| 0.755 \| 0.7247 \| 0.2062 \| 0.1052 \| \| 0.0366 \| 108.96 \| 1362 \| 0.9146 \| 0.755 \| 0.3744 \| 1.4513 \| 0.755 \| 0.7247 \| 0.2073 \| 0.1047 \| \| 0.0366 \| 110.0 \| 1375 \| 0.9157 \| 0.755 \| 0.3764 \| 1.4486 \| 0.755 \| 0.7247 \| 0.2094 \| 0.1046 \| \| 0.0366 \| 110.96 \| 1387 \| 0.9201 \| 0.755 \| 0.3767 \| 1.4529 \| 0.755 \| 0.7247 \| 0.2093 \| 0.1051 \| \| 0.0366 \| 112.0 \| 1400 \| 0.9190 \| 0.755 \| 0.3759 \| 1.4533 \| 0.755 \| 0.7247 \| 0.2020 \| 0.1049 \| \| 0.0366 \| 112.96 \| 1412 \| 0.9230 \| 0.755 \| 0.3782 \| 1.4529 \| 0.755 \| 0.7247 \| 0.2054 \| 0.1050 \| \| 0.0366 \| 114.0 \| 1425 \| 0.9234 \| 0.755 \| 0.3778 \| 1.4505 \| 0.755 \| 0.7247 \| 0.2009 \| 0.1049 \| \| 0.0366 \| 114.96 \| 1437 \| 0.9238 \| 0.755 \| 0.3780 \| 1.4469 \| 0.755 \| 0.7247 \| 0.1974 \| 0.1051 \| \| 0.0366 \| 116.0 \| 1450 \| 0.9264 \| 0.755 \| 0.3786 \| 1.4538 \| 0.755 \| 0.7247 \| 0.2016 \| 0.1051 \| \| 0.0366 \| 116.96 \| 1462 \| 0.9275 \| 0.755 \| 0.3787 \| 1.4553 \| 0.755 \| 0.7247 \| 0.2012 \| 0.1054 \| \| 0.0366 \| 118.0 \| 1475 \| 0.9306 \| 0.755 \| 0.3798 \| 1.4595 \| 0.755 \| 0.7247 \| 0.2139 \| 0.1057 \| \| 0.0366 \| 118.96 \| 1487 \| 0.9291 \| 0.76 \| 0.3789 \| 1.4529 \| 0.76 \| 0.7273 \| 0.2014 \| 0.1054 \| \| 0.0214 \| 120.0 \| 1500 \| 0.9318 \| 0.755 \| 0.3804 \| 1.4520 \| 0.755 \| 0.7247 \| 0.2017 \| 0.1050 \| \| 0.0214 \| 120.96 \| 1512 \| 0.9325 \| 0.755 \| 0.3796 \| 1.4535 \| 0.755 \| 0.7247 \| 0.2087 \| 0.1053 \| \| 0.0214 \| 122.0 \| 1525 \| 0.9339 \| 0.755 \| 0.3804 \| 1.4534 \| 0.755 \| 0.7247 \| 0.2068 \| 0.1056 \| \| 0.0214 \| 122.96 \| 1537 \| 0.9342 \| 0.755 \| 0.3807 \| 1.4519 \| 0.755 \| 0.7247 \| 0.1986 \| 0.1052 \| \| 0.0214 \| 124.0 \| 1550 \| 0.9357 \| 0.755 \| 0.3808 \| 1.4524 \| 0.755 \| 0.7247 \| 0.2058 \| 0.1054 \| \| 0.0214 \| 124.96 \| 1562 \| 0.9360 \| 0.755 \| 0.3808 \| 1.4514 \| 0.755 \| 0.7247 \| 0.2061 \| 0.1052 \| \| 0.0214 \| 126.0 \| 1575 \| 0.9409 \| 0.755 \| 0.3828 \| 1.4557 \| 0.755 \| 0.7247 \| 0.2044 \| 0.1058 \| \| 0.0214 \| 126.96 \| 1587 \| 0.9390 \| 0.755 \| 0.3819 \| 1.4523 \| 0.755 \| 0.7247 \| 0.2062 \| 0.1052 \| \| 0.0214 \| 128.0 \| 1600 \| 0.9425 \| 0.755 \| 0.3833 \| 1.4559 \| 0.755 \| 0.7247 \| 0.2049 \| 0.1058 \| \| 0.0214 \| 128.96 \| 1612 \| 0.9421 \| 0.755 \| 0.3824 \| 1.4534 \| 0.755 \| 0.7247 \| 0.1978 \| 0.1055 \| \| 0.0214 \| 130.0 \| 1625 \| 0.9433 \| 0.755 \| 0.3831 \| 1.4530 \| 0.755 \| 0.7247 \| 0.2069 \| 0.1056 \| \| 0.0214 \| 130.96 \| 1637 \| 0.9463 \| 0.755 \| 0.3842 \| 1.4535 \| 0.755 \| 0.7247 \| 0.2127 \| 0.1058 \| \| 0.0214 \| 132.0 \| 1650 \| 0.9462 \| 0.755 \| 0.3835 \| 1.4546 \| 0.755 \| 0.7247 \| 0.1975 \| 0.1057 \| \| 0.0214 \| 132.96 \| 1662 \| 0.9479 \| 0.755 \| 0.3842 \| 1.4562 \| 0.755 \| 0.7247 \| 0.2012 \| 0.1059 \| \| 0.0214 \| 134.0 \| 1675 \| 0.9493 \| 0.755 \| 0.3852 \| 1.4547 \| 0.755 \| 0.7247 \| 0.2161 \| 0.1057 \| \| 0.0214 \| 134.96 \| 1687 \| 0.9484 \| 0.755 \| 0.3842 \| 1.4563 \| 0.755 \| 0.7247 \| 0.2046 \| 0.1055 \| \| 0.0214 \| 136.0 \| 1700 \| 0.9500 \| 0.755 \| 0.3849 \| 1.4528 \| 0.755 \| 0.7247 \| 0.2081 \| 0.1058 \| \| 0.0214 \| 136.96 \| 1712 \| 0.9510 \| 0.755 \| 0.3854 \| 1.4510 \| 0.755 \| 0.7247 \| 0.1976 \| 0.1053 \| \| 0.0214 \| 138.0 \| 1725 \| 0.9519 \| 0.755 \| 0.3855 \| 1.4516 \| 0.755 \| 0.7247 \| 0.2090 \| 0.1056 \| \| 0.0214 \| 138.96 \| 1737 \| 0.9535 \| 0.755 \| 0.3857 \| 1.4537 \| 0.755 \| 0.7247 \| 0.2091 \| 0.1057 \| \| 0.0214 \| 140.0 \| 1750 \| 0.9546 \| 0.755 \| 0.3862 \| 1.4557 \| 0.755 \| 0.7247 \| 0.2020 \| 0.1057 \| \| 0.0214 \| 140.96 \| 1762 \| 0.9558 \| 0.755 \| 0.3867 \| 1.4530 \| 0.755 \| 0.7247 \| 0.2018 \| 0.1059 \| \| 0.0214 \| 142.0 \| 1775 \| 0.9567 \| 0.755 \| 0.3870 \| 1.4522 \| 0.755 \| 0.7247 \| 0.2059 \| 0.1055 \| \| 0.0214 \| 142.96 \| 1787 \| 0.9589 \| 0.755 \| 0.3876 \| 1.4555 \| 0.755 \| 0.7247 \| 0.2063 \| 0.1060 \| \| 0.0214 \| 144.0 \| 1800 \| 0.9584 \| 0.755 \| 0.3872 \| 1.4568 \| 0.755 \| 0.7245 \| 0.2162 \| 0.1056 \| \| 0.0214 \| 144.96 \| 1812 \| 0.9613 \| 0.755 \| 0.3881 \| 1.4594 \| 0.755 \| 0.7247 \| 0.2085 \| 0.1063 \| \| 0.0214 \| 146.0 \| 1825 \| 0.9604 \| 0.755 \| 0.3873 \| 1.5132 \| 0.755 \| 0.7247 \| 0.2047 \| 0.1056 \| \| 0.0214 \| 146.96 \| 1837 \| 0.9627 \| 0.755 \| 0.3887 \| 1.4573 \| 0.755 \| 0.7247 \| 0.2107 \| 0.1059 \| \| 0.0214 \| 148.0 \| 1850 \| 0.9643 \| 0.755 \| 0.3890 \| 1.4570 \| 0.755 \| 0.7247 \| 0.2041 \| 0.1065 \| \| 0.0214 \| 148.96 \| 1862 \| 0.9633 \| 0.755 \| 0.3886 \| 1.4526 \| 0.755 \| 0.7247 \| 0.2085 \| 0.1055 \| \| 0.0214 \| 150.0 \| 1875 \| 0.9637 \| 0.755 \| 0.3887 \| 1.4551 \| 0.755 \| 0.7245 \| 0.2096 \| 0.1054 \| \| 0.0214 \| 150.96 \| 1887 \| 0.9645 \| 0.755 \| 0.3886 \| 1.4534 \| 0.755 \| 0.7247 \| 0.2100 \| 0.1058 \| \| 0.0214 \| 152.0 \| 1900 \| 0.9661 \| 0.755 \| 0.3891 \| 1.4550 \| 0.755 \| 0.7247 \| 0.2072 \| 0.1060 \| \| 0.0214 \| 152.96 \| 1912 \| 0.9665 \| 0.755 \| 0.3894 \| 1.4543 \| 0.755 \| 0.7247 \| 0.2092 \| 0.1057 \| \| 0.0214 \| 154.0 \| 1925 \| 0.9667 \| 0.755 \| 0.3894 \| 1.4570 \| 0.755 \| 0.7245 \| 0.2019 \| 0.1056 \| \| 0.0214 \| 154.96 \| 1937 \| 0.9681 \| 0.755 \| 0.3900 \| 1.4537 \| 0.755 \| 0.7245 \| 0.2098 \| 0.1059 \| \| 0.0214 \| 156.0 \| 1950 \| 0.9689 \| 0.755 \| 0.3903 \| 1.4533 \| 0.755 \| 0.7245 \| 0.2022 \| 0.1056 \| \| 0.0214 \| 156.96 \| 1962 \| 0.9689 \| 0.755 \| 0.3898 \| 1.4553 \| 0.755 \| 0.7247 \| 0.2074 \| 0.1058 \| \| 0.0214 \| 158.0 \| 1975 \| 0.9705 \| 0.755 \| 0.3901 \| 1.4566 \| 0.755 \| 0.7247 \| 0.2072 \| 0.1062 \| \| 0.0214 \| 158.96 \| 1987 \| 0.9721 \| 0.755 \| 0.3911 \| 1.4570 \| 0.755 \| 0.7245 \| 0.2055 \| 0.1062 \| \| 0.0155 \| 160.0 \| 2000 \| 0.9712 \| 0.755 \| 0.3904 \| 1.4551 \| 0.755 \| 0.7245 \| 0.2070 \| 0.1060 \| \| 0.0155 \| 160.96 \| 2012 \| 0.9721 \| 0.755 \| 0.3906 \| 1.4563 \| 0.755 \| 0.7245 \| 0.2108 \| 0.1059 \| \| 0.0155 \| 162.0 \| 2025 \| 0.9734 \| 0.755 \| 0.3914 \| 1.4550 \| 0.755 \| 0.7245 \| 0.2071 \| 0.1057 \| \| 0.0155 \| 162.96 \| 2037 \| 0.9740 \| 0.755 \| 0.3913 \| 1.4565 \| 0.755 \| 0.7245 \| 0.2073 \| 0.1062 \| \| 0.0155 \| 164.0 \| 2050 \| 0.9744 \| 0.755 \| 0.3915 \| 1.4563 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 164.96 \| 2062 \| 0.9752 \| 0.755 \| 0.3917 \| 1.4558 \| 0.755 \| 0.7245 \| 0.2102 \| 0.1059 \| \| 0.0155 \| 166.0 \| 2075 \| 0.9755 \| 0.755 \| 0.3916 \| 1.4566 \| 0.755 \| 0.7245 \| 0.2102 \| 0.1062 \| \| 0.0155 \| 166.96 \| 2087 \| 0.9755 \| 0.755 \| 0.3917 \| 1.4542 \| 0.755 \| 0.7245 \| 0.2100 \| 0.1057 \| \| 0.0155 \| 168.0 \| 2100 \| 0.9768 \| 0.755 \| 0.3921 \| 1.4578 \| 0.755 \| 0.7245 \| 0.2103 \| 0.1059 \| \| 0.0155 \| 168.96 \| 2112 \| 0.9767 \| 0.755 \| 0.3919 \| 1.4575 \| 0.755 \| 0.7245 \| 0.2106 \| 0.1057 \| \| 0.0155 \| 170.0 \| 2125 \| 0.9776 \| 0.755 \| 0.3922 \| 1.4567 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 170.96 \| 2137 \| 0.9783 \| 0.755 \| 0.3925 \| 1.4560 \| 0.755 \| 0.7245 \| 0.2080 \| 0.1062 \| \| 0.0155 \| 172.0 \| 2150 \| 0.9793 \| 0.755 \| 0.3927 \| 1.4578 \| 0.755 \| 0.7245 \| 0.2102 \| 0.1064 \| \| 0.0155 \| 172.96 \| 2162 \| 0.9783 \| 0.755 \| 0.3923 \| 1.4557 \| 0.755 \| 0.7245 \| 0.2098 \| 0.1058 \| \| 0.0155 \| 174.0 \| 2175 \| 0.9790 \| 0.755 \| 0.3927 \| 1.4550 \| 0.755 \| 0.7245 \| 0.2087 \| 0.1057 \| \| 0.0155 \| 174.96 \| 2187 \| 0.9801 \| 0.755 \| 0.3929 \| 1.4577 \| 0.755 \| 0.7245 \| 0.2100 \| 0.1059 \| \| 0.0155 \| 176.0 \| 2200 \| 0.9800 \| 0.755 \| 0.3928 \| 1.4566 \| 0.755 \| 0.7245 \| 0.2081 \| 0.1058 \| \| 0.0155 \| 176.96 \| 2212 \| 0.9809 \| 0.755 \| 0.3932 \| 1.4568 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 178.0 \| 2225 \| 0.9808 \| 0.755 \| 0.3930 \| 1.4571 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 178.96 \| 2237 \| 0.9808 \| 0.755 \| 0.3930 \| 1.4566 \| 0.755 \| 0.7245 \| 0.2082 \| 0.1057 \| \| 0.0155 \| 180.0 \| 2250 \| 0.9813 \| 0.755 \| 0.3931 \| 1.4567 \| 0.755 \| 0.7245 \| 0.2082 \| 0.1058 \| \| 0.0155 \| 180.96 \| 2262 \| 0.9817 \| 0.755 \| 0.3933 \| 1.4571 \| 0.755 \| 0.7245 \| 0.2032 \| 0.1059 \| \| 0.0155 \| 182.0 \| 2275 \| 0.9819 \| 0.755 \| 0.3933 \| 1.4570 \| 0.755 \| 0.7245 \| 0.2103 \| 0.1058 \| \| 0.0155 \| 182.96 \| 2287 \| 0.9822 \| 0.755 \| 0.3934 \| 1.4567 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 184.0 \| 2300 \| 0.9824 \| 0.755 \| 0.3934 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 184.96 \| 2312 \| 0.9825 \| 0.755 \| 0.3935 \| 1.4569 \| 0.755 \| 0.7245 \| 0.2081 \| 0.1060 \| \| 0.0155 \| 186.0 \| 2325 \| 0.9825 \| 0.755 \| 0.3934 \| 1.4574 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 186.96 \| 2337 \| 0.9829 \| 0.755 \| 0.3935 \| 1.4571 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 188.0 \| 2350 \| 0.9830 \| 0.755 \| 0.3936 \| 1.4569 \| 0.755 \| 0.7245 \| 0.2081 \| 0.1060 \| \| 0.0155 \| 188.96 \| 2362 \| 0.9831 \| 0.755 \| 0.3936 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2082 \| 0.1059 \| \| 0.0155 \| 190.0 \| 2375 \| 0.9830 \| 0.755 \| 0.3935 \| 1.4575 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 190.96 \| 2387 \| 0.9831 \| 0.755 \| 0.3936 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 192.0 \| 2400 \| 0.9831 \| 0.755 \| 0.3936 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3
BlueAvenir/model_growth_restructuring_V_0_3	BlueAvenir	2023-07-12T18:03:50Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-07-12T18:03:20Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 153 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 153, "warmup_steps": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
VK246/IC_ver5a_coco_swin_gpt2_05pc_1e	VK246	2023-07-12T18:03:19Z	45	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-07-12T17:53:07Z	--- tags: - generated_from_trainer datasets: - coco metrics: - rouge - bleu model-index: - name: IC_ver5a_coco_swin_gpt2_05pc_1e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IC_ver5a_coco_swin_gpt2_05pc_1e This model is a fine-tuned version of [](https://huggingface.co/) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 1.2245 - Rouge1: 22.0956 - Rouge2: 2.7707 - Rougel: 20.6806 - Rougelsum: 20.6915 - Bleu: 1.2101 - Gen Len: 11.1124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:------:\|:-------:\| \| 2.1625 \| 0.42 \| 25 \| 1.2744 \| 22.7707 \| 2.4514 \| 20.6431 \| 20.6829 \| 0.0 \| 11.1124 \| \| 1.3438 \| 0.85 \| 50 \| 1.2374 \| 21.3271 \| 2.4116 \| 19.7122 \| 19.7385 \| 0.7474 \| 11.1124 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
NasimB/gpt2-concat-mod-datasets1-iorder-rarity-all-5p5k	NasimB	2023-07-12T18:03:11Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-12T16:28:12Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-mod-datasets1-iorder-rarity-all-5p5k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-mod-datasets1-iorder-rarity-all-5p5k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.7791 \| 0.32 \| 500 \| 5.6460 \| \| 5.4436 \| 0.63 \| 1000 \| 5.2273 \| \| 5.0944 \| 0.95 \| 1500 \| 4.9700 \| \| 4.809 \| 1.26 \| 2000 \| 4.8294 \| \| 4.6757 \| 1.58 \| 2500 \| 4.7121 \| \| 4.5526 \| 1.9 \| 3000 \| 4.6044 \| \| 4.3523 \| 2.21 \| 3500 \| 4.5477 \| \| 4.2579 \| 2.53 \| 4000 \| 4.4848 \| \| 4.2187 \| 2.84 \| 4500 \| 4.4262 \| \| 4.0519 \| 3.16 \| 5000 \| 4.4090 \| \| 3.9359 \| 3.47 \| 5500 \| 4.3808 \| \| 3.9128 \| 3.79 \| 6000 \| 4.3460 \| \| 3.8052 \| 4.11 \| 6500 \| 4.3424 \| \| 3.6404 \| 4.42 \| 7000 \| 4.3367 \| \| 3.6307 \| 4.74 \| 7500 \| 4.3246 \| \| 3.5885 \| 5.05 \| 8000 \| 4.3236 \| \| 3.4332 \| 5.37 \| 8500 \| 4.3296 \| \| 3.4351 \| 5.69 \| 9000 \| 4.3281 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
tyavika/lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi	tyavika	2023-07-12T17:59:27Z	77	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-12T15:42:47Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3813 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.5317 \| 1.0 \| 3290 \| 1.3385 \| \| 1.0853 \| 2.0 \| 6580 \| 1.1885 \| \| 0.7993 \| 3.0 \| 9870 \| 1.2330 \| \| 0.5808 \| 4.0 \| 13160 \| 1.3813 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
rmcl7/sd-class-butterflies-32	rmcl7	2023-07-12T17:54:58Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-12T17:53:25Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('rmcl7/sd-class-butterflies-32') image = pipeline().images[0] image ```
DipanAI/falcon_law_7Bat	DipanAI	2023-07-12T17:53:31Z	1	0	peft	[ "peft", "tensorboard", "region:us" ]	null	2023-07-12T17:49:30Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0
ayanban011/vit-base_tobacco_lr5e-6_e_200	ayanban011	2023-07-12T17:42:48Z	167	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-12T15:04:59Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_tobacco_lr5e-6_e_200 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_lr5e-6_e_200 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7893 - Accuracy: 0.78 - Brier Loss: 0.3364 - Nll: 1.4430 - F1 Micro: 0.78 - F1 Macro: 0.7488 - Ece: 0.1922 - Aurc: 0.1018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7450 \| 0.815 \| 0.3079 \| 1.1882 \| 0.815 \| 0.7942 \| 0.2383 \| 0.0734 \| \| No log \| 2.0 \| 25 \| 0.7440 \| 0.815 \| 0.3074 \| 1.1877 \| 0.815 \| 0.7922 \| 0.2303 \| 0.0734 \| \| No log \| 2.96 \| 37 \| 0.7429 \| 0.81 \| 0.3071 \| 1.1883 \| 0.81 \| 0.7914 \| 0.2367 \| 0.0714 \| \| No log \| 4.0 \| 50 \| 0.7463 \| 0.815 \| 0.3083 \| 1.1912 \| 0.815 \| 0.7942 \| 0.2334 \| 0.0768 \| \| No log \| 4.96 \| 62 \| 0.7453 \| 0.815 \| 0.3080 \| 1.1927 \| 0.815 \| 0.7922 \| 0.2224 \| 0.0751 \| \| No log \| 6.0 \| 75 \| 0.7416 \| 0.81 \| 0.3067 \| 1.1870 \| 0.81 \| 0.7914 \| 0.2232 \| 0.0716 \| \| No log \| 6.96 \| 87 \| 0.7420 \| 0.81 \| 0.3070 \| 1.1858 \| 0.81 \| 0.7914 \| 0.2309 \| 0.0730 \| \| No log \| 8.0 \| 100 \| 0.7409 \| 0.81 \| 0.3062 \| 1.1879 \| 0.81 \| 0.7871 \| 0.2186 \| 0.0749 \| \| No log \| 8.96 \| 112 \| 0.7444 \| 0.815 \| 0.3079 \| 1.1984 \| 0.815 \| 0.8059 \| 0.2342 \| 0.0766 \| \| No log \| 10.0 \| 125 \| 0.7445 \| 0.81 \| 0.3079 \| 1.1918 \| 0.81 \| 0.7894 \| 0.2353 \| 0.0775 \| \| No log \| 10.96 \| 137 \| 0.7451 \| 0.81 \| 0.3085 \| 1.1888 \| 0.81 \| 0.7871 \| 0.2319 \| 0.0784 \| \| No log \| 12.0 \| 150 \| 0.7411 \| 0.805 \| 0.3060 \| 1.1914 \| 0.805 \| 0.7829 \| 0.2186 \| 0.0779 \| \| No log \| 12.96 \| 162 \| 0.7406 \| 0.815 \| 0.3075 \| 1.1967 \| 0.815 \| 0.8057 \| 0.2472 \| 0.0746 \| \| No log \| 14.0 \| 175 \| 0.7360 \| 0.805 \| 0.3048 \| 1.2494 \| 0.805 \| 0.7841 \| 0.2383 \| 0.0770 \| \| No log \| 14.96 \| 187 \| 0.7331 \| 0.81 \| 0.3037 \| 1.1896 \| 0.81 \| 0.7912 \| 0.2306 \| 0.0721 \| \| No log \| 16.0 \| 200 \| 0.7345 \| 0.81 \| 0.3042 \| 1.2488 \| 0.81 \| 0.7956 \| 0.2064 \| 0.0794 \| \| No log \| 16.96 \| 212 \| 0.7329 \| 0.815 \| 0.3018 \| 1.2595 \| 0.815 \| 0.8043 \| 0.2331 \| 0.0797 \| \| No log \| 18.0 \| 225 \| 0.7354 \| 0.795 \| 0.3055 \| 1.1743 \| 0.795 \| 0.7765 \| 0.2154 \| 0.0742 \| \| No log \| 18.96 \| 237 \| 0.7282 \| 0.805 \| 0.3001 \| 1.1920 \| 0.805 \| 0.7844 \| 0.2444 \| 0.0748 \| \| No log \| 20.0 \| 250 \| 0.7225 \| 0.805 \| 0.2981 \| 1.1804 \| 0.805 \| 0.7874 \| 0.2041 \| 0.0744 \| \| No log \| 20.96 \| 262 \| 0.7250 \| 0.81 \| 0.3000 \| 1.2516 \| 0.81 \| 0.7900 \| 0.2148 \| 0.0761 \| \| No log \| 22.0 \| 275 \| 0.7252 \| 0.81 \| 0.3009 \| 1.3033 \| 0.81 \| 0.7954 \| 0.2375 \| 0.0782 \| \| No log \| 22.96 \| 287 \| 0.7293 \| 0.8 \| 0.3016 \| 1.2557 \| 0.8000 \| 0.7796 \| 0.2243 \| 0.0810 \| \| No log \| 24.0 \| 300 \| 0.7344 \| 0.805 \| 0.3057 \| 1.3122 \| 0.805 \| 0.7853 \| 0.2096 \| 0.0815 \| \| No log \| 24.96 \| 312 \| 0.7314 \| 0.81 \| 0.3044 \| 1.2501 \| 0.81 \| 0.7909 \| 0.2068 \| 0.0808 \| \| No log \| 26.0 \| 325 \| 0.7293 \| 0.81 \| 0.3036 \| 1.3066 \| 0.81 \| 0.7909 \| 0.2015 \| 0.0804 \| \| No log \| 26.96 \| 337 \| 0.7323 \| 0.805 \| 0.3042 \| 1.3147 \| 0.805 \| 0.7853 \| 0.2242 \| 0.0827 \| \| No log \| 28.0 \| 350 \| 0.7288 \| 0.805 \| 0.3029 \| 1.3109 \| 0.805 \| 0.7853 \| 0.1976 \| 0.0826 \| \| No log \| 28.96 \| 362 \| 0.7343 \| 0.805 \| 0.3058 \| 1.3108 \| 0.805 \| 0.7853 \| 0.2128 \| 0.0851 \| \| No log \| 30.0 \| 375 \| 0.7351 \| 0.8 \| 0.3062 \| 1.3129 \| 0.8000 \| 0.7733 \| 0.2088 \| 0.0845 \| \| No log \| 30.96 \| 387 \| 0.7301 \| 0.8 \| 0.3044 \| 1.2452 \| 0.8000 \| 0.7733 \| 0.2272 \| 0.0836 \| \| No log \| 32.0 \| 400 \| 0.7340 \| 0.8 \| 0.3055 \| 1.3769 \| 0.8000 \| 0.7806 \| 0.2207 \| 0.0855 \| \| No log \| 32.96 \| 412 \| 0.7322 \| 0.805 \| 0.3052 \| 1.3132 \| 0.805 \| 0.7784 \| 0.2470 \| 0.0865 \| \| No log \| 34.0 \| 425 \| 0.7301 \| 0.8 \| 0.3045 \| 1.2543 \| 0.8000 \| 0.7733 \| 0.2021 \| 0.0863 \| \| No log \| 34.96 \| 437 \| 0.7297 \| 0.8 \| 0.3046 \| 1.3056 \| 0.8000 \| 0.7731 \| 0.1886 \| 0.0836 \| \| No log \| 36.0 \| 450 \| 0.7326 \| 0.805 \| 0.3056 \| 1.3102 \| 0.805 \| 0.7784 \| 0.2256 \| 0.0892 \| \| No log \| 36.96 \| 462 \| 0.7311 \| 0.8 \| 0.3046 \| 1.3681 \| 0.8000 \| 0.7733 \| 0.2065 \| 0.0880 \| \| No log \| 38.0 \| 475 \| 0.7356 \| 0.8 \| 0.3077 \| 1.3666 \| 0.8000 \| 0.7733 \| 0.2034 \| 0.0895 \| \| No log \| 38.96 \| 487 \| 0.7302 \| 0.8 \| 0.3042 \| 1.3114 \| 0.8000 \| 0.7733 \| 0.1996 \| 0.0894 \| \| 0.1528 \| 40.0 \| 500 \| 0.7289 \| 0.805 \| 0.3035 \| 1.3669 \| 0.805 \| 0.7797 \| 0.1951 \| 0.0874 \| \| 0.1528 \| 40.96 \| 512 \| 0.7292 \| 0.8 \| 0.3037 \| 1.3685 \| 0.8000 \| 0.7737 \| 0.2103 \| 0.0878 \| \| 0.1528 \| 42.0 \| 525 \| 0.7347 \| 0.805 \| 0.3077 \| 1.3640 \| 0.805 \| 0.7784 \| 0.2005 \| 0.0894 \| \| 0.1528 \| 42.96 \| 537 \| 0.7343 \| 0.805 \| 0.3063 \| 1.3684 \| 0.805 \| 0.7784 \| 0.2055 \| 0.0899 \| \| 0.1528 \| 44.0 \| 550 \| 0.7283 \| 0.805 \| 0.3038 \| 1.3660 \| 0.805 \| 0.7797 \| 0.2142 \| 0.0881 \| \| 0.1528 \| 44.96 \| 562 \| 0.7325 \| 0.805 \| 0.3061 \| 1.3658 \| 0.805 \| 0.7784 \| 0.1967 \| 0.0896 \| \| 0.1528 \| 46.0 \| 575 \| 0.7303 \| 0.81 \| 0.3049 \| 1.3659 \| 0.81 \| 0.7844 \| 0.1985 \| 0.0883 \| \| 0.1528 \| 46.96 \| 587 \| 0.7321 \| 0.805 \| 0.3054 \| 1.3657 \| 0.805 \| 0.7797 \| 0.1925 \| 0.0888 \| \| 0.1528 \| 48.0 \| 600 \| 0.7322 \| 0.8 \| 0.3054 \| 1.3680 \| 0.8000 \| 0.7704 \| 0.1837 \| 0.0903 \| \| 0.1528 \| 48.96 \| 612 \| 0.7368 \| 0.8 \| 0.3079 \| 1.3234 \| 0.8000 \| 0.7704 \| 0.2111 \| 0.0905 \| \| 0.1528 \| 50.0 \| 625 \| 0.7358 \| 0.8 \| 0.3074 \| 1.3682 \| 0.8000 \| 0.7704 \| 0.1971 \| 0.0899 \| \| 0.1528 \| 50.96 \| 637 \| 0.7318 \| 0.8 \| 0.3054 \| 1.3661 \| 0.8000 \| 0.7704 \| 0.2111 \| 0.0889 \| \| 0.1528 \| 52.0 \| 650 \| 0.7473 \| 0.795 \| 0.3133 \| 1.3707 \| 0.795 \| 0.7660 \| 0.2032 \| 0.0954 \| \| 0.1528 \| 52.96 \| 662 \| 0.7299 \| 0.805 \| 0.3050 \| 1.3631 \| 0.805 \| 0.7797 \| 0.1977 \| 0.0891 \| \| 0.1528 \| 54.0 \| 675 \| 0.7427 \| 0.795 \| 0.3116 \| 1.3673 \| 0.795 \| 0.7644 \| 0.2181 \| 0.0923 \| \| 0.1528 \| 54.96 \| 687 \| 0.7371 \| 0.8 \| 0.3087 \| 1.3648 \| 0.8000 \| 0.7704 \| 0.1984 \| 0.0902 \| \| 0.1528 \| 56.0 \| 700 \| 0.7399 \| 0.8 \| 0.3100 \| 1.3671 \| 0.8000 \| 0.7704 \| 0.2178 \| 0.0920 \| \| 0.1528 \| 56.96 \| 712 \| 0.7420 \| 0.8 \| 0.3114 \| 1.3671 \| 0.8000 \| 0.7689 \| 0.2035 \| 0.0925 \| \| 0.1528 \| 58.0 \| 725 \| 0.7380 \| 0.8 \| 0.3088 \| 1.3646 \| 0.8000 \| 0.7704 \| 0.2016 \| 0.0916 \| \| 0.1528 \| 58.96 \| 737 \| 0.7390 \| 0.8 \| 0.3091 \| 1.3671 \| 0.8000 \| 0.7704 \| 0.2005 \| 0.0921 \| \| 0.1528 \| 60.0 \| 750 \| 0.7370 \| 0.8 \| 0.3085 \| 1.3651 \| 0.8000 \| 0.7704 \| 0.1891 \| 0.0908 \| \| 0.1528 \| 60.96 \| 762 \| 0.7388 \| 0.8 \| 0.3097 \| 1.3622 \| 0.8000 \| 0.7704 \| 0.2010 \| 0.0906 \| \| 0.1528 \| 62.0 \| 775 \| 0.7403 \| 0.795 \| 0.3102 \| 1.3691 \| 0.795 \| 0.7654 \| 0.2142 \| 0.0910 \| \| 0.1528 \| 62.96 \| 787 \| 0.7390 \| 0.8 \| 0.3088 \| 1.3950 \| 0.8000 \| 0.7684 \| 0.2058 \| 0.0910 \| \| 0.1528 \| 64.0 \| 800 \| 0.7431 \| 0.795 \| 0.3110 \| 1.3722 \| 0.795 \| 0.7656 \| 0.1959 \| 0.0914 \| \| 0.1528 \| 64.96 \| 812 \| 0.7429 \| 0.8 \| 0.3117 \| 1.3673 \| 0.8000 \| 0.7704 \| 0.1933 \| 0.0925 \| \| 0.1528 \| 66.0 \| 825 \| 0.7419 \| 0.8 \| 0.3115 \| 1.3663 \| 0.8000 \| 0.7704 \| 0.1986 \| 0.0913 \| \| 0.1528 \| 66.96 \| 837 \| 0.7434 \| 0.8 \| 0.3120 \| 1.3672 \| 0.8000 \| 0.7704 \| 0.1929 \| 0.0927 \| \| 0.1528 \| 68.0 \| 850 \| 0.7414 \| 0.8 \| 0.3115 \| 1.3649 \| 0.8000 \| 0.7704 \| 0.1988 \| 0.0923 \| \| 0.1528 \| 68.96 \| 862 \| 0.7448 \| 0.8 \| 0.3129 \| 1.3685 \| 0.8000 \| 0.7704 \| 0.2251 \| 0.0928 \| \| 0.1528 \| 70.0 \| 875 \| 0.7450 \| 0.8 \| 0.3130 \| 1.3657 \| 0.8000 \| 0.7704 \| 0.1969 \| 0.0934 \| \| 0.1528 \| 70.96 \| 887 \| 0.7464 \| 0.8 \| 0.3132 \| 1.3686 \| 0.8000 \| 0.7704 \| 0.1988 \| 0.0946 \| \| 0.1528 \| 72.0 \| 900 \| 0.7465 \| 0.8 \| 0.3138 \| 1.3682 \| 0.8000 \| 0.7707 \| 0.2015 \| 0.0935 \| \| 0.1528 \| 72.96 \| 912 \| 0.7471 \| 0.8 \| 0.3142 \| 1.3685 \| 0.8000 \| 0.7696 \| 0.2093 \| 0.0936 \| \| 0.1528 \| 74.0 \| 925 \| 0.7476 \| 0.8 \| 0.3145 \| 1.3685 \| 0.8000 \| 0.7704 \| 0.2120 \| 0.0942 \| \| 0.1528 \| 74.96 \| 937 \| 0.7433 \| 0.8 \| 0.3126 \| 1.3655 \| 0.8000 \| 0.7696 \| 0.1907 \| 0.0918 \| \| 0.1528 \| 76.0 \| 950 \| 0.7519 \| 0.8 \| 0.3166 \| 1.3683 \| 0.8000 \| 0.7704 \| 0.2111 \| 0.0949 \| \| 0.1528 \| 76.96 \| 962 \| 0.7485 \| 0.8 \| 0.3152 \| 1.3679 \| 0.8000 \| 0.7696 \| 0.2035 \| 0.0942 \| \| 0.1528 \| 78.0 \| 975 \| 0.7496 \| 0.8 \| 0.3155 \| 1.3716 \| 0.8000 \| 0.7704 \| 0.1931 \| 0.0940 \| \| 0.1528 \| 78.96 \| 987 \| 0.7513 \| 0.8 \| 0.3163 \| 1.3684 \| 0.8000 \| 0.7704 \| 0.1888 \| 0.0952 \| \| 0.1059 \| 80.0 \| 1000 \| 0.7490 \| 0.8 \| 0.3157 \| 1.3707 \| 0.8000 \| 0.7691 \| 0.2118 \| 0.0943 \| \| 0.1059 \| 80.96 \| 1012 \| 0.7482 \| 0.8 \| 0.3151 \| 1.3687 \| 0.8000 \| 0.7696 \| 0.2060 \| 0.0945 \| \| 0.1059 \| 82.0 \| 1025 \| 0.7516 \| 0.8 \| 0.3163 \| 1.3682 \| 0.8000 \| 0.7704 \| 0.2248 \| 0.0950 \| \| 0.1059 \| 82.96 \| 1037 \| 0.7527 \| 0.8 \| 0.3174 \| 1.3660 \| 0.8000 \| 0.7707 \| 0.2204 \| 0.0948 \| \| 0.1059 \| 84.0 \| 1050 \| 0.7495 \| 0.8 \| 0.3156 \| 1.3663 \| 0.8000 \| 0.7704 \| 0.2067 \| 0.0939 \| \| 0.1059 \| 84.96 \| 1062 \| 0.7506 \| 0.8 \| 0.3161 \| 1.3659 \| 0.8000 \| 0.7707 \| 0.2086 \| 0.0947 \| \| 0.1059 \| 86.0 \| 1075 \| 0.7537 \| 0.8 \| 0.3179 \| 1.3687 \| 0.8000 \| 0.7698 \| 0.2001 \| 0.0955 \| \| 0.1059 \| 86.96 \| 1087 \| 0.7525 \| 0.8 \| 0.3172 \| 1.3693 \| 0.8000 \| 0.7696 \| 0.2096 \| 0.0947 \| \| 0.1059 \| 88.0 \| 1100 \| 0.7535 \| 0.8 \| 0.3175 \| 1.3675 \| 0.8000 \| 0.7704 \| 0.2091 \| 0.0951 \| \| 0.1059 \| 88.96 \| 1112 \| 0.7525 \| 0.8 \| 0.3172 \| 1.3680 \| 0.8000 \| 0.7696 \| 0.2009 \| 0.0946 \| \| 0.1059 \| 90.0 \| 1125 \| 0.7566 \| 0.8 \| 0.3190 \| 1.3725 \| 0.8000 \| 0.7698 \| 0.1881 \| 0.0964 \| \| 0.1059 \| 90.96 \| 1137 \| 0.7578 \| 0.8 \| 0.3195 \| 1.3726 \| 0.8000 \| 0.7704 \| 0.1880 \| 0.0968 \| \| 0.1059 \| 92.0 \| 1150 \| 0.7560 \| 0.8 \| 0.3186 \| 1.3715 \| 0.8000 \| 0.7707 \| 0.2095 \| 0.0969 \| \| 0.1059 \| 92.96 \| 1162 \| 0.7623 \| 0.795 \| 0.3219 \| 1.3765 \| 0.795 \| 0.7681 \| 0.1907 \| 0.0979 \| \| 0.1059 \| 94.0 \| 1175 \| 0.7567 \| 0.8 \| 0.3192 \| 1.3709 \| 0.8000 \| 0.7698 \| 0.2000 \| 0.0953 \| \| 0.1059 \| 94.96 \| 1187 \| 0.7538 \| 0.8 \| 0.3181 \| 1.3708 \| 0.8000 \| 0.7691 \| 0.1986 \| 0.0958 \| \| 0.1059 \| 96.0 \| 1200 \| 0.7530 \| 0.8 \| 0.3175 \| 1.3725 \| 0.8000 \| 0.7693 \| 0.2122 \| 0.0958 \| \| 0.1059 \| 96.96 \| 1212 \| 0.7607 \| 0.8 \| 0.3207 \| 1.3730 \| 0.8000 \| 0.7709 \| 0.1906 \| 0.0972 \| \| 0.1059 \| 98.0 \| 1225 \| 0.7647 \| 0.79 \| 0.3229 \| 1.3411 \| 0.79 \| 0.7633 \| 0.1889 \| 0.0984 \| \| 0.1059 \| 98.96 \| 1237 \| 0.7568 \| 0.79 \| 0.3191 \| 1.4425 \| 0.79 \| 0.7644 \| 0.1911 \| 0.0976 \| \| 0.1059 \| 100.0 \| 1250 \| 0.7586 \| 0.8 \| 0.3200 \| 1.4486 \| 0.8000 \| 0.7696 \| 0.2009 \| 0.0952 \| \| 0.1059 \| 100.96 \| 1262 \| 0.7552 \| 0.8 \| 0.3192 \| 1.3728 \| 0.8000 \| 0.7696 \| 0.1962 \| 0.0953 \| \| 0.1059 \| 102.0 \| 1275 \| 0.7601 \| 0.8 \| 0.3217 \| 1.4309 \| 0.8000 \| 0.7691 \| 0.2071 \| 0.0969 \| \| 0.1059 \| 102.96 \| 1287 \| 0.7608 \| 0.795 \| 0.3213 \| 1.3702 \| 0.795 \| 0.7668 \| 0.2068 \| 0.0967 \| \| 0.1059 \| 104.0 \| 1300 \| 0.7590 \| 0.795 \| 0.3202 \| 1.3748 \| 0.795 \| 0.7668 \| 0.1922 \| 0.0967 \| \| 0.1059 \| 104.96 \| 1312 \| 0.7626 \| 0.795 \| 0.3222 \| 1.3775 \| 0.795 \| 0.7673 \| 0.1917 \| 0.0974 \| \| 0.1059 \| 106.0 \| 1325 \| 0.7632 \| 0.795 \| 0.3228 \| 1.3765 \| 0.795 \| 0.7664 \| 0.2072 \| 0.0977 \| \| 0.1059 \| 106.96 \| 1337 \| 0.7612 \| 0.795 \| 0.3223 \| 1.3764 \| 0.795 \| 0.7666 \| 0.2011 \| 0.0974 \| \| 0.1059 \| 108.0 \| 1350 \| 0.7669 \| 0.79 \| 0.3246 \| 1.3777 \| 0.79 \| 0.7639 \| 0.2005 \| 0.0981 \| \| 0.1059 \| 108.96 \| 1362 \| 0.7658 \| 0.795 \| 0.3238 \| 1.3782 \| 0.795 \| 0.7673 \| 0.2099 \| 0.0983 \| \| 0.1059 \| 110.0 \| 1375 \| 0.7632 \| 0.79 \| 0.3232 \| 1.3766 \| 0.79 \| 0.7639 \| 0.2002 \| 0.0978 \| \| 0.1059 \| 110.96 \| 1387 \| 0.7651 \| 0.79 \| 0.3236 \| 1.3758 \| 0.79 \| 0.7580 \| 0.2134 \| 0.0980 \| \| 0.1059 \| 112.0 \| 1400 \| 0.7649 \| 0.79 \| 0.3235 \| 1.3765 \| 0.79 \| 0.7583 \| 0.1975 \| 0.0982 \| \| 0.1059 \| 112.96 \| 1412 \| 0.7683 \| 0.79 \| 0.3253 \| 1.3759 \| 0.79 \| 0.7639 \| 0.1913 \| 0.0986 \| \| 0.1059 \| 114.0 \| 1425 \| 0.7682 \| 0.795 \| 0.3253 \| 1.3778 \| 0.795 \| 0.7668 \| 0.1986 \| 0.0984 \| \| 0.1059 \| 114.96 \| 1437 \| 0.7666 \| 0.79 \| 0.3245 \| 1.3771 \| 0.79 \| 0.7639 \| 0.1838 \| 0.0976 \| \| 0.1059 \| 116.0 \| 1450 \| 0.7685 \| 0.79 \| 0.3255 \| 1.3802 \| 0.79 \| 0.7639 \| 0.1962 \| 0.0988 \| \| 0.1059 \| 116.96 \| 1462 \| 0.7676 \| 0.785 \| 0.3249 \| 1.3783 \| 0.785 \| 0.7554 \| 0.2011 \| 0.0991 \| \| 0.1059 \| 118.0 \| 1475 \| 0.7704 \| 0.785 \| 0.3262 \| 1.3789 \| 0.785 \| 0.7516 \| 0.2098 \| 0.0995 \| \| 0.1059 \| 118.96 \| 1487 \| 0.7701 \| 0.785 \| 0.3262 \| 1.3806 \| 0.785 \| 0.7554 \| 0.2167 \| 0.0991 \| \| 0.0842 \| 120.0 \| 1500 \| 0.7708 \| 0.79 \| 0.3267 \| 1.3815 \| 0.79 \| 0.7639 \| 0.1934 \| 0.0990 \| \| 0.0842 \| 120.96 \| 1512 \| 0.7710 \| 0.785 \| 0.3265 \| 1.3790 \| 0.785 \| 0.7516 \| 0.1928 \| 0.0995 \| \| 0.0842 \| 122.0 \| 1525 \| 0.7728 \| 0.79 \| 0.3274 \| 1.3830 \| 0.79 \| 0.7639 \| 0.1917 \| 0.0996 \| \| 0.0842 \| 122.96 \| 1537 \| 0.7708 \| 0.785 \| 0.3267 \| 1.3826 \| 0.785 \| 0.7554 \| 0.2057 \| 0.0992 \| \| 0.0842 \| 124.0 \| 1550 \| 0.7698 \| 0.785 \| 0.3262 \| 1.3777 \| 0.785 \| 0.7554 \| 0.2021 \| 0.0996 \| \| 0.0842 \| 124.96 \| 1562 \| 0.7706 \| 0.785 \| 0.3267 \| 1.3825 \| 0.785 \| 0.7554 \| 0.1949 \| 0.0994 \| \| 0.0842 \| 126.0 \| 1575 \| 0.7751 \| 0.79 \| 0.3290 \| 1.3827 \| 0.79 \| 0.7639 \| 0.1945 \| 0.1002 \| \| 0.0842 \| 126.96 \| 1587 \| 0.7734 \| 0.79 \| 0.3282 \| 1.3855 \| 0.79 \| 0.7639 \| 0.1882 \| 0.0998 \| \| 0.0842 \| 128.0 \| 1600 \| 0.7753 \| 0.785 \| 0.3290 \| 1.3842 \| 0.785 \| 0.7554 \| 0.1926 \| 0.1006 \| \| 0.0842 \| 128.96 \| 1612 \| 0.7731 \| 0.78 \| 0.3278 \| 1.3808 \| 0.78 \| 0.7488 \| 0.2115 \| 0.0994 \| \| 0.0842 \| 130.0 \| 1625 \| 0.7723 \| 0.78 \| 0.3276 \| 1.3830 \| 0.78 \| 0.7486 \| 0.2005 \| 0.0994 \| \| 0.0842 \| 130.96 \| 1637 \| 0.7746 \| 0.78 \| 0.3287 \| 1.3810 \| 0.78 \| 0.7488 \| 0.2103 \| 0.0999 \| \| 0.0842 \| 132.0 \| 1650 \| 0.7758 \| 0.78 \| 0.3291 \| 1.3848 \| 0.78 \| 0.7488 \| 0.2138 \| 0.1004 \| \| 0.0842 \| 132.96 \| 1662 \| 0.7771 \| 0.78 \| 0.3299 \| 1.3833 \| 0.78 \| 0.7488 \| 0.2080 \| 0.1007 \| \| 0.0842 \| 134.0 \| 1675 \| 0.7757 \| 0.78 \| 0.3295 \| 1.3837 \| 0.78 \| 0.7488 \| 0.2060 \| 0.0999 \| \| 0.0842 \| 134.96 \| 1687 \| 0.7754 \| 0.78 \| 0.3292 \| 1.3836 \| 0.78 \| 0.7488 \| 0.2070 \| 0.1001 \| \| 0.0842 \| 136.0 \| 1700 \| 0.7755 \| 0.78 \| 0.3294 \| 1.3834 \| 0.78 \| 0.7488 \| 0.2057 \| 0.0999 \| \| 0.0842 \| 136.96 \| 1712 \| 0.7755 \| 0.78 \| 0.3295 \| 1.3874 \| 0.78 \| 0.7488 \| 0.1999 \| 0.0998 \| \| 0.0842 \| 138.0 \| 1725 \| 0.7759 \| 0.78 \| 0.3296 \| 1.4380 \| 0.78 \| 0.7488 \| 0.2007 \| 0.1002 \| \| 0.0842 \| 138.96 \| 1737 \| 0.7776 \| 0.78 \| 0.3303 \| 1.3868 \| 0.78 \| 0.7488 \| 0.2091 \| 0.1002 \| \| 0.0842 \| 140.0 \| 1750 \| 0.7780 \| 0.78 \| 0.3304 \| 1.3868 \| 0.78 \| 0.7488 \| 0.2086 \| 0.1005 \| \| 0.0842 \| 140.96 \| 1762 \| 0.7780 \| 0.78 \| 0.3306 \| 1.3855 \| 0.78 \| 0.7488 \| 0.2065 \| 0.1001 \| \| 0.0842 \| 142.0 \| 1775 \| 0.7789 \| 0.78 \| 0.3311 \| 1.3855 \| 0.78 \| 0.7488 \| 0.2067 \| 0.1003 \| \| 0.0842 \| 142.96 \| 1787 \| 0.7798 \| 0.78 \| 0.3314 \| 1.3856 \| 0.78 \| 0.7488 \| 0.2083 \| 0.1010 \| \| 0.0842 \| 144.0 \| 1800 \| 0.7799 \| 0.78 \| 0.3315 \| 1.3914 \| 0.78 \| 0.7488 \| 0.2161 \| 0.1004 \| \| 0.0842 \| 144.96 \| 1812 \| 0.7806 \| 0.78 \| 0.3317 \| 1.3857 \| 0.78 \| 0.7488 \| 0.2024 \| 0.1008 \| \| 0.0842 \| 146.0 \| 1825 \| 0.7817 \| 0.78 \| 0.3322 \| 1.3947 \| 0.78 \| 0.7488 \| 0.2043 \| 0.1009 \| \| 0.0842 \| 146.96 \| 1837 \| 0.7815 \| 0.78 \| 0.3324 \| 1.3898 \| 0.78 \| 0.7488 \| 0.2118 \| 0.1006 \| \| 0.0842 \| 148.0 \| 1850 \| 0.7820 \| 0.78 \| 0.3326 \| 1.3874 \| 0.78 \| 0.7488 \| 0.2114 \| 0.1008 \| \| 0.0842 \| 148.96 \| 1862 \| 0.7821 \| 0.78 \| 0.3327 \| 1.4391 \| 0.78 \| 0.7488 \| 0.2087 \| 0.1006 \| \| 0.0842 \| 150.0 \| 1875 \| 0.7816 \| 0.78 \| 0.3324 \| 1.4410 \| 0.78 \| 0.7488 \| 0.2160 \| 0.1006 \| \| 0.0842 \| 150.96 \| 1887 \| 0.7826 \| 0.78 \| 0.3328 \| 1.3927 \| 0.78 \| 0.7488 \| 0.2011 \| 0.1007 \| \| 0.0842 \| 152.0 \| 1900 \| 0.7834 \| 0.78 \| 0.3332 \| 1.4411 \| 0.78 \| 0.7488 \| 0.1994 \| 0.1009 \| \| 0.0842 \| 152.96 \| 1912 \| 0.7830 \| 0.78 \| 0.3331 \| 1.4409 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1008 \| \| 0.0842 \| 154.0 \| 1925 \| 0.7825 \| 0.78 \| 0.3329 \| 1.4412 \| 0.78 \| 0.7488 \| 0.2070 \| 0.1008 \| \| 0.0842 \| 154.96 \| 1937 \| 0.7827 \| 0.78 \| 0.3332 \| 1.4399 \| 0.78 \| 0.7488 \| 0.2097 \| 0.1007 \| \| 0.0842 \| 156.0 \| 1950 \| 0.7822 \| 0.78 \| 0.3329 \| 1.4391 \| 0.78 \| 0.7488 \| 0.1986 \| 0.1006 \| \| 0.0842 \| 156.96 \| 1962 \| 0.7838 \| 0.78 \| 0.3335 \| 1.4414 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1007 \| \| 0.0842 \| 158.0 \| 1975 \| 0.7845 \| 0.78 \| 0.3337 \| 1.3899 \| 0.78 \| 0.7488 \| 0.2087 \| 0.1008 \| \| 0.0842 \| 158.96 \| 1987 \| 0.7846 \| 0.78 \| 0.3340 \| 1.4419 \| 0.78 \| 0.7488 \| 0.2002 \| 0.1007 \| \| 0.0731 \| 160.0 \| 2000 \| 0.7853 \| 0.78 \| 0.3343 \| 1.4419 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1010 \| \| 0.0731 \| 160.96 \| 2012 \| 0.7853 \| 0.78 \| 0.3343 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1963 \| 0.1012 \| \| 0.0731 \| 162.0 \| 2025 \| 0.7846 \| 0.78 \| 0.3341 \| 1.4411 \| 0.78 \| 0.7488 \| 0.1998 \| 0.1008 \| \| 0.0731 \| 162.96 \| 2037 \| 0.7856 \| 0.78 \| 0.3345 \| 1.4419 \| 0.78 \| 0.7488 \| 0.2000 \| 0.1011 \| \| 0.0731 \| 164.0 \| 2050 \| 0.7863 \| 0.78 \| 0.3348 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1991 \| 0.1014 \| \| 0.0731 \| 164.96 \| 2062 \| 0.7863 \| 0.78 \| 0.3349 \| 1.4414 \| 0.78 \| 0.7488 \| 0.1969 \| 0.1013 \| \| 0.0731 \| 166.0 \| 2075 \| 0.7865 \| 0.78 \| 0.3349 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1994 \| 0.1015 \| \| 0.0731 \| 166.96 \| 2087 \| 0.7863 \| 0.78 \| 0.3349 \| 1.4412 \| 0.78 \| 0.7488 \| 0.1966 \| 0.1015 \| \| 0.0731 \| 168.0 \| 2100 \| 0.7870 \| 0.78 \| 0.3352 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1944 \| 0.1016 \| \| 0.0731 \| 168.96 \| 2112 \| 0.7868 \| 0.78 \| 0.3351 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1919 \| 0.1017 \| \| 0.0731 \| 170.0 \| 2125 \| 0.7873 \| 0.78 \| 0.3354 \| 1.4424 \| 0.78 \| 0.7488 \| 0.1965 \| 0.1017 \| \| 0.0731 \| 170.96 \| 2137 \| 0.7870 \| 0.78 \| 0.3352 \| 1.4418 \| 0.78 \| 0.7488 \| 0.2081 \| 0.1014 \| \| 0.0731 \| 172.0 \| 2150 \| 0.7879 \| 0.78 \| 0.3356 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1017 \| \| 0.0731 \| 172.96 \| 2162 \| 0.7874 \| 0.78 \| 0.3355 \| 1.4421 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1015 \| \| 0.0731 \| 174.0 \| 2175 \| 0.7872 \| 0.78 \| 0.3355 \| 1.4418 \| 0.78 \| 0.7488 \| 0.1965 \| 0.1016 \| \| 0.0731 \| 174.96 \| 2187 \| 0.7882 \| 0.78 \| 0.3358 \| 1.4433 \| 0.78 \| 0.7488 \| 0.1945 \| 0.1017 \| \| 0.0731 \| 176.0 \| 2200 \| 0.7882 \| 0.78 \| 0.3358 \| 1.4423 \| 0.78 \| 0.7488 \| 0.1969 \| 0.1016 \| \| 0.0731 \| 176.96 \| 2212 \| 0.7885 \| 0.78 \| 0.3361 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1948 \| 0.1017 \| \| 0.0731 \| 178.0 \| 2225 \| 0.7883 \| 0.78 \| 0.3359 \| 1.4428 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 178.96 \| 2237 \| 0.7882 \| 0.78 \| 0.3359 \| 1.4426 \| 0.78 \| 0.7488 \| 0.1920 \| 0.1017 \| \| 0.0731 \| 180.0 \| 2250 \| 0.7884 \| 0.78 \| 0.3360 \| 1.4425 \| 0.78 \| 0.7488 \| 0.2000 \| 0.1016 \| \| 0.0731 \| 180.96 \| 2262 \| 0.7886 \| 0.78 \| 0.3361 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 182.0 \| 2275 \| 0.7888 \| 0.78 \| 0.3362 \| 1.4428 \| 0.78 \| 0.7488 \| 0.1977 \| 0.1016 \| \| 0.0731 \| 182.96 \| 2287 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4428 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1017 \| \| 0.0731 \| 184.0 \| 2300 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 184.96 \| 2312 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4427 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 186.0 \| 2325 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1018 \| \| 0.0731 \| 186.96 \| 2337 \| 0.7892 \| 0.78 \| 0.3364 \| 1.4432 \| 0.78 \| 0.7488 \| 0.1921 \| 0.1017 \| \| 0.0731 \| 188.0 \| 2350 \| 0.7890 \| 0.78 \| 0.3363 \| 1.4427 \| 0.78 \| 0.7488 \| 0.1921 \| 0.1017 \| \| 0.0731 \| 188.96 \| 2362 \| 0.7892 \| 0.78 \| 0.3364 \| 1.4429 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1017 \| \| 0.0731 \| 190.0 \| 2375 \| 0.7893 \| 0.78 \| 0.3364 \| 1.4433 \| 0.78 \| 0.7488 \| 0.1945 \| 0.1017 \| \| 0.0731 \| 190.96 \| 2387 \| 0.7893 \| 0.78 \| 0.3364 \| 1.4430 \| 0.78 \| 0.7488 \| 0.1921 \| 0.1017 \| \| 0.0731 \| 192.0 \| 2400 \| 0.7893 \| 0.78 \| 0.3364 \| 1.4430 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1018 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3
asrimanth/person-thumbs-up-lora-no-cap	asrimanth	2023-07-12T17:31:28Z	5	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-10T21:45:20Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - asrimanth/person-thumbs-up-lora-no-cap These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
zblaaa/t5-base-finetuned-ner_docred_30	zblaaa	2023-07-12T17:30:08Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-12T11:00:35Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-ner_docred_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-ner_docred_30 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1900 - Rouge1: 6.698 - Rouge2: 5.261 - Rougel: 6.6835 - Rougelsum: 6.6818 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 125 \| 0.5156 \| 6.5406 \| 4.9855 \| 6.4905 \| 6.494 \| 20.0 \| \| No log \| 2.0 \| 250 \| 0.3949 \| 6.5113 \| 4.9122 \| 6.4534 \| 6.4453 \| 20.0 \| \| No log \| 3.0 \| 375 \| 0.3280 \| 6.5165 \| 4.9088 \| 6.4537 \| 6.451 \| 20.0 \| \| 0.7311 \| 4.0 \| 500 \| 0.2949 \| 6.424 \| 4.7298 \| 6.3672 \| 6.3627 \| 20.0 \| \| 0.7311 \| 5.0 \| 625 \| 0.2764 \| 6.6189 \| 5.1219 \| 6.5651 \| 6.5672 \| 20.0 \| \| 0.7311 \| 6.0 \| 750 \| 0.2633 \| 6.628 \| 5.1335 \| 6.5664 \| 6.5721 \| 20.0 \| \| 0.7311 \| 7.0 \| 875 \| 0.2547 \| 6.5591 \| 4.9979 \| 6.5075 \| 6.5057 \| 20.0 \| \| 0.3331 \| 8.0 \| 1000 \| 0.2482 \| 6.6612 \| 5.1918 \| 6.5987 \| 6.6068 \| 20.0 \| \| 0.3331 \| 9.0 \| 1125 \| 0.2413 \| 6.6093 \| 5.0954 \| 6.5515 \| 6.5553 \| 20.0 \| \| 0.3331 \| 10.0 \| 1250 \| 0.2357 \| 6.6264 \| 5.1201 \| 6.5681 \| 6.5723 \| 20.0 \| \| 0.3331 \| 11.0 \| 1375 \| 0.2300 \| 6.6487 \| 5.1525 \| 6.6176 \| 6.6177 \| 20.0 \| \| 0.2788 \| 12.0 \| 1500 \| 0.2226 \| 6.6858 \| 5.2325 \| 6.6745 \| 6.6762 \| 20.0 \| \| 0.2788 \| 13.0 \| 1625 \| 0.2166 \| 6.6495 \| 5.1531 \| 6.6378 \| 6.6377 \| 20.0 \| \| 0.2788 \| 14.0 \| 1750 \| 0.2108 \| 6.6807 \| 5.2212 \| 6.6653 \| 6.6664 \| 20.0 \| \| 0.2788 \| 15.0 \| 1875 \| 0.2068 \| 6.6811 \| 5.2248 \| 6.6699 \| 6.6697 \| 20.0 \| \| 0.2435 \| 16.0 \| 2000 \| 0.2030 \| 6.6701 \| 5.2077 \| 6.652 \| 6.6492 \| 20.0 \| \| 0.2435 \| 17.0 \| 2125 \| 0.1997 \| 6.6845 \| 5.2334 \| 6.6647 \| 6.6624 \| 20.0 \| \| 0.2435 \| 18.0 \| 2250 \| 0.1978 \| 6.6762 \| 5.2202 \| 6.6571 \| 6.6559 \| 20.0 \| \| 0.2435 \| 19.0 \| 2375 \| 0.1964 \| 6.684 \| 5.2358 \| 6.6695 \| 6.6683 \| 20.0 \| \| 0.2188 \| 20.0 \| 2500 \| 0.1957 \| 6.6882 \| 5.2426 \| 6.675 \| 6.6735 \| 20.0 \| \| 0.2188 \| 21.0 \| 2625 \| 0.1942 \| 6.6882 \| 5.2426 \| 6.675 \| 6.6735 \| 20.0 \| \| 0.2188 \| 22.0 \| 2750 \| 0.1932 \| 6.6935 \| 5.2513 \| 6.6784 \| 6.6762 \| 20.0 \| \| 0.2188 \| 23.0 \| 2875 \| 0.1924 \| 6.6935 \| 5.2513 \| 6.6784 \| 6.6762 \| 20.0 \| \| 0.2052 \| 24.0 \| 3000 \| 0.1918 \| 6.6882 \| 5.2426 \| 6.675 \| 6.6735 \| 20.0 \| \| 0.2052 \| 25.0 \| 3125 \| 0.1915 \| 6.6935 \| 5.2513 \| 6.6784 \| 6.6762 \| 20.0 \| \| 0.2052 \| 26.0 \| 3250 \| 0.1908 \| 6.698 \| 5.261 \| 6.6835 \| 6.6818 \| 20.0 \| \| 0.2052 \| 27.0 \| 3375 \| 0.1905 \| 6.698 \| 5.261 \| 6.6835 \| 6.6818 \| 20.0 \| \| 0.1977 \| 28.0 \| 3500 \| 0.1901 \| 6.698 \| 5.261 \| 6.6835 \| 6.6818 \| 20.0 \| \| 0.1977 \| 29.0 \| 3625 \| 0.1900 \| 6.698 \| 5.261 \| 6.6835 \| 6.6818 \| 20.0 \| \| 0.1977 \| 30.0 \| 3750 \| 0.1900 \| 6.698 \| 5.261 \| 6.6835 \| 6.6818 \| 20.0 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.1.0.dev20230611+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
SHENMU007/neunit_BASE_V12.3	SHENMU007	2023-07-12T17:27:42Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-12T14:29:43Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
sl-alex/llama-7b-alpaca-stepwise-lora	sl-alex	2023-07-12T17:22:09Z	0	0	null	[ "en", "dataset:sl-alex/openai-prm800k-solutions-only", "license:apache-2.0", "region:us" ]	null	2023-07-12T15:00:40Z	--- license: apache-2.0 language: - en datasets: - sl-alex/openai-prm800k-solutions-only --- Finetunes Llama-7b+Alpaca to solve problems via stepwise reasoning (OpenAI [PRM800k dataset](https://github.com/openai/prm800k), or rather our postprocessed version, [`sl-alex/openai-prm800k-solutions-only`](https://huggingface.co/datasets/sl-alex/openai-prm800k-solutions-only)). ## Model description This is a fork of [`llama-7b`](https://huggingface.co/huggyllama/llama-7b) + [`tloen/alpaca-lora-7b`](https://huggingface.co/tloen/alpaca-lora-7b). That is: we loaded Llama-7b, we applied Alpaca LoRA, expanded vocabulary, then QLoRA 4-bit finetuned from there. Parts: - base model [`llama-7b`](https://huggingface.co/huggyllama/llama-7b) - LoRA 0 [`tloen/alpaca-lora-7b`](https://huggingface.co/tloen/alpaca-lora-7b) - LoRA 1 - `adapter_config.json` - `adapter_model.bin` - tokenizer (this is just [`llama-7b`](https://huggingface.co/huggyllama/llama-7b)'s tokenizer, plus a `[PAD]` token for which no embedding was learned. in practice you can inference a batch-of-1 using the original [`llama-7b`](https://huggingface.co/huggyllama/llama-7b) tokenizer instead) - `added_tokens.json` - `special_tokens_map.json` - `tokenizer.model` - `tokenizer_config.json` ## Training Trained using [`qlora.py`](https://github.com/scottlogic-alex/qlora/blob/stepwise/qlora.py) from our [`stepwise`](https://github.com/scottlogic-alex/qlora/tree/stepwise) branch of [qlora](https://github.com/artidoro/qlora). Known-good as of commit [`3a86919`](https://github.com/scottlogic-alex/qlora/blob/3a8691986b6718562bcd8e3522447b52842c1d9a/qlora.py). `python -m qlora --model_name_or_path huggyllama/llama-7b --lora_name_or_path tloen/alpaca-lora-7b --dataset prm800k-solutions --dataset_format prm800k-solutions --bf16 --max_memory_MB 24000 --use_bos_token_in_prompt --truncate_toward_center --source_max_len 184 --target_max_len 998 --gradient_accumulation_steps 4 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --learning_rate 0.0002 --run_name 13b_alpaca_special_tokens_long --report_to wandb --save_steps 64 --save_total_limit 3 --max_steps 1664 --evaluation_strategy steps --eval_steps 64 --generate_steps 16` ## Usage You can load using [`evaluate.py`](https://github.com/scottlogic-alex/qlora/blob/stepwise/evaluate.py#L209-L278) from our [`stepwise`](https://github.com/scottlogic-alex/qlora/tree/stepwise) branch of [qlora](https://github.com/artidoro/qlora). Known-good as of commit [`3a86919`](https://github.com/scottlogic-alex/qlora/blob/3a8691986b6718562bcd8e3522447b52842c1d9a/evaluate.py). ```bash git clone -b stepwise https://github.com/scottlogic-alex/qlora.git cd qlora python -m evaluate \ --model_name_or_path huggyllama/llama-7b \ --base_lora_model_name_or_path tloen/alpaca-lora-7b \ --tokenizer_model_name_or_path huggyllama/llama-7b \ --lora_model_name_or_path sl-alex/llama-7b-alpaca-stepwise-lora \ --use_bos_token_in_prompt \ --overrun_countermeasures False ``` ## Prompt style The model is finetuned for instruction-response. `prompt = '<s>Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nHow do I feed a family of 5 without ingredients?\n\n### Response:\n<\|step_start\|>'` ## Example output: `$ How do I drink a matcha latte?` `I’ve never drunk a matcha latte before, but I’ve seen people do it online. It looks like they dump the entire contents of a little teabag into a frothed milk drink and then mix it well. I think I could do that.<\|step_end\|><\|step_start\|>I’ll have to buy a matcha latte kit first, because I don’t know what they have in the teabags. I’ll see if my favorite coffee shop sells them.<\|step_end\|><\|step_start\|>I’ll also need a whisk or a mixer. I’ll look online for the best one. Which one do you recommend?<\|step_end\|><\|step_start\|>I’ll need to heat up the milk first. Which will you use -- the microwave or the stovetop?<\|step_end\|><\|step_start\|>I’ll warm up 16 ounces of milk in the microwave. For the stovetop, I’ll just put it on the burner for two minutes. I don’t want it to boil.` `$ What is the third Fibonacci number?` `I need to find the third Fibonacci number, which is the number after the second, which is F(2).<\|step_end\|><\|step_start\|>There are two ways to get the third Fibonacci number. I could either wait one more day and get F(3) using the Fibonacci sequence, or I could do something more clever and faster by using a computer. <\|step_end\|><\|step_start\|>Computers are great for mathematics, so I'll see if I can get F(3) quicker with a computer.<\|step_end\|><\|step_start\|>First, though, I'll explain what Fibonacci means. It's named after a 13th-century Italian accountant named Leonardo Fibonacci who created a sequence of numbers where each following number is the sum of the two previous ones. <\|step_end\|><\|step_start\|>The Fibonacci sequence begins 0, 1, 1, 2, 3, 5, 8, 13, 21, and so on. <\|step_end\|><\|` `$ How do I make a cup of tea?` `First, I find a teaspoonful of loose-leaf Assam tea in a teapot. Then, I fill the teapot with water that has been warming up over a stovetop burner.<\|step_end\|><\|step_start\|>Once the water has reached a rolling boil, I pour it over the tea, cover the pot, and steam it for at least two minutes. Uncover the pot, remove the lid, and serve. That's how to make a cup of tea.` `<\|response_start\|>I've always thought Assam tea was an Indian tea, but this web site says it comes from the north-center part of Bangladesh. I wonder if there's a difference between Assam and Bangladesh tea.<\|Response_End\|><\|step_start\|>BANGLADESH tea has all the qualities of Assam, but the difference in the final cup lies in the aroma and the lingering aftertaste.` `<\|step_end\|><\|response_start\|>The Assam tea comes in very large, flat pieces of processed leaves. They are cut from the tree and rolled` `$ How do I open a jar?` `I need to find a jar opener at my school. I can't just use my mouth because it's too dangerous to choke on the lid, and it would ruin my school uniform. I could bring a bottle opener from home, but it might not work. I also can't use a knife, because that could get me into trouble with the headmaster. The solution is to ask a friendly student to open the jar for me.</\|step_end\|><\|step_start\|>I find a student who is willing to help. We both hold the jar with our hands on the inside and outside of the rim, and she uses her nails to pry the lid off. She has been doing this for years, and it looks easy, but I should be careful not to scratch myself.</\|step_end\|><\|step_start\|>We set the jar down and all the pieces are there: the lid, the jar, and the plastic bag. I can never open another jar without feeling proud of myself. My classmates are impressed by my skills as a consumer.</\|step_end\|><\|step_start\|>I should keep the lid as a souvenir, but`
henda/bert-multilingual-ner	henda	2023-07-12T17:17:04Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-12T08:39:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-multilingual-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-multilingual-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0678 - Precision: 0.7057 - Recall: 0.7305 - F1: 0.7179 - Accuracy: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0946 \| 0.06 \| 10000 \| 0.0974 \| 0.5971 \| 0.6368 \| 0.6163 \| 0.9612 \| \| 0.0935 \| 0.12 \| 20000 \| 0.0892 \| 0.6258 \| 0.6633 \| 0.6440 \| 0.9652 \| \| 0.0829 \| 0.17 \| 30000 \| 0.0913 \| 0.6393 \| 0.6557 \| 0.6474 \| 0.9653 \| \| 0.086 \| 0.23 \| 40000 \| 0.0829 \| 0.6868 \| 0.6131 \| 0.6478 \| 0.9679 \| \| 0.0836 \| 0.29 \| 50000 \| 0.0806 \| 0.6450 \| 0.7019 \| 0.6722 \| 0.9673 \| \| 0.0813 \| 0.35 \| 60000 \| 0.0784 \| 0.6718 \| 0.6823 \| 0.6770 \| 0.9694 \| \| 0.0756 \| 0.41 \| 70000 \| 0.0771 \| 0.6559 \| 0.7217 \| 0.6872 \| 0.9696 \| \| 0.0753 \| 0.47 \| 80000 \| 0.0733 \| 0.6944 \| 0.6660 \| 0.6799 \| 0.9706 \| \| 0.0716 \| 0.52 \| 90000 \| 0.0781 \| 0.6698 \| 0.7151 \| 0.6917 \| 0.9704 \| \| 0.0785 \| 0.58 \| 100000 \| 0.0723 \| 0.6936 \| 0.6960 \| 0.6948 \| 0.9714 \| \| 0.0707 \| 0.64 \| 110000 \| 0.0729 \| 0.6943 \| 0.7098 \| 0.7020 \| 0.9718 \| \| 0.0699 \| 0.7 \| 120000 \| 0.0714 \| 0.6928 \| 0.7200 \| 0.7061 \| 0.9717 \| \| 0.0729 \| 0.76 \| 130000 \| 0.0715 \| 0.6887 \| 0.7305 \| 0.7090 \| 0.9722 \| \| 0.0696 \| 0.82 \| 140000 \| 0.0703 \| 0.6826 \| 0.7479 \| 0.7137 \| 0.9722 \| \| 0.0654 \| 0.87 \| 150000 \| 0.0697 \| 0.6851 \| 0.7500 \| 0.7161 \| 0.9727 \| \| 0.0636 \| 0.93 \| 160000 \| 0.0684 \| 0.6968 \| 0.7410 \| 0.7183 \| 0.9731 \| \| 0.0641 \| 0.99 \| 170000 \| 0.0679 \| 0.7047 \| 0.7315 \| 0.7179 \| 0.9734 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3
asrimanth/person-thumbs-up-plain-lora	asrimanth	2023-07-12T17:12:05Z	2	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-11T22:22:40Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - asrimanth/person-thumbs-up-plain-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
Winmodel/poca-SoccerTwos	Winmodel	2023-07-12T17:03:16Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-07-12T17:02:13Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Winmodel/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jmarcolan/joao_funciona2	jmarcolan	2023-07-12T17:03:08Z	2	0	transformers	[ "transformers", "en", "endpoints_compatible", "region:us" ]	null	2023-07-12T16:50:45Z	--- language: en --- <p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p> Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```
veluchs/q-FrozenLake-v1-4x4-noSlippery	veluchs	2023-07-12T16:47:45Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T16:47:43Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="veluchs/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Rui31415/ppo-Huggy	Rui31415	2023-07-12T16:26:21Z	25	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-12T16:26:17Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Rui31415/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Hinata197/videomae-base-finetuned-ucf101-subset	Hinata197	2023-07-12T16:20:32Z	59	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-07-12T16:02:00Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1006 - Accuracy: 0.9714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 600 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6882 \| 0.25 \| 150 \| 1.3392 \| 0.4714 \| \| 0.3556 \| 1.25 \| 300 \| 0.5520 \| 0.7857 \| \| 0.1375 \| 2.25 \| 450 \| 0.2829 \| 0.8857 \| \| 0.0361 \| 3.25 \| 600 \| 0.1006 \| 0.9714 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
0sunfire0/q-FrozenLake-v1-4x4-noSlippery	0sunfire0	2023-07-12T16:13:41Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T16:13:39Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="0sunfire0/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
YanJiangJerry/covid-augment-tweet-roberta-large-e4	YanJiangJerry	2023-07-12T16:11:00Z	116	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T14:31:14Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: covid-augment-tweet-roberta-large-e4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # covid-augment-tweet-roberta-large-e4 This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5214 - Accuracy: 0.9510 - F1: 0.8707 - Precision: 0.8562 - Recall: 0.8858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.1484 \| 1.0 \| 4089 \| 0.4570 \| 0.9265 \| 0.8273 \| 0.7358 \| 0.9446 \| \| 0.0738 \| 2.0 \| 8178 \| 0.4873 \| 0.9368 \| 0.8414 \| 0.7903 \| 0.8997 \| \| 0.0265 \| 3.0 \| 12267 \| 0.4970 \| 0.9484 \| 0.8587 \| 0.8773 \| 0.8408 \| \| 0.0043 \| 4.0 \| 16356 \| 0.5214 \| 0.9510 \| 0.8707 \| 0.8562 \| 0.8858 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
phoenix-glr/wav2vec2-base-finetuned-ks	phoenix-glr	2023-07-12T15:57:35Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-12T13:54:28Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0855 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6422 \| 1.0 \| 399 \| 0.5417 \| 0.9622 \| \| 0.2815 \| 2.0 \| 798 \| 0.1712 \| 0.9771 \| \| 0.2034 \| 3.0 \| 1197 \| 0.1125 \| 0.9815 \| \| 0.1486 \| 4.0 \| 1597 \| 0.0961 \| 0.9809 \| \| 0.1262 \| 5.0 \| 1995 \| 0.0855 \| 0.9844 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
LarryAIDraw/brj-saori-V1	LarryAIDraw	2023-07-12T15:55:54Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-12T15:27:13Z	--- license: creativeml-openrail-m --- https://civitai.com/models/81104/saori-blue-archive
LarryAIDraw/silverwolf-000002	LarryAIDraw	2023-07-12T15:55:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-12T15:26:29Z	--- license: creativeml-openrail-m --- https://civitai.com/models/106319/lora-honkai-star-rail-silverwolf
LarryAIDraw/rezerowitches-25	LarryAIDraw	2023-07-12T15:54:25Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-12T15:23:05Z	--- license: creativeml-openrail-m --- https://civitai.com/models/107626/rezero-witches-pack-7-in-1-echidna-satella-minerva-daphne-typhon-carmilla-sekhmet
Ruxun/yolov8-player	Ruxun	2023-07-12T15:35:10Z	0	0	null	[ "region:us" ]	null	2023-06-05T21:17:20Z	Finetuned Model based on yolov8 for harvard basketball team player detection
bofenghuang/vigogne-7b-chat	bofenghuang	2023-07-12T15:33:38Z	1,478	4	transformers	[ "transformers", "pytorch", "llama", "text-generation", "LLM", "fr", "license:openrail", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-05-15T14:07:53Z	--- license: openrail language: - fr pipeline_tag: text-generation library_name: transformers tags: - llama - LLM inference: false --- <p align="center" width="100%"> <img src="https://huggingface.co/bofenghuang/vigogne-7b-chat/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;"> </p> # Vigogne-7B-Chat: A French Chat LLaMA Model Vigogne-7B-Chat is a LLaMA-7B model fine-tuned to conduct multi-turn dialogues in 🇫🇷 French between human user and AI assistant. For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne Usage and License Notices: Same as [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Vigogne is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from vigogne.preprocess import generate_inference_chat_prompt model_name_or_path = "bofenghuang/vigogne-7b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto") user_query = "Expliquez la différence entre DoS et phishing." prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer) input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device) input_length = input_ids.shape[1] generated_outputs = model.generate( input_ids=input_ids, generation_config=GenerationConfig( temperature=0.1, do_sample=True, repetition_penalty=1.0, max_new_tokens=512, ), return_dict_in_generate=True, ) generated_tokens = generated_outputs.sequences[0, input_length:] generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True) print(generated_text) ``` You can infer this model by using the following Google Colab Notebook. <a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> ## Limitations Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7	jordyvl	2023-07-12T15:32:48Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-12T14:55:18Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7789 - Accuracy: 0.565 - Brier Loss: 0.5798 - Nll: 2.3548 - F1 Micro: 0.565 - F1 Macro: 0.5569 - Ece: 0.1677 - Aurc: 0.2032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.7719 \| 0.04 \| 1.0712 \| 7.3456 \| 0.04 \| 0.0315 \| 0.2816 \| 0.9543 \| \| No log \| 2.0 \| 14 \| 3.7636 \| 0.1 \| 0.9486 \| 5.7989 \| 0.1000 \| 0.0863 \| 0.1625 \| 0.8854 \| \| No log \| 3.0 \| 21 \| 3.3871 \| 0.235 \| 0.8838 \| 5.3315 \| 0.235 \| 0.1857 \| 0.1589 \| 0.6274 \| \| No log \| 4.0 \| 28 \| 2.8780 \| 0.2975 \| 0.7900 \| 3.5467 \| 0.2975 \| 0.2884 \| 0.1672 \| 0.4712 \| \| No log \| 5.0 \| 35 \| 2.5223 \| 0.3875 \| 0.7164 \| 3.0188 \| 0.3875 \| 0.3630 \| 0.1596 \| 0.3495 \| \| No log \| 6.0 \| 42 \| 2.3634 \| 0.43 \| 0.6927 \| 3.1378 \| 0.4300 \| 0.4031 \| 0.1877 \| 0.3055 \| \| No log \| 7.0 \| 49 \| 2.3487 \| 0.445 \| 0.7009 \| 3.0084 \| 0.445 \| 0.4123 \| 0.2026 \| 0.3003 \| \| No log \| 8.0 \| 56 \| 2.2521 \| 0.47 \| 0.6857 \| 2.8634 \| 0.47 \| 0.4607 \| 0.1977 \| 0.2918 \| \| No log \| 9.0 \| 63 \| 2.3597 \| 0.4775 \| 0.6955 \| 2.6768 \| 0.4775 \| 0.4642 \| 0.2215 \| 0.2787 \| \| No log \| 10.0 \| 70 \| 2.3524 \| 0.46 \| 0.7130 \| 2.7830 \| 0.46 \| 0.4441 \| 0.2429 \| 0.2941 \| \| No log \| 11.0 \| 77 \| 2.4233 \| 0.46 \| 0.7108 \| 3.0867 \| 0.46 \| 0.4418 \| 0.2247 \| 0.2828 \| \| No log \| 12.0 \| 84 \| 2.2723 \| 0.485 \| 0.6901 \| 2.7313 \| 0.485 \| 0.4742 \| 0.2293 \| 0.2774 \| \| No log \| 13.0 \| 91 \| 2.2818 \| 0.49 \| 0.7007 \| 2.5829 \| 0.49 \| 0.4859 \| 0.2473 \| 0.2791 \| \| No log \| 14.0 \| 98 \| 2.1682 \| 0.4975 \| 0.6695 \| 2.8292 \| 0.4975 \| 0.4815 \| 0.2170 \| 0.2653 \| \| No log \| 15.0 \| 105 \| 2.0652 \| 0.52 \| 0.6520 \| 2.4319 \| 0.52 \| 0.5120 \| 0.2079 \| 0.2617 \| \| No log \| 16.0 \| 112 \| 2.0524 \| 0.5225 \| 0.6384 \| 2.5273 \| 0.5225 \| 0.5128 \| 0.1980 \| 0.2391 \| \| No log \| 17.0 \| 119 \| 2.1736 \| 0.48 \| 0.6765 \| 2.4887 \| 0.48 \| 0.4809 \| 0.2112 \| 0.2834 \| \| No log \| 18.0 \| 126 \| 2.0096 \| 0.515 \| 0.6496 \| 2.4230 \| 0.515 \| 0.5215 \| 0.1957 \| 0.2513 \| \| No log \| 19.0 \| 133 \| 2.0207 \| 0.5175 \| 0.6417 \| 2.4591 \| 0.5175 \| 0.5104 \| 0.2011 \| 0.2526 \| \| No log \| 20.0 \| 140 \| 1.9152 \| 0.5425 \| 0.6044 \| 2.5069 \| 0.5425 \| 0.5405 \| 0.1826 \| 0.2147 \| \| No log \| 21.0 \| 147 \| 1.9600 \| 0.52 \| 0.6332 \| 2.5016 \| 0.52 \| 0.5204 \| 0.1871 \| 0.2427 \| \| No log \| 22.0 \| 154 \| 1.9325 \| 0.515 \| 0.6226 \| 2.4981 \| 0.515 \| 0.5078 \| 0.1860 \| 0.2323 \| \| No log \| 23.0 \| 161 \| 1.9172 \| 0.53 \| 0.6144 \| 2.4601 \| 0.53 \| 0.5221 \| 0.1941 \| 0.2311 \| \| No log \| 24.0 \| 168 \| 1.8891 \| 0.5425 \| 0.6091 \| 2.4653 \| 0.5425 \| 0.5399 \| 0.1933 \| 0.2260 \| \| No log \| 25.0 \| 175 \| 1.9460 \| 0.5175 \| 0.6214 \| 2.4785 \| 0.5175 \| 0.5168 \| 0.1694 \| 0.2335 \| \| No log \| 26.0 \| 182 \| 1.9060 \| 0.5525 \| 0.5970 \| 2.4789 \| 0.5525 \| 0.5430 \| 0.1934 \| 0.2137 \| \| No log \| 27.0 \| 189 \| 1.9421 \| 0.5375 \| 0.6205 \| 2.4814 \| 0.5375 \| 0.5313 \| 0.2135 \| 0.2402 \| \| No log \| 28.0 \| 196 \| 2.0195 \| 0.545 \| 0.6187 \| 2.5330 \| 0.545 \| 0.5256 \| 0.1800 \| 0.2360 \| \| No log \| 29.0 \| 203 \| 1.9428 \| 0.535 \| 0.6167 \| 2.4894 \| 0.535 \| 0.5111 \| 0.1862 \| 0.2322 \| \| No log \| 30.0 \| 210 \| 1.8996 \| 0.5225 \| 0.6207 \| 2.4810 \| 0.5225 \| 0.5137 \| 0.1994 \| 0.2330 \| \| No log \| 31.0 \| 217 \| 1.8462 \| 0.54 \| 0.6119 \| 2.4201 \| 0.54 \| 0.5341 \| 0.1817 \| 0.2190 \| \| No log \| 32.0 \| 224 \| 1.8324 \| 0.55 \| 0.5988 \| 2.4230 \| 0.55 \| 0.5427 \| 0.1888 \| 0.2171 \| \| No log \| 33.0 \| 231 \| 1.8393 \| 0.545 \| 0.5977 \| 2.3943 \| 0.545 \| 0.5339 \| 0.1838 \| 0.2172 \| \| No log \| 34.0 \| 238 \| 1.8704 \| 0.5475 \| 0.6081 \| 2.4488 \| 0.5475 \| 0.5427 \| 0.1768 \| 0.2200 \| \| No log \| 35.0 \| 245 \| 1.8546 \| 0.54 \| 0.6022 \| 2.3273 \| 0.54 \| 0.5316 \| 0.1847 \| 0.2226 \| \| No log \| 36.0 \| 252 \| 1.8608 \| 0.53 \| 0.5972 \| 2.5153 \| 0.53 \| 0.5139 \| 0.1810 \| 0.2202 \| \| No log \| 37.0 \| 259 \| 1.8663 \| 0.5325 \| 0.6057 \| 2.4642 \| 0.5325 \| 0.5243 \| 0.1836 \| 0.2205 \| \| No log \| 38.0 \| 266 \| 1.8300 \| 0.545 \| 0.5954 \| 2.5101 \| 0.545 \| 0.5418 \| 0.1890 \| 0.2141 \| \| No log \| 39.0 \| 273 \| 1.8121 \| 0.5625 \| 0.5853 \| 2.4397 \| 0.5625 \| 0.5550 \| 0.1704 \| 0.2110 \| \| No log \| 40.0 \| 280 \| 1.7916 \| 0.54 \| 0.5884 \| 2.3565 \| 0.54 \| 0.5361 \| 0.1685 \| 0.2135 \| \| No log \| 41.0 \| 287 \| 1.8353 \| 0.5575 \| 0.5929 \| 2.4252 \| 0.5575 \| 0.5451 \| 0.1823 \| 0.2116 \| \| No log \| 42.0 \| 294 \| 1.7999 \| 0.5675 \| 0.5839 \| 2.4820 \| 0.5675 \| 0.5631 \| 0.1729 \| 0.2045 \| \| No log \| 43.0 \| 301 \| 1.8622 \| 0.52 \| 0.6106 \| 2.4823 \| 0.52 \| 0.5028 \| 0.1948 \| 0.2270 \| \| No log \| 44.0 \| 308 \| 1.7892 \| 0.55 \| 0.5892 \| 2.3342 \| 0.55 \| 0.5450 \| 0.1798 \| 0.2126 \| \| No log \| 45.0 \| 315 \| 1.7978 \| 0.545 \| 0.5868 \| 2.4345 \| 0.545 \| 0.5439 \| 0.1894 \| 0.2094 \| \| No log \| 46.0 \| 322 \| 1.7697 \| 0.56 \| 0.5772 \| 2.4272 \| 0.56 \| 0.5585 \| 0.1601 \| 0.1997 \| \| No log \| 47.0 \| 329 \| 1.7754 \| 0.5475 \| 0.5835 \| 2.3977 \| 0.5475 \| 0.5438 \| 0.1759 \| 0.2059 \| \| No log \| 48.0 \| 336 \| 1.7922 \| 0.545 \| 0.5929 \| 2.4119 \| 0.545 \| 0.5390 \| 0.1891 \| 0.2131 \| \| No log \| 49.0 \| 343 \| 1.8055 \| 0.5625 \| 0.5872 \| 2.3654 \| 0.5625 \| 0.5497 \| 0.1759 \| 0.2073 \| \| No log \| 50.0 \| 350 \| 1.7972 \| 0.56 \| 0.5894 \| 2.3366 \| 0.56 \| 0.5487 \| 0.1803 \| 0.2083 \| \| No log \| 51.0 \| 357 \| 1.7890 \| 0.555 \| 0.5815 \| 2.3858 \| 0.555 \| 0.5501 \| 0.1693 \| 0.2067 \| \| No log \| 52.0 \| 364 \| 1.7958 \| 0.5475 \| 0.5883 \| 2.4244 \| 0.5475 \| 0.5355 \| 0.1910 \| 0.2105 \| \| No log \| 53.0 \| 371 \| 1.7881 \| 0.5675 \| 0.5834 \| 2.4135 \| 0.5675 \| 0.5603 \| 0.1836 \| 0.2028 \| \| No log \| 54.0 \| 378 \| 1.7675 \| 0.555 \| 0.5766 \| 2.4043 \| 0.555 \| 0.5563 \| 0.1653 \| 0.2047 \| \| No log \| 55.0 \| 385 \| 1.7688 \| 0.55 \| 0.5843 \| 2.3641 \| 0.55 \| 0.5505 \| 0.1729 \| 0.2092 \| \| No log \| 56.0 \| 392 \| 1.7796 \| 0.55 \| 0.5861 \| 2.3404 \| 0.55 \| 0.5458 \| 0.1808 \| 0.2114 \| \| No log \| 57.0 \| 399 \| 1.7861 \| 0.54 \| 0.5885 \| 2.3460 \| 0.54 \| 0.5323 \| 0.1902 \| 0.2073 \| \| No log \| 58.0 \| 406 \| 1.7746 \| 0.56 \| 0.5818 \| 2.3715 \| 0.56 \| 0.5557 \| 0.1643 \| 0.2034 \| \| No log \| 59.0 \| 413 \| 1.7828 \| 0.5575 \| 0.5868 \| 2.3086 \| 0.5575 \| 0.5526 \| 0.1956 \| 0.2088 \| \| No log \| 60.0 \| 420 \| 1.7735 \| 0.565 \| 0.5825 \| 2.3405 \| 0.565 \| 0.5619 \| 0.1696 \| 0.2058 \| \| No log \| 61.0 \| 427 \| 1.7651 \| 0.5675 \| 0.5760 \| 2.4771 \| 0.5675 \| 0.5636 \| 0.1847 \| 0.2027 \| \| No log \| 62.0 \| 434 \| 1.7751 \| 0.5575 \| 0.5834 \| 2.3727 \| 0.5575 \| 0.5524 \| 0.1638 \| 0.2052 \| \| No log \| 63.0 \| 441 \| 1.7900 \| 0.56 \| 0.5834 \| 2.3635 \| 0.56 \| 0.5502 \| 0.1789 \| 0.2061 \| \| No log \| 64.0 \| 448 \| 1.7729 \| 0.56 \| 0.5821 \| 2.3797 \| 0.56 \| 0.5554 \| 0.1676 \| 0.2046 \| \| No log \| 65.0 \| 455 \| 1.7743 \| 0.5625 \| 0.5826 \| 2.4174 \| 0.5625 \| 0.5581 \| 0.1538 \| 0.2052 \| \| No log \| 66.0 \| 462 \| 1.7749 \| 0.5625 \| 0.5801 \| 2.3799 \| 0.5625 \| 0.5592 \| 0.1709 \| 0.2036 \| \| No log \| 67.0 \| 469 \| 1.7795 \| 0.5625 \| 0.5814 \| 2.3169 \| 0.5625 \| 0.5533 \| 0.1883 \| 0.2037 \| \| No log \| 68.0 \| 476 \| 1.7773 \| 0.5675 \| 0.5794 \| 2.3588 \| 0.5675 \| 0.5622 \| 0.1779 \| 0.2013 \| \| No log \| 69.0 \| 483 \| 1.7762 \| 0.56 \| 0.5793 \| 2.3514 \| 0.56 \| 0.5566 \| 0.1707 \| 0.2039 \| \| No log \| 70.0 \| 490 \| 1.7762 \| 0.5625 \| 0.5787 \| 2.3620 \| 0.5625 \| 0.5529 \| 0.1607 \| 0.2017 \| \| No log \| 71.0 \| 497 \| 1.7740 \| 0.5675 \| 0.5798 \| 2.3235 \| 0.5675 \| 0.5612 \| 0.1637 \| 0.2046 \| \| 0.4215 \| 72.0 \| 504 \| 1.7739 \| 0.56 \| 0.5790 \| 2.3235 \| 0.56 \| 0.5542 \| 0.1583 \| 0.2023 \| \| 0.4215 \| 73.0 \| 511 \| 1.7783 \| 0.56 \| 0.5806 \| 2.4187 \| 0.56 \| 0.5545 \| 0.1674 \| 0.2040 \| \| 0.4215 \| 74.0 \| 518 \| 1.7785 \| 0.56 \| 0.5805 \| 2.3302 \| 0.56 \| 0.5544 \| 0.1748 \| 0.2033 \| \| 0.4215 \| 75.0 \| 525 \| 1.7777 \| 0.5625 \| 0.5795 \| 2.3321 \| 0.5625 \| 0.5548 \| 0.1754 \| 0.2029 \| \| 0.4215 \| 76.0 \| 532 \| 1.7785 \| 0.565 \| 0.5799 \| 2.3249 \| 0.565 \| 0.5586 \| 0.1696 \| 0.2023 \| \| 0.4215 \| 77.0 \| 539 \| 1.7763 \| 0.565 \| 0.5790 \| 2.3561 \| 0.565 \| 0.5573 \| 0.1574 \| 0.2022 \| \| 0.4215 \| 78.0 \| 546 \| 1.7767 \| 0.565 \| 0.5790 \| 2.3296 \| 0.565 \| 0.5572 \| 0.1633 \| 0.2024 \| \| 0.4215 \| 79.0 \| 553 \| 1.7763 \| 0.565 \| 0.5790 \| 2.3555 \| 0.565 \| 0.5580 \| 0.1687 \| 0.2016 \| \| 0.4215 \| 80.0 \| 560 \| 1.7783 \| 0.565 \| 0.5800 \| 2.3254 \| 0.565 \| 0.5576 \| 0.1752 \| 0.2032 \| \| 0.4215 \| 81.0 \| 567 \| 1.7773 \| 0.5675 \| 0.5796 \| 2.3530 \| 0.5675 \| 0.5605 \| 0.1519 \| 0.2023 \| \| 0.4215 \| 82.0 \| 574 \| 1.7774 \| 0.5625 \| 0.5797 \| 2.3253 \| 0.5625 \| 0.5549 \| 0.1911 \| 0.2028 \| \| 0.4215 \| 83.0 \| 581 \| 1.7784 \| 0.5625 \| 0.5794 \| 2.3554 \| 0.5625 \| 0.5544 \| 0.1659 \| 0.2030 \| \| 0.4215 \| 84.0 \| 588 \| 1.7769 \| 0.565 \| 0.5793 \| 2.3527 \| 0.565 \| 0.5585 \| 0.1588 \| 0.2024 \| \| 0.4215 \| 85.0 \| 595 \| 1.7787 \| 0.565 \| 0.5799 \| 2.3549 \| 0.565 \| 0.5576 \| 0.1687 \| 0.2032 \| \| 0.4215 \| 86.0 \| 602 \| 1.7778 \| 0.565 \| 0.5795 \| 2.3548 \| 0.565 \| 0.5574 \| 0.1577 \| 0.2029 \| \| 0.4215 \| 87.0 \| 609 \| 1.7787 \| 0.5625 \| 0.5798 \| 2.3545 \| 0.5625 \| 0.5549 \| 0.1643 \| 0.2032 \| \| 0.4215 \| 88.0 \| 616 \| 1.7786 \| 0.565 \| 0.5796 \| 2.3554 \| 0.565 \| 0.5574 \| 0.1667 \| 0.2031 \| \| 0.4215 \| 89.0 \| 623 \| 1.7785 \| 0.565 \| 0.5799 \| 2.3546 \| 0.565 \| 0.5574 \| 0.1691 \| 0.2032 \| \| 0.4215 \| 90.0 \| 630 \| 1.7784 \| 0.565 \| 0.5797 \| 2.3548 \| 0.565 \| 0.5574 \| 0.1656 \| 0.2031 \| \| 0.4215 \| 91.0 \| 637 \| 1.7784 \| 0.565 \| 0.5797 \| 2.3550 \| 0.565 \| 0.5569 \| 0.1753 \| 0.2032 \| \| 0.4215 \| 92.0 \| 644 \| 1.7786 \| 0.565 \| 0.5797 \| 2.3545 \| 0.565 \| 0.5574 \| 0.1744 \| 0.2030 \| \| 0.4215 \| 93.0 \| 651 \| 1.7785 \| 0.565 \| 0.5797 \| 2.3545 \| 0.565 \| 0.5574 \| 0.1709 \| 0.2031 \| \| 0.4215 \| 94.0 \| 658 \| 1.7787 \| 0.565 \| 0.5797 \| 2.3543 \| 0.565 \| 0.5574 \| 0.1704 \| 0.2032 \| \| 0.4215 \| 95.0 \| 665 \| 1.7787 \| 0.565 \| 0.5798 \| 2.3545 \| 0.565 \| 0.5574 \| 0.1713 \| 0.2032 \| \| 0.4215 \| 96.0 \| 672 \| 1.7788 \| 0.565 \| 0.5798 \| 2.3549 \| 0.565 \| 0.5569 \| 0.1777 \| 0.2031 \| \| 0.4215 \| 97.0 \| 679 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3550 \| 0.565 \| 0.5569 \| 0.1677 \| 0.2032 \| \| 0.4215 \| 98.0 \| 686 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3549 \| 0.565 \| 0.5569 \| 0.1648 \| 0.2032 \| \| 0.4215 \| 99.0 \| 693 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3548 \| 0.565 \| 0.5569 \| 0.1728 \| 0.2032 \| \| 0.4215 \| 100.0 \| 700 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3548 \| 0.565 \| 0.5569 \| 0.1677 \| 0.2032 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
banden/dqn-SpaceInvadersNoFrameskip-v1	banden	2023-07-12T15:31:37Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T15:30:56Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 517.00 +/- 204.56 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga banden -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga banden -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga banden ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
yashwantk/finetuning-sentiment-model-3000-samples	yashwantk	2023-07-12T15:26:18Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-30T05:27:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8633333333333333 - name: F1 type: f1 value: 0.8646864686468646 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3200 - Accuracy: 0.8633 - F1: 0.8647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Phips/dqn-BeamRiderNoFrameskip-v4	Phips	2023-07-12T15:26:02Z	4	0	stable-baselines3	[ "stable-baselines3", "BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T15:23:51Z	--- library_name: stable-baselines3 tags: - BeamRiderNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BeamRiderNoFrameskip-v4 type: BeamRiderNoFrameskip-v4 metrics: - type: mean_reward value: 4036.80 +/- 1681.31 name: mean_reward verified: false --- # DQN Agent playing BeamRiderNoFrameskip-v4 This is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga Phips -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga Phips -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga Phips ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ChaimaaBouafoud/arabicSent-ChamaBert	ChaimaaBouafoud	2023-07-12T15:24:41Z	106	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "ar", "fr", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T13:38:21Z	--- language: - ar - fr - en metrics: - accuracy - f1 - roc_auc pipeline_tag: text-classification --- # arabicSent-ChamaBert This model is a fine-tuned version of the <u> aubmindlab/bert-base-arabertv02-twitter </u> model on a webscraped dataset of Arabic comments. It has been trained specifically for sentiment classification tasks in Moroccan Arabic, covering both Standard Arabic and dialectal variations. The model's performance on the evaluation set is as follows: - Loss: 0.1626 - Accuracy: 0.9073 - F1: 0.9129 - Roc Auc: 0.9337 # Dataset The dataset used for training and evaluation consists of a collection of Moroccan Arabic comments specifically focused on sentiments towards the effects of vaccines. It contains a total of 81,971 comments, with sentiment labels of "Negative" and "Positive". The dataset provides ground truth annotations that enable the model to learn the association between the language used in comments and the corresponding sentiment expressed. The data collection process adhered to ethical considerations, respecting user privacy and complying with applicable data protection regulations. Measures were taken to ensure the anonymization of user identities and the removal of any personally identifiable information. # Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
jrosenzw/autotrain-diabetes-detection-74053139456	jrosenzw	2023-07-12T15:23:51Z	4	0	transformers	[ "transformers", "joblib", "xgboost", "autotrain", "tabular", "classification", "tabular-classification", "dataset:jrosenzw/autotrain-data-diabetes-detection", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	tabular-classification	2023-07-12T15:12:14Z	--- tags: - autotrain - tabular - classification - tabular-classification datasets: - jrosenzw/autotrain-data-diabetes-detection co2_eq_emissions: emissions: 0.8803900471704509 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 74053139456 - CO2 Emissions (in grams): 0.8804 ## Validation Metrics - Loss: 0.473 - Accuracy: 0.760 - Precision: 0.667 - Recall: 0.630 - AUC: 0.835 - F1: 0.648 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```
ynklab/XCodeBERT	ynklab	2023-07-12T15:17:51Z	105	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "arxiv:2306.15604", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-29T14:01:20Z	--- license: mit --- [Paper on arXiv](https://arxiv.org/abs/2306.15604) ### Code Java/PHP/Python/Go ### Query En/Fr/Ja/Zh ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('ynklab/XCodeBERT') # Same as `xlm-roberta-base` model = AutoModel.from_pretrained('ynklab/XCodeBERT') ```
alujjdnd/10-Social-Dimensions	alujjdnd	2023-07-12T15:11:43Z	0	0	null	[ "license:cc-by-4.0", "region:us" ]	null	2023-06-12T11:19:49Z	--- license: cc-by-4.0 --- # 10 Social Dimensions A mirror of https://github.com/minjechoi/10dimensions/tree/master/weights - notably the Dropbox links ## Overview Contains pretrained weights for LSTM and BERT classifiers, for the 10 social dimensions. ### From Paper https://dl.acm.org/doi/10.1145/3366423.3380224 ### ACM Reference Format Minje Choi, Luca Maria Aiello, Krisztián Zsolt Varga, and Daniele Quercia. 2020. Ten Social Dimensions of Conversations and Relationships. In Proceedings of The Web Conference 2020 (WWW ’20), April 20–24, 2020, Taipei, Taiwan. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3366423.3380224
vineetsharma/q-Taxi-v3_million_steps	vineetsharma	2023-07-12T15:08:50Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T15:05:01Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3_million_steps results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="vineetsharma/q-Taxi-v3_million_steps", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
bofenghuang/vigogne-13b-chat	bofenghuang	2023-07-12T14:59:28Z	1,466	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "LLM", "fr", "license:openrail", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-12T13:59:04Z	--- license: openrail language: - fr pipeline_tag: text-generation library_name: transformers tags: - llama - LLM inference: false --- <p align="center" width="100%"> <img src="https://huggingface.co/bofenghuang/vigogne-13b-chat/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;"> </p> # Vigogne-13B-Chat: A French Chat LLaMA Model Vigogne-13B-Chat is a LLaMA-13B model fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant. For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne Usage and License Notices: Same as [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Vigogne is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from vigogne.preprocess import generate_inference_chat_prompt model_name_or_path = "bofenghuang/vigogne-13b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto") user_query = "Expliquez la différence entre DoS et phishing." prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer) input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device) input_length = input_ids.shape[1] generated_outputs = model.generate( input_ids=input_ids, generation_config=GenerationConfig( temperature=0.1, do_sample=True, repetition_penalty=1.0, max_new_tokens=512, ), return_dict_in_generate=True, ) generated_tokens = generated_outputs.sequences[0, input_length:] generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True) print(generated_text) ``` You can infer this model by using the following Google Colab Notebook. <a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> ## Limitations Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.
bardsai/finance-sentiment-zh-base	bardsai	2023-07-12T14:54:22Z	200	18	transformers	[ "transformers", "pytorch", "bert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "zh", "dataset:datasets/financial_phrasebank", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T14:41:06Z	--- language: zh tags: - text-classification - financial-sentiment-analysis - sentiment-analysis datasets: - datasets/financial_phrasebank metrics: - f1 - accuracy - precision - recall widget: - text: "净销售额增长30%，达到3600万欧元。" example_title: "Example 1" - text: "黑色星期五拉开帷幕。店内促销活动列表。" example_title: "Example 2" - text: "CDPROJEKT股价在WSE上市公司中跌幅最大。" example_title: "Example 3" --- # Finance Sentiment ZH (base) Finance Sentiment ZH (base) is a model based on [bert-base-chinese](https://huggingface.co/bert-base-chinese) for analyzing sentiment of Chinese financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu. The model will give you a three labels: positive, negative and neutral. ## How to use You can use this model directly with a pipeline for sentiment-analysis: ```python from transformers import pipeline nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-zh-base") nlp("净销售额增长30%，达到3600万欧元。") ``` ```bash [{'label': 'negative', 'score': 0.9999314546585083}] ``` ## Performance \| Metric \| Value \| \| --- \| ----------- \| \| f1 macro \| 0.966 \| \| precision macro \| 0.963 \| \| recall macro \| 0.969 \| \| accuracy \| 0.973 \| \| samples per second \| 135.2 \| (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-07-12: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai
DipanAI/Low_falcan_7b	DipanAI	2023-07-12T14:49:33Z	0	0	null	[ "pytorch", "tensorboard", "generated_from_trainer", "region:us" ]	null	2023-07-12T14:00:09Z	--- tags: - generated_from_trainer model-index: - name: Low_falcan_7b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Low_falcan_7b This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 800 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
bardsai/finance-sentiment-zh-fast	bardsai	2023-07-12T14:48:43Z	533	7	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "zh", "dataset:datasets/financial_phrasebank", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-12T14:21:24Z	--- language: zh tags: - text-classification - financial-sentiment-analysis - sentiment-analysis datasets: - datasets/financial_phrasebank metrics: - f1 - accuracy - precision - recall widget: - text: "净销售额增长30%，达到3600万欧元。" example_title: "Example 1" - text: "黑色星期五拉开帷幕。店内促销活动列表。" example_title: "Example 2" - text: "CDPROJEKT股价在WSE上市公司中跌幅最大。" example_title: "Example 3" --- # Finance Sentiment ZH (fast) Finance Sentiment ZH (fast) is a [distiluse](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1)-based model for analyzing sentiment of Chinese financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu. The model will give you a three labels: positive, negative and neutral. ## How to use You can use this model directly with a pipeline for sentiment-analysis: ```python from transformers import pipeline nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-zh-fast") nlp("净销售额增长30%，达到3600万欧元。") ``` ```bash [{'label': 'positive', 'score': 0.9996095299720764}] ``` ## Performance \| Metric \| Value \| \| --- \| ----------- \| \| f1 macro \| 0.953 \| \| precision macro \| 0.959 \| \| recall macro \| 0.949 \| \| accuracy \| 0.961 \| \| samples per second \| 264.6 \| (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-07-12: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai
vineetsharma/q-Taxi-v3	vineetsharma	2023-07-12T14:35:12Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T14:35:11Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="vineetsharma/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
cenkersisman/chatbotgpt-turkish-latin	cenkersisman	2023-07-12T14:22:53Z	163	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "tr", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-02T04:19:39Z	--- widget: - text: '[Q] cengiz han kac yilinda dogmustur' example_title: Örnek 1 - text: '[Q] etimesgut havaalani nerede bulunmaktadir' example_title: Örnek 2 - text: '[Q] spartakus nereli bir gladyatordur' example_title: Örnek 3 - text: '[Q] ajda pekkan hangi sarkisiyla iyi cikis yakalamistir' example_title: Örnek 4 - text: '[Q] kobalt camlari kim tarafindan kesfedilmistir' example_title: Örnek 5 - text: '[Q] john turk ne zaman dogmustur' example_title: Örnek 6 - text: '[Q] tayvan ne zaman kurulmustur' example_title: Örnek 7 - text: '[Q] dunyanin her bolgesinden kutuphaneler hangi imkani verir' example_title: Örnek 8 - text: '[Q] gozun en distaki birinci tabakaya ne denir' example_title: Örnek 9 language: - tr --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Bu model test amaçlı hazırlanmıştır ve fikir vermesi açısından geliştirilmiştir. Model için Vikipedi üzerinden üretilen 40 bin soru cevap GPT ile eğitilmiştir. Daha büyük veri setlerinde daha iyi sonuçlar alınabilir. ## Önemli Notlar * Inference için soruların başında [Q] kullanılmalıdır. * Sorular latin karakterlerden oluşmalıdır. ç ğ I ö ş ü gibi harfler içermemelidir. * Sorular küçük harflerden oluşmalıdır. Büyük harf veya sembol kullanımı farklı ve istenmeyen cevaplar üretecektir. ## Model Details ## Başlıklara göre en fazla soru cevap içeren konular aşağıdadır: * Cengiz Han: 665 adet * Futbol rekabetleri listesi: 409 adet * Lüleburgaz Muharebesi: 336 adet * I. Baybars: 263 adet * Sovyetler Birliği'nin askerî tarihi: 258 adet * Zümrüdüanka Yoldaşlığı: 245 adet * Gilles Deleuze: 208 adet * Ermenistan Sovyet Sosyalist Cumhuriyeti: 195 adet * Nâzım Hikmet: 173 adet * Hermann Göring: 169 adet * V. Leon: 163 adet * Gökhan Türkmen: 156 adet * Dumbledore'un Ordusu: 153 adet * Ajda Pekkan: 152 adet * Kırkpınar Yağlı Güreşleri: 152 adet * Mehdi Savaşı: 150 adet * İmamiye (Şiilik öğretisi): 149 adet * Rumyantsev Harekâtı: 145 adet * II. Dünya Savaşı tankları: 144 adet * Emîn: 142 adet * Boshin Savaşı: 137 adet * Wolfgang Amadeus Mozart: 136 adet * Faşizm: 134 adet * Kâzım Koyuncu: 133 adet * Suvorov Harekâtı: 128 adet * Mao Zedong: 127 adet * Mehdî (Abbâsî halifesi): 127 adet * Madagaskar: 124 adet * Oscar Niemeyer: 123 adet * Adolf Eichmann: 123 adet * Joachim von Ribbentrop: 121 adet * Crystal Palace FC: 119 adet * IV. Mihail: 111 adet * VI. Leon: 107 adet * Han Hanedanı: 105 adet * Portekiz coğrafi keşifleri: 104 adet * II. Nikiforos: 102 adet * III. Mihail: 98 adet * Fidel Castro: 96 adet * Tsushima Muharebesi: 94 adet * I. Basileios: 93 adet * VI. Konstantinos: 92 adet * Bijan Muharebesi: 91 adet * Otto Skorzeny: 90 adet * Antimon: 89 adet * Dijitalleştirme: 88 adet * Theofilos: 79 adet * Sovyet Hava Kuvvetleri: 78 adet * IV. Henry, Bölüm 1: 77 adet ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Cenker Sisman - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model : redrussianarmy/gpt2-turkish-cased ![Loss değerleri](https://huggingface.co/cenkersisman/chatbotgpt-turkish-cased/resolve/main/v.png) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Sınırlamalar ve Önyargılar chatbotgpt-turkish, bir özyineli dil modeli olarak eğitildi. Bu, temel işlevinin bir metin dizisi alıp bir sonraki belirteci tahmin etmek olduğu anlamına gelir. Dil modelleri bunun dışında birçok görev için yaygın olarak kullanılsa da, bu çalışmayla ilgili birçok bilinmeyen bulunmaktadır. chatbotgpt-turkish, küfür, açık saçıklık ve aksi davranışlara yol açan metinleri içerdiği bilinen bir veri kümesi olan Pile üzerinde eğitildi. Kullanım durumunuza bağlı olarak, chatbotgpt-turkish toplumsal olarak kabul edilemez metinler üretebilir. Pile makalesinin 5. ve 6. bölümlerinde Pile'daki önyargıların daha ayrıntılı bir analizini bulabilirsiniz. Tüm dil modellerinde olduğu gibi, chatbotgpt-turkish'in belirli bir girişe nasıl yanıt vereceğini önceden tahmin etmek zordur ve uyarı olmaksızın saldırgan içerik ortaya çıkabilir. Sonuçları yayınlamadan önce insanların çıktıları denetlemesini veya filtrelemesini öneririz, hem istenmeyen içeriği sansürlemek hem de sonuçların kalitesini iyileştirmek için. ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model ```python """Inference""" from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel, GPT2TokenizerFast, GPT2Tokenizer def load_model(model_path): model = GPT2LMHeadModel.from_pretrained(model_path) return model def load_tokenizer(tokenizer_path): tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path) return tokenizer def generate_text(model_path, sequence, max_length): model = load_model(model_path) tokenizer = load_tokenizer(model_path) ids = tokenizer.encode(sequence, return_tensors='pt') outputs = model.generate( ids, do_sample=True, max_length=max_length, pad_token_id=model.config.eos_token_id, top_k=1, top_p=0.99, ) converted = tokenizer.convert_ids_to_tokens(outputs[0]) valid_tokens = [token if token is not None else '.' for token in converted] generated_text = tokenizer.convert_tokens_to_string(valid_tokens) print(generated_text) model2_path = "cenkersisman/chatbotgpt-turkish" sequence2 = "[Q] cengiz han kimdir" max_len = 120 generate_text(model2_path, sequence2, max_len) ``` ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SHENMU007/neunit_BASE_V12.2	SHENMU007	2023-07-12T14:17:28Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-12T11:35:11Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9	jordyvl	2023-07-12T14:10:56Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-12T13:24:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5908 - Accuracy: 0.54 - Brier Loss: 0.6121 - Nll: 2.4999 - F1 Micro: 0.54 - F1 Macro: 0.5334 - Ece: 0.1895 - Aurc: 0.2228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 3.9334 \| 0.0425 \| 1.0719 \| 7.3494 \| 0.0425 \| 0.0341 \| 0.2781 \| 0.9557 \| \| No log \| 2.0 \| 14 \| 3.0206 \| 0.09 \| 0.9526 \| 5.9271 \| 0.09 \| 0.0682 \| 0.1659 \| 0.8896 \| \| No log \| 3.0 \| 21 \| 2.6955 \| 0.2275 \| 0.8867 \| 5.4041 \| 0.2275 \| 0.1804 \| 0.1637 \| 0.6351 \| \| No log \| 4.0 \| 28 \| 2.3029 \| 0.29 \| 0.8005 \| 3.5699 \| 0.29 \| 0.2727 \| 0.1512 \| 0.4877 \| \| No log \| 5.0 \| 35 \| 2.0293 \| 0.37 \| 0.7219 \| 2.9856 \| 0.37 \| 0.3519 \| 0.1495 \| 0.3632 \| \| No log \| 6.0 \| 42 \| 1.9194 \| 0.44 \| 0.6926 \| 2.9746 \| 0.44 \| 0.4240 \| 0.1778 \| 0.3091 \| \| No log \| 7.0 \| 49 \| 1.9640 \| 0.4675 \| 0.7000 \| 3.0857 \| 0.4675 \| 0.4390 \| 0.2009 \| 0.3004 \| \| No log \| 8.0 \| 56 \| 1.9049 \| 0.4625 \| 0.7003 \| 2.8472 \| 0.4625 \| 0.4602 \| 0.2053 \| 0.3051 \| \| No log \| 9.0 \| 63 \| 2.0561 \| 0.4675 \| 0.7168 \| 2.8229 \| 0.4675 \| 0.4501 \| 0.2288 \| 0.2994 \| \| No log \| 10.0 \| 70 \| 2.1002 \| 0.45 \| 0.7433 \| 2.7915 \| 0.45 \| 0.4234 \| 0.2691 \| 0.3022 \| \| No log \| 11.0 \| 77 \| 2.2528 \| 0.4525 \| 0.7686 \| 3.0103 \| 0.4525 \| 0.4320 \| 0.2921 \| 0.3183 \| \| No log \| 12.0 \| 84 \| 2.1190 \| 0.475 \| 0.7427 \| 2.6715 \| 0.4750 \| 0.4660 \| 0.2832 \| 0.3077 \| \| No log \| 13.0 \| 91 \| 2.3102 \| 0.445 \| 0.7825 \| 2.9698 \| 0.445 \| 0.4252 \| 0.3093 \| 0.3100 \| \| No log \| 14.0 \| 98 \| 2.3501 \| 0.42 \| 0.8145 \| 2.7585 \| 0.4200 \| 0.4248 \| 0.3206 \| 0.3662 \| \| No log \| 15.0 \| 105 \| 2.2402 \| 0.495 \| 0.7423 \| 3.0313 \| 0.495 \| 0.4702 \| 0.2692 \| 0.2818 \| \| No log \| 16.0 \| 112 \| 2.2266 \| 0.49 \| 0.7349 \| 2.8824 \| 0.49 \| 0.4714 \| 0.2763 \| 0.2895 \| \| No log \| 17.0 \| 119 \| 2.2989 \| 0.4725 \| 0.7509 \| 3.0951 \| 0.4725 \| 0.4499 \| 0.2863 \| 0.2855 \| \| No log \| 18.0 \| 126 \| 2.1355 \| 0.47 \| 0.7322 \| 2.9349 \| 0.47 \| 0.4616 \| 0.2725 \| 0.2845 \| \| No log \| 19.0 \| 133 \| 2.0965 \| 0.505 \| 0.7067 \| 2.8254 \| 0.505 \| 0.4956 \| 0.2523 \| 0.2757 \| \| No log \| 20.0 \| 140 \| 2.1961 \| 0.485 \| 0.7358 \| 3.1604 \| 0.485 \| 0.4567 \| 0.2825 \| 0.2841 \| \| No log \| 21.0 \| 147 \| 2.1287 \| 0.5025 \| 0.7247 \| 2.5998 \| 0.5025 \| 0.5074 \| 0.2703 \| 0.3064 \| \| No log \| 22.0 \| 154 \| 2.2280 \| 0.4675 \| 0.7760 \| 2.8571 \| 0.4675 \| 0.4636 \| 0.2911 \| 0.3232 \| \| No log \| 23.0 \| 161 \| 1.9649 \| 0.5025 \| 0.6828 \| 2.8224 \| 0.5025 \| 0.4970 \| 0.2410 \| 0.2633 \| \| No log \| 24.0 \| 168 \| 1.9361 \| 0.5125 \| 0.6780 \| 2.7309 \| 0.5125 \| 0.5035 \| 0.2326 \| 0.2553 \| \| No log \| 25.0 \| 175 \| 2.0161 \| 0.5 \| 0.6980 \| 2.9958 \| 0.5 \| 0.4912 \| 0.2580 \| 0.2556 \| \| No log \| 26.0 \| 182 \| 1.8763 \| 0.5025 \| 0.6624 \| 2.8291 \| 0.5025 \| 0.4952 \| 0.2305 \| 0.2431 \| \| No log \| 27.0 \| 189 \| 1.9057 \| 0.525 \| 0.6793 \| 2.5627 \| 0.525 \| 0.5174 \| 0.2161 \| 0.2634 \| \| No log \| 28.0 \| 196 \| 1.8529 \| 0.52 \| 0.6683 \| 2.7191 \| 0.52 \| 0.5132 \| 0.2375 \| 0.2535 \| \| No log \| 29.0 \| 203 \| 1.9603 \| 0.5125 \| 0.6831 \| 2.7822 \| 0.5125 \| 0.5076 \| 0.2395 \| 0.2657 \| \| No log \| 30.0 \| 210 \| 1.8247 \| 0.52 \| 0.6533 \| 2.8547 \| 0.52 \| 0.5058 \| 0.2080 \| 0.2426 \| \| No log \| 31.0 \| 217 \| 1.8275 \| 0.5125 \| 0.6547 \| 2.6194 \| 0.5125 \| 0.5032 \| 0.2208 \| 0.2488 \| \| No log \| 32.0 \| 224 \| 1.8003 \| 0.52 \| 0.6455 \| 2.6138 \| 0.52 \| 0.5124 \| 0.2302 \| 0.2370 \| \| No log \| 33.0 \| 231 \| 1.8714 \| 0.505 \| 0.6694 \| 2.6643 \| 0.505 \| 0.4970 \| 0.2195 \| 0.2553 \| \| No log \| 34.0 \| 238 \| 1.8018 \| 0.5075 \| 0.6659 \| 2.5423 \| 0.5075 \| 0.4978 \| 0.2241 \| 0.2515 \| \| No log \| 35.0 \| 245 \| 1.7844 \| 0.5225 \| 0.6503 \| 2.6100 \| 0.5225 \| 0.5181 \| 0.2181 \| 0.2435 \| \| No log \| 36.0 \| 252 \| 1.8321 \| 0.5225 \| 0.6674 \| 2.7821 \| 0.5225 \| 0.5020 \| 0.2285 \| 0.2462 \| \| No log \| 37.0 \| 259 \| 1.7859 \| 0.4975 \| 0.6725 \| 2.6066 \| 0.4975 \| 0.4974 \| 0.2351 \| 0.2627 \| \| No log \| 38.0 \| 266 \| 1.7790 \| 0.5125 \| 0.6595 \| 2.6983 \| 0.5125 \| 0.5023 \| 0.2172 \| 0.2497 \| \| No log \| 39.0 \| 273 \| 1.6989 \| 0.5225 \| 0.6401 \| 2.6743 \| 0.5225 \| 0.5151 \| 0.2100 \| 0.2407 \| \| No log \| 40.0 \| 280 \| 1.7568 \| 0.52 \| 0.6488 \| 2.5294 \| 0.52 \| 0.5132 \| 0.2208 \| 0.2442 \| \| No log \| 41.0 \| 287 \| 1.6896 \| 0.5275 \| 0.6362 \| 2.5489 \| 0.5275 \| 0.5141 \| 0.2045 \| 0.2323 \| \| No log \| 42.0 \| 294 \| 1.7193 \| 0.5275 \| 0.6517 \| 2.5525 \| 0.5275 \| 0.5232 \| 0.1986 \| 0.2467 \| \| No log \| 43.0 \| 301 \| 1.7199 \| 0.535 \| 0.6403 \| 2.5974 \| 0.535 \| 0.5279 \| 0.2104 \| 0.2432 \| \| No log \| 44.0 \| 308 \| 1.6594 \| 0.5375 \| 0.6330 \| 2.4854 \| 0.5375 \| 0.5316 \| 0.2015 \| 0.2321 \| \| No log \| 45.0 \| 315 \| 1.6543 \| 0.5275 \| 0.6239 \| 2.4955 \| 0.5275 \| 0.5223 \| 0.2144 \| 0.2308 \| \| No log \| 46.0 \| 322 \| 1.6490 \| 0.5425 \| 0.6262 \| 2.5215 \| 0.5425 \| 0.5358 \| 0.2104 \| 0.2273 \| \| No log \| 47.0 \| 329 \| 1.6570 \| 0.54 \| 0.6233 \| 2.5454 \| 0.54 \| 0.5380 \| 0.2047 \| 0.2301 \| \| No log \| 48.0 \| 336 \| 1.6359 \| 0.5375 \| 0.6218 \| 2.5546 \| 0.5375 \| 0.5320 \| 0.2171 \| 0.2257 \| \| No log \| 49.0 \| 343 \| 1.6320 \| 0.55 \| 0.6214 \| 2.4958 \| 0.55 \| 0.5452 \| 0.2014 \| 0.2267 \| \| No log \| 50.0 \| 350 \| 1.6230 \| 0.53 \| 0.6208 \| 2.4979 \| 0.53 \| 0.5243 \| 0.2017 \| 0.2315 \| \| No log \| 51.0 \| 357 \| 1.6374 \| 0.535 \| 0.6257 \| 2.4644 \| 0.535 \| 0.5293 \| 0.2038 \| 0.2286 \| \| No log \| 52.0 \| 364 \| 1.6190 \| 0.5375 \| 0.6199 \| 2.5279 \| 0.5375 \| 0.5310 \| 0.1855 \| 0.2290 \| \| No log \| 53.0 \| 371 \| 1.6155 \| 0.5475 \| 0.6158 \| 2.4738 \| 0.5475 \| 0.5435 \| 0.1913 \| 0.2239 \| \| No log \| 54.0 \| 378 \| 1.6131 \| 0.5425 \| 0.6184 \| 2.4982 \| 0.5425 \| 0.5377 \| 0.1969 \| 0.2248 \| \| No log \| 55.0 \| 385 \| 1.6035 \| 0.545 \| 0.6138 \| 2.4690 \| 0.545 \| 0.5406 \| 0.2164 \| 0.2223 \| \| No log \| 56.0 \| 392 \| 1.5990 \| 0.54 \| 0.6153 \| 2.4701 \| 0.54 \| 0.5356 \| 0.2019 \| 0.2249 \| \| No log \| 57.0 \| 399 \| 1.6024 \| 0.5425 \| 0.6153 \| 2.4626 \| 0.5425 \| 0.5375 \| 0.1826 \| 0.2237 \| \| No log \| 58.0 \| 406 \| 1.5935 \| 0.545 \| 0.6141 \| 2.4390 \| 0.545 \| 0.5415 \| 0.1933 \| 0.2238 \| \| No log \| 59.0 \| 413 \| 1.6016 \| 0.545 \| 0.6137 \| 2.4640 \| 0.545 \| 0.5401 \| 0.2021 \| 0.2230 \| \| No log \| 60.0 \| 420 \| 1.5976 \| 0.54 \| 0.6146 \| 2.4618 \| 0.54 \| 0.5355 \| 0.1912 \| 0.2245 \| \| No log \| 61.0 \| 427 \| 1.5984 \| 0.545 \| 0.6133 \| 2.4683 \| 0.545 \| 0.5408 \| 0.1971 \| 0.2228 \| \| No log \| 62.0 \| 434 \| 1.5941 \| 0.54 \| 0.6131 \| 2.4639 \| 0.54 \| 0.5358 \| 0.1898 \| 0.2236 \| \| No log \| 63.0 \| 441 \| 1.5936 \| 0.545 \| 0.6123 \| 2.4689 \| 0.545 \| 0.5404 \| 0.1953 \| 0.2222 \| \| No log \| 64.0 \| 448 \| 1.5970 \| 0.5425 \| 0.6138 \| 2.4647 \| 0.5425 \| 0.5384 \| 0.2015 \| 0.2238 \| \| No log \| 65.0 \| 455 \| 1.5943 \| 0.545 \| 0.6130 \| 2.4963 \| 0.545 \| 0.5400 \| 0.1979 \| 0.2229 \| \| No log \| 66.0 \| 462 \| 1.5936 \| 0.545 \| 0.6127 \| 2.4977 \| 0.545 \| 0.5400 \| 0.1933 \| 0.2229 \| \| No log \| 67.0 \| 469 \| 1.5928 \| 0.5425 \| 0.6127 \| 2.4965 \| 0.5425 \| 0.5381 \| 0.1976 \| 0.2233 \| \| No log \| 68.0 \| 476 \| 1.5946 \| 0.5425 \| 0.6128 \| 2.4768 \| 0.5425 \| 0.5383 \| 0.2149 \| 0.2233 \| \| No log \| 69.0 \| 483 \| 1.5924 \| 0.54 \| 0.6126 \| 2.4946 \| 0.54 \| 0.5356 \| 0.2094 \| 0.2233 \| \| No log \| 70.0 \| 490 \| 1.5921 \| 0.54 \| 0.6120 \| 2.4964 \| 0.54 \| 0.5356 \| 0.1801 \| 0.2230 \| \| No log \| 71.0 \| 497 \| 1.5926 \| 0.54 \| 0.6126 \| 2.4955 \| 0.54 \| 0.5356 \| 0.2039 \| 0.2235 \| \| 0.3138 \| 72.0 \| 504 \| 1.5916 \| 0.5425 \| 0.6121 \| 2.4964 \| 0.5425 \| 0.5366 \| 0.1898 \| 0.2229 \| \| 0.3138 \| 73.0 \| 511 \| 1.5917 \| 0.54 \| 0.6119 \| 2.4966 \| 0.54 \| 0.5356 \| 0.2039 \| 0.2231 \| \| 0.3138 \| 74.0 \| 518 \| 1.5918 \| 0.54 \| 0.6123 \| 2.4964 \| 0.54 \| 0.5351 \| 0.2035 \| 0.2229 \| \| 0.3138 \| 75.0 \| 525 \| 1.5912 \| 0.54 \| 0.6118 \| 2.4975 \| 0.54 \| 0.5351 \| 0.2059 \| 0.2228 \| \| 0.3138 \| 76.0 \| 532 \| 1.5918 \| 0.54 \| 0.6124 \| 2.4965 \| 0.54 \| 0.5351 \| 0.1971 \| 0.2231 \| \| 0.3138 \| 77.0 \| 539 \| 1.5919 \| 0.5425 \| 0.6120 \| 2.4974 \| 0.5425 \| 0.5358 \| 0.2087 \| 0.2227 \| \| 0.3138 \| 78.0 \| 546 \| 1.5903 \| 0.54 \| 0.6118 \| 2.4978 \| 0.54 \| 0.5341 \| 0.2169 \| 0.2228 \| \| 0.3138 \| 79.0 \| 553 \| 1.5922 \| 0.54 \| 0.6124 \| 2.4976 \| 0.54 \| 0.5351 \| 0.2109 \| 0.2234 \| \| 0.3138 \| 80.0 \| 560 \| 1.5914 \| 0.54 \| 0.6122 \| 2.4983 \| 0.54 \| 0.5345 \| 0.2041 \| 0.2228 \| \| 0.3138 \| 81.0 \| 567 \| 1.5907 \| 0.54 \| 0.6119 \| 2.4981 \| 0.54 \| 0.5345 \| 0.2128 \| 0.2226 \| \| 0.3138 \| 82.0 \| 574 \| 1.5921 \| 0.5425 \| 0.6124 \| 2.4986 \| 0.5425 \| 0.5362 \| 0.2084 \| 0.2227 \| \| 0.3138 \| 83.0 \| 581 \| 1.5918 \| 0.5425 \| 0.6125 \| 2.4987 \| 0.5425 \| 0.5362 \| 0.2038 \| 0.2230 \| \| 0.3138 \| 84.0 \| 588 \| 1.5902 \| 0.54 \| 0.6120 \| 2.4989 \| 0.54 \| 0.5345 \| 0.2043 \| 0.2226 \| \| 0.3138 \| 85.0 \| 595 \| 1.5919 \| 0.5425 \| 0.6124 \| 2.4988 \| 0.5425 \| 0.5360 \| 0.1998 \| 0.2228 \| \| 0.3138 \| 86.0 \| 602 \| 1.5916 \| 0.5425 \| 0.6124 \| 2.4990 \| 0.5425 \| 0.5362 \| 0.2079 \| 0.2227 \| \| 0.3138 \| 87.0 \| 609 \| 1.5906 \| 0.54 \| 0.6120 \| 2.4990 \| 0.54 \| 0.5345 \| 0.2037 \| 0.2227 \| \| 0.3138 \| 88.0 \| 616 \| 1.5908 \| 0.54 \| 0.6120 \| 2.4989 \| 0.54 \| 0.5345 \| 0.2091 \| 0.2230 \| \| 0.3138 \| 89.0 \| 623 \| 1.5909 \| 0.54 \| 0.6120 \| 2.4995 \| 0.54 \| 0.5344 \| 0.2113 \| 0.2228 \| \| 0.3138 \| 90.0 \| 630 \| 1.5906 \| 0.54 \| 0.6119 \| 2.4996 \| 0.54 \| 0.5345 \| 0.1969 \| 0.2228 \| \| 0.3138 \| 91.0 \| 637 \| 1.5911 \| 0.5425 \| 0.6121 \| 2.4999 \| 0.5425 \| 0.5360 \| 0.1954 \| 0.2226 \| \| 0.3138 \| 92.0 \| 644 \| 1.5909 \| 0.54 \| 0.6121 \| 2.4994 \| 0.54 \| 0.5344 \| 0.1928 \| 0.2228 \| \| 0.3138 \| 93.0 \| 651 \| 1.5907 \| 0.5425 \| 0.6121 \| 2.4999 \| 0.5425 \| 0.5360 \| 0.2034 \| 0.2225 \| \| 0.3138 \| 94.0 \| 658 \| 1.5910 \| 0.5425 \| 0.6122 \| 2.4996 \| 0.5425 \| 0.5360 \| 0.1974 \| 0.2227 \| \| 0.3138 \| 95.0 \| 665 \| 1.5909 \| 0.5375 \| 0.6121 \| 2.4995 \| 0.5375 \| 0.5319 \| 0.1990 \| 0.2230 \| \| 0.3138 \| 96.0 \| 672 \| 1.5907 \| 0.5375 \| 0.6120 \| 2.4997 \| 0.5375 \| 0.5318 \| 0.1980 \| 0.2229 \| \| 0.3138 \| 97.0 \| 679 \| 1.5907 \| 0.54 \| 0.6120 \| 2.4998 \| 0.54 \| 0.5344 \| 0.1900 \| 0.2228 \| \| 0.3138 \| 98.0 \| 686 \| 1.5907 \| 0.5425 \| 0.6120 \| 2.4999 \| 0.5425 \| 0.5362 \| 0.1899 \| 0.2226 \| \| 0.3138 \| 99.0 \| 693 \| 1.5908 \| 0.54 \| 0.6121 \| 2.4999 \| 0.54 \| 0.5334 \| 0.1936 \| 0.2228 \| \| 0.3138 \| 100.0 \| 700 \| 1.5908 \| 0.54 \| 0.6121 \| 2.4999 \| 0.54 \| 0.5334 \| 0.1895 \| 0.2228 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
YojitShinde/Reinforce-CartPole-v1	YojitShinde	2023-07-12T14:06:04Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T14:05:54Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
fireday/ppo-LunarLander-v2	fireday	2023-07-12T14:05:26Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T14:05:07Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 291.16 +/- 20.30 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
malditoguisante/ppo-LunarLander-v2	malditoguisante	2023-07-12T14:04:14Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T14:03:55Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 272.05 +/- 16.08 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
aalloott/ppo-LunarLander-v2	aalloott	2023-07-12T14:01:04Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T14:00:45Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 250.52 +/- 23.91 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
alexbrandsen/bert-base-german-cased-archaeo-NER	alexbrandsen	2023-07-12T13:59:45Z	121	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "Archaeology", "Named Entity Recognition", "NER", "de", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-12T13:22:47Z	--- license: cc0-1.0 language: - de tags: - Archaeology - Named Entity Recognition - NER metrics: - f1 --- # bert-base-german-cased-archaeo-NER A German BERT model for Named Entity Recognition in the Archaeology domain This is the bert-base-german-cased-archaeo model finetuned for NER, targeting the following entities: - Time periods (PER) - Places (LOC) - Artefacts (ART) - Contexts (CON) - Materials (MAT) - Species (SPE)
MUNDOJU/ppo-LunarLander-v2	MUNDOJU	2023-07-12T13:55:46Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T13:55:27Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.13 +/- 13.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Mateo2008/ppo-LunarLander-v2	Mateo2008	2023-07-12T13:54:02Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T13:53:38Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.21 +/- 22.06 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
PisoF/ppo-LunarLander-v2	PisoF	2023-07-12T13:53:40Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-12T13:53:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.80 +/- 20.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Samuel1234/ppo-LunarLander-v2	Samuel1234	2023-07-12T13:52:00Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T18:25:26Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.25 +/- 19.97 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Aceituna0813/ppo-LunarLander-v2	Aceituna0813	2023-07-12T13:47:32Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T18:22:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.22 +/- 34.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7	jordyvl	2023-07-12T13:24:05Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-12T12:43:57Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5923 - Accuracy: 0.57 - Brier Loss: 0.5750 - Nll: 2.3088 - F1 Micro: 0.57 - F1 Macro: 0.5661 - Ece: 0.1722 - Aurc: 0.2058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.3150 \| 0.0425 \| 1.0714 \| 7.3695 \| 0.0425 \| 0.0339 \| 0.2866 \| 0.9549 \| \| No log \| 2.0 \| 14 \| 3.3516 \| 0.1 \| 0.9507 \| 5.8558 \| 0.1000 \| 0.0866 \| 0.1647 \| 0.8831 \| \| No log \| 3.0 \| 21 \| 2.9890 \| 0.225 \| 0.8838 \| 5.3580 \| 0.225 \| 0.1805 \| 0.1576 \| 0.6316 \| \| No log \| 4.0 \| 28 \| 2.5376 \| 0.29 \| 0.7946 \| 3.5543 \| 0.29 \| 0.2749 \| 0.1832 \| 0.4807 \| \| No log \| 5.0 \| 35 \| 2.2193 \| 0.3875 \| 0.7186 \| 3.0794 \| 0.3875 \| 0.3677 \| 0.1577 \| 0.3531 \| \| No log \| 6.0 \| 42 \| 2.0818 \| 0.43 \| 0.6905 \| 2.9853 \| 0.4300 \| 0.4165 \| 0.1668 \| 0.3056 \| \| No log \| 7.0 \| 49 \| 2.1032 \| 0.45 \| 0.7019 \| 3.1044 \| 0.45 \| 0.4208 \| 0.2121 \| 0.2997 \| \| No log \| 8.0 \| 56 \| 2.0360 \| 0.455 \| 0.6994 \| 2.8491 \| 0.455 \| 0.4492 \| 0.2131 \| 0.3026 \| \| No log \| 9.0 \| 63 \| 2.1719 \| 0.475 \| 0.7092 \| 2.7831 \| 0.4750 \| 0.4549 \| 0.2332 \| 0.2870 \| \| No log \| 10.0 \| 70 \| 2.1820 \| 0.4525 \| 0.7393 \| 2.8185 \| 0.4525 \| 0.4318 \| 0.2813 \| 0.2994 \| \| No log \| 11.0 \| 77 \| 2.2659 \| 0.4475 \| 0.7485 \| 2.8020 \| 0.4475 \| 0.4227 \| 0.2677 \| 0.3046 \| \| No log \| 12.0 \| 84 \| 2.1798 \| 0.4575 \| 0.7325 \| 2.6772 \| 0.4575 \| 0.4555 \| 0.2738 \| 0.3081 \| \| No log \| 13.0 \| 91 \| 2.3031 \| 0.465 \| 0.7431 \| 2.8956 \| 0.465 \| 0.4390 \| 0.2771 \| 0.2945 \| \| No log \| 14.0 \| 98 \| 2.0867 \| 0.49 \| 0.7048 \| 2.5312 \| 0.49 \| 0.4823 \| 0.2528 \| 0.2921 \| \| No log \| 15.0 \| 105 \| 2.1671 \| 0.49 \| 0.7218 \| 2.7779 \| 0.49 \| 0.4749 \| 0.2396 \| 0.2877 \| \| No log \| 16.0 \| 112 \| 2.0091 \| 0.485 \| 0.6857 \| 2.7234 \| 0.485 \| 0.4608 \| 0.2493 \| 0.2577 \| \| No log \| 17.0 \| 119 \| 1.9494 \| 0.515 \| 0.6714 \| 2.4841 \| 0.515 \| 0.5072 \| 0.2380 \| 0.2614 \| \| No log \| 18.0 \| 126 \| 1.9132 \| 0.505 \| 0.6665 \| 2.4777 \| 0.505 \| 0.4945 \| 0.2206 \| 0.2622 \| \| No log \| 19.0 \| 133 \| 2.0539 \| 0.505 \| 0.6776 \| 2.7885 \| 0.505 \| 0.4986 \| 0.2209 \| 0.2724 \| \| No log \| 20.0 \| 140 \| 1.9533 \| 0.5125 \| 0.6666 \| 2.7287 \| 0.5125 \| 0.5044 \| 0.2385 \| 0.2645 \| \| No log \| 21.0 \| 147 \| 1.9038 \| 0.525 \| 0.6365 \| 2.8345 \| 0.525 \| 0.5099 \| 0.2021 \| 0.2290 \| \| No log \| 22.0 \| 154 \| 1.8525 \| 0.5075 \| 0.6448 \| 2.6337 \| 0.5075 \| 0.4958 \| 0.2083 \| 0.2494 \| \| No log \| 23.0 \| 161 \| 1.7880 \| 0.51 \| 0.6386 \| 2.4856 \| 0.51 \| 0.5078 \| 0.2186 \| 0.2478 \| \| No log \| 24.0 \| 168 \| 1.8363 \| 0.505 \| 0.6456 \| 2.5075 \| 0.505 \| 0.4966 \| 0.1962 \| 0.2399 \| \| No log \| 25.0 \| 175 \| 1.9655 \| 0.4725 \| 0.6864 \| 2.6331 \| 0.4725 \| 0.4608 \| 0.2291 \| 0.2669 \| \| No log \| 26.0 \| 182 \| 1.8660 \| 0.5175 \| 0.6547 \| 2.5404 \| 0.5175 \| 0.5076 \| 0.2252 \| 0.2489 \| \| No log \| 27.0 \| 189 \| 1.8693 \| 0.525 \| 0.6446 \| 2.6230 \| 0.525 \| 0.5145 \| 0.2047 \| 0.2540 \| \| No log \| 28.0 \| 196 \| 1.8113 \| 0.51 \| 0.6407 \| 2.4380 \| 0.51 \| 0.4978 \| 0.2030 \| 0.2454 \| \| No log \| 29.0 \| 203 \| 1.8313 \| 0.53 \| 0.6445 \| 2.4777 \| 0.53 \| 0.5284 \| 0.2071 \| 0.2575 \| \| No log \| 30.0 \| 210 \| 1.7310 \| 0.5425 \| 0.6197 \| 2.4559 \| 0.5425 \| 0.5384 \| 0.1869 \| 0.2367 \| \| No log \| 31.0 \| 217 \| 1.8023 \| 0.5325 \| 0.6351 \| 2.5026 \| 0.5325 \| 0.5216 \| 0.2081 \| 0.2496 \| \| No log \| 32.0 \| 224 \| 1.7652 \| 0.5325 \| 0.6186 \| 2.4794 \| 0.5325 \| 0.5145 \| 0.1715 \| 0.2338 \| \| No log \| 33.0 \| 231 \| 1.7583 \| 0.525 \| 0.6363 \| 2.4889 \| 0.525 \| 0.5275 \| 0.1984 \| 0.2463 \| \| No log \| 34.0 \| 238 \| 1.7552 \| 0.5475 \| 0.6164 \| 2.4291 \| 0.5475 \| 0.5305 \| 0.2026 \| 0.2377 \| \| No log \| 35.0 \| 245 \| 1.6839 \| 0.5375 \| 0.6085 \| 2.5915 \| 0.5375 \| 0.5253 \| 0.1828 \| 0.2214 \| \| No log \| 36.0 \| 252 \| 1.7480 \| 0.5425 \| 0.6104 \| 2.5809 \| 0.5425 \| 0.5366 \| 0.1716 \| 0.2232 \| \| No log \| 37.0 \| 259 \| 1.7534 \| 0.525 \| 0.6225 \| 2.3614 \| 0.525 \| 0.5183 \| 0.1930 \| 0.2249 \| \| No log \| 38.0 \| 266 \| 1.7484 \| 0.5425 \| 0.6125 \| 2.5714 \| 0.5425 \| 0.5282 \| 0.1792 \| 0.2272 \| \| No log \| 39.0 \| 273 \| 1.7073 \| 0.55 \| 0.6172 \| 2.4200 \| 0.55 \| 0.5370 \| 0.1902 \| 0.2314 \| \| No log \| 40.0 \| 280 \| 1.7303 \| 0.55 \| 0.6134 \| 2.4829 \| 0.55 \| 0.5394 \| 0.1916 \| 0.2324 \| \| No log \| 41.0 \| 287 \| 1.6684 \| 0.54 \| 0.6060 \| 2.4632 \| 0.54 \| 0.5350 \| 0.2028 \| 0.2251 \| \| No log \| 42.0 \| 294 \| 1.7171 \| 0.5375 \| 0.6055 \| 2.4705 \| 0.5375 \| 0.5213 \| 0.1776 \| 0.2262 \| \| No log \| 43.0 \| 301 \| 1.6493 \| 0.545 \| 0.5991 \| 2.5207 \| 0.545 \| 0.5412 \| 0.1779 \| 0.2214 \| \| No log \| 44.0 \| 308 \| 1.6548 \| 0.5625 \| 0.5920 \| 2.4810 \| 0.5625 \| 0.5568 \| 0.1892 \| 0.2182 \| \| No log \| 45.0 \| 315 \| 1.6392 \| 0.565 \| 0.5943 \| 2.3771 \| 0.565 \| 0.5586 \| 0.2165 \| 0.2162 \| \| No log \| 46.0 \| 322 \| 1.6923 \| 0.5225 \| 0.6159 \| 2.3661 \| 0.5225 \| 0.5158 \| 0.1775 \| 0.2400 \| \| No log \| 47.0 \| 329 \| 1.6266 \| 0.5525 \| 0.5827 \| 2.4385 \| 0.5525 \| 0.5468 \| 0.1845 \| 0.2100 \| \| No log \| 48.0 \| 336 \| 1.6804 \| 0.55 \| 0.6019 \| 2.3884 \| 0.55 \| 0.5481 \| 0.1895 \| 0.2291 \| \| No log \| 49.0 \| 343 \| 1.6202 \| 0.5725 \| 0.5847 \| 2.4882 \| 0.5725 \| 0.5596 \| 0.1642 \| 0.2125 \| \| No log \| 50.0 \| 350 \| 1.6222 \| 0.54 \| 0.5882 \| 2.4144 \| 0.54 \| 0.5311 \| 0.1830 \| 0.2226 \| \| No log \| 51.0 \| 357 \| 1.6119 \| 0.5775 \| 0.5794 \| 2.4063 \| 0.5775 \| 0.5731 \| 0.1647 \| 0.2019 \| \| No log \| 52.0 \| 364 \| 1.5958 \| 0.57 \| 0.5757 \| 2.3342 \| 0.57 \| 0.5642 \| 0.1778 \| 0.2094 \| \| No log \| 53.0 \| 371 \| 1.6206 \| 0.545 \| 0.5913 \| 2.3884 \| 0.545 \| 0.5365 \| 0.1799 \| 0.2187 \| \| No log \| 54.0 \| 378 \| 1.5982 \| 0.5675 \| 0.5745 \| 2.4276 \| 0.5675 \| 0.5640 \| 0.1746 \| 0.2050 \| \| No log \| 55.0 \| 385 \| 1.6258 \| 0.5525 \| 0.5856 \| 2.4005 \| 0.5525 \| 0.5373 \| 0.1890 \| 0.2124 \| \| No log \| 56.0 \| 392 \| 1.5763 \| 0.57 \| 0.5744 \| 2.4477 \| 0.57 \| 0.5729 \| 0.1651 \| 0.2081 \| \| No log \| 57.0 \| 399 \| 1.6249 \| 0.5525 \| 0.5861 \| 2.3791 \| 0.5525 \| 0.5432 \| 0.1531 \| 0.2114 \| \| No log \| 58.0 \| 406 \| 1.6240 \| 0.5775 \| 0.5791 \| 2.4540 \| 0.5775 \| 0.5730 \| 0.1582 \| 0.2054 \| \| No log \| 59.0 \| 413 \| 1.6149 \| 0.545 \| 0.5851 \| 2.3134 \| 0.545 \| 0.5395 \| 0.1870 \| 0.2137 \| \| No log \| 60.0 \| 420 \| 1.6163 \| 0.5775 \| 0.5792 \| 2.3778 \| 0.5775 \| 0.5708 \| 0.1762 \| 0.2076 \| \| No log \| 61.0 \| 427 \| 1.6132 \| 0.5575 \| 0.5868 \| 2.3759 \| 0.5575 \| 0.5530 \| 0.1842 \| 0.2159 \| \| No log \| 62.0 \| 434 \| 1.5940 \| 0.5725 \| 0.5756 \| 2.3394 \| 0.5725 \| 0.5731 \| 0.2102 \| 0.2054 \| \| No log \| 63.0 \| 441 \| 1.6167 \| 0.56 \| 0.5841 \| 2.4117 \| 0.56 \| 0.5541 \| 0.1806 \| 0.2160 \| \| No log \| 64.0 \| 448 \| 1.5988 \| 0.57 \| 0.5775 \| 2.3388 \| 0.57 \| 0.5667 \| 0.1680 \| 0.2064 \| \| No log \| 65.0 \| 455 \| 1.5893 \| 0.5725 \| 0.5752 \| 2.4281 \| 0.5725 \| 0.5695 \| 0.1624 \| 0.2050 \| \| No log \| 66.0 \| 462 \| 1.5975 \| 0.5725 \| 0.5737 \| 2.3760 \| 0.5725 \| 0.5662 \| 0.1733 \| 0.2026 \| \| No log \| 67.0 \| 469 \| 1.5903 \| 0.57 \| 0.5772 \| 2.2921 \| 0.57 \| 0.5675 \| 0.1888 \| 0.2112 \| \| No log \| 68.0 \| 476 \| 1.5878 \| 0.575 \| 0.5730 \| 2.3676 \| 0.575 \| 0.5706 \| 0.1683 \| 0.2039 \| \| No log \| 69.0 \| 483 \| 1.5950 \| 0.57 \| 0.5775 \| 2.3006 \| 0.57 \| 0.5641 \| 0.1639 \| 0.2076 \| \| No log \| 70.0 \| 490 \| 1.5916 \| 0.58 \| 0.5728 \| 2.3424 \| 0.58 \| 0.5769 \| 0.1714 \| 0.2026 \| \| No log \| 71.0 \| 497 \| 1.5960 \| 0.5675 \| 0.5784 \| 2.3057 \| 0.5675 \| 0.5624 \| 0.1600 \| 0.2073 \| \| 0.3705 \| 72.0 \| 504 \| 1.5907 \| 0.575 \| 0.5755 \| 2.3322 \| 0.575 \| 0.5723 \| 0.1578 \| 0.2066 \| \| 0.3705 \| 73.0 \| 511 \| 1.5918 \| 0.5675 \| 0.5762 \| 2.3182 \| 0.5675 \| 0.5605 \| 0.1942 \| 0.2071 \| \| 0.3705 \| 74.0 \| 518 \| 1.5894 \| 0.585 \| 0.5747 \| 2.3335 \| 0.585 \| 0.5818 \| 0.1739 \| 0.2035 \| \| 0.3705 \| 75.0 \| 525 \| 1.5878 \| 0.565 \| 0.5750 \| 2.3019 \| 0.565 \| 0.5607 \| 0.1649 \| 0.2060 \| \| 0.3705 \| 76.0 \| 532 \| 1.5923 \| 0.575 \| 0.5742 \| 2.3376 \| 0.575 \| 0.5699 \| 0.1779 \| 0.2048 \| \| 0.3705 \| 77.0 \| 539 \| 1.5891 \| 0.565 \| 0.5760 \| 2.2978 \| 0.565 \| 0.5616 \| 0.1691 \| 0.2066 \| \| 0.3705 \| 78.0 \| 546 \| 1.5896 \| 0.575 \| 0.5738 \| 2.3748 \| 0.575 \| 0.5703 \| 0.1733 \| 0.2048 \| \| 0.3705 \| 79.0 \| 553 \| 1.5901 \| 0.5675 \| 0.5757 \| 2.3039 \| 0.5675 \| 0.5634 \| 0.1710 \| 0.2064 \| \| 0.3705 \| 80.0 \| 560 \| 1.5906 \| 0.57 \| 0.5746 \| 2.3125 \| 0.57 \| 0.5657 \| 0.1692 \| 0.2054 \| \| 0.3705 \| 81.0 \| 567 \| 1.5907 \| 0.57 \| 0.5751 \| 2.3097 \| 0.57 \| 0.5659 \| 0.1600 \| 0.2047 \| \| 0.3705 \| 82.0 \| 574 \| 1.5902 \| 0.57 \| 0.5746 \| 2.3072 \| 0.57 \| 0.5657 \| 0.1797 \| 0.2055 \| \| 0.3705 \| 83.0 \| 581 \| 1.5906 \| 0.5725 \| 0.5746 \| 2.3145 \| 0.5725 \| 0.5681 \| 0.1547 \| 0.2050 \| \| 0.3705 \| 84.0 \| 588 \| 1.5909 \| 0.5725 \| 0.5750 \| 2.3057 \| 0.5725 \| 0.5684 \| 0.1746 \| 0.2055 \| \| 0.3705 \| 85.0 \| 595 \| 1.5906 \| 0.57 \| 0.5746 \| 2.3098 \| 0.57 \| 0.5661 \| 0.1721 \| 0.2054 \| \| 0.3705 \| 86.0 \| 602 \| 1.5916 \| 0.57 \| 0.5749 \| 2.3093 \| 0.57 \| 0.5661 \| 0.1659 \| 0.2058 \| \| 0.3705 \| 87.0 \| 609 \| 1.5913 \| 0.57 \| 0.5748 \| 2.3084 \| 0.57 \| 0.5661 \| 0.1631 \| 0.2058 \| \| 0.3705 \| 88.0 \| 616 \| 1.5918 \| 0.57 \| 0.5749 \| 2.3082 \| 0.57 \| 0.5661 \| 0.1652 \| 0.2058 \| \| 0.3705 \| 89.0 \| 623 \| 1.5919 \| 0.57 \| 0.5750 \| 2.3084 \| 0.57 \| 0.5661 \| 0.1658 \| 0.2059 \| \| 0.3705 \| 90.0 \| 630 \| 1.5918 \| 0.5725 \| 0.5749 \| 2.3087 \| 0.5725 \| 0.5685 \| 0.1650 \| 0.2056 \| \| 0.3705 \| 91.0 \| 637 \| 1.5921 \| 0.57 \| 0.5750 \| 2.3076 \| 0.57 \| 0.5661 \| 0.1549 \| 0.2059 \| \| 0.3705 \| 92.0 \| 644 \| 1.5920 \| 0.57 \| 0.5750 \| 2.3079 \| 0.57 \| 0.5661 \| 0.1581 \| 0.2058 \| \| 0.3705 \| 93.0 \| 651 \| 1.5917 \| 0.57 \| 0.5749 \| 2.3080 \| 0.57 \| 0.5661 \| 0.1680 \| 0.2057 \| \| 0.3705 \| 94.0 \| 658 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3083 \| 0.57 \| 0.5661 \| 0.1643 \| 0.2058 \| \| 0.3705 \| 95.0 \| 665 \| 1.5924 \| 0.57 \| 0.5751 \| 2.3085 \| 0.57 \| 0.5661 \| 0.1543 \| 0.2059 \| \| 0.3705 \| 96.0 \| 672 \| 1.5922 \| 0.57 \| 0.5750 \| 2.3085 \| 0.57 \| 0.5661 \| 0.1530 \| 0.2058 \| \| 0.3705 \| 97.0 \| 679 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3088 \| 0.57 \| 0.5661 \| 0.1688 \| 0.2058 \| \| 0.3705 \| 98.0 \| 686 \| 1.5923 \| 0.57 \| 0.5749 \| 2.3089 \| 0.57 \| 0.5661 \| 0.1733 \| 0.2058 \| \| 0.3705 \| 99.0 \| 693 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3088 \| 0.57 \| 0.5661 \| 0.1735 \| 0.2058 \| \| 0.3705 \| 100.0 \| 700 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3088 \| 0.57 \| 0.5661 \| 0.1722 \| 0.2058 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

SHENMU007/neunit_BASE_V12.5

SHENMU007

2023-07-12T23:39:19Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-12T20:39:15Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

ayanban011/vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split

ayanban011

2023-07-12T23:33:16Z

165

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-12T18:58:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split results: [] ---  # vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0411 - Accuracy: 0.8333 - Brier Loss: 0.3084 - Nll: 1.3568 - F1 Micro: 0.8333 - F1 Macro: 0.8183 - Ece: 0.1563 - Aurc: 0.0847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.99 | 43 | 0.7544 | 0.7960 | 0.3088 | 1.3391 | 0.7960 | 0.7715 | 0.1991 | 0.0817 | | No log | 2.0 | 87 | 0.7158 | 0.8218 | 0.2920 | 1.1888 | 0.8218 | 0.7941 | 0.1863 | 0.0741 | | No log | 2.98 | 130 | 0.7144 | 0.7989 | 0.2932 | 1.2958 | 0.7989 | 0.7701 | 0.1628 | 0.0749 | | No log | 3.99 | 174 | 0.6762 | 0.8305 | 0.2749 | 1.1916 | 0.8305 | 0.8076 | 0.1844 | 0.0678 | | No log | 4.98 | 217 | 0.6710 | 0.8362 | 0.2745 | 1.0739 | 0.8362 | 0.8076 | 0.1696 | 0.0664 | | No log | 5.99 | 261 | 0.6532 | 0.8362 | 0.2675 | 1.0011 | 0.8362 | 0.8115 | 0.1750 | 0.0602 | | No log | 6.98 | 304 | 0.6404 | 0.8362 | 0.2635 | 1.0072 | 0.8362 | 0.8106 | 0.1714 | 0.0633 | | No log | 7.99 | 348 | 0.6635 | 0.8218 | 0.2707 | 1.0903 | 0.8218 | 0.8030 | 0.1513 | 0.0770 | | No log | 9.0 | 392 | 0.6167 | 0.8420 | 0.2534 | 1.0176 | 0.8420 | 0.8259 | 0.1613 | 0.0796 | | No log | 9.99 | 435 | 0.6496 | 0.8276 | 0.2703 | 0.9646 | 0.8276 | 0.8085 | 0.1643 | 0.0588 | | No log | 11.0 | 479 | 0.6091 | 0.8506 | 0.2467 | 1.1036 | 0.8506 | 0.8308 | 0.1483 | 0.0650 | | 0.4309 | 11.98 | 522 | 0.6075 | 0.8420 | 0.2483 | 0.9144 | 0.8420 | 0.8246 | 0.1391 | 0.0519 | | 0.4309 | 12.99 | 566 | 0.6164 | 0.8276 | 0.2576 | 0.9703 | 0.8276 | 0.8092 | 0.1467 | 0.0645 | | 0.4309 | 13.98 | 609 | 0.5893 | 0.8592 | 0.2347 | 1.1493 | 0.8592 | 0.8483 | 0.1347 | 0.0715 | | 0.4309 | 14.99 | 653 | 0.6123 | 0.8477 | 0.2485 | 1.1889 | 0.8477 | 0.8232 | 0.1587 | 0.0764 | | 0.4309 | 16.0 | 697 | 0.6352 | 0.8420 | 0.2615 | 1.1999 | 0.8420 | 0.8403 | 0.1368 | 0.0668 | | 0.4309 | 16.99 | 740 | 0.6329 | 0.8333 | 0.2625 | 1.1748 | 0.8333 | 0.8249 | 0.1267 | 0.0744 | | 0.4309 | 18.0 | 784 | 0.6350 | 0.8448 | 0.2590 | 1.2154 | 0.8448 | 0.8386 | 0.1423 | 0.0688 | | 0.4309 | 18.98 | 827 | 0.5892 | 0.8592 | 0.2383 | 1.1001 | 0.8592 | 0.8515 | 0.1293 | 0.0630 | | 0.4309 | 19.99 | 871 | 0.5981 | 0.8477 | 0.2476 | 1.0104 | 0.8477 | 0.8375 | 0.1345 | 0.0630 | | 0.4309 | 20.98 | 914 | 0.6484 | 0.8420 | 0.2642 | 1.3553 | 0.8420 | 0.8292 | 0.1490 | 0.0770 | | 0.4309 | 21.99 | 958 | 0.6298 | 0.8305 | 0.2657 | 1.1220 | 0.8305 | 0.8208 | 0.1292 | 0.0670 | | 0.1285 | 22.98 | 1001 | 0.6325 | 0.8391 | 0.2633 | 1.2549 | 0.8391 | 0.8362 | 0.1328 | 0.0708 | | 0.1285 | 23.99 | 1045 | 0.6032 | 0.8534 | 0.2486 | 1.1258 | 0.8534 | 0.8444 | 0.1229 | 0.0706 | | 0.1285 | 25.0 | 1089 | 0.6080 | 0.8534 | 0.2460 | 1.2033 | 0.8534 | 0.8414 | 0.1257 | 0.0755 | | 0.1285 | 25.99 | 1132 | 0.6321 | 0.8391 | 0.2667 | 1.2242 | 0.8391 | 0.8355 | 0.1349 | 0.0697 | | 0.1285 | 27.0 | 1176 | 0.6325 | 0.8592 | 0.2522 | 1.2029 | 0.8592 | 0.8493 | 0.1278 | 0.0778 | | 0.1285 | 27.98 | 1219 | 0.6585 | 0.8534 | 0.2546 | 1.3669 | 0.8534 | 0.8378 | 0.1368 | 0.0890 | | 0.1285 | 28.99 | 1263 | 0.6302 | 0.8563 | 0.2517 | 1.2419 | 0.8563 | 0.8508 | 0.1294 | 0.0751 | | 0.1285 | 29.98 | 1306 | 0.6663 | 0.8477 | 0.2637 | 1.4132 | 0.8477 | 0.8339 | 0.1399 | 0.0828 | | 0.1285 | 30.99 | 1350 | 0.7063 | 0.8362 | 0.2799 | 1.4323 | 0.8362 | 0.8330 | 0.1441 | 0.0863 | | 0.1285 | 32.0 | 1394 | 0.6564 | 0.8506 | 0.2570 | 1.1583 | 0.8506 | 0.8417 | 0.1358 | 0.0847 | | 0.1285 | 32.99 | 1437 | 0.6738 | 0.8477 | 0.2647 | 1.3855 | 0.8477 | 0.8398 | 0.1305 | 0.0775 | | 0.1285 | 34.0 | 1481 | 0.6528 | 0.8563 | 0.2559 | 1.2601 | 0.8563 | 0.8462 | 0.1310 | 0.0789 | | 0.0385 | 34.98 | 1524 | 0.6534 | 0.8563 | 0.2537 | 1.2931 | 0.8563 | 0.8461 | 0.1241 | 0.0773 | | 0.0385 | 35.99 | 1568 | 0.6541 | 0.8534 | 0.2525 | 1.2589 | 0.8534 | 0.8449 | 0.1315 | 0.0833 | | 0.0385 | 36.98 | 1611 | 0.6769 | 0.8592 | 0.2545 | 1.4351 | 0.8592 | 0.8492 | 0.1242 | 0.0792 | | 0.0385 | 37.99 | 1655 | 0.6824 | 0.8592 | 0.2576 | 1.2241 | 0.8592 | 0.8472 | 0.1327 | 0.0810 | | 0.0385 | 38.98 | 1698 | 0.6843 | 0.8563 | 0.2589 | 1.3394 | 0.8563 | 0.8450 | 0.1311 | 0.0802 | | 0.0385 | 39.99 | 1742 | 0.6964 | 0.8506 | 0.2630 | 1.2625 | 0.8506 | 0.8405 | 0.1310 | 0.0789 | | 0.0385 | 41.0 | 1786 | 0.7051 | 0.8534 | 0.2671 | 1.3296 | 0.8534 | 0.8434 | 0.1353 | 0.0794 | | 0.0385 | 41.99 | 1829 | 0.7006 | 0.8506 | 0.2645 | 1.2965 | 0.8506 | 0.8400 | 0.1373 | 0.0796 | | 0.0385 | 43.0 | 1873 | 0.7054 | 0.8563 | 0.2646 | 1.2973 | 0.8563 | 0.8450 | 0.1313 | 0.0790 | | 0.0385 | 43.98 | 1916 | 0.7143 | 0.8506 | 0.2673 | 1.2640 | 0.8506 | 0.8399 | 0.1359 | 0.0803 | | 0.0385 | 44.99 | 1960 | 0.7168 | 0.8534 | 0.2665 | 1.3058 | 0.8534 | 0.8429 | 0.1389 | 0.0820 | | 0.0206 | 45.98 | 2003 | 0.7204 | 0.8506 | 0.2669 | 1.3009 | 0.8506 | 0.8384 | 0.1336 | 0.0805 | | 0.0206 | 46.99 | 2047 | 0.7265 | 0.8534 | 0.2683 | 1.2633 | 0.8534 | 0.8415 | 0.1319 | 0.0806 | | 0.0206 | 48.0 | 2091 | 0.7311 | 0.8506 | 0.2695 | 1.2725 | 0.8506 | 0.8396 | 0.1372 | 0.0811 | | 0.0206 | 48.99 | 2134 | 0.7384 | 0.8477 | 0.2729 | 1.3385 | 0.8477 | 0.8364 | 0.1387 | 0.0807 | | 0.0206 | 50.0 | 2178 | 0.7383 | 0.8534 | 0.2695 | 1.1951 | 0.8534 | 0.8406 | 0.1344 | 0.0827 | | 0.0206 | 50.98 | 2221 | 0.7440 | 0.8506 | 0.2740 | 1.3360 | 0.8506 | 0.8394 | 0.1418 | 0.0812 | | 0.0206 | 51.99 | 2265 | 0.7455 | 0.8506 | 0.2727 | 1.2704 | 0.8506 | 0.8388 | 0.1351 | 0.0816 | | 0.0206 | 52.98 | 2308 | 0.7474 | 0.8506 | 0.2708 | 1.2622 | 0.8506 | 0.8384 | 0.1334 | 0.0823 | | 0.0206 | 53.99 | 2352 | 0.7581 | 0.8477 | 0.2750 | 1.3446 | 0.8477 | 0.8374 | 0.1406 | 0.0826 | | 0.0206 | 54.98 | 2395 | 0.7571 | 0.8477 | 0.2751 | 1.3703 | 0.8477 | 0.8363 | 0.1378 | 0.0814 | | 0.0206 | 55.99 | 2439 | 0.7618 | 0.8477 | 0.2752 | 1.3702 | 0.8477 | 0.8363 | 0.1363 | 0.0827 | | 0.0206 | 57.0 | 2483 | 0.7638 | 0.8477 | 0.2749 | 1.3774 | 0.8477 | 0.8363 | 0.1394 | 0.0819 | | 0.0135 | 57.99 | 2526 | 0.7693 | 0.8477 | 0.2760 | 1.3370 | 0.8477 | 0.8363 | 0.1378 | 0.0824 | | 0.0135 | 59.0 | 2570 | 0.7724 | 0.8448 | 0.2779 | 1.3710 | 0.8448 | 0.8344 | 0.1431 | 0.0823 | | 0.0135 | 59.98 | 2613 | 0.7780 | 0.8477 | 0.2784 | 1.3328 | 0.8477 | 0.8363 | 0.1463 | 0.0828 | | 0.0135 | 60.99 | 2657 | 0.7818 | 0.8477 | 0.2795 | 1.3289 | 0.8477 | 0.8363 | 0.1466 | 0.0828 | | 0.0135 | 61.98 | 2700 | 0.7847 | 0.8420 | 0.2805 | 1.3308 | 0.8420 | 0.8308 | 0.1418 | 0.0830 | | 0.0135 | 62.99 | 2744 | 0.7851 | 0.8448 | 0.2782 | 1.3650 | 0.8448 | 0.8344 | 0.1411 | 0.0834 | | 0.0135 | 64.0 | 2788 | 0.7925 | 0.8420 | 0.2829 | 1.4383 | 0.8420 | 0.8319 | 0.1425 | 0.0821 | | 0.0135 | 64.99 | 2831 | 0.7959 | 0.8448 | 0.2826 | 1.4130 | 0.8448 | 0.8353 | 0.1431 | 0.0826 | | 0.0135 | 66.0 | 2875 | 0.7989 | 0.8420 | 0.2821 | 1.4040 | 0.8420 | 0.8285 | 0.1446 | 0.0833 | | 0.0135 | 66.98 | 2918 | 0.7996 | 0.8477 | 0.2807 | 1.3296 | 0.8477 | 0.8363 | 0.1464 | 0.0837 | | 0.0135 | 67.99 | 2962 | 0.8042 | 0.8448 | 0.2824 | 1.3637 | 0.8448 | 0.8344 | 0.1434 | 0.0837 | | 0.0097 | 68.98 | 3005 | 0.8095 | 0.8391 | 0.2845 | 1.3635 | 0.8391 | 0.8275 | 0.1468 | 0.0835 | | 0.0097 | 69.99 | 3049 | 0.8073 | 0.8448 | 0.2824 | 1.3640 | 0.8448 | 0.8344 | 0.1413 | 0.0833 | | 0.0097 | 70.98 | 3092 | 0.8140 | 0.8477 | 0.2834 | 1.3617 | 0.8477 | 0.8363 | 0.1444 | 0.0837 | | 0.0097 | 71.99 | 3136 | 0.8152 | 0.8420 | 0.2842 | 1.4009 | 0.8420 | 0.8277 | 0.1439 | 0.0840 | | 0.0097 | 73.0 | 3180 | 0.8163 | 0.8391 | 0.2858 | 1.4029 | 0.8391 | 0.8246 | 0.1482 | 0.0836 | | 0.0097 | 73.99 | 3223 | 0.8192 | 0.8391 | 0.2844 | 1.3644 | 0.8391 | 0.8240 | 0.1475 | 0.0843 | | 0.0097 | 75.0 | 3267 | 0.8225 | 0.8448 | 0.2836 | 1.3593 | 0.8448 | 0.8344 | 0.1473 | 0.0847 | | 0.0097 | 75.98 | 3310 | 0.8267 | 0.8362 | 0.2859 | 1.3642 | 0.8362 | 0.8207 | 0.1473 | 0.0840 | | 0.0097 | 76.99 | 3354 | 0.8275 | 0.8391 | 0.2847 | 1.3618 | 0.8391 | 0.8240 | 0.1450 | 0.0849 | | 0.0097 | 77.98 | 3397 | 0.8325 | 0.8362 | 0.2879 | 1.3686 | 0.8362 | 0.8207 | 0.1491 | 0.0843 | | 0.0097 | 78.99 | 3441 | 0.8389 | 0.8448 | 0.2885 | 1.3629 | 0.8448 | 0.8329 | 0.1504 | 0.0833 | | 0.0097 | 80.0 | 3485 | 0.8420 | 0.8420 | 0.2887 | 1.3610 | 0.8420 | 0.8261 | 0.1458 | 0.0837 | | 0.0073 | 80.99 | 3528 | 0.8452 | 0.8362 | 0.2900 | 1.4064 | 0.8362 | 0.8221 | 0.1488 | 0.0833 | | 0.0073 | 82.0 | 3572 | 0.8492 | 0.8362 | 0.2898 | 1.4076 | 0.8362 | 0.8221 | 0.1500 | 0.0837 | | 0.0073 | 82.98 | 3615 | 0.8478 | 0.8362 | 0.2895 | 1.3609 | 0.8362 | 0.8207 | 0.1485 | 0.0847 | | 0.0073 | 83.99 | 3659 | 0.8483 | 0.8391 | 0.2880 | 1.3622 | 0.8391 | 0.8243 | 0.1480 | 0.0842 | | 0.0073 | 84.98 | 3702 | 0.8534 | 0.8420 | 0.2892 | 1.3609 | 0.8420 | 0.8261 | 0.1468 | 0.0843 | | 0.0073 | 85.99 | 3746 | 0.8547 | 0.8333 | 0.2898 | 1.4028 | 0.8333 | 0.8186 | 0.1513 | 0.0846 | | 0.0073 | 86.98 | 3789 | 0.8618 | 0.8391 | 0.2906 | 1.3597 | 0.8391 | 0.8243 | 0.1445 | 0.0846 | | 0.0073 | 87.99 | 3833 | 0.8594 | 0.8420 | 0.2885 | 1.3265 | 0.8420 | 0.8311 | 0.1462 | 0.0848 | | 0.0073 | 89.0 | 3877 | 0.8669 | 0.8391 | 0.2911 | 1.3592 | 0.8391 | 0.8243 | 0.1471 | 0.0843 | | 0.0073 | 89.99 | 3920 | 0.8664 | 0.8391 | 0.2901 | 1.3597 | 0.8391 | 0.8243 | 0.1468 | 0.0852 | | 0.0073 | 91.0 | 3964 | 0.8678 | 0.8420 | 0.2905 | 1.3253 | 0.8420 | 0.8296 | 0.1462 | 0.0854 | | 0.0057 | 91.98 | 4007 | 0.8719 | 0.8391 | 0.2909 | 1.3585 | 0.8391 | 0.8243 | 0.1475 | 0.0853 | | 0.0057 | 92.99 | 4051 | 0.8768 | 0.8391 | 0.2930 | 1.3595 | 0.8391 | 0.8243 | 0.1493 | 0.0852 | | 0.0057 | 93.98 | 4094 | 0.8785 | 0.8333 | 0.2928 | 1.4034 | 0.8333 | 0.8203 | 0.1529 | 0.0849 | | 0.0057 | 94.99 | 4138 | 0.8859 | 0.8333 | 0.2942 | 1.3684 | 0.8333 | 0.8183 | 0.1543 | 0.0844 | | 0.0057 | 96.0 | 4182 | 0.8839 | 0.8362 | 0.2937 | 1.3597 | 0.8362 | 0.8221 | 0.1497 | 0.0852 | | 0.0057 | 96.99 | 4225 | 0.8864 | 0.8333 | 0.2940 | 1.4012 | 0.8333 | 0.8203 | 0.1532 | 0.0850 | | 0.0057 | 98.0 | 4269 | 0.8879 | 0.8362 | 0.2941 | 1.3607 | 0.8362 | 0.8221 | 0.1504 | 0.0849 | | 0.0057 | 98.98 | 4312 | 0.8921 | 0.8333 | 0.2954 | 1.3609 | 0.8333 | 0.8183 | 0.1521 | 0.0851 | | 0.0057 | 99.99 | 4356 | 0.8949 | 0.8391 | 0.2945 | 1.3575 | 0.8391 | 0.8243 | 0.1491 | 0.0854 | | 0.0057 | 100.98 | 4399 | 0.8945 | 0.8362 | 0.2945 | 1.3591 | 0.8362 | 0.8221 | 0.1500 | 0.0856 | | 0.0057 | 101.99 | 4443 | 0.8985 | 0.8333 | 0.2944 | 1.3599 | 0.8333 | 0.8183 | 0.1530 | 0.0854 | | 0.0057 | 102.98 | 4486 | 0.8987 | 0.8391 | 0.2951 | 1.3586 | 0.8391 | 0.8246 | 0.1499 | 0.0850 | | 0.0045 | 103.99 | 4530 | 0.9025 | 0.8362 | 0.2957 | 1.3592 | 0.8362 | 0.8221 | 0.1510 | 0.0857 | | 0.0045 | 105.0 | 4574 | 0.9082 | 0.8305 | 0.2972 | 1.3625 | 0.8305 | 0.8165 | 0.1568 | 0.0852 | | 0.0045 | 105.99 | 4617 | 0.9087 | 0.8362 | 0.2958 | 1.3579 | 0.8362 | 0.8221 | 0.1505 | 0.0858 | | 0.0045 | 107.0 | 4661 | 0.9105 | 0.8305 | 0.2977 | 1.3619 | 0.8305 | 0.8165 | 0.1561 | 0.0844 | | 0.0045 | 107.98 | 4704 | 0.9136 | 0.8305 | 0.2978 | 1.3994 | 0.8305 | 0.8165 | 0.1559 | 0.0851 | | 0.0045 | 108.99 | 4748 | 0.9148 | 0.8391 | 0.2968 | 1.3573 | 0.8391 | 0.8243 | 0.1504 | 0.0856 | | 0.0045 | 109.98 | 4791 | 0.9188 | 0.8333 | 0.2974 | 1.3569 | 0.8333 | 0.8183 | 0.1532 | 0.0850 | | 0.0045 | 110.99 | 4835 | 0.9164 | 0.8362 | 0.2959 | 1.3595 | 0.8362 | 0.8221 | 0.1507 | 0.0857 | | 0.0045 | 112.0 | 4879 | 0.9221 | 0.8333 | 0.2977 | 1.3573 | 0.8333 | 0.8183 | 0.1550 | 0.0857 | | 0.0045 | 112.99 | 4922 | 0.9256 | 0.8305 | 0.2990 | 1.3599 | 0.8305 | 0.8165 | 0.1574 | 0.0852 | | 0.0045 | 114.0 | 4966 | 0.9284 | 0.8305 | 0.2994 | 1.3610 | 0.8305 | 0.8165 | 0.1572 | 0.0848 | | 0.0037 | 114.98 | 5009 | 0.9312 | 0.8333 | 0.2998 | 1.3565 | 0.8333 | 0.8183 | 0.1537 | 0.0857 | | 0.0037 | 115.99 | 5053 | 0.9322 | 0.8333 | 0.2995 | 1.3583 | 0.8333 | 0.8183 | 0.1543 | 0.0852 | | 0.0037 | 116.98 | 5096 | 0.9385 | 0.8305 | 0.3007 | 1.3593 | 0.8305 | 0.8165 | 0.1577 | 0.0852 | | 0.0037 | 117.99 | 5140 | 0.9386 | 0.8305 | 0.3009 | 1.4329 | 0.8305 | 0.8165 | 0.1582 | 0.0851 | | 0.0037 | 118.98 | 5183 | 0.9386 | 0.8333 | 0.2996 | 1.3570 | 0.8333 | 0.8183 | 0.1542 | 0.0855 | | 0.0037 | 119.99 | 5227 | 0.9406 | 0.8333 | 0.2995 | 1.3554 | 0.8333 | 0.8183 | 0.1540 | 0.0848 | | 0.0037 | 121.0 | 5271 | 0.9442 | 0.8305 | 0.3006 | 1.3589 | 0.8305 | 0.8165 | 0.1570 | 0.0849 | | 0.0037 | 121.99 | 5314 | 0.9435 | 0.8333 | 0.3000 | 1.3551 | 0.8333 | 0.8183 | 0.1546 | 0.0855 | | 0.0037 | 123.0 | 5358 | 0.9456 | 0.8333 | 0.2996 | 1.3550 | 0.8333 | 0.8183 | 0.1544 | 0.0848 | | 0.0037 | 123.98 | 5401 | 0.9490 | 0.8333 | 0.3008 | 1.3561 | 0.8333 | 0.8183 | 0.1547 | 0.0850 | | 0.0037 | 124.99 | 5445 | 0.9500 | 0.8333 | 0.3011 | 1.3592 | 0.8333 | 0.8183 | 0.1551 | 0.0846 | | 0.0037 | 125.98 | 5488 | 0.9513 | 0.8333 | 0.3003 | 1.3549 | 0.8333 | 0.8183 | 0.1544 | 0.0845 | | 0.0031 | 126.99 | 5532 | 0.9575 | 0.8305 | 0.3024 | 1.3580 | 0.8305 | 0.8165 | 0.1581 | 0.0849 | | 0.0031 | 128.0 | 5576 | 0.9593 | 0.8305 | 0.3025 | 1.4028 | 0.8305 | 0.8165 | 0.1591 | 0.0851 | | 0.0031 | 128.99 | 5619 | 0.9594 | 0.8305 | 0.3021 | 1.3619 | 0.8305 | 0.8165 | 0.1579 | 0.0849 | | 0.0031 | 130.0 | 5663 | 0.9628 | 0.8305 | 0.3025 | 1.3589 | 0.8305 | 0.8165 | 0.1587 | 0.0847 | | 0.0031 | 130.98 | 5706 | 0.9652 | 0.8305 | 0.3031 | 1.3599 | 0.8305 | 0.8165 | 0.1593 | 0.0844 | | 0.0031 | 131.99 | 5750 | 0.9646 | 0.8362 | 0.3005 | 1.3353 | 0.8362 | 0.8205 | 0.1520 | 0.0851 | | 0.0031 | 132.98 | 5793 | 0.9658 | 0.8333 | 0.3021 | 1.3562 | 0.8333 | 0.8183 | 0.1555 | 0.0849 | | 0.0031 | 133.99 | 5837 | 0.9698 | 0.8333 | 0.3023 | 1.3545 | 0.8333 | 0.8183 | 0.1554 | 0.0845 | | 0.0031 | 134.98 | 5880 | 0.9716 | 0.8333 | 0.3032 | 1.3559 | 0.8333 | 0.8183 | 0.1555 | 0.0852 | | 0.0031 | 135.99 | 5924 | 0.9736 | 0.8305 | 0.3037 | 1.3624 | 0.8305 | 0.8165 | 0.1584 | 0.0849 | | 0.0031 | 137.0 | 5968 | 0.9760 | 0.8333 | 0.3039 | 1.3575 | 0.8333 | 0.8183 | 0.1551 | 0.0845 | | 0.0026 | 137.99 | 6011 | 0.9789 | 0.8305 | 0.3041 | 1.3569 | 0.8305 | 0.8165 | 0.1592 | 0.0848 | | 0.0026 | 139.0 | 6055 | 0.9801 | 0.8305 | 0.3040 | 1.3574 | 0.8305 | 0.8165 | 0.1598 | 0.0854 | | 0.0026 | 139.98 | 6098 | 0.9806 | 0.8333 | 0.3035 | 1.3552 | 0.8333 | 0.8183 | 0.1557 | 0.0852 | | 0.0026 | 140.99 | 6142 | 0.9835 | 0.8333 | 0.3041 | 1.3574 | 0.8333 | 0.8183 | 0.1564 | 0.0846 | | 0.0026 | 141.98 | 6185 | 0.9838 | 0.8333 | 0.3037 | 1.3549 | 0.8333 | 0.8183 | 0.1557 | 0.0849 | | 0.0026 | 142.99 | 6229 | 0.9872 | 0.8333 | 0.3044 | 1.3544 | 0.8333 | 0.8183 | 0.1557 | 0.0851 | | 0.0026 | 144.0 | 6273 | 0.9900 | 0.8305 | 0.3056 | 1.3654 | 0.8305 | 0.8165 | 0.1597 | 0.0847 | | 0.0026 | 144.99 | 6316 | 0.9907 | 0.8333 | 0.3049 | 1.3551 | 0.8333 | 0.8183 | 0.1565 | 0.0854 | | 0.0026 | 146.0 | 6360 | 0.9896 | 0.8333 | 0.3044 | 1.3569 | 0.8333 | 0.8183 | 0.1563 | 0.0843 | | 0.0026 | 146.98 | 6403 | 0.9938 | 0.8333 | 0.3053 | 1.3550 | 0.8333 | 0.8183 | 0.1562 | 0.0844 | | 0.0026 | 147.99 | 6447 | 0.9962 | 0.8305 | 0.3056 | 1.3615 | 0.8305 | 0.8165 | 0.1594 | 0.0844 | | 0.0026 | 148.98 | 6490 | 0.9954 | 0.8305 | 0.3051 | 1.3601 | 0.8305 | 0.8165 | 0.1590 | 0.0847 | | 0.0022 | 149.99 | 6534 | 0.9961 | 0.8333 | 0.3043 | 1.3550 | 0.8333 | 0.8183 | 0.1554 | 0.0847 | | 0.0022 | 150.98 | 6577 | 1.0026 | 0.8333 | 0.3059 | 1.3555 | 0.8333 | 0.8183 | 0.1563 | 0.0853 | | 0.0022 | 151.99 | 6621 | 1.0004 | 0.8333 | 0.3049 | 1.3544 | 0.8333 | 0.8183 | 0.1566 | 0.0847 | | 0.0022 | 153.0 | 6665 | 1.0024 | 0.8305 | 0.3058 | 1.3606 | 0.8305 | 0.8165 | 0.1595 | 0.0846 | | 0.0022 | 153.99 | 6708 | 1.0054 | 0.8305 | 0.3064 | 1.3598 | 0.8305 | 0.8165 | 0.1591 | 0.0848 | | 0.0022 | 155.0 | 6752 | 1.0053 | 0.8333 | 0.3054 | 1.3548 | 0.8333 | 0.8183 | 0.1562 | 0.0845 | | 0.0022 | 155.98 | 6795 | 1.0068 | 0.8333 | 0.3053 | 1.3548 | 0.8333 | 0.8183 | 0.1562 | 0.0846 | | 0.0022 | 156.99 | 6839 | 1.0076 | 0.8333 | 0.3055 | 1.3551 | 0.8333 | 0.8183 | 0.1561 | 0.0844 | | 0.0022 | 157.98 | 6882 | 1.0105 | 0.8333 | 0.3059 | 1.3546 | 0.8333 | 0.8183 | 0.1563 | 0.0845 | | 0.0022 | 158.99 | 6926 | 1.0114 | 0.8333 | 0.3061 | 1.3555 | 0.8333 | 0.8183 | 0.1559 | 0.0851 | | 0.0022 | 160.0 | 6970 | 1.0108 | 0.8333 | 0.3061 | 1.3586 | 0.8333 | 0.8183 | 0.1561 | 0.0848 | | 0.002 | 160.99 | 7013 | 1.0129 | 0.8333 | 0.3064 | 1.3577 | 0.8333 | 0.8183 | 0.1560 | 0.0845 | | 0.002 | 162.0 | 7057 | 1.0141 | 0.8333 | 0.3060 | 1.3542 | 0.8333 | 0.8183 | 0.1562 | 0.0845 | | 0.002 | 162.98 | 7100 | 1.0150 | 0.8333 | 0.3063 | 1.3555 | 0.8333 | 0.8183 | 0.1563 | 0.0847 | | 0.002 | 163.99 | 7144 | 1.0181 | 0.8305 | 0.3071 | 1.3616 | 0.8305 | 0.8165 | 0.1587 | 0.0847 | | 0.002 | 164.98 | 7187 | 1.0197 | 0.8305 | 0.3073 | 1.3610 | 0.8305 | 0.8165 | 0.1585 | 0.0847 | | 0.002 | 165.99 | 7231 | 1.0203 | 0.8333 | 0.3071 | 1.3566 | 0.8333 | 0.8183 | 0.1565 | 0.0846 | | 0.002 | 166.98 | 7274 | 1.0214 | 0.8333 | 0.3070 | 1.3561 | 0.8333 | 0.8183 | 0.1564 | 0.0845 | | 0.002 | 167.99 | 7318 | 1.0211 | 0.8333 | 0.3067 | 1.3558 | 0.8333 | 0.8183 | 0.1562 | 0.0846 | | 0.002 | 169.0 | 7362 | 1.0255 | 0.8305 | 0.3077 | 1.3564 | 0.8305 | 0.8165 | 0.1592 | 0.0846 | | 0.002 | 169.99 | 7405 | 1.0238 | 0.8333 | 0.3066 | 1.3535 | 0.8333 | 0.8183 | 0.1567 | 0.0844 | | 0.002 | 171.0 | 7449 | 1.0258 | 0.8333 | 0.3075 | 1.3580 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.002 | 171.98 | 7492 | 1.0260 | 0.8333 | 0.3073 | 1.3594 | 0.8333 | 0.8183 | 0.1559 | 0.0846 | | 0.0018 | 172.99 | 7536 | 1.0281 | 0.8305 | 0.3077 | 1.3584 | 0.8305 | 0.8165 | 0.1586 | 0.0847 | | 0.0018 | 173.98 | 7579 | 1.0274 | 0.8333 | 0.3073 | 1.3577 | 0.8333 | 0.8183 | 0.1560 | 0.0851 | | 0.0018 | 174.99 | 7623 | 1.0323 | 0.8305 | 0.3082 | 1.3577 | 0.8305 | 0.8165 | 0.1596 | 0.0848 | | 0.0018 | 176.0 | 7667 | 1.0303 | 0.8333 | 0.3076 | 1.3579 | 0.8333 | 0.8183 | 0.1561 | 0.0846 | | 0.0018 | 176.99 | 7710 | 1.0325 | 0.8333 | 0.3081 | 1.3567 | 0.8333 | 0.8183 | 0.1565 | 0.0845 | | 0.0018 | 178.0 | 7754 | 1.0319 | 0.8333 | 0.3077 | 1.3569 | 0.8333 | 0.8183 | 0.1560 | 0.0847 | | 0.0018 | 178.98 | 7797 | 1.0340 | 0.8333 | 0.3081 | 1.3568 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.0018 | 179.99 | 7841 | 1.0331 | 0.8333 | 0.3072 | 1.3550 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0018 | 180.98 | 7884 | 1.0346 | 0.8333 | 0.3079 | 1.3563 | 0.8333 | 0.8183 | 0.1561 | 0.0847 | | 0.0018 | 181.99 | 7928 | 1.0344 | 0.8333 | 0.3079 | 1.3577 | 0.8333 | 0.8183 | 0.1565 | 0.0847 | | 0.0018 | 182.98 | 7971 | 1.0363 | 0.8333 | 0.3080 | 1.3556 | 0.8333 | 0.8183 | 0.1566 | 0.0850 | | 0.0016 | 183.99 | 8015 | 1.0368 | 0.8333 | 0.3080 | 1.3569 | 0.8333 | 0.8183 | 0.1561 | 0.0847 | | 0.0016 | 185.0 | 8059 | 1.0369 | 0.8333 | 0.3080 | 1.3563 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.0016 | 185.99 | 8102 | 1.0373 | 0.8333 | 0.3080 | 1.3565 | 0.8333 | 0.8183 | 0.1561 | 0.0850 | | 0.0016 | 187.0 | 8146 | 1.0377 | 0.8333 | 0.3080 | 1.3568 | 0.8333 | 0.8183 | 0.1561 | 0.0846 | | 0.0016 | 187.98 | 8189 | 1.0392 | 0.8333 | 0.3084 | 1.3577 | 0.8333 | 0.8183 | 0.1565 | 0.0846 | | 0.0016 | 188.99 | 8233 | 1.0391 | 0.8333 | 0.3082 | 1.3564 | 0.8333 | 0.8183 | 0.1564 | 0.0848 | | 0.0016 | 189.98 | 8276 | 1.0393 | 0.8333 | 0.3081 | 1.3561 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.0016 | 190.99 | 8320 | 1.0398 | 0.8333 | 0.3084 | 1.3582 | 0.8333 | 0.8183 | 0.1562 | 0.0846 | | 0.0016 | 192.0 | 8364 | 1.0405 | 0.8333 | 0.3083 | 1.3558 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0016 | 192.99 | 8407 | 1.0401 | 0.8333 | 0.3082 | 1.3558 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0016 | 194.0 | 8451 | 1.0407 | 0.8333 | 0.3083 | 1.3564 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0016 | 194.98 | 8494 | 1.0414 | 0.8333 | 0.3086 | 1.3573 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0015 | 195.99 | 8538 | 1.0410 | 0.8333 | 0.3084 | 1.3567 | 0.8333 | 0.8183 | 0.1564 | 0.0848 | | 0.0015 | 196.98 | 8581 | 1.0411 | 0.8333 | 0.3084 | 1.3568 | 0.8333 | 0.8183 | 0.1563 | 0.0846 | | 0.0015 | 197.42 | 8600 | 1.0411 | 0.8333 | 0.3084 | 1.3568 | 0.8333 | 0.8183 | 0.1563 | 0.0847 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

Isotonic/informal_to_formal

Isotonic

2023-07-12T22:55:28Z

111

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "style-transfer", "seq2seq", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-01T05:59:36Z

--- language: "en" tags: - style-transfer - text2text-generation - seq2seq inference: true --- # Formality Style Transfer ## Model description T5 Model for Formality Style Transfer. Trained on the GYAFC dataset. ## How to use PyTorch model available. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Isotonic/informal_to_formal") model = AutoModelForSeq2SeqLM.from_pretrained("Isotonic/informal_to_formal") sentence = "will you look into these two deals and let me know" text = "Make the following sentence Formal: " + sentence + " </s>" encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") outputs = model.generate( input_ids=input_ids, attention_mask=attention_masks, max_length=256, do_sample=True, top_k=120, top_p=0.95, early_stopping=True, num_return_sequences=5 ) for output in outputs: line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True) print(line) Output: "Would you look into the two deals in question, then let me know?" ```

komo-dono/rammatra_jp

komo-dono

2023-07-12T22:52:40Z

0

null

[ "region:us" ]

null

2023-07-12T22:51:26Z

--- license: openrail language: - ja tags: - music rammatra jp 500 epoch

KingShmeeky/KingshmeekyRVC

KingShmeeky

2023-07-12T22:43:21Z

0

null

[ "music", "en", "license:openrail", "region:us" ]

null

2023-07-12T22:30:27Z

--- license: openrail language: - en tags: - music ---

nolanaatama/nglshdbhtcvbrnnknckrbckrgnshnmpctrvcv2150pchmklgn

nolanaatama

2023-07-12T22:40:26Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-12T22:08:55Z

--- license: creativeml-openrail-m ---

lovelyxs/Pyramids

lovelyxs

2023-07-12T22:37:03Z

3

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-07-12T22:36:58Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: lovelyxs/Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

komo-dono/collei_jp

komo-dono

2023-07-12T22:28:58Z

0

null

[ "region:us" ]

null

2023-07-12T22:27:51Z

--- license: openrail language: - ja tags: - music collei japanese 500 epoch

cworthingtonfujitsu/falcon-7b-instruct-jukebox

cworthingtonfujitsu

2023-07-12T21:58:59Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-12T21:58:36Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

lovelyxs/ppo-SnowballTarget

lovelyxs

2023-07-12T21:43:03Z

9

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-07-12T21:42:55Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: lovelyxs/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

whywynn/q-FrozenLake-v1-4x4-noSlippery

whywynn

2023-07-12T21:24:58Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T21:24:49Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="whywynn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

yanex0/cn-v1-1

yanex0

2023-07-12T21:20:20Z

0

null

[ "license:openrail", "region:us" ]

null

2023-07-12T21:14:00Z

--- license: openrail --- This is the model files for [ControlNet 1.1](https://github.com/lllyasviel/ControlNet-v1-1-nightly). This model card will be filled in a more detailed way after 1.1 is officially merged into ControlNet.

SrPrieto/ppo-LunarLander-v2

SrPrieto

2023-07-12T21:14:49Z

5

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T21:14:30Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.18 +/- 13.06 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

lovelyxs/Reinforce-Pixelcopter-PLE-v0

lovelyxs

2023-07-12T21:06:20Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T21:06:13Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.30 +/- 39.51 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

carbon225/byt5-abbreviations-pl

carbon225

2023-07-12T21:00:28Z

104

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "pl", "dataset:carbon225/poleval-abbreviation-disambiguation-wiki", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-09T21:40:24Z

--- datasets: - carbon225/poleval-abbreviation-disambiguation-wiki language: - pl widget: - text: "Kolejne 0,12 <mask>pkt. proc.</mask> wynika ze spadku popytu na polski eksport, a 0,08 z zaburzeń na rynku wewnętrznym" example_title: "Example 1" --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MarcoIPolo/distilbert-base-uncased-finetuned-emotion

MarcoIPolo

2023-07-12T20:50:49Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T16:05:46Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9245630401134893 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2193 - Accuracy: 0.9245 - F1: 0.9246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3295 | 0.899 | 0.8946 | | No log | 2.0 | 500 | 0.2193 | 0.9245 | 0.9246 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

grace-pro/afriberta-small-finetuned-hausa-2e-4

grace-pro

2023-07-12T20:50:37Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-12T20:15:26Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-small-finetuned-hausa-2e-4 results: [] ---  # afriberta-small-finetuned-hausa-2e-4 This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2081 - Precision: 0.6383 - Recall: 0.4793 - F1: 0.5475 - Accuracy: 0.9589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1575 | 1.0 | 1312 | 0.1439 | 0.6452 | 0.3971 | 0.4917 | 0.9569 | | 0.1201 | 2.0 | 2624 | 0.1371 | 0.6344 | 0.4451 | 0.5231 | 0.9578 | | 0.0831 | 3.0 | 3936 | 0.1544 | 0.6444 | 0.4727 | 0.5454 | 0.9591 | | 0.0523 | 4.0 | 5248 | 0.1836 | 0.6500 | 0.4683 | 0.5444 | 0.9592 | | 0.0318 | 5.0 | 6560 | 0.2081 | 0.6383 | 0.4793 | 0.5475 | 0.9589 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

foreverip/q-FrozenLake-v1-4x4-noSlippery

foreverip

2023-07-12T20:49:59Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T20:49:56Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="foreverip/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

kimnguyenwork/Taxi-v3

kimnguyenwork

2023-07-12T20:45:36Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T20:45:29Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="kimnguyenwork/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

1aurent/diy-ppo-LunarLander-v2

1aurent

2023-07-12T20:42:54Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T20:03:20Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -41.63 +/- 67.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'repo_id': '1aurent/diy-ppo-LunarLander-v2' 'exp_name': 'hugggingface_colab' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.0003 'num_envs': 32 'num_steps': 64 'anneal_lr': True 'gae': True 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'batch_size': 2048 'minibatch_size': 512} ```

kimnguyenwork/q-FrozenLake-v1

kimnguyenwork

2023-07-12T20:42:26Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T20:42:20Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="kimnguyenwork/q-FrozenLake-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

NasimB/gpt2-concat-guten-rarity-no-cut

NasimB

2023-07-12T20:33:38Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-12T18:48:47Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-rarity-no-cut results: [] ---  # gpt2-concat-guten-rarity-no-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6869 | 0.29 | 500 | 5.6385 | | 5.3235 | 0.59 | 1000 | 5.2015 | | 4.9865 | 0.88 | 1500 | 4.9498 | | 4.7068 | 1.18 | 2000 | 4.8080 | | 4.5674 | 1.47 | 2500 | 4.6941 | | 4.4601 | 1.76 | 3000 | 4.5872 | | 4.3293 | 2.06 | 3500 | 4.5155 | | 4.1497 | 2.35 | 4000 | 4.4676 | | 4.1182 | 2.64 | 4500 | 4.4072 | | 4.0826 | 2.94 | 5000 | 4.3514 | | 3.8664 | 3.23 | 5500 | 4.3488 | | 3.8272 | 3.53 | 6000 | 4.3168 | | 3.8034 | 3.82 | 6500 | 4.2843 | | 3.6795 | 4.11 | 7000 | 4.2836 | | 3.5333 | 4.41 | 7500 | 4.2764 | | 3.534 | 4.7 | 8000 | 4.2603 | | 3.5182 | 4.99 | 8500 | 4.2478 | | 3.3437 | 5.29 | 9000 | 4.2620 | | 3.3384 | 5.58 | 9500 | 4.2601 | | 3.3385 | 5.88 | 10000 | 4.2595 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

SHENMU007/neunit_BASE_V12.4

SHENMU007

2023-07-12T20:26:54Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-12T17:42:58Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

VK246/IC_ver5b_coco_swin_gpt2_01pc_1e

VK246

2023-07-12T20:14:54Z

46

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-07-12T19:47:07Z

--- tags: - generated_from_trainer datasets: - coco metrics: - rouge - bleu model-index: - name: IC_ver5b_coco_swin_gpt2_01pc_1e results: [] ---  # IC_ver5b_coco_swin_gpt2_01pc_1e This model is a fine-tuned version of [VK246/IC_ver5a_coco_swin_gpt2_05pc_1e](https://huggingface.co/VK246/IC_ver5a_coco_swin_gpt2_05pc_1e) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 1.1266 - Rouge1: 27.4772 - Rouge2: 5.9305 - Rougel: 25.1138 - Rougelsum: 25.1235 - Bleu: 2.437 - Gen Len: 11.1124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:------:|:-------:| | 1.2093 | 0.42 | 25 | 1.1552 | 22.8898 | 3.6353 | 20.6781 | 20.6737 | 1.1554 | 11.1124 | | 1.2149 | 0.85 | 50 | 1.1358 | 26.2857 | 5.2765 | 24.0266 | 24.0308 | 2.1954 | 11.1124 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jd06/TwoSentenceHorrorModel

jd06

2023-07-12T20:14:37Z

211

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-11T20:51:49Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: TwoSentenceHorrorModel results: [] ---  # TwoSentenceHorrorModel This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3563 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 4.7786 | | No log | 2.0 | 2 | 4.4930 | | No log | 3.0 | 3 | 4.3563 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

odunola/transcriber-t5-v8

odunola

2023-07-12T20:08:13Z

114

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-06T12:24:29Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: transcriber-t5-v8 results: [] ---  # transcriber-t5-v8 This model is a fine-tuned version of [odunola/transcriber-t5-v7](https://huggingface.co/odunola/transcriber-t5-v7) on the [odunola/transcriberv3](https://huggingface.co/datasets/odunola/transcriberv3) dataset. It achieves the following results on the evaluation set: - Loss: 0.1924 ## Model description This model, a fine-tuned T5, is designed to pinpoint and extract specific Bible scriptures' chapter and verse from a string of text. It's a major component of a broader project but is also versatile enough for your other applications. The model's learning is driven by a unique dataset, painstakingly compiled from transcripts of sermons on YouTube and various online platforms. This handpicked, curated data equips the model with a specialized understanding of religious discourse and biblical references. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2215 | 0.23 | 500 | 0.2216 | | 0.1814 | 0.47 | 1000 | 0.2197 | | 0.2011 | 0.7 | 1500 | 0.2059 | | 0.2595 | 0.94 | 2000 | 0.2009 | | 0.2412 | 1.17 | 2500 | 0.2019 | | 0.1785 | 1.41 | 3000 | 0.1970 | | 0.1962 | 1.64 | 3500 | 0.1983 | | 0.2009 | 1.88 | 4000 | 0.1963 | | 0.2013 | 2.11 | 4500 | 0.1923 | | 0.2715 | 2.35 | 5000 | 0.1929 | | 0.2488 | 2.58 | 5500 | 0.1936 | | 0.1185 | 2.81 | 6000 | 0.1924 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

newsrx/instructor-large

newsrx

2023-07-12T19:56:14Z

7

0

sentence-transformers

[ "sentence-transformers", "pytorch", "t5", "text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "prompt-retrieval", "text-reranking", "feature-extraction", "sentence-similarity", "transformers", "English", "Sentence Similarity", "natural_questions", "ms_marco", "fever", "hotpot_qa", "mteb", "en", "arxiv:2212.09741", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "region:us" ]

sentence-similarity

2023-07-12T19:56:14Z

--- pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - prompt-retrieval - text-reranking - sentence-transformers - feature-extraction - sentence-similarity - transformers - t5 - English - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb language: en inference: false license: apache-2.0 model-index: - name: INSTRUCTOR results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 88.13432835820896 - type: ap value: 59.298209334395665 - type: f1 value: 83.31769058643586 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.526375 - type: ap value: 88.16327709705504 - type: f1 value: 91.51095801287843 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.856 - type: f1 value: 45.41490917650942 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 31.223 - type: map_at_10 value: 47.947 - type: map_at_100 value: 48.742000000000004 - type: map_at_1000 value: 48.745 - type: map_at_3 value: 43.137 - type: map_at_5 value: 45.992 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 48.4 - type: mrr_at_100 value: 49.202 - type: mrr_at_1000 value: 49.205 - type: mrr_at_3 value: 43.551 - type: mrr_at_5 value: 46.467999999999996 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 57.045 - type: ndcg_at_100 value: 60.175 - type: ndcg_at_1000 value: 60.233000000000004 - type: ndcg_at_3 value: 47.171 - type: ndcg_at_5 value: 52.322 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 8.599 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.63 - type: precision_at_5 value: 14.282 - type: recall_at_1 value: 31.223 - type: recall_at_10 value: 85.989 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 58.89 - type: recall_at_5 value: 71.408 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 43.1621946393635 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.56417132407894 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.29539304390207 - type: mrr value: 76.44484017060196 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_spearman value: 84.38746499431112 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.51298701298701 - type: f1 value: 77.49041754069235 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.61848554098577 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 31.32623280148178 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.803000000000004 - type: map_at_10 value: 48.848 - type: map_at_100 value: 50.5 - type: map_at_1000 value: 50.602999999999994 - type: map_at_3 value: 45.111000000000004 - type: map_at_5 value: 47.202 - type: mrr_at_1 value: 44.635000000000005 - type: mrr_at_10 value: 55.593 - type: mrr_at_100 value: 56.169999999999995 - type: mrr_at_1000 value: 56.19499999999999 - type: mrr_at_3 value: 53.361999999999995 - type: mrr_at_5 value: 54.806999999999995 - type: ndcg_at_1 value: 44.635000000000005 - type: ndcg_at_10 value: 55.899 - type: ndcg_at_100 value: 60.958 - type: ndcg_at_1000 value: 62.302 - type: ndcg_at_3 value: 51.051 - type: ndcg_at_5 value: 53.351000000000006 - type: precision_at_1 value: 44.635000000000005 - type: precision_at_10 value: 10.786999999999999 - type: precision_at_100 value: 1.6580000000000001 - type: precision_at_1000 value: 0.213 - type: precision_at_3 value: 24.893 - type: precision_at_5 value: 17.740000000000002 - type: recall_at_1 value: 35.803000000000004 - type: recall_at_10 value: 68.657 - type: recall_at_100 value: 89.77199999999999 - type: recall_at_1000 value: 97.67 - type: recall_at_3 value: 54.066 - type: recall_at_5 value: 60.788 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.706 - type: map_at_10 value: 44.896 - type: map_at_100 value: 46.299 - type: map_at_1000 value: 46.44 - type: map_at_3 value: 41.721000000000004 - type: map_at_5 value: 43.486000000000004 - type: mrr_at_1 value: 41.592 - type: mrr_at_10 value: 50.529 - type: mrr_at_100 value: 51.22 - type: mrr_at_1000 value: 51.258 - type: mrr_at_3 value: 48.205999999999996 - type: mrr_at_5 value: 49.528 - type: ndcg_at_1 value: 41.592 - type: ndcg_at_10 value: 50.77199999999999 - type: ndcg_at_100 value: 55.383 - type: ndcg_at_1000 value: 57.288 - type: ndcg_at_3 value: 46.324 - type: ndcg_at_5 value: 48.346000000000004 - type: precision_at_1 value: 41.592 - type: precision_at_10 value: 9.516 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 22.399 - type: precision_at_5 value: 15.770999999999999 - type: recall_at_1 value: 33.706 - type: recall_at_10 value: 61.353 - type: recall_at_100 value: 80.182 - type: recall_at_1000 value: 91.896 - type: recall_at_3 value: 48.204 - type: recall_at_5 value: 53.89699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 44.424 - type: map_at_10 value: 57.169000000000004 - type: map_at_100 value: 58.202 - type: map_at_1000 value: 58.242000000000004 - type: map_at_3 value: 53.825 - type: map_at_5 value: 55.714 - type: mrr_at_1 value: 50.470000000000006 - type: mrr_at_10 value: 60.489000000000004 - type: mrr_at_100 value: 61.096 - type: mrr_at_1000 value: 61.112 - type: mrr_at_3 value: 58.192 - type: mrr_at_5 value: 59.611999999999995 - type: ndcg_at_1 value: 50.470000000000006 - type: ndcg_at_10 value: 63.071999999999996 - type: ndcg_at_100 value: 66.964 - type: ndcg_at_1000 value: 67.659 - type: ndcg_at_3 value: 57.74399999999999 - type: ndcg_at_5 value: 60.367000000000004 - type: precision_at_1 value: 50.470000000000006 - type: precision_at_10 value: 10.019 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.558999999999997 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 44.424 - type: recall_at_10 value: 77.02 - type: recall_at_100 value: 93.738 - type: recall_at_1000 value: 98.451 - type: recall_at_3 value: 62.888 - type: recall_at_5 value: 69.138 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.294 - type: map_at_10 value: 34.503 - type: map_at_100 value: 35.641 - type: map_at_1000 value: 35.724000000000004 - type: map_at_3 value: 31.753999999999998 - type: map_at_5 value: 33.190999999999995 - type: mrr_at_1 value: 28.362 - type: mrr_at_10 value: 36.53 - type: mrr_at_100 value: 37.541000000000004 - type: mrr_at_1000 value: 37.602000000000004 - type: mrr_at_3 value: 33.917 - type: mrr_at_5 value: 35.358000000000004 - type: ndcg_at_1 value: 28.362 - type: ndcg_at_10 value: 39.513999999999996 - type: ndcg_at_100 value: 44.815 - type: ndcg_at_1000 value: 46.839 - type: ndcg_at_3 value: 34.02 - type: ndcg_at_5 value: 36.522 - type: precision_at_1 value: 28.362 - type: precision_at_10 value: 6.101999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.161999999999999 - type: precision_at_5 value: 9.966 - type: recall_at_1 value: 26.294 - type: recall_at_10 value: 53.098 - type: recall_at_100 value: 76.877 - type: recall_at_1000 value: 91.834 - type: recall_at_3 value: 38.266 - type: recall_at_5 value: 44.287 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.407 - type: map_at_10 value: 25.185999999999996 - type: map_at_100 value: 26.533 - type: map_at_1000 value: 26.657999999999998 - type: map_at_3 value: 22.201999999999998 - type: map_at_5 value: 23.923 - type: mrr_at_1 value: 20.522000000000002 - type: mrr_at_10 value: 29.522 - type: mrr_at_100 value: 30.644 - type: mrr_at_1000 value: 30.713 - type: mrr_at_3 value: 26.679000000000002 - type: mrr_at_5 value: 28.483000000000004 - type: ndcg_at_1 value: 20.522000000000002 - type: ndcg_at_10 value: 30.656 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.675 - type: ndcg_at_3 value: 25.319000000000003 - type: ndcg_at_5 value: 27.992 - type: precision_at_1 value: 20.522000000000002 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 16.407 - type: recall_at_10 value: 43.164 - type: recall_at_100 value: 69.695 - type: recall_at_1000 value: 89.41900000000001 - type: recall_at_3 value: 28.634999999999998 - type: recall_at_5 value: 35.308 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.473 - type: map_at_10 value: 41.676 - type: map_at_100 value: 43.120999999999995 - type: map_at_1000 value: 43.230000000000004 - type: map_at_3 value: 38.306000000000004 - type: map_at_5 value: 40.355999999999995 - type: mrr_at_1 value: 37.536 - type: mrr_at_10 value: 47.643 - type: mrr_at_100 value: 48.508 - type: mrr_at_1000 value: 48.551 - type: mrr_at_3 value: 45.348 - type: mrr_at_5 value: 46.744 - type: ndcg_at_1 value: 37.536 - type: ndcg_at_10 value: 47.823 - type: ndcg_at_100 value: 53.395 - type: ndcg_at_1000 value: 55.271 - type: ndcg_at_3 value: 42.768 - type: ndcg_at_5 value: 45.373000000000005 - type: precision_at_1 value: 37.536 - type: precision_at_10 value: 8.681 - type: precision_at_100 value: 1.34 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.468 - type: precision_at_5 value: 14.495 - type: recall_at_1 value: 30.473 - type: recall_at_10 value: 60.092999999999996 - type: recall_at_100 value: 82.733 - type: recall_at_1000 value: 94.875 - type: recall_at_3 value: 45.734 - type: recall_at_5 value: 52.691 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.976000000000003 - type: map_at_10 value: 41.097 - type: map_at_100 value: 42.547000000000004 - type: map_at_1000 value: 42.659000000000006 - type: map_at_3 value: 37.251 - type: map_at_5 value: 39.493 - type: mrr_at_1 value: 37.557 - type: mrr_at_10 value: 46.605000000000004 - type: mrr_at_100 value: 47.487 - type: mrr_at_1000 value: 47.54 - type: mrr_at_3 value: 43.721 - type: mrr_at_5 value: 45.411 - type: ndcg_at_1 value: 37.557 - type: ndcg_at_10 value: 47.449000000000005 - type: ndcg_at_100 value: 53.052 - type: ndcg_at_1000 value: 55.010999999999996 - type: ndcg_at_3 value: 41.439 - type: ndcg_at_5 value: 44.292 - type: precision_at_1 value: 37.557 - type: precision_at_10 value: 8.847 - type: precision_at_100 value: 1.357 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 20.091 - type: precision_at_5 value: 14.384 - type: recall_at_1 value: 29.976000000000003 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 84.245 - type: recall_at_1000 value: 96.97200000000001 - type: recall_at_3 value: 43.794 - type: recall_at_5 value: 51.778999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.099166666666665 - type: map_at_10 value: 38.1365 - type: map_at_100 value: 39.44491666666667 - type: map_at_1000 value: 39.55858333333334 - type: map_at_3 value: 35.03641666666666 - type: map_at_5 value: 36.79833333333334 - type: mrr_at_1 value: 33.39966666666667 - type: mrr_at_10 value: 42.42583333333333 - type: mrr_at_100 value: 43.28575 - type: mrr_at_1000 value: 43.33741666666667 - type: mrr_at_3 value: 39.94975 - type: mrr_at_5 value: 41.41633333333334 - type: ndcg_at_1 value: 33.39966666666667 - type: ndcg_at_10 value: 43.81741666666667 - type: ndcg_at_100 value: 49.08166666666667 - type: ndcg_at_1000 value: 51.121166666666674 - type: ndcg_at_3 value: 38.73575 - type: ndcg_at_5 value: 41.18158333333333 - type: precision_at_1 value: 33.39966666666667 - type: precision_at_10 value: 7.738916666666667 - type: precision_at_100 value: 1.2265833333333331 - type: precision_at_1000 value: 0.15983333333333336 - type: precision_at_3 value: 17.967416666666665 - type: precision_at_5 value: 12.78675 - type: recall_at_1 value: 28.099166666666665 - type: recall_at_10 value: 56.27049999999999 - type: recall_at_100 value: 78.93291666666667 - type: recall_at_1000 value: 92.81608333333334 - type: recall_at_3 value: 42.09775 - type: recall_at_5 value: 48.42533333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.663 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.426 - type: map_at_1000 value: 31.519000000000002 - type: map_at_3 value: 28.069 - type: map_at_5 value: 29.256999999999998 - type: mrr_at_1 value: 26.687 - type: mrr_at_10 value: 33.107 - type: mrr_at_100 value: 34.055 - type: mrr_at_1000 value: 34.117999999999995 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.14 - type: ndcg_at_1 value: 26.687 - type: ndcg_at_10 value: 34.615 - type: ndcg_at_100 value: 39.776 - type: ndcg_at_1000 value: 42.05 - type: ndcg_at_3 value: 30.322 - type: ndcg_at_5 value: 32.157000000000004 - type: precision_at_1 value: 26.687 - type: precision_at_10 value: 5.491 - type: precision_at_100 value: 0.877 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.139000000000001 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.663 - type: recall_at_10 value: 45.035 - type: recall_at_100 value: 68.554 - type: recall_at_1000 value: 85.077 - type: recall_at_3 value: 32.982 - type: recall_at_5 value: 37.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.403 - type: map_at_10 value: 25.197000000000003 - type: map_at_100 value: 26.355 - type: map_at_1000 value: 26.487 - type: map_at_3 value: 22.733 - type: map_at_5 value: 24.114 - type: mrr_at_1 value: 21.37 - type: mrr_at_10 value: 29.091 - type: mrr_at_100 value: 30.018 - type: mrr_at_1000 value: 30.096 - type: mrr_at_3 value: 26.887 - type: mrr_at_5 value: 28.157 - type: ndcg_at_1 value: 21.37 - type: ndcg_at_10 value: 30.026000000000003 - type: ndcg_at_100 value: 35.416 - type: ndcg_at_1000 value: 38.45 - type: ndcg_at_3 value: 25.764 - type: ndcg_at_5 value: 27.742 - type: precision_at_1 value: 21.37 - type: precision_at_10 value: 5.609 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 12.423 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 17.403 - type: recall_at_10 value: 40.573 - type: recall_at_100 value: 64.818 - type: recall_at_1000 value: 86.53699999999999 - type: recall_at_3 value: 28.493000000000002 - type: recall_at_5 value: 33.660000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.639 - type: map_at_10 value: 38.951 - type: map_at_100 value: 40.238 - type: map_at_1000 value: 40.327 - type: map_at_3 value: 35.842 - type: map_at_5 value: 37.617 - type: mrr_at_1 value: 33.769 - type: mrr_at_10 value: 43.088 - type: mrr_at_100 value: 44.03 - type: mrr_at_1000 value: 44.072 - type: mrr_at_3 value: 40.656 - type: mrr_at_5 value: 42.138999999999996 - type: ndcg_at_1 value: 33.769 - type: ndcg_at_10 value: 44.676 - type: ndcg_at_100 value: 50.416000000000004 - type: ndcg_at_1000 value: 52.227999999999994 - type: ndcg_at_3 value: 39.494 - type: ndcg_at_5 value: 42.013 - type: precision_at_1 value: 33.769 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.221 - type: precision_at_5 value: 12.966 - type: recall_at_1 value: 28.639 - type: recall_at_10 value: 57.687999999999995 - type: recall_at_100 value: 82.541 - type: recall_at_1000 value: 94.896 - type: recall_at_3 value: 43.651 - type: recall_at_5 value: 49.925999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.57 - type: map_at_10 value: 40.004 - type: map_at_100 value: 41.75 - type: map_at_1000 value: 41.97 - type: map_at_3 value: 36.788 - type: map_at_5 value: 38.671 - type: mrr_at_1 value: 35.375 - type: mrr_at_10 value: 45.121 - type: mrr_at_100 value: 45.994 - type: mrr_at_1000 value: 46.04 - type: mrr_at_3 value: 42.227 - type: mrr_at_5 value: 43.995 - type: ndcg_at_1 value: 35.375 - type: ndcg_at_10 value: 46.392 - type: ndcg_at_100 value: 52.196 - type: ndcg_at_1000 value: 54.274 - type: ndcg_at_3 value: 41.163 - type: ndcg_at_5 value: 43.813 - type: precision_at_1 value: 35.375 - type: precision_at_10 value: 8.676 - type: precision_at_100 value: 1.678 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 19.104 - type: precision_at_5 value: 13.913 - type: recall_at_1 value: 29.57 - type: recall_at_10 value: 58.779 - type: recall_at_100 value: 83.337 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 44.005 - type: recall_at_5 value: 50.975 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.832 - type: map_at_10 value: 29.733999999999998 - type: map_at_100 value: 30.727 - type: map_at_1000 value: 30.843999999999998 - type: map_at_3 value: 26.834999999999997 - type: map_at_5 value: 28.555999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 31.791999999999998 - type: mrr_at_100 value: 32.666000000000004 - type: mrr_at_1000 value: 32.751999999999995 - type: mrr_at_3 value: 29.144 - type: mrr_at_5 value: 30.622 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.744 - type: ndcg_at_1000 value: 42.407000000000004 - type: ndcg_at_3 value: 29.421000000000003 - type: ndcg_at_5 value: 32.211 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 5.675 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.753999999999998 - type: precision_at_5 value: 9.353 - type: recall_at_1 value: 20.832 - type: recall_at_10 value: 48.795 - type: recall_at_100 value: 70.703 - type: recall_at_1000 value: 90.187 - type: recall_at_3 value: 34.455000000000005 - type: recall_at_5 value: 40.967 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.334 - type: map_at_10 value: 19.009999999999998 - type: map_at_100 value: 21.129 - type: map_at_1000 value: 21.328 - type: map_at_3 value: 15.152 - type: map_at_5 value: 17.084 - type: mrr_at_1 value: 23.453 - type: mrr_at_10 value: 36.099 - type: mrr_at_100 value: 37.069 - type: mrr_at_1000 value: 37.104 - type: mrr_at_3 value: 32.096000000000004 - type: mrr_at_5 value: 34.451 - type: ndcg_at_1 value: 23.453 - type: ndcg_at_10 value: 27.739000000000004 - type: ndcg_at_100 value: 35.836 - type: ndcg_at_1000 value: 39.242 - type: ndcg_at_3 value: 21.263 - type: ndcg_at_5 value: 23.677 - type: precision_at_1 value: 23.453 - type: precision_at_10 value: 9.199 - type: precision_at_100 value: 1.791 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.2 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 10.334 - type: recall_at_10 value: 35.177 - type: recall_at_100 value: 63.009 - type: recall_at_1000 value: 81.938 - type: recall_at_3 value: 19.914 - type: recall_at_5 value: 26.077 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.212 - type: map_at_10 value: 17.386 - type: map_at_100 value: 24.234 - type: map_at_1000 value: 25.724999999999998 - type: map_at_3 value: 12.727 - type: map_at_5 value: 14.785 - type: mrr_at_1 value: 59.25 - type: mrr_at_10 value: 68.687 - type: mrr_at_100 value: 69.133 - type: mrr_at_1000 value: 69.14099999999999 - type: mrr_at_3 value: 66.917 - type: mrr_at_5 value: 67.742 - type: ndcg_at_1 value: 48.625 - type: ndcg_at_10 value: 36.675999999999995 - type: ndcg_at_100 value: 41.543 - type: ndcg_at_1000 value: 49.241 - type: ndcg_at_3 value: 41.373 - type: ndcg_at_5 value: 38.707 - type: precision_at_1 value: 59.25 - type: precision_at_10 value: 28.525 - type: precision_at_100 value: 9.027000000000001 - type: precision_at_1000 value: 1.8339999999999999 - type: precision_at_3 value: 44.833 - type: precision_at_5 value: 37.35 - type: recall_at_1 value: 8.212 - type: recall_at_10 value: 23.188 - type: recall_at_100 value: 48.613 - type: recall_at_1000 value: 73.093 - type: recall_at_3 value: 14.419 - type: recall_at_5 value: 17.798 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.725 - type: f1 value: 46.50743309855908 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 55.086 - type: map_at_10 value: 66.914 - type: map_at_100 value: 67.321 - type: map_at_1000 value: 67.341 - type: map_at_3 value: 64.75800000000001 - type: map_at_5 value: 66.189 - type: mrr_at_1 value: 59.28600000000001 - type: mrr_at_10 value: 71.005 - type: mrr_at_100 value: 71.304 - type: mrr_at_1000 value: 71.313 - type: mrr_at_3 value: 69.037 - type: mrr_at_5 value: 70.35 - type: ndcg_at_1 value: 59.28600000000001 - type: ndcg_at_10 value: 72.695 - type: ndcg_at_100 value: 74.432 - type: ndcg_at_1000 value: 74.868 - type: ndcg_at_3 value: 68.72200000000001 - type: ndcg_at_5 value: 71.081 - type: precision_at_1 value: 59.28600000000001 - type: precision_at_10 value: 9.499 - type: precision_at_100 value: 1.052 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 27.503 - type: precision_at_5 value: 17.854999999999997 - type: recall_at_1 value: 55.086 - type: recall_at_10 value: 86.453 - type: recall_at_100 value: 94.028 - type: recall_at_1000 value: 97.052 - type: recall_at_3 value: 75.821 - type: recall_at_5 value: 81.6 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.262999999999998 - type: map_at_10 value: 37.488 - type: map_at_100 value: 39.498 - type: map_at_1000 value: 39.687 - type: map_at_3 value: 32.529 - type: map_at_5 value: 35.455 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.239000000000004 - type: mrr_at_100 value: 54.086 - type: mrr_at_1000 value: 54.122 - type: mrr_at_3 value: 51.235 - type: mrr_at_5 value: 52.415 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 45.446 - type: ndcg_at_100 value: 52.429 - type: ndcg_at_1000 value: 55.169000000000004 - type: ndcg_at_3 value: 41.882000000000005 - type: ndcg_at_5 value: 43.178 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.931999999999999 - type: precision_at_100 value: 2.025 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 28.652 - type: precision_at_5 value: 21.204 - type: recall_at_1 value: 22.262999999999998 - type: recall_at_10 value: 52.447 - type: recall_at_100 value: 78.045 - type: recall_at_1000 value: 94.419 - type: recall_at_3 value: 38.064 - type: recall_at_5 value: 44.769 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 32.519 - type: map_at_10 value: 45.831 - type: map_at_100 value: 46.815 - type: map_at_1000 value: 46.899 - type: map_at_3 value: 42.836 - type: map_at_5 value: 44.65 - type: mrr_at_1 value: 65.037 - type: mrr_at_10 value: 72.16 - type: mrr_at_100 value: 72.51100000000001 - type: mrr_at_1000 value: 72.53 - type: mrr_at_3 value: 70.682 - type: mrr_at_5 value: 71.54599999999999 - type: ndcg_at_1 value: 65.037 - type: ndcg_at_10 value: 55.17999999999999 - type: ndcg_at_100 value: 58.888 - type: ndcg_at_1000 value: 60.648 - type: ndcg_at_3 value: 50.501 - type: ndcg_at_5 value: 52.977 - type: precision_at_1 value: 65.037 - type: precision_at_10 value: 11.530999999999999 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 31.483 - type: precision_at_5 value: 20.845 - type: recall_at_1 value: 32.519 - type: recall_at_10 value: 57.657000000000004 - type: recall_at_100 value: 72.30199999999999 - type: recall_at_1000 value: 84.024 - type: recall_at_3 value: 47.225 - type: recall_at_5 value: 52.113 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.3168 - type: ap value: 83.80165516037135 - type: f1 value: 88.29942471066407 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.724999999999998 - type: map_at_10 value: 32.736 - type: map_at_100 value: 33.938 - type: map_at_1000 value: 33.991 - type: map_at_3 value: 28.788000000000004 - type: map_at_5 value: 31.016 - type: mrr_at_1 value: 21.361 - type: mrr_at_10 value: 33.323 - type: mrr_at_100 value: 34.471000000000004 - type: mrr_at_1000 value: 34.518 - type: mrr_at_3 value: 29.453000000000003 - type: mrr_at_5 value: 31.629 - type: ndcg_at_1 value: 21.361 - type: ndcg_at_10 value: 39.649 - type: ndcg_at_100 value: 45.481 - type: ndcg_at_1000 value: 46.775 - type: ndcg_at_3 value: 31.594 - type: ndcg_at_5 value: 35.543 - type: precision_at_1 value: 21.361 - type: precision_at_10 value: 6.3740000000000006 - type: precision_at_100 value: 0.931 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.514999999999999 - type: precision_at_5 value: 10.100000000000001 - type: recall_at_1 value: 20.724999999999998 - type: recall_at_10 value: 61.034 - type: recall_at_100 value: 88.062 - type: recall_at_1000 value: 97.86399999999999 - type: recall_at_3 value: 39.072 - type: recall_at_5 value: 48.53 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.8919288645691 - type: f1 value: 93.57059586398059 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.97993616051072 - type: f1 value: 48.244319183606535 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.90047074646941 - type: f1 value: 66.48999056063725 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.34566240753195 - type: f1 value: 73.54164154290658 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.21866934757011 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.000936217235534 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.68189362520352 - type: mrr value: 32.69603637784303 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.078 - type: map_at_10 value: 12.671 - type: map_at_100 value: 16.291 - type: map_at_1000 value: 17.855999999999998 - type: map_at_3 value: 9.610000000000001 - type: map_at_5 value: 11.152 - type: mrr_at_1 value: 43.963 - type: mrr_at_10 value: 53.173 - type: mrr_at_100 value: 53.718999999999994 - type: mrr_at_1000 value: 53.756 - type: mrr_at_3 value: 50.980000000000004 - type: mrr_at_5 value: 52.42 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.086 - type: ndcg_at_100 value: 32.545 - type: ndcg_at_1000 value: 41.144999999999996 - type: ndcg_at_3 value: 39.434999999999995 - type: ndcg_at_5 value: 37.888 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.014999999999997 - type: precision_at_100 value: 8.594 - type: precision_at_1000 value: 2.169 - type: precision_at_3 value: 37.049 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 6.078 - type: recall_at_10 value: 16.17 - type: recall_at_100 value: 34.512 - type: recall_at_1000 value: 65.447 - type: recall_at_3 value: 10.706 - type: recall_at_5 value: 13.158 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 27.378000000000004 - type: map_at_10 value: 42.178 - type: map_at_100 value: 43.32 - type: map_at_1000 value: 43.358000000000004 - type: map_at_3 value: 37.474000000000004 - type: map_at_5 value: 40.333000000000006 - type: mrr_at_1 value: 30.823 - type: mrr_at_10 value: 44.626 - type: mrr_at_100 value: 45.494 - type: mrr_at_1000 value: 45.519 - type: mrr_at_3 value: 40.585 - type: mrr_at_5 value: 43.146 - type: ndcg_at_1 value: 30.794 - type: ndcg_at_10 value: 50.099000000000004 - type: ndcg_at_100 value: 54.900999999999996 - type: ndcg_at_1000 value: 55.69499999999999 - type: ndcg_at_3 value: 41.238 - type: ndcg_at_5 value: 46.081 - type: precision_at_1 value: 30.794 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 18.926000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 27.378000000000004 - type: recall_at_10 value: 71.842 - type: recall_at_100 value: 92.565 - type: recall_at_1000 value: 98.402 - type: recall_at_3 value: 49.053999999999995 - type: recall_at_5 value: 60.207 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.557 - type: map_at_10 value: 84.729 - type: map_at_100 value: 85.369 - type: map_at_1000 value: 85.382 - type: map_at_3 value: 81.72 - type: map_at_5 value: 83.613 - type: mrr_at_1 value: 81.3 - type: mrr_at_10 value: 87.488 - type: mrr_at_100 value: 87.588 - type: mrr_at_1000 value: 87.589 - type: mrr_at_3 value: 86.53 - type: mrr_at_5 value: 87.18599999999999 - type: ndcg_at_1 value: 81.28999999999999 - type: ndcg_at_10 value: 88.442 - type: ndcg_at_100 value: 89.637 - type: ndcg_at_1000 value: 89.70700000000001 - type: ndcg_at_3 value: 85.55199999999999 - type: ndcg_at_5 value: 87.154 - type: precision_at_1 value: 81.28999999999999 - type: precision_at_10 value: 13.489999999999998 - type: precision_at_100 value: 1.54 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.708 - type: recall_at_1 value: 70.557 - type: recall_at_10 value: 95.645 - type: recall_at_100 value: 99.693 - type: recall_at_1000 value: 99.995 - type: recall_at_3 value: 87.359 - type: recall_at_5 value: 91.89699999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 63.65060114776209 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.63271250680617 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.263 - type: map_at_10 value: 10.801 - type: map_at_100 value: 12.888 - type: map_at_1000 value: 13.224 - type: map_at_3 value: 7.362 - type: map_at_5 value: 9.149000000000001 - type: mrr_at_1 value: 21 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.513 - type: mrr_at_1000 value: 32.58 - type: mrr_at_3 value: 28.116999999999997 - type: mrr_at_5 value: 29.976999999999997 - type: ndcg_at_1 value: 21 - type: ndcg_at_10 value: 18.551000000000002 - type: ndcg_at_100 value: 26.657999999999998 - type: ndcg_at_1000 value: 32.485 - type: ndcg_at_3 value: 16.834 - type: ndcg_at_5 value: 15.204999999999998 - type: precision_at_1 value: 21 - type: precision_at_10 value: 9.84 - type: precision_at_100 value: 2.16 - type: precision_at_1000 value: 0.35500000000000004 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 13.62 - type: recall_at_1 value: 4.263 - type: recall_at_10 value: 19.922 - type: recall_at_100 value: 43.808 - type: recall_at_1000 value: 72.14500000000001 - type: recall_at_3 value: 9.493 - type: recall_at_5 value: 13.767999999999999 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_spearman value: 81.27446313317233 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_spearman value: 76.27963301217527 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_spearman value: 88.18495048450949 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_spearman value: 81.91982338692046 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_spearman value: 89.00896818385291 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_spearman value: 85.48814644586132 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_spearman value: 90.30116926966582 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_spearman value: 67.74132963032342 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_spearman value: 86.87741355780479 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.0019012295875 - type: mrr value: 94.70267024188593 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 50.05 - type: map_at_10 value: 59.36 - type: map_at_100 value: 59.967999999999996 - type: map_at_1000 value: 60.023 - type: map_at_3 value: 56.515 - type: map_at_5 value: 58.272999999999996 - type: mrr_at_1 value: 53 - type: mrr_at_10 value: 61.102000000000004 - type: mrr_at_100 value: 61.476 - type: mrr_at_1000 value: 61.523 - type: mrr_at_3 value: 58.778 - type: mrr_at_5 value: 60.128 - type: ndcg_at_1 value: 53 - type: ndcg_at_10 value: 64.43100000000001 - type: ndcg_at_100 value: 66.73599999999999 - type: ndcg_at_1000 value: 68.027 - type: ndcg_at_3 value: 59.279 - type: ndcg_at_5 value: 61.888 - type: precision_at_1 value: 53 - type: precision_at_10 value: 8.767 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.444000000000003 - type: precision_at_5 value: 15.667 - type: recall_at_1 value: 50.05 - type: recall_at_10 value: 78.511 - type: recall_at_100 value: 88.5 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 64.117 - type: recall_at_5 value: 70.867 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72178217821782 - type: cos_sim_ap value: 93.0728601593541 - type: cos_sim_f1 value: 85.6727976766699 - type: cos_sim_precision value: 83.02063789868667 - type: cos_sim_recall value: 88.5 - type: dot_accuracy value: 99.72178217821782 - type: dot_ap value: 93.07287396168348 - type: dot_f1 value: 85.6727976766699 - type: dot_precision value: 83.02063789868667 - type: dot_recall value: 88.5 - type: euclidean_accuracy value: 99.72178217821782 - type: euclidean_ap value: 93.07285657982895 - type: euclidean_f1 value: 85.6727976766699 - type: euclidean_precision value: 83.02063789868667 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.72475247524753 - type: manhattan_ap value: 93.02792973059809 - type: manhattan_f1 value: 85.7727737973388 - type: manhattan_precision value: 87.84067085953879 - type: manhattan_recall value: 83.8 - type: max_accuracy value: 99.72475247524753 - type: max_ap value: 93.07287396168348 - type: max_f1 value: 85.7727737973388 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.77583615550819 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.151636938606956 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.16607939471187 - type: mrr value: 52.95172046091163 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.314646669495666 - type: cos_sim_spearman value: 31.83562491439455 - type: dot_pearson value: 31.314590842874157 - type: dot_spearman value: 31.83363065810437 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.3010000000000002 - type: map_at_100 value: 7.2139999999999995 - type: map_at_1000 value: 20.179 - type: map_at_3 value: 0.528 - type: map_at_5 value: 0.8019999999999999 - type: mrr_at_1 value: 72 - type: mrr_at_10 value: 83.39999999999999 - type: mrr_at_100 value: 83.39999999999999 - type: mrr_at_1000 value: 83.39999999999999 - type: mrr_at_3 value: 81.667 - type: mrr_at_5 value: 83.06700000000001 - type: ndcg_at_1 value: 66 - type: ndcg_at_10 value: 58.059000000000005 - type: ndcg_at_100 value: 44.316 - type: ndcg_at_1000 value: 43.147000000000006 - type: ndcg_at_3 value: 63.815999999999995 - type: ndcg_at_5 value: 63.005 - type: precision_at_1 value: 72 - type: precision_at_10 value: 61.4 - type: precision_at_100 value: 45.62 - type: precision_at_1000 value: 19.866 - type: precision_at_3 value: 70 - type: precision_at_5 value: 68.8 - type: recall_at_1 value: 0.198 - type: recall_at_10 value: 1.517 - type: recall_at_100 value: 10.587 - type: recall_at_1000 value: 41.233 - type: recall_at_3 value: 0.573 - type: recall_at_5 value: 0.907 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.894 - type: map_at_10 value: 8.488999999999999 - type: map_at_100 value: 14.445 - type: map_at_1000 value: 16.078 - type: map_at_3 value: 4.589 - type: map_at_5 value: 6.019 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 39.82 - type: mrr_at_100 value: 40.752 - type: mrr_at_1000 value: 40.771 - type: mrr_at_3 value: 34.354 - type: mrr_at_5 value: 37.721 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 21.563 - type: ndcg_at_100 value: 33.857 - type: ndcg_at_1000 value: 46.199 - type: ndcg_at_3 value: 22.296 - type: ndcg_at_5 value: 21.770999999999997 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.142999999999999 - type: precision_at_1000 value: 1.541 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 1.894 - type: recall_at_10 value: 14.931 - type: recall_at_100 value: 45.524 - type: recall_at_1000 value: 83.243 - type: recall_at_3 value: 5.712 - type: recall_at_5 value: 8.386000000000001 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.049 - type: ap value: 13.85116971310922 - type: f1 value: 54.37504302487686 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.1312959818902 - type: f1 value: 64.11413877009383 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 54.13103431861502 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.327889372355 - type: cos_sim_ap value: 77.42059895975699 - type: cos_sim_f1 value: 71.02706903250873 - type: cos_sim_precision value: 69.75324344950394 - type: cos_sim_recall value: 72.34828496042216 - type: dot_accuracy value: 87.327889372355 - type: dot_ap value: 77.4209479346677 - type: dot_f1 value: 71.02706903250873 - type: dot_precision value: 69.75324344950394 - type: dot_recall value: 72.34828496042216 - type: euclidean_accuracy value: 87.327889372355 - type: euclidean_ap value: 77.42096495861037 - type: euclidean_f1 value: 71.02706903250873 - type: euclidean_precision value: 69.75324344950394 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.31000774870358 - type: manhattan_ap value: 77.38930750711619 - type: manhattan_f1 value: 71.07935314027831 - type: manhattan_precision value: 67.70957726295677 - type: manhattan_recall value: 74.80211081794195 - type: max_accuracy value: 87.327889372355 - type: max_ap value: 77.42096495861037 - type: max_f1 value: 71.07935314027831 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.58939729110878 - type: cos_sim_ap value: 87.17594155025475 - type: cos_sim_f1 value: 79.21146953405018 - type: cos_sim_precision value: 76.8918527109307 - type: cos_sim_recall value: 81.67539267015707 - type: dot_accuracy value: 89.58939729110878 - type: dot_ap value: 87.17593963273593 - type: dot_f1 value: 79.21146953405018 - type: dot_precision value: 76.8918527109307 - type: dot_recall value: 81.67539267015707 - type: euclidean_accuracy value: 89.58939729110878 - type: euclidean_ap value: 87.17592466925834 - type: euclidean_f1 value: 79.21146953405018 - type: euclidean_precision value: 76.8918527109307 - type: euclidean_recall value: 81.67539267015707 - type: manhattan_accuracy value: 89.62626615438352 - type: manhattan_ap value: 87.16589873161546 - type: manhattan_f1 value: 79.25143598295348 - type: manhattan_precision value: 76.39494177323712 - type: manhattan_recall value: 82.32984293193716 - type: max_accuracy value: 89.62626615438352 - type: max_ap value: 87.17594155025475 - type: max_f1 value: 79.25143598295348 duplicated_from: hkunlp/instructor-large --- # hkunlp/instructor-large We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! **************************** **Updates** **************************** * 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance. * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out! ## Quick start <hr /> ## Installation ```bash pip install InstructorEmbedding ``` ## Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-large') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title:" embeddings = model.encode([[instruction,sentence]]) print(embeddings) ``` ## Use cases <hr /> ## Calculate embeddings for your customized texts If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:                           Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. ## Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] embeddings_a = model.encode(sentences_a) embeddings_b = model.encode(sentences_b) similarities = cosine_similarity(embeddings_a,embeddings_b) print(similarities) ``` ## Information Retrieval You can also use **customized embeddings** for information retrieval. ```python import numpy as np from sklearn.metrics.pairwise import cosine_similarity query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loansâ€”and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] query_embeddings = model.encode(query) corpus_embeddings = model.encode(corpus) similarities = cosine_similarity(query_embeddings,corpus_embeddings) retrieved_doc_id = np.argmax(similarities) print(retrieved_doc_id) ``` ## Clustering Use **customized embeddings** for clustering texts in groups. ```python import sklearn.cluster sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] embeddings = model.encode(sentences) clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) clustering_model.fit(embeddings) cluster_assignment = clustering_model.labels_ print(cluster_assignment) ```

chh6/v0TaxiAttempt

chh6

2023-07-12T19:47:12Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T19:47:10Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: v0TaxiAttempt results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="chh6/v0TaxiAttempt", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

veluchs/dqn-SpaceInvadersNoFrameskip-v4-4

veluchs

2023-07-12T19:41:22Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T19:40:57Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 264.50 +/- 87.36 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga veluchs -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga veluchs -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga veluchs ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ImanAndrea/BART-eng-to-ro

ImanAndrea

2023-07-12T19:33:27Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:wmt16-en-ro-pre-processed", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-12T06:56:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16-en-ro-pre-processed model-index: - name: BART-eng-to-ro results: [] ---  # BART-eng-to-ro This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the wmt16-en-ro-pre-processed dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.10.1 - Tokenizers 0.13.3

vuiseng9/baseline-ft-mrpc-IRoberta-b-8bit

vuiseng9

2023-07-12T19:21:04Z

6

0

transformers

[ "transformers", "pytorch", "ibert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T18:39:16Z

--- language: - en tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: baseline-ft-mrpc-IRoberta-b-8bit results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8970588235294118 - name: F1 type: f1 value: 0.9257950530035336 ---  # baseline-ft-mrpc-IRoberta-b-8bit This model is a fine-tuned version of [vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized](https://huggingface.co/vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.3871 - Accuracy: 0.8971 - F1: 0.9258 - Combined Score: 0.9114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.0021 | 1.0 | 230 | 0.4017 | 0.8848 | 0.9147 | 0.8998 | | 0.0026 | 2.0 | 460 | 0.4105 | 0.8873 | 0.9173 | 0.9023 | | 0.0026 | 3.0 | 690 | 0.3707 | 0.8946 | 0.9236 | 0.9091 | | 0.0037 | 4.0 | 920 | 0.3893 | 0.8946 | 0.9228 | 0.9087 | | 1.324 | 5.0 | 1150 | 0.3871 | 0.8897 | 0.9204 | 0.9050 | | 0.0227 | 6.0 | 1380 | 0.3951 | 0.8897 | 0.9201 | 0.9049 | | 0.0081 | 7.0 | 1610 | 0.3818 | 0.8824 | 0.9155 | 0.8989 | | 0.0054 | 8.0 | 1840 | 0.3902 | 0.8873 | 0.9181 | 0.9027 | | 0.0383 | 9.0 | 2070 | 0.3659 | 0.8922 | 0.9225 | 0.9073 | | 0.3861 | 10.0 | 2300 | 0.4260 | 0.8652 | 0.9030 | 0.8841 | | 0.0028 | 11.0 | 2530 | 0.3619 | 0.8946 | 0.9234 | 0.9090 | | 0.0957 | 12.0 | 2760 | 0.3871 | 0.8971 | 0.9258 | 0.9114 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

4bit/WizardLM-13B-V1.1-GPTQ

4bit

2023-07-12T19:06:41Z

8

3

transformers

[ "transformers", "llama", "text-generation", "arxiv:2304.12244", "license:other", "autotrain_compatible", "region:us" ]

text-generation

2023-07-12T19:03:12Z

--- inference: false license: other ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div>  # WizardLM's WizardLM 13B V1.1 GPTQ These files are GPTQ model files for [WizardLM's WizardLM 13B V1.1](https://huggingface.co/WizardLM/WizardLM-13B-V1.1). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate). ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.1) ## Prompt template: Vicuna ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ``` ## Provided files Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. | Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description | | ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- | | main | 4 | 128 | False | 7.45 GB | True | GPTQ-for-LLaMa | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. | | gptq-4bit-32g-actorder_True | 4 | 32 | True | 8.00 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. | | gptq-4bit-64g-actorder_True | 4 | 64 | True | 7.51 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | | gptq-4bit-128g-actorder_True | 4 | 128 | True | 7.26 GB | True | AutoGPTQ | 4-bit, with Act Order androup size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | | gptq-8bit--1g-actorder_True | 8 | None | True | 13.36 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. | | gptq-8bit-128g-actorder_False | 8 | 128 | False | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/WizardLM-13B-V1.1-GPTQ:gptq-4bit-32g-actorder_True` - With Git, you can clone a branch with: ``` git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ` ``` - In Python Transformers code, the branch is the `revision` parameter; see below. ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/WizardLM-13B-V1.1-GPTQ`. - To download from a specific branch, enter for example `TheBloke/WizardLM-13B-V1.1-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done" 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `WizardLM-13B-V1.1-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! ## How to use this GPTQ model from Python code First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: `GITHUB_ACTIONS=true pip install auto-gptq` Then try the following example code: ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name_or_path = "TheBloke/WizardLM-13B-V1.1-GPTQ" model_basename = "wizardlm-13b-v1.1-GPTQ-4bit-128g.no-act.order" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename use_safetensors=True, trust_remote_code=True, device="cuda:0", use_triton=use_triton, quantize_config=None) """ To download from a specific branch, use the revision parameter, as in this example: model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, revision="gptq-4bit-32g-actorder_True", model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Compatibility The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork. ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz. **Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang. Thank you to all my generous patrons and donaters!  # Original model card: WizardLM's WizardLM 13B V1.1 This is the **Full-Weight** of WizardLM-13B V1.1 model. **Repository**: https://github.com/nlpxucan/WizardLM **Twitter**: https://twitter.com/WizardLM_AI/status/1677282955490918401 - 🔥🔥🔥 [7/7/2023] We released **WizardLM V1.1** models. The **WizardLM-13B-V1.1** is here ([Demo_13B-V1.1](https://e8a06366ccd1c4d1.gradio.app), [Demo_13B-V1.1_bak-1](https://59da107262a25764.gradio.app), [Demo_13B-V1.1_bak-2](https://dfc5113f66739c80.gradio.app), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.1)). **WizardLM-7B-V1.1**, **WizardLM-30B-V1.1**, and **WizardLM-65B-V1.1** are coming soon. Please checkout the [Full Model Weights](https://huggingface.co/WizardLM) and [paper](https://arxiv.org/abs/2304.12244). - 🔥🔥🔥 [7/7/2023] The **WizardLM-13B-V1.1** achieves **6.74** on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), **86.32%** on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and **99.3%** on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)

ramymohamed/ppo-LunarLander-v2

ramymohamed

2023-07-12T19:05:33Z

4

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T19:05:12Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.71 +/- 24.27 name: mean_reward verified: false --- # **ppo** Agent playing **LunarLander-v2** This is a trained model of a **ppo** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

gmurillo/set-fit-goup-6-f

gmurillo

2023-07-12T19:00:20Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bart", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-07-12T18:59:06Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gmurillo/set-fit-goup-6-f This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-6-f") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

gmurillo/set-fit-goup-4-f

gmurillo

2023-07-12T18:57:16Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bart", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-07-12T18:55:50Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gmurillo/set-fit-goup-4-f This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-4-f") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

gmurillo/set-fit-goup-1-f

gmurillo

2023-07-12T18:51:50Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bart", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-07-12T18:50:32Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gmurillo/set-fit-goup-1-f This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gmurillo/set-fit-goup-1-f") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

komo-dono/dashiegames

komo-dono

2023-07-12T18:44:13Z

0

null

[ "region:us" ]

null

2023-07-12T18:42:38Z

--- license: openrail language: - en tags: - music dashiegames 500 epoch

tyavika/LR1E4-BS16-Distilbert-QA-Pytorch-FULL

tyavika

2023-07-12T18:39:38Z

105

0

transformers

[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-07T04:59:00Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Distilbert-QA-Pytorch-FULL results: [] ---  # LR1E4-BS16-Distilbert-QA-Pytorch-FULL This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4071 | 1.0 | 3290 | 1.2792 | | 1.0123 | 2.0 | 6580 | 1.2843 | | 0.6916 | 3.0 | 9870 | 1.3888 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized

vuiseng9

2023-07-12T18:33:30Z

107

0

transformers

[ "transformers", "pytorch", "ibert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T18:24:52Z

--- language: - en tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: baseline-ft-mrpc-IRoberta-b-unquantized results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8995098039215687 - name: F1 type: f1 value: 0.9266547406082289 ---  # baseline-ft-mrpc-IRoberta-b-unquantized This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5354 - Accuracy: 0.8995 - F1: 0.9267 - Combined Score: 0.9131 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.1212 | 1.0 | 230 | 0.3401 | 0.8799 | 0.9136 | 0.8967 | | 0.0347 | 2.0 | 460 | 0.3085 | 0.8676 | 0.9059 | 0.8868 | | 0.0495 | 3.0 | 690 | 0.3552 | 0.8848 | 0.9174 | 0.9011 | | 0.0024 | 4.0 | 920 | 0.4960 | 0.8824 | 0.9158 | 0.8991 | | 0.0046 | 5.0 | 1150 | 0.5354 | 0.8995 | 0.9267 | 0.9131 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Taki3d/CrackDetectionLowRes

Taki3d

2023-07-12T18:24:18Z

202

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-11T22:00:17Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: CrackDetectionLowRes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9940476190476191 ---  # CrackDetectionLowRes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9940 - Loss: 0.0183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.0126 | 1.0 | 992 | 0.9879 | 0.0344 | | 0.0788 | 2.0 | 1904 | 0.9933 | 0.0220 | | 0.1336 | 3.0 | 2856 | 0.9933 | 0.0222 | | 0.0066 | 4.0 | 3808 | 0.9933 | 0.0190 | | 0.0528 | 5.0 | 4760 | 0.9940 | 0.0183 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

hsultanbey/autocomplete_trainer

hsultanbey

2023-07-12T18:23:42Z

143

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-12T18:22:39Z

--- license: mit tags: - generated_from_trainer model-index: - name: autocomplete_trainer results: [] ---  # autocomplete_trainer This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

wenbowen/BundleSDF

wenbowen

2023-07-12T18:21:31Z

0

null

[ "arxiv:2303.14158", "region:us" ]

null

2023-07-12T18:19:41Z

Paper https://arxiv.org/abs/2303.14158 Project page https://bundlesdf.github.io/

asrimanth/person-thumbs-up-lora

asrimanth

2023-07-12T18:19:11Z

2

3

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-12T18:18:41Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - asrimanth/person-thumbs-up-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

Danish-summarisation/DanSumT5-pilot

Danish-summarisation

2023-07-12T18:12:28Z

122

2

transformers

[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "summarization", "da", "arxiv:1804.11283", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-07-05T10:06:53Z

--- language: - da tags: - summarization widget: - text: "De strejkende SAS-piloter melder sig nu klar til gøre en undtagelse fra strejken for at hente strandede chartergæster hjem fra flere ferieområder. Undtagelsen skal gælde nogle uger frem, men piloterne vil under ingen omstændigheder have nye gæster med sig ned til de samme destinationer. Det skriver SAS Pilot Group i en pressemeddelelse. - Vi forstår, at det er uundgåeligt, at vores passagerer bliver ramt af strejken. Men vi piloter er altid fokuseret på at opføre os ansvarligt med passagersikkerheden som højeste prioritet, siger Martin Lindgren, der er formand for SAS Pilot Group i Norden. Men for at hjælpe strandede gæster kræver de strejkende piloter samtidig, at SAS' trækker sin lockout af piloterne tilbage. Samtidig ser SAS Pilot Group det som en forudsætning, at SAS ikke får hjælp fra andre flyselskaber til at flyve nye passagerer til de samme destinationer, som piloterne tilbyder at flyve gæster hjem fra, skriver fagforeningen." example_title: "Example 1" - text: "Mere end 21.000 krigsforbrydelser. Så mange efterforsker de ukrainske myndigheder lige nu ifølge den ukrainske rigsadvokat, Iryna Venediktova. Hun oplyser til britiske BBC, at der bliver anmeldt mellem 200 og 300 nye sager om dagen. Forbrydelserne er ifølge Venediktova svære at efterforske, fordi det kan være vanskeligt at komme frem til de relevante områder og mennesker. Men hun understreger overfor BBC, at russiske soldater, der har dræbt, tortureret eller voldtaget civile, bør forstå, at det kun er et spørgsmål om tid, før de alle vil komme for retten. Rusland er blevet anklaget for en lang række krigsforbrydelser, siden landet invaderede Ukraine den 24. februar, men afviser alle anklager." example_title: "Example 2" - text: "Det nye studie Cognitive Science på Aarhus Universitet, som i år havde Østjyllands højeste adgangskrav på 11,7 i karaktergennemsnit, udklækker det første hold bachelorer til sommer. Men når de skal læse videre på kandidaten må de til udlandet, hvis ikke de vil skifte til et andet fag. Aarhus Universitet kan nemlig ikke nå at oprette en kandidat i Cognitive Science til næste sommer, hvor det første hold bachelorer er færdige. Det rammer blandt andre Julie Sohn, der startede på uddannelsen i sommeren 2015, og derfor kun mangler et år, før hun er bachelor. - Jeg synes, at det er ærgerligt, at vi som nye studerende på et populært studie ikke kan tage en kandidat i Danmark, siger hun. Bacheloruddannelsen i Cognitive Science blev oprettet af Aarhus Universitet i 2015, og uddannelsen kombinerer viden om menneskelig adfærd med avanceret statistik. Da der endnu ikke er oprettet en kandidatuddannelse indenfor dette område, har Julie Sohn i stedet mulighed for at læse en kandidatgrad i for eksempel informationsvidenskab. Hun vil dog hellere fortsætte på Cognitive Science, og derfor overvejer hun nu at læse videre i udlandet. - Det ser ud til, at det er den eneste mulighed, hvis man gerne vil læse videre på noget, der faktisk passer ind til vores studie, siger hun. Nye regler giver forsinkelse På Aarhus Universitet havde man håbet på at have kandidatuddannelsen klar, når det første hold bachelorer bliver færdige til sommer. Arbejdet er dog blevet forsinket, fordi der er kommet nye regler for, hvornår man må oprette en uddannelse, fortæller Niels Lehmann, prodekan på fakultetet Arts, som Cognitive Science hører under. Det er nogle meget dygtige studerende, der kommer ind på uddannelsen, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast. NIELS LEHMANN, PRODEKAN, AARHUS UNIVERSITET Tidligere skulle Danmarks Akkrediteringsinstitution se alle nye uddannelser efter i sømmene for at sikre, at kvaliteten var i orden. Nu skal uddannelsesinstitutionerne selv stå for det kvalitetstjek. Men det tjek har Aarhus Universitet endnu ikke fået grønt lys til selv at udføre, fortæller prodekanen. - Vi ville meget gerne have kunnet nå at få et udbud på kandidaten i gang i 2018, men så længe man er under institutionsakkreditering, så kan man ikke ansøge om nye uddannelser, siger han. Det er endnu usikkert, hvornår Aarhus Universitet kan oprette kandidaten i Cognitive Science. Hvis de får alle de nødvendige godkendelser, kan den tidligst være klar i 2019. Prodekan Niels Lehmann frygter, at Danmark kommer til at miste nogle af landets skarpeste studerende, hvis de er nødt til at rejse til udlandet for at gøre deres uddannelse færdig. - Det er nogle meget, meget dygtige studerende, der kommer ind på denne uddannelse, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast, siger han. Hos Danmarks Akkrediteringsinstitution forstår man godt, at universitets ansatte og studenrede ærgrer sig. - Jeg kan godt forstå, at Aarhus Universitet ærgrer sig over, at det trækker ud, og at der går noget tid, før man får mulighed for at oprette nye uddannelser, og at man ikke har fået den genvej til at oprette nye uddannelser, som ville være fuldt med, hvis man havde opnået en positiv institutionsakkreditering, siger kommunikationsansvarlig Daniel Sebastian Larsen. I år var Cognitive Science i Aarhus den uddannelse i Danmark, der havde det fjerde højeste karakterkrav - det højeste var 'AP Graduate in Marketing Management' på Erhvervsakademi Sjælland med et krav på 12,3." example_title: "Example 3" --- # mT5-base fine-tuned for News article Summarisation ✏️🧾 [Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for **summarisation** downstream task. # Model summary This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019). # References Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283 Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831

arstep/q-FrozenLake-v1-4x4-noSlippery

arstep

2023-07-12T18:12:13Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T18:12:10Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="arstep/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

ayanban011/vit-base_tobacco_wr_0.01_wd_0.2

ayanban011

2023-07-12T18:09:56Z

165

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-12T15:22:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_tobacco_wr_0.01_wd_0.2 results: [] ---  # vit-base_tobacco_wr_0.01_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9831 - Accuracy: 0.755 - Brier Loss: 0.3936 - Nll: 1.4572 - F1 Micro: 0.755 - F1 Macro: 0.7245 - Ece: 0.2101 - Aurc: 0.1059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7384 | 0.815 | 0.3056 | 1.1863 | 0.815 | 0.8021 | 0.2211 | 0.0789 | | No log | 2.0 | 25 | 0.7504 | 0.795 | 0.3109 | 1.2594 | 0.795 | 0.7691 | 0.2235 | 0.0789 | | No log | 2.96 | 37 | 0.7415 | 0.765 | 0.3109 | 1.0445 | 0.765 | 0.7422 | 0.2323 | 0.0762 | | No log | 4.0 | 50 | 0.7222 | 0.805 | 0.2989 | 1.3200 | 0.805 | 0.7951 | 0.2028 | 0.0682 | | No log | 4.96 | 62 | 0.7160 | 0.815 | 0.2989 | 1.2307 | 0.815 | 0.8067 | 0.2381 | 0.0688 | | No log | 6.0 | 75 | 0.7149 | 0.825 | 0.2950 | 1.3097 | 0.825 | 0.8127 | 0.2110 | 0.0809 | | No log | 6.96 | 87 | 0.7157 | 0.815 | 0.2986 | 1.2310 | 0.815 | 0.8007 | 0.2195 | 0.0779 | | No log | 8.0 | 100 | 0.7245 | 0.81 | 0.3020 | 1.3030 | 0.81 | 0.7949 | 0.1997 | 0.0834 | | No log | 8.96 | 112 | 0.7111 | 0.815 | 0.2972 | 1.2310 | 0.815 | 0.8009 | 0.2090 | 0.0793 | | No log | 10.0 | 125 | 0.7344 | 0.81 | 0.3087 | 1.2974 | 0.81 | 0.7956 | 0.2240 | 0.0913 | | No log | 10.96 | 137 | 0.7264 | 0.81 | 0.3057 | 1.3216 | 0.81 | 0.7907 | 0.2074 | 0.0879 | | No log | 12.0 | 150 | 0.7301 | 0.805 | 0.3056 | 1.2448 | 0.805 | 0.7919 | 0.2112 | 0.0932 | | No log | 12.96 | 162 | 0.7224 | 0.805 | 0.3020 | 1.2950 | 0.805 | 0.7899 | 0.1930 | 0.0915 | | No log | 14.0 | 175 | 0.7235 | 0.81 | 0.3045 | 1.2965 | 0.81 | 0.7956 | 0.2025 | 0.0902 | | No log | 14.96 | 187 | 0.7239 | 0.81 | 0.3039 | 1.2970 | 0.81 | 0.7956 | 0.2156 | 0.0906 | | No log | 16.0 | 200 | 0.7293 | 0.81 | 0.3060 | 1.4336 | 0.81 | 0.7924 | 0.2029 | 0.0955 | | No log | 16.96 | 212 | 0.7253 | 0.805 | 0.3044 | 1.2984 | 0.805 | 0.7853 | 0.1946 | 0.0911 | | No log | 18.0 | 225 | 0.7293 | 0.805 | 0.3076 | 1.1852 | 0.805 | 0.7861 | 0.2015 | 0.0916 | | No log | 18.96 | 237 | 0.7328 | 0.8 | 0.3050 | 1.3844 | 0.8000 | 0.7836 | 0.1900 | 0.0986 | | No log | 20.0 | 250 | 0.7263 | 0.8 | 0.3058 | 1.2943 | 0.8000 | 0.7754 | 0.2003 | 0.0900 | | No log | 20.96 | 262 | 0.7370 | 0.805 | 0.3102 | 1.3612 | 0.805 | 0.7818 | 0.1998 | 0.0939 | | No log | 22.0 | 275 | 0.7412 | 0.795 | 0.3118 | 1.3673 | 0.795 | 0.7714 | 0.1952 | 0.0957 | | No log | 22.96 | 287 | 0.7326 | 0.795 | 0.3077 | 1.3572 | 0.795 | 0.7666 | 0.1935 | 0.0925 | | No log | 24.0 | 300 | 0.7308 | 0.805 | 0.3076 | 1.4265 | 0.805 | 0.7818 | 0.1968 | 0.0906 | | No log | 24.96 | 312 | 0.7424 | 0.8 | 0.3122 | 1.3666 | 0.8000 | 0.7774 | 0.1853 | 0.0960 | | No log | 26.0 | 325 | 0.7383 | 0.8 | 0.3097 | 1.3644 | 0.8000 | 0.7774 | 0.1911 | 0.0952 | | No log | 26.96 | 337 | 0.7468 | 0.805 | 0.3143 | 1.4295 | 0.805 | 0.7829 | 0.1952 | 0.0971 | | No log | 28.0 | 350 | 0.7484 | 0.795 | 0.3135 | 1.4327 | 0.795 | 0.7668 | 0.1906 | 0.0989 | | No log | 28.96 | 362 | 0.7459 | 0.8 | 0.3135 | 1.3624 | 0.8000 | 0.7704 | 0.1945 | 0.0944 | | No log | 30.0 | 375 | 0.7513 | 0.8 | 0.3160 | 1.4282 | 0.8000 | 0.7698 | 0.1991 | 0.0953 | | No log | 30.96 | 387 | 0.7535 | 0.795 | 0.3168 | 1.4336 | 0.795 | 0.7656 | 0.1799 | 0.0980 | | No log | 32.0 | 400 | 0.7540 | 0.8 | 0.3171 | 1.4469 | 0.8000 | 0.7774 | 0.1943 | 0.0959 | | No log | 32.96 | 412 | 0.7566 | 0.8 | 0.3180 | 1.3772 | 0.8000 | 0.7704 | 0.1809 | 0.0972 | | No log | 34.0 | 425 | 0.7641 | 0.795 | 0.3220 | 1.3878 | 0.795 | 0.7691 | 0.1937 | 0.1021 | | No log | 34.96 | 437 | 0.7602 | 0.795 | 0.3192 | 1.3748 | 0.795 | 0.7656 | 0.1908 | 0.0973 | | No log | 36.0 | 450 | 0.7577 | 0.8 | 0.3186 | 1.4397 | 0.8000 | 0.7704 | 0.1858 | 0.0951 | | No log | 36.96 | 462 | 0.7724 | 0.795 | 0.3233 | 1.3800 | 0.795 | 0.7656 | 0.1796 | 0.1002 | | No log | 38.0 | 475 | 0.7675 | 0.795 | 0.3223 | 1.3659 | 0.795 | 0.7654 | 0.1862 | 0.0976 | | No log | 38.96 | 487 | 0.7772 | 0.79 | 0.3261 | 1.4406 | 0.79 | 0.7633 | 0.1877 | 0.1062 | | 0.0949 | 40.0 | 500 | 0.7631 | 0.795 | 0.3211 | 1.4302 | 0.795 | 0.7654 | 0.1896 | 0.0953 | | 0.0949 | 40.96 | 512 | 0.7878 | 0.79 | 0.3304 | 1.5689 | 0.79 | 0.7631 | 0.1770 | 0.1014 | | 0.0949 | 42.0 | 525 | 0.7639 | 0.815 | 0.3198 | 1.5524 | 0.815 | 0.7879 | 0.1890 | 0.0938 | | 0.0949 | 42.96 | 537 | 0.7967 | 0.785 | 0.3350 | 1.4516 | 0.785 | 0.7606 | 0.1809 | 0.1040 | | 0.0949 | 44.0 | 550 | 0.7735 | 0.81 | 0.3237 | 1.4915 | 0.81 | 0.7784 | 0.2079 | 0.0963 | | 0.0949 | 44.96 | 562 | 0.7859 | 0.79 | 0.3300 | 1.5076 | 0.79 | 0.7594 | 0.1851 | 0.0991 | | 0.0949 | 46.0 | 575 | 0.7917 | 0.79 | 0.3323 | 1.5062 | 0.79 | 0.7631 | 0.1944 | 0.1015 | | 0.0949 | 46.96 | 587 | 0.7879 | 0.795 | 0.3310 | 1.4304 | 0.795 | 0.7656 | 0.1761 | 0.0987 | | 0.0949 | 48.0 | 600 | 0.7917 | 0.79 | 0.3320 | 1.4382 | 0.79 | 0.7629 | 0.1828 | 0.0994 | | 0.0949 | 48.96 | 612 | 0.7950 | 0.79 | 0.3333 | 1.4929 | 0.79 | 0.7549 | 0.1811 | 0.0995 | | 0.0949 | 50.0 | 625 | 0.8005 | 0.79 | 0.3363 | 1.4351 | 0.79 | 0.7631 | 0.1953 | 0.1009 | | 0.0949 | 50.96 | 637 | 0.8019 | 0.79 | 0.3363 | 1.4891 | 0.79 | 0.7631 | 0.1837 | 0.0998 | | 0.0949 | 52.0 | 650 | 0.8021 | 0.79 | 0.3354 | 1.4899 | 0.79 | 0.7586 | 0.1990 | 0.0994 | | 0.0949 | 52.96 | 662 | 0.8012 | 0.795 | 0.3346 | 1.4913 | 0.795 | 0.7648 | 0.1878 | 0.0984 | | 0.0949 | 54.0 | 675 | 0.8078 | 0.785 | 0.3388 | 1.4920 | 0.785 | 0.7559 | 0.1938 | 0.1000 | | 0.0949 | 54.96 | 687 | 0.8092 | 0.785 | 0.3391 | 1.5003 | 0.785 | 0.7561 | 0.1878 | 0.1004 | | 0.0949 | 56.0 | 700 | 0.8095 | 0.795 | 0.3378 | 1.4914 | 0.795 | 0.7634 | 0.1879 | 0.0994 | | 0.0949 | 56.96 | 712 | 0.8124 | 0.785 | 0.3396 | 1.4926 | 0.785 | 0.7555 | 0.1996 | 0.1005 | | 0.0949 | 58.0 | 725 | 0.8120 | 0.78 | 0.3391 | 1.4405 | 0.78 | 0.7469 | 0.1924 | 0.1005 | | 0.0949 | 58.96 | 737 | 0.8185 | 0.785 | 0.3406 | 1.5007 | 0.785 | 0.7523 | 0.1910 | 0.1013 | | 0.0949 | 60.0 | 750 | 0.8182 | 0.785 | 0.3421 | 1.4371 | 0.785 | 0.7555 | 0.1727 | 0.1010 | | 0.0949 | 60.96 | 762 | 0.8224 | 0.78 | 0.3443 | 1.4404 | 0.78 | 0.7475 | 0.1941 | 0.1019 | | 0.0949 | 62.0 | 775 | 0.8267 | 0.78 | 0.3463 | 1.4995 | 0.78 | 0.7535 | 0.1927 | 0.1016 | | 0.0949 | 62.96 | 787 | 0.8252 | 0.775 | 0.3447 | 1.4965 | 0.775 | 0.7465 | 0.1798 | 0.1016 | | 0.0949 | 64.0 | 800 | 0.8286 | 0.78 | 0.3446 | 1.4987 | 0.78 | 0.7475 | 0.1911 | 0.1014 | | 0.0949 | 64.96 | 812 | 0.8308 | 0.78 | 0.3467 | 1.4438 | 0.78 | 0.7469 | 0.1866 | 0.1019 | | 0.0949 | 66.0 | 825 | 0.8346 | 0.775 | 0.3476 | 1.4592 | 0.775 | 0.7444 | 0.1878 | 0.1034 | | 0.0949 | 66.96 | 837 | 0.8348 | 0.78 | 0.3484 | 1.4423 | 0.78 | 0.7515 | 0.1821 | 0.1021 | | 0.0949 | 68.0 | 850 | 0.8376 | 0.78 | 0.3481 | 1.4970 | 0.78 | 0.7469 | 0.1922 | 0.1015 | | 0.0949 | 68.96 | 862 | 0.8379 | 0.78 | 0.3497 | 1.4388 | 0.78 | 0.7515 | 0.1850 | 0.1018 | | 0.0949 | 70.0 | 875 | 0.8452 | 0.78 | 0.3512 | 1.4974 | 0.78 | 0.7525 | 0.1828 | 0.1036 | | 0.0949 | 70.96 | 887 | 0.8437 | 0.78 | 0.3511 | 1.4519 | 0.78 | 0.7469 | 0.2076 | 0.1032 | | 0.0949 | 72.0 | 900 | 0.8485 | 0.775 | 0.3535 | 1.5163 | 0.775 | 0.7444 | 0.1863 | 0.1033 | | 0.0949 | 72.96 | 912 | 0.8516 | 0.77 | 0.3555 | 1.5015 | 0.7700 | 0.7404 | 0.1868 | 0.1030 | | 0.0949 | 74.0 | 925 | 0.8507 | 0.77 | 0.3541 | 1.4407 | 0.7700 | 0.7404 | 0.1996 | 0.1026 | | 0.0949 | 74.96 | 937 | 0.8508 | 0.77 | 0.3540 | 1.4424 | 0.7700 | 0.7400 | 0.1996 | 0.1025 | | 0.0949 | 76.0 | 950 | 0.8559 | 0.77 | 0.3558 | 1.4487 | 0.7700 | 0.7400 | 0.2001 | 0.1031 | | 0.0949 | 76.96 | 962 | 0.8564 | 0.77 | 0.3568 | 1.4389 | 0.7700 | 0.7400 | 0.1804 | 0.1024 | | 0.0949 | 78.0 | 975 | 0.8611 | 0.775 | 0.3569 | 1.4966 | 0.775 | 0.7440 | 0.1831 | 0.1031 | | 0.0949 | 78.96 | 987 | 0.8573 | 0.77 | 0.3557 | 1.4946 | 0.7700 | 0.7400 | 0.1933 | 0.1013 | | 0.0366 | 80.0 | 1000 | 0.8640 | 0.775 | 0.3587 | 1.4998 | 0.775 | 0.7440 | 0.1871 | 0.1032 | | 0.0366 | 80.96 | 1012 | 0.8640 | 0.77 | 0.3585 | 1.4983 | 0.7700 | 0.7400 | 0.1921 | 0.1032 | | 0.0366 | 82.0 | 1025 | 0.8696 | 0.77 | 0.3608 | 1.5032 | 0.7700 | 0.7400 | 0.2033 | 0.1036 | | 0.0366 | 82.96 | 1037 | 0.8702 | 0.77 | 0.3613 | 1.4987 | 0.7700 | 0.7400 | 0.2022 | 0.1029 | | 0.0366 | 84.0 | 1050 | 0.8686 | 0.77 | 0.3597 | 1.4446 | 0.7700 | 0.7400 | 0.1887 | 0.1028 | | 0.0366 | 84.96 | 1062 | 0.8700 | 0.77 | 0.3607 | 1.4365 | 0.7700 | 0.7400 | 0.1900 | 0.1025 | | 0.0366 | 86.0 | 1075 | 0.8756 | 0.765 | 0.3621 | 1.5009 | 0.765 | 0.7308 | 0.1983 | 0.1040 | | 0.0366 | 86.96 | 1087 | 0.8768 | 0.76 | 0.3623 | 1.5035 | 0.76 | 0.7282 | 0.1956 | 0.1040 | | 0.0366 | 88.0 | 1100 | 0.8762 | 0.765 | 0.3618 | 1.4409 | 0.765 | 0.7308 | 0.1957 | 0.1033 | | 0.0366 | 88.96 | 1112 | 0.8777 | 0.765 | 0.3629 | 1.4427 | 0.765 | 0.7308 | 0.1974 | 0.1036 | | 0.0366 | 90.0 | 1125 | 0.8854 | 0.76 | 0.3661 | 1.4681 | 0.76 | 0.7282 | 0.2023 | 0.1049 | | 0.0366 | 90.96 | 1137 | 0.8867 | 0.76 | 0.3660 | 1.5057 | 0.76 | 0.7282 | 0.1995 | 0.1049 | | 0.0366 | 92.0 | 1150 | 0.8849 | 0.765 | 0.3648 | 1.5011 | 0.765 | 0.7308 | 0.1953 | 0.1039 | | 0.0366 | 92.96 | 1162 | 0.8898 | 0.76 | 0.3670 | 1.5077 | 0.76 | 0.7282 | 0.2083 | 0.1045 | | 0.0366 | 94.0 | 1175 | 0.8891 | 0.765 | 0.3662 | 1.4520 | 0.765 | 0.7308 | 0.2091 | 0.1040 | | 0.0366 | 94.96 | 1187 | 0.8910 | 0.755 | 0.3679 | 1.4460 | 0.755 | 0.7247 | 0.2039 | 0.1043 | | 0.0366 | 96.0 | 1200 | 0.8935 | 0.76 | 0.3684 | 1.4435 | 0.76 | 0.7273 | 0.1929 | 0.1041 | | 0.0366 | 96.96 | 1212 | 0.8964 | 0.755 | 0.3689 | 1.4526 | 0.755 | 0.7247 | 0.1980 | 0.1048 | | 0.0366 | 98.0 | 1225 | 0.8979 | 0.755 | 0.3701 | 1.4507 | 0.755 | 0.7247 | 0.2017 | 0.1045 | | 0.0366 | 98.96 | 1237 | 0.8965 | 0.755 | 0.3692 | 1.4474 | 0.755 | 0.7247 | 0.2057 | 0.1044 | | 0.0366 | 100.0 | 1250 | 0.9019 | 0.755 | 0.3716 | 1.4526 | 0.755 | 0.7247 | 0.2051 | 0.1047 | | 0.0366 | 100.96 | 1262 | 0.8994 | 0.755 | 0.3694 | 1.4485 | 0.755 | 0.7247 | 0.1979 | 0.1041 | | 0.0366 | 102.0 | 1275 | 0.9023 | 0.755 | 0.3715 | 1.4465 | 0.755 | 0.7247 | 0.2140 | 0.1044 | | 0.0366 | 102.96 | 1287 | 0.9048 | 0.755 | 0.3720 | 1.4472 | 0.755 | 0.7247 | 0.2066 | 0.1045 | | 0.0366 | 104.0 | 1300 | 0.9060 | 0.755 | 0.3719 | 1.4565 | 0.755 | 0.7247 | 0.2003 | 0.1044 | | 0.0366 | 104.96 | 1312 | 0.9105 | 0.755 | 0.3735 | 1.4625 | 0.755 | 0.7247 | 0.2094 | 0.1053 | | 0.0366 | 106.0 | 1325 | 0.9099 | 0.76 | 0.3738 | 1.4463 | 0.76 | 0.7273 | 0.2050 | 0.1044 | | 0.0366 | 106.96 | 1337 | 0.9111 | 0.755 | 0.3751 | 1.4486 | 0.755 | 0.7247 | 0.2173 | 0.1041 | | 0.0366 | 108.0 | 1350 | 0.9149 | 0.755 | 0.3745 | 1.5081 | 0.755 | 0.7247 | 0.2062 | 0.1052 | | 0.0366 | 108.96 | 1362 | 0.9146 | 0.755 | 0.3744 | 1.4513 | 0.755 | 0.7247 | 0.2073 | 0.1047 | | 0.0366 | 110.0 | 1375 | 0.9157 | 0.755 | 0.3764 | 1.4486 | 0.755 | 0.7247 | 0.2094 | 0.1046 | | 0.0366 | 110.96 | 1387 | 0.9201 | 0.755 | 0.3767 | 1.4529 | 0.755 | 0.7247 | 0.2093 | 0.1051 | | 0.0366 | 112.0 | 1400 | 0.9190 | 0.755 | 0.3759 | 1.4533 | 0.755 | 0.7247 | 0.2020 | 0.1049 | | 0.0366 | 112.96 | 1412 | 0.9230 | 0.755 | 0.3782 | 1.4529 | 0.755 | 0.7247 | 0.2054 | 0.1050 | | 0.0366 | 114.0 | 1425 | 0.9234 | 0.755 | 0.3778 | 1.4505 | 0.755 | 0.7247 | 0.2009 | 0.1049 | | 0.0366 | 114.96 | 1437 | 0.9238 | 0.755 | 0.3780 | 1.4469 | 0.755 | 0.7247 | 0.1974 | 0.1051 | | 0.0366 | 116.0 | 1450 | 0.9264 | 0.755 | 0.3786 | 1.4538 | 0.755 | 0.7247 | 0.2016 | 0.1051 | | 0.0366 | 116.96 | 1462 | 0.9275 | 0.755 | 0.3787 | 1.4553 | 0.755 | 0.7247 | 0.2012 | 0.1054 | | 0.0366 | 118.0 | 1475 | 0.9306 | 0.755 | 0.3798 | 1.4595 | 0.755 | 0.7247 | 0.2139 | 0.1057 | | 0.0366 | 118.96 | 1487 | 0.9291 | 0.76 | 0.3789 | 1.4529 | 0.76 | 0.7273 | 0.2014 | 0.1054 | | 0.0214 | 120.0 | 1500 | 0.9318 | 0.755 | 0.3804 | 1.4520 | 0.755 | 0.7247 | 0.2017 | 0.1050 | | 0.0214 | 120.96 | 1512 | 0.9325 | 0.755 | 0.3796 | 1.4535 | 0.755 | 0.7247 | 0.2087 | 0.1053 | | 0.0214 | 122.0 | 1525 | 0.9339 | 0.755 | 0.3804 | 1.4534 | 0.755 | 0.7247 | 0.2068 | 0.1056 | | 0.0214 | 122.96 | 1537 | 0.9342 | 0.755 | 0.3807 | 1.4519 | 0.755 | 0.7247 | 0.1986 | 0.1052 | | 0.0214 | 124.0 | 1550 | 0.9357 | 0.755 | 0.3808 | 1.4524 | 0.755 | 0.7247 | 0.2058 | 0.1054 | | 0.0214 | 124.96 | 1562 | 0.9360 | 0.755 | 0.3808 | 1.4514 | 0.755 | 0.7247 | 0.2061 | 0.1052 | | 0.0214 | 126.0 | 1575 | 0.9409 | 0.755 | 0.3828 | 1.4557 | 0.755 | 0.7247 | 0.2044 | 0.1058 | | 0.0214 | 126.96 | 1587 | 0.9390 | 0.755 | 0.3819 | 1.4523 | 0.755 | 0.7247 | 0.2062 | 0.1052 | | 0.0214 | 128.0 | 1600 | 0.9425 | 0.755 | 0.3833 | 1.4559 | 0.755 | 0.7247 | 0.2049 | 0.1058 | | 0.0214 | 128.96 | 1612 | 0.9421 | 0.755 | 0.3824 | 1.4534 | 0.755 | 0.7247 | 0.1978 | 0.1055 | | 0.0214 | 130.0 | 1625 | 0.9433 | 0.755 | 0.3831 | 1.4530 | 0.755 | 0.7247 | 0.2069 | 0.1056 | | 0.0214 | 130.96 | 1637 | 0.9463 | 0.755 | 0.3842 | 1.4535 | 0.755 | 0.7247 | 0.2127 | 0.1058 | | 0.0214 | 132.0 | 1650 | 0.9462 | 0.755 | 0.3835 | 1.4546 | 0.755 | 0.7247 | 0.1975 | 0.1057 | | 0.0214 | 132.96 | 1662 | 0.9479 | 0.755 | 0.3842 | 1.4562 | 0.755 | 0.7247 | 0.2012 | 0.1059 | | 0.0214 | 134.0 | 1675 | 0.9493 | 0.755 | 0.3852 | 1.4547 | 0.755 | 0.7247 | 0.2161 | 0.1057 | | 0.0214 | 134.96 | 1687 | 0.9484 | 0.755 | 0.3842 | 1.4563 | 0.755 | 0.7247 | 0.2046 | 0.1055 | | 0.0214 | 136.0 | 1700 | 0.9500 | 0.755 | 0.3849 | 1.4528 | 0.755 | 0.7247 | 0.2081 | 0.1058 | | 0.0214 | 136.96 | 1712 | 0.9510 | 0.755 | 0.3854 | 1.4510 | 0.755 | 0.7247 | 0.1976 | 0.1053 | | 0.0214 | 138.0 | 1725 | 0.9519 | 0.755 | 0.3855 | 1.4516 | 0.755 | 0.7247 | 0.2090 | 0.1056 | | 0.0214 | 138.96 | 1737 | 0.9535 | 0.755 | 0.3857 | 1.4537 | 0.755 | 0.7247 | 0.2091 | 0.1057 | | 0.0214 | 140.0 | 1750 | 0.9546 | 0.755 | 0.3862 | 1.4557 | 0.755 | 0.7247 | 0.2020 | 0.1057 | | 0.0214 | 140.96 | 1762 | 0.9558 | 0.755 | 0.3867 | 1.4530 | 0.755 | 0.7247 | 0.2018 | 0.1059 | | 0.0214 | 142.0 | 1775 | 0.9567 | 0.755 | 0.3870 | 1.4522 | 0.755 | 0.7247 | 0.2059 | 0.1055 | | 0.0214 | 142.96 | 1787 | 0.9589 | 0.755 | 0.3876 | 1.4555 | 0.755 | 0.7247 | 0.2063 | 0.1060 | | 0.0214 | 144.0 | 1800 | 0.9584 | 0.755 | 0.3872 | 1.4568 | 0.755 | 0.7245 | 0.2162 | 0.1056 | | 0.0214 | 144.96 | 1812 | 0.9613 | 0.755 | 0.3881 | 1.4594 | 0.755 | 0.7247 | 0.2085 | 0.1063 | | 0.0214 | 146.0 | 1825 | 0.9604 | 0.755 | 0.3873 | 1.5132 | 0.755 | 0.7247 | 0.2047 | 0.1056 | | 0.0214 | 146.96 | 1837 | 0.9627 | 0.755 | 0.3887 | 1.4573 | 0.755 | 0.7247 | 0.2107 | 0.1059 | | 0.0214 | 148.0 | 1850 | 0.9643 | 0.755 | 0.3890 | 1.4570 | 0.755 | 0.7247 | 0.2041 | 0.1065 | | 0.0214 | 148.96 | 1862 | 0.9633 | 0.755 | 0.3886 | 1.4526 | 0.755 | 0.7247 | 0.2085 | 0.1055 | | 0.0214 | 150.0 | 1875 | 0.9637 | 0.755 | 0.3887 | 1.4551 | 0.755 | 0.7245 | 0.2096 | 0.1054 | | 0.0214 | 150.96 | 1887 | 0.9645 | 0.755 | 0.3886 | 1.4534 | 0.755 | 0.7247 | 0.2100 | 0.1058 | | 0.0214 | 152.0 | 1900 | 0.9661 | 0.755 | 0.3891 | 1.4550 | 0.755 | 0.7247 | 0.2072 | 0.1060 | | 0.0214 | 152.96 | 1912 | 0.9665 | 0.755 | 0.3894 | 1.4543 | 0.755 | 0.7247 | 0.2092 | 0.1057 | | 0.0214 | 154.0 | 1925 | 0.9667 | 0.755 | 0.3894 | 1.4570 | 0.755 | 0.7245 | 0.2019 | 0.1056 | | 0.0214 | 154.96 | 1937 | 0.9681 | 0.755 | 0.3900 | 1.4537 | 0.755 | 0.7245 | 0.2098 | 0.1059 | | 0.0214 | 156.0 | 1950 | 0.9689 | 0.755 | 0.3903 | 1.4533 | 0.755 | 0.7245 | 0.2022 | 0.1056 | | 0.0214 | 156.96 | 1962 | 0.9689 | 0.755 | 0.3898 | 1.4553 | 0.755 | 0.7247 | 0.2074 | 0.1058 | | 0.0214 | 158.0 | 1975 | 0.9705 | 0.755 | 0.3901 | 1.4566 | 0.755 | 0.7247 | 0.2072 | 0.1062 | | 0.0214 | 158.96 | 1987 | 0.9721 | 0.755 | 0.3911 | 1.4570 | 0.755 | 0.7245 | 0.2055 | 0.1062 | | 0.0155 | 160.0 | 2000 | 0.9712 | 0.755 | 0.3904 | 1.4551 | 0.755 | 0.7245 | 0.2070 | 0.1060 | | 0.0155 | 160.96 | 2012 | 0.9721 | 0.755 | 0.3906 | 1.4563 | 0.755 | 0.7245 | 0.2108 | 0.1059 | | 0.0155 | 162.0 | 2025 | 0.9734 | 0.755 | 0.3914 | 1.4550 | 0.755 | 0.7245 | 0.2071 | 0.1057 | | 0.0155 | 162.96 | 2037 | 0.9740 | 0.755 | 0.3913 | 1.4565 | 0.755 | 0.7245 | 0.2073 | 0.1062 | | 0.0155 | 164.0 | 2050 | 0.9744 | 0.755 | 0.3915 | 1.4563 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 164.96 | 2062 | 0.9752 | 0.755 | 0.3917 | 1.4558 | 0.755 | 0.7245 | 0.2102 | 0.1059 | | 0.0155 | 166.0 | 2075 | 0.9755 | 0.755 | 0.3916 | 1.4566 | 0.755 | 0.7245 | 0.2102 | 0.1062 | | 0.0155 | 166.96 | 2087 | 0.9755 | 0.755 | 0.3917 | 1.4542 | 0.755 | 0.7245 | 0.2100 | 0.1057 | | 0.0155 | 168.0 | 2100 | 0.9768 | 0.755 | 0.3921 | 1.4578 | 0.755 | 0.7245 | 0.2103 | 0.1059 | | 0.0155 | 168.96 | 2112 | 0.9767 | 0.755 | 0.3919 | 1.4575 | 0.755 | 0.7245 | 0.2106 | 0.1057 | | 0.0155 | 170.0 | 2125 | 0.9776 | 0.755 | 0.3922 | 1.4567 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 170.96 | 2137 | 0.9783 | 0.755 | 0.3925 | 1.4560 | 0.755 | 0.7245 | 0.2080 | 0.1062 | | 0.0155 | 172.0 | 2150 | 0.9793 | 0.755 | 0.3927 | 1.4578 | 0.755 | 0.7245 | 0.2102 | 0.1064 | | 0.0155 | 172.96 | 2162 | 0.9783 | 0.755 | 0.3923 | 1.4557 | 0.755 | 0.7245 | 0.2098 | 0.1058 | | 0.0155 | 174.0 | 2175 | 0.9790 | 0.755 | 0.3927 | 1.4550 | 0.755 | 0.7245 | 0.2087 | 0.1057 | | 0.0155 | 174.96 | 2187 | 0.9801 | 0.755 | 0.3929 | 1.4577 | 0.755 | 0.7245 | 0.2100 | 0.1059 | | 0.0155 | 176.0 | 2200 | 0.9800 | 0.755 | 0.3928 | 1.4566 | 0.755 | 0.7245 | 0.2081 | 0.1058 | | 0.0155 | 176.96 | 2212 | 0.9809 | 0.755 | 0.3932 | 1.4568 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 178.0 | 2225 | 0.9808 | 0.755 | 0.3930 | 1.4571 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 178.96 | 2237 | 0.9808 | 0.755 | 0.3930 | 1.4566 | 0.755 | 0.7245 | 0.2082 | 0.1057 | | 0.0155 | 180.0 | 2250 | 0.9813 | 0.755 | 0.3931 | 1.4567 | 0.755 | 0.7245 | 0.2082 | 0.1058 | | 0.0155 | 180.96 | 2262 | 0.9817 | 0.755 | 0.3933 | 1.4571 | 0.755 | 0.7245 | 0.2032 | 0.1059 | | 0.0155 | 182.0 | 2275 | 0.9819 | 0.755 | 0.3933 | 1.4570 | 0.755 | 0.7245 | 0.2103 | 0.1058 | | 0.0155 | 182.96 | 2287 | 0.9822 | 0.755 | 0.3934 | 1.4567 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 184.0 | 2300 | 0.9824 | 0.755 | 0.3934 | 1.4572 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 184.96 | 2312 | 0.9825 | 0.755 | 0.3935 | 1.4569 | 0.755 | 0.7245 | 0.2081 | 0.1060 | | 0.0155 | 186.0 | 2325 | 0.9825 | 0.755 | 0.3934 | 1.4574 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 186.96 | 2337 | 0.9829 | 0.755 | 0.3935 | 1.4571 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 188.0 | 2350 | 0.9830 | 0.755 | 0.3936 | 1.4569 | 0.755 | 0.7245 | 0.2081 | 0.1060 | | 0.0155 | 188.96 | 2362 | 0.9831 | 0.755 | 0.3936 | 1.4572 | 0.755 | 0.7245 | 0.2082 | 0.1059 | | 0.0155 | 190.0 | 2375 | 0.9830 | 0.755 | 0.3935 | 1.4575 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 190.96 | 2387 | 0.9831 | 0.755 | 0.3936 | 1.4572 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 192.0 | 2400 | 0.9831 | 0.755 | 0.3936 | 1.4572 | 0.755 | 0.7245 | 0.2101 | 0.1059 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

BlueAvenir/model_growth_restructuring_V_0_3

BlueAvenir

2023-07-12T18:03:50Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-07-12T18:03:20Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 153 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 153, "warmup_steps": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

VK246/IC_ver5a_coco_swin_gpt2_05pc_1e

VK246

2023-07-12T18:03:19Z

45

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-07-12T17:53:07Z

--- tags: - generated_from_trainer datasets: - coco metrics: - rouge - bleu model-index: - name: IC_ver5a_coco_swin_gpt2_05pc_1e results: [] ---  # IC_ver5a_coco_swin_gpt2_05pc_1e This model is a fine-tuned version of [](https://huggingface.co/) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 1.2245 - Rouge1: 22.0956 - Rouge2: 2.7707 - Rougel: 20.6806 - Rougelsum: 20.6915 - Bleu: 1.2101 - Gen Len: 11.1124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:------:|:-------:| | 2.1625 | 0.42 | 25 | 1.2744 | 22.7707 | 2.4514 | 20.6431 | 20.6829 | 0.0 | 11.1124 | | 1.3438 | 0.85 | 50 | 1.2374 | 21.3271 | 2.4116 | 19.7122 | 19.7385 | 0.7474 | 11.1124 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

NasimB/gpt2-concat-mod-datasets1-iorder-rarity-all-5p5k

NasimB

2023-07-12T18:03:11Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-12T16:28:12Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-mod-datasets1-iorder-rarity-all-5p5k results: [] ---  # gpt2-concat-mod-datasets1-iorder-rarity-all-5p5k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7791 | 0.32 | 500 | 5.6460 | | 5.4436 | 0.63 | 1000 | 5.2273 | | 5.0944 | 0.95 | 1500 | 4.9700 | | 4.809 | 1.26 | 2000 | 4.8294 | | 4.6757 | 1.58 | 2500 | 4.7121 | | 4.5526 | 1.9 | 3000 | 4.6044 | | 4.3523 | 2.21 | 3500 | 4.5477 | | 4.2579 | 2.53 | 4000 | 4.4848 | | 4.2187 | 2.84 | 4500 | 4.4262 | | 4.0519 | 3.16 | 5000 | 4.4090 | | 3.9359 | 3.47 | 5500 | 4.3808 | | 3.9128 | 3.79 | 6000 | 4.3460 | | 3.8052 | 4.11 | 6500 | 4.3424 | | 3.6404 | 4.42 | 7000 | 4.3367 | | 3.6307 | 4.74 | 7500 | 4.3246 | | 3.5885 | 5.05 | 8000 | 4.3236 | | 3.4332 | 5.37 | 8500 | 4.3296 | | 3.4351 | 5.69 | 9000 | 4.3281 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

tyavika/lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi

tyavika

2023-07-12T17:59:27Z

77

0

transformers

[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-12T15:42:47Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi results: [] ---  # lr1e5-layer1-bs16-Distil-CNN128LSTM128NoBi This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3813 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.5317 | 1.0 | 3290 | 1.3385 | | 1.0853 | 2.0 | 6580 | 1.1885 | | 0.7993 | 3.0 | 9870 | 1.2330 | | 0.5808 | 4.0 | 13160 | 1.3813 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

rmcl7/sd-class-butterflies-32

rmcl7

2023-07-12T17:54:58Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-07-12T17:53:25Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('rmcl7/sd-class-butterflies-32') image = pipeline().images[0] image ```

DipanAI/falcon_law_7Bat

DipanAI

2023-07-12T17:53:31Z

1

0

peft

[ "peft", "tensorboard", "region:us" ]

null

2023-07-12T17:49:30Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0

ayanban011/vit-base_tobacco_lr5e-6_e_200

ayanban011

2023-07-12T17:42:48Z

167

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-12T15:04:59Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_tobacco_lr5e-6_e_200 results: [] ---  # vit-base_tobacco_lr5e-6_e_200 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7893 - Accuracy: 0.78 - Brier Loss: 0.3364 - Nll: 1.4430 - F1 Micro: 0.78 - F1 Macro: 0.7488 - Ece: 0.1922 - Aurc: 0.1018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7450 | 0.815 | 0.3079 | 1.1882 | 0.815 | 0.7942 | 0.2383 | 0.0734 | | No log | 2.0 | 25 | 0.7440 | 0.815 | 0.3074 | 1.1877 | 0.815 | 0.7922 | 0.2303 | 0.0734 | | No log | 2.96 | 37 | 0.7429 | 0.81 | 0.3071 | 1.1883 | 0.81 | 0.7914 | 0.2367 | 0.0714 | | No log | 4.0 | 50 | 0.7463 | 0.815 | 0.3083 | 1.1912 | 0.815 | 0.7942 | 0.2334 | 0.0768 | | No log | 4.96 | 62 | 0.7453 | 0.815 | 0.3080 | 1.1927 | 0.815 | 0.7922 | 0.2224 | 0.0751 | | No log | 6.0 | 75 | 0.7416 | 0.81 | 0.3067 | 1.1870 | 0.81 | 0.7914 | 0.2232 | 0.0716 | | No log | 6.96 | 87 | 0.7420 | 0.81 | 0.3070 | 1.1858 | 0.81 | 0.7914 | 0.2309 | 0.0730 | | No log | 8.0 | 100 | 0.7409 | 0.81 | 0.3062 | 1.1879 | 0.81 | 0.7871 | 0.2186 | 0.0749 | | No log | 8.96 | 112 | 0.7444 | 0.815 | 0.3079 | 1.1984 | 0.815 | 0.8059 | 0.2342 | 0.0766 | | No log | 10.0 | 125 | 0.7445 | 0.81 | 0.3079 | 1.1918 | 0.81 | 0.7894 | 0.2353 | 0.0775 | | No log | 10.96 | 137 | 0.7451 | 0.81 | 0.3085 | 1.1888 | 0.81 | 0.7871 | 0.2319 | 0.0784 | | No log | 12.0 | 150 | 0.7411 | 0.805 | 0.3060 | 1.1914 | 0.805 | 0.7829 | 0.2186 | 0.0779 | | No log | 12.96 | 162 | 0.7406 | 0.815 | 0.3075 | 1.1967 | 0.815 | 0.8057 | 0.2472 | 0.0746 | | No log | 14.0 | 175 | 0.7360 | 0.805 | 0.3048 | 1.2494 | 0.805 | 0.7841 | 0.2383 | 0.0770 | | No log | 14.96 | 187 | 0.7331 | 0.81 | 0.3037 | 1.1896 | 0.81 | 0.7912 | 0.2306 | 0.0721 | | No log | 16.0 | 200 | 0.7345 | 0.81 | 0.3042 | 1.2488 | 0.81 | 0.7956 | 0.2064 | 0.0794 | | No log | 16.96 | 212 | 0.7329 | 0.815 | 0.3018 | 1.2595 | 0.815 | 0.8043 | 0.2331 | 0.0797 | | No log | 18.0 | 225 | 0.7354 | 0.795 | 0.3055 | 1.1743 | 0.795 | 0.7765 | 0.2154 | 0.0742 | | No log | 18.96 | 237 | 0.7282 | 0.805 | 0.3001 | 1.1920 | 0.805 | 0.7844 | 0.2444 | 0.0748 | | No log | 20.0 | 250 | 0.7225 | 0.805 | 0.2981 | 1.1804 | 0.805 | 0.7874 | 0.2041 | 0.0744 | | No log | 20.96 | 262 | 0.7250 | 0.81 | 0.3000 | 1.2516 | 0.81 | 0.7900 | 0.2148 | 0.0761 | | No log | 22.0 | 275 | 0.7252 | 0.81 | 0.3009 | 1.3033 | 0.81 | 0.7954 | 0.2375 | 0.0782 | | No log | 22.96 | 287 | 0.7293 | 0.8 | 0.3016 | 1.2557 | 0.8000 | 0.7796 | 0.2243 | 0.0810 | | No log | 24.0 | 300 | 0.7344 | 0.805 | 0.3057 | 1.3122 | 0.805 | 0.7853 | 0.2096 | 0.0815 | | No log | 24.96 | 312 | 0.7314 | 0.81 | 0.3044 | 1.2501 | 0.81 | 0.7909 | 0.2068 | 0.0808 | | No log | 26.0 | 325 | 0.7293 | 0.81 | 0.3036 | 1.3066 | 0.81 | 0.7909 | 0.2015 | 0.0804 | | No log | 26.96 | 337 | 0.7323 | 0.805 | 0.3042 | 1.3147 | 0.805 | 0.7853 | 0.2242 | 0.0827 | | No log | 28.0 | 350 | 0.7288 | 0.805 | 0.3029 | 1.3109 | 0.805 | 0.7853 | 0.1976 | 0.0826 | | No log | 28.96 | 362 | 0.7343 | 0.805 | 0.3058 | 1.3108 | 0.805 | 0.7853 | 0.2128 | 0.0851 | | No log | 30.0 | 375 | 0.7351 | 0.8 | 0.3062 | 1.3129 | 0.8000 | 0.7733 | 0.2088 | 0.0845 | | No log | 30.96 | 387 | 0.7301 | 0.8 | 0.3044 | 1.2452 | 0.8000 | 0.7733 | 0.2272 | 0.0836 | | No log | 32.0 | 400 | 0.7340 | 0.8 | 0.3055 | 1.3769 | 0.8000 | 0.7806 | 0.2207 | 0.0855 | | No log | 32.96 | 412 | 0.7322 | 0.805 | 0.3052 | 1.3132 | 0.805 | 0.7784 | 0.2470 | 0.0865 | | No log | 34.0 | 425 | 0.7301 | 0.8 | 0.3045 | 1.2543 | 0.8000 | 0.7733 | 0.2021 | 0.0863 | | No log | 34.96 | 437 | 0.7297 | 0.8 | 0.3046 | 1.3056 | 0.8000 | 0.7731 | 0.1886 | 0.0836 | | No log | 36.0 | 450 | 0.7326 | 0.805 | 0.3056 | 1.3102 | 0.805 | 0.7784 | 0.2256 | 0.0892 | | No log | 36.96 | 462 | 0.7311 | 0.8 | 0.3046 | 1.3681 | 0.8000 | 0.7733 | 0.2065 | 0.0880 | | No log | 38.0 | 475 | 0.7356 | 0.8 | 0.3077 | 1.3666 | 0.8000 | 0.7733 | 0.2034 | 0.0895 | | No log | 38.96 | 487 | 0.7302 | 0.8 | 0.3042 | 1.3114 | 0.8000 | 0.7733 | 0.1996 | 0.0894 | | 0.1528 | 40.0 | 500 | 0.7289 | 0.805 | 0.3035 | 1.3669 | 0.805 | 0.7797 | 0.1951 | 0.0874 | | 0.1528 | 40.96 | 512 | 0.7292 | 0.8 | 0.3037 | 1.3685 | 0.8000 | 0.7737 | 0.2103 | 0.0878 | | 0.1528 | 42.0 | 525 | 0.7347 | 0.805 | 0.3077 | 1.3640 | 0.805 | 0.7784 | 0.2005 | 0.0894 | | 0.1528 | 42.96 | 537 | 0.7343 | 0.805 | 0.3063 | 1.3684 | 0.805 | 0.7784 | 0.2055 | 0.0899 | | 0.1528 | 44.0 | 550 | 0.7283 | 0.805 | 0.3038 | 1.3660 | 0.805 | 0.7797 | 0.2142 | 0.0881 | | 0.1528 | 44.96 | 562 | 0.7325 | 0.805 | 0.3061 | 1.3658 | 0.805 | 0.7784 | 0.1967 | 0.0896 | | 0.1528 | 46.0 | 575 | 0.7303 | 0.81 | 0.3049 | 1.3659 | 0.81 | 0.7844 | 0.1985 | 0.0883 | | 0.1528 | 46.96 | 587 | 0.7321 | 0.805 | 0.3054 | 1.3657 | 0.805 | 0.7797 | 0.1925 | 0.0888 | | 0.1528 | 48.0 | 600 | 0.7322 | 0.8 | 0.3054 | 1.3680 | 0.8000 | 0.7704 | 0.1837 | 0.0903 | | 0.1528 | 48.96 | 612 | 0.7368 | 0.8 | 0.3079 | 1.3234 | 0.8000 | 0.7704 | 0.2111 | 0.0905 | | 0.1528 | 50.0 | 625 | 0.7358 | 0.8 | 0.3074 | 1.3682 | 0.8000 | 0.7704 | 0.1971 | 0.0899 | | 0.1528 | 50.96 | 637 | 0.7318 | 0.8 | 0.3054 | 1.3661 | 0.8000 | 0.7704 | 0.2111 | 0.0889 | | 0.1528 | 52.0 | 650 | 0.7473 | 0.795 | 0.3133 | 1.3707 | 0.795 | 0.7660 | 0.2032 | 0.0954 | | 0.1528 | 52.96 | 662 | 0.7299 | 0.805 | 0.3050 | 1.3631 | 0.805 | 0.7797 | 0.1977 | 0.0891 | | 0.1528 | 54.0 | 675 | 0.7427 | 0.795 | 0.3116 | 1.3673 | 0.795 | 0.7644 | 0.2181 | 0.0923 | | 0.1528 | 54.96 | 687 | 0.7371 | 0.8 | 0.3087 | 1.3648 | 0.8000 | 0.7704 | 0.1984 | 0.0902 | | 0.1528 | 56.0 | 700 | 0.7399 | 0.8 | 0.3100 | 1.3671 | 0.8000 | 0.7704 | 0.2178 | 0.0920 | | 0.1528 | 56.96 | 712 | 0.7420 | 0.8 | 0.3114 | 1.3671 | 0.8000 | 0.7689 | 0.2035 | 0.0925 | | 0.1528 | 58.0 | 725 | 0.7380 | 0.8 | 0.3088 | 1.3646 | 0.8000 | 0.7704 | 0.2016 | 0.0916 | | 0.1528 | 58.96 | 737 | 0.7390 | 0.8 | 0.3091 | 1.3671 | 0.8000 | 0.7704 | 0.2005 | 0.0921 | | 0.1528 | 60.0 | 750 | 0.7370 | 0.8 | 0.3085 | 1.3651 | 0.8000 | 0.7704 | 0.1891 | 0.0908 | | 0.1528 | 60.96 | 762 | 0.7388 | 0.8 | 0.3097 | 1.3622 | 0.8000 | 0.7704 | 0.2010 | 0.0906 | | 0.1528 | 62.0 | 775 | 0.7403 | 0.795 | 0.3102 | 1.3691 | 0.795 | 0.7654 | 0.2142 | 0.0910 | | 0.1528 | 62.96 | 787 | 0.7390 | 0.8 | 0.3088 | 1.3950 | 0.8000 | 0.7684 | 0.2058 | 0.0910 | | 0.1528 | 64.0 | 800 | 0.7431 | 0.795 | 0.3110 | 1.3722 | 0.795 | 0.7656 | 0.1959 | 0.0914 | | 0.1528 | 64.96 | 812 | 0.7429 | 0.8 | 0.3117 | 1.3673 | 0.8000 | 0.7704 | 0.1933 | 0.0925 | | 0.1528 | 66.0 | 825 | 0.7419 | 0.8 | 0.3115 | 1.3663 | 0.8000 | 0.7704 | 0.1986 | 0.0913 | | 0.1528 | 66.96 | 837 | 0.7434 | 0.8 | 0.3120 | 1.3672 | 0.8000 | 0.7704 | 0.1929 | 0.0927 | | 0.1528 | 68.0 | 850 | 0.7414 | 0.8 | 0.3115 | 1.3649 | 0.8000 | 0.7704 | 0.1988 | 0.0923 | | 0.1528 | 68.96 | 862 | 0.7448 | 0.8 | 0.3129 | 1.3685 | 0.8000 | 0.7704 | 0.2251 | 0.0928 | | 0.1528 | 70.0 | 875 | 0.7450 | 0.8 | 0.3130 | 1.3657 | 0.8000 | 0.7704 | 0.1969 | 0.0934 | | 0.1528 | 70.96 | 887 | 0.7464 | 0.8 | 0.3132 | 1.3686 | 0.8000 | 0.7704 | 0.1988 | 0.0946 | | 0.1528 | 72.0 | 900 | 0.7465 | 0.8 | 0.3138 | 1.3682 | 0.8000 | 0.7707 | 0.2015 | 0.0935 | | 0.1528 | 72.96 | 912 | 0.7471 | 0.8 | 0.3142 | 1.3685 | 0.8000 | 0.7696 | 0.2093 | 0.0936 | | 0.1528 | 74.0 | 925 | 0.7476 | 0.8 | 0.3145 | 1.3685 | 0.8000 | 0.7704 | 0.2120 | 0.0942 | | 0.1528 | 74.96 | 937 | 0.7433 | 0.8 | 0.3126 | 1.3655 | 0.8000 | 0.7696 | 0.1907 | 0.0918 | | 0.1528 | 76.0 | 950 | 0.7519 | 0.8 | 0.3166 | 1.3683 | 0.8000 | 0.7704 | 0.2111 | 0.0949 | | 0.1528 | 76.96 | 962 | 0.7485 | 0.8 | 0.3152 | 1.3679 | 0.8000 | 0.7696 | 0.2035 | 0.0942 | | 0.1528 | 78.0 | 975 | 0.7496 | 0.8 | 0.3155 | 1.3716 | 0.8000 | 0.7704 | 0.1931 | 0.0940 | | 0.1528 | 78.96 | 987 | 0.7513 | 0.8 | 0.3163 | 1.3684 | 0.8000 | 0.7704 | 0.1888 | 0.0952 | | 0.1059 | 80.0 | 1000 | 0.7490 | 0.8 | 0.3157 | 1.3707 | 0.8000 | 0.7691 | 0.2118 | 0.0943 | | 0.1059 | 80.96 | 1012 | 0.7482 | 0.8 | 0.3151 | 1.3687 | 0.8000 | 0.7696 | 0.2060 | 0.0945 | | 0.1059 | 82.0 | 1025 | 0.7516 | 0.8 | 0.3163 | 1.3682 | 0.8000 | 0.7704 | 0.2248 | 0.0950 | | 0.1059 | 82.96 | 1037 | 0.7527 | 0.8 | 0.3174 | 1.3660 | 0.8000 | 0.7707 | 0.2204 | 0.0948 | | 0.1059 | 84.0 | 1050 | 0.7495 | 0.8 | 0.3156 | 1.3663 | 0.8000 | 0.7704 | 0.2067 | 0.0939 | | 0.1059 | 84.96 | 1062 | 0.7506 | 0.8 | 0.3161 | 1.3659 | 0.8000 | 0.7707 | 0.2086 | 0.0947 | | 0.1059 | 86.0 | 1075 | 0.7537 | 0.8 | 0.3179 | 1.3687 | 0.8000 | 0.7698 | 0.2001 | 0.0955 | | 0.1059 | 86.96 | 1087 | 0.7525 | 0.8 | 0.3172 | 1.3693 | 0.8000 | 0.7696 | 0.2096 | 0.0947 | | 0.1059 | 88.0 | 1100 | 0.7535 | 0.8 | 0.3175 | 1.3675 | 0.8000 | 0.7704 | 0.2091 | 0.0951 | | 0.1059 | 88.96 | 1112 | 0.7525 | 0.8 | 0.3172 | 1.3680 | 0.8000 | 0.7696 | 0.2009 | 0.0946 | | 0.1059 | 90.0 | 1125 | 0.7566 | 0.8 | 0.3190 | 1.3725 | 0.8000 | 0.7698 | 0.1881 | 0.0964 | | 0.1059 | 90.96 | 1137 | 0.7578 | 0.8 | 0.3195 | 1.3726 | 0.8000 | 0.7704 | 0.1880 | 0.0968 | | 0.1059 | 92.0 | 1150 | 0.7560 | 0.8 | 0.3186 | 1.3715 | 0.8000 | 0.7707 | 0.2095 | 0.0969 | | 0.1059 | 92.96 | 1162 | 0.7623 | 0.795 | 0.3219 | 1.3765 | 0.795 | 0.7681 | 0.1907 | 0.0979 | | 0.1059 | 94.0 | 1175 | 0.7567 | 0.8 | 0.3192 | 1.3709 | 0.8000 | 0.7698 | 0.2000 | 0.0953 | | 0.1059 | 94.96 | 1187 | 0.7538 | 0.8 | 0.3181 | 1.3708 | 0.8000 | 0.7691 | 0.1986 | 0.0958 | | 0.1059 | 96.0 | 1200 | 0.7530 | 0.8 | 0.3175 | 1.3725 | 0.8000 | 0.7693 | 0.2122 | 0.0958 | | 0.1059 | 96.96 | 1212 | 0.7607 | 0.8 | 0.3207 | 1.3730 | 0.8000 | 0.7709 | 0.1906 | 0.0972 | | 0.1059 | 98.0 | 1225 | 0.7647 | 0.79 | 0.3229 | 1.3411 | 0.79 | 0.7633 | 0.1889 | 0.0984 | | 0.1059 | 98.96 | 1237 | 0.7568 | 0.79 | 0.3191 | 1.4425 | 0.79 | 0.7644 | 0.1911 | 0.0976 | | 0.1059 | 100.0 | 1250 | 0.7586 | 0.8 | 0.3200 | 1.4486 | 0.8000 | 0.7696 | 0.2009 | 0.0952 | | 0.1059 | 100.96 | 1262 | 0.7552 | 0.8 | 0.3192 | 1.3728 | 0.8000 | 0.7696 | 0.1962 | 0.0953 | | 0.1059 | 102.0 | 1275 | 0.7601 | 0.8 | 0.3217 | 1.4309 | 0.8000 | 0.7691 | 0.2071 | 0.0969 | | 0.1059 | 102.96 | 1287 | 0.7608 | 0.795 | 0.3213 | 1.3702 | 0.795 | 0.7668 | 0.2068 | 0.0967 | | 0.1059 | 104.0 | 1300 | 0.7590 | 0.795 | 0.3202 | 1.3748 | 0.795 | 0.7668 | 0.1922 | 0.0967 | | 0.1059 | 104.96 | 1312 | 0.7626 | 0.795 | 0.3222 | 1.3775 | 0.795 | 0.7673 | 0.1917 | 0.0974 | | 0.1059 | 106.0 | 1325 | 0.7632 | 0.795 | 0.3228 | 1.3765 | 0.795 | 0.7664 | 0.2072 | 0.0977 | | 0.1059 | 106.96 | 1337 | 0.7612 | 0.795 | 0.3223 | 1.3764 | 0.795 | 0.7666 | 0.2011 | 0.0974 | | 0.1059 | 108.0 | 1350 | 0.7669 | 0.79 | 0.3246 | 1.3777 | 0.79 | 0.7639 | 0.2005 | 0.0981 | | 0.1059 | 108.96 | 1362 | 0.7658 | 0.795 | 0.3238 | 1.3782 | 0.795 | 0.7673 | 0.2099 | 0.0983 | | 0.1059 | 110.0 | 1375 | 0.7632 | 0.79 | 0.3232 | 1.3766 | 0.79 | 0.7639 | 0.2002 | 0.0978 | | 0.1059 | 110.96 | 1387 | 0.7651 | 0.79 | 0.3236 | 1.3758 | 0.79 | 0.7580 | 0.2134 | 0.0980 | | 0.1059 | 112.0 | 1400 | 0.7649 | 0.79 | 0.3235 | 1.3765 | 0.79 | 0.7583 | 0.1975 | 0.0982 | | 0.1059 | 112.96 | 1412 | 0.7683 | 0.79 | 0.3253 | 1.3759 | 0.79 | 0.7639 | 0.1913 | 0.0986 | | 0.1059 | 114.0 | 1425 | 0.7682 | 0.795 | 0.3253 | 1.3778 | 0.795 | 0.7668 | 0.1986 | 0.0984 | | 0.1059 | 114.96 | 1437 | 0.7666 | 0.79 | 0.3245 | 1.3771 | 0.79 | 0.7639 | 0.1838 | 0.0976 | | 0.1059 | 116.0 | 1450 | 0.7685 | 0.79 | 0.3255 | 1.3802 | 0.79 | 0.7639 | 0.1962 | 0.0988 | | 0.1059 | 116.96 | 1462 | 0.7676 | 0.785 | 0.3249 | 1.3783 | 0.785 | 0.7554 | 0.2011 | 0.0991 | | 0.1059 | 118.0 | 1475 | 0.7704 | 0.785 | 0.3262 | 1.3789 | 0.785 | 0.7516 | 0.2098 | 0.0995 | | 0.1059 | 118.96 | 1487 | 0.7701 | 0.785 | 0.3262 | 1.3806 | 0.785 | 0.7554 | 0.2167 | 0.0991 | | 0.0842 | 120.0 | 1500 | 0.7708 | 0.79 | 0.3267 | 1.3815 | 0.79 | 0.7639 | 0.1934 | 0.0990 | | 0.0842 | 120.96 | 1512 | 0.7710 | 0.785 | 0.3265 | 1.3790 | 0.785 | 0.7516 | 0.1928 | 0.0995 | | 0.0842 | 122.0 | 1525 | 0.7728 | 0.79 | 0.3274 | 1.3830 | 0.79 | 0.7639 | 0.1917 | 0.0996 | | 0.0842 | 122.96 | 1537 | 0.7708 | 0.785 | 0.3267 | 1.3826 | 0.785 | 0.7554 | 0.2057 | 0.0992 | | 0.0842 | 124.0 | 1550 | 0.7698 | 0.785 | 0.3262 | 1.3777 | 0.785 | 0.7554 | 0.2021 | 0.0996 | | 0.0842 | 124.96 | 1562 | 0.7706 | 0.785 | 0.3267 | 1.3825 | 0.785 | 0.7554 | 0.1949 | 0.0994 | | 0.0842 | 126.0 | 1575 | 0.7751 | 0.79 | 0.3290 | 1.3827 | 0.79 | 0.7639 | 0.1945 | 0.1002 | | 0.0842 | 126.96 | 1587 | 0.7734 | 0.79 | 0.3282 | 1.3855 | 0.79 | 0.7639 | 0.1882 | 0.0998 | | 0.0842 | 128.0 | 1600 | 0.7753 | 0.785 | 0.3290 | 1.3842 | 0.785 | 0.7554 | 0.1926 | 0.1006 | | 0.0842 | 128.96 | 1612 | 0.7731 | 0.78 | 0.3278 | 1.3808 | 0.78 | 0.7488 | 0.2115 | 0.0994 | | 0.0842 | 130.0 | 1625 | 0.7723 | 0.78 | 0.3276 | 1.3830 | 0.78 | 0.7486 | 0.2005 | 0.0994 | | 0.0842 | 130.96 | 1637 | 0.7746 | 0.78 | 0.3287 | 1.3810 | 0.78 | 0.7488 | 0.2103 | 0.0999 | | 0.0842 | 132.0 | 1650 | 0.7758 | 0.78 | 0.3291 | 1.3848 | 0.78 | 0.7488 | 0.2138 | 0.1004 | | 0.0842 | 132.96 | 1662 | 0.7771 | 0.78 | 0.3299 | 1.3833 | 0.78 | 0.7488 | 0.2080 | 0.1007 | | 0.0842 | 134.0 | 1675 | 0.7757 | 0.78 | 0.3295 | 1.3837 | 0.78 | 0.7488 | 0.2060 | 0.0999 | | 0.0842 | 134.96 | 1687 | 0.7754 | 0.78 | 0.3292 | 1.3836 | 0.78 | 0.7488 | 0.2070 | 0.1001 | | 0.0842 | 136.0 | 1700 | 0.7755 | 0.78 | 0.3294 | 1.3834 | 0.78 | 0.7488 | 0.2057 | 0.0999 | | 0.0842 | 136.96 | 1712 | 0.7755 | 0.78 | 0.3295 | 1.3874 | 0.78 | 0.7488 | 0.1999 | 0.0998 | | 0.0842 | 138.0 | 1725 | 0.7759 | 0.78 | 0.3296 | 1.4380 | 0.78 | 0.7488 | 0.2007 | 0.1002 | | 0.0842 | 138.96 | 1737 | 0.7776 | 0.78 | 0.3303 | 1.3868 | 0.78 | 0.7488 | 0.2091 | 0.1002 | | 0.0842 | 140.0 | 1750 | 0.7780 | 0.78 | 0.3304 | 1.3868 | 0.78 | 0.7488 | 0.2086 | 0.1005 | | 0.0842 | 140.96 | 1762 | 0.7780 | 0.78 | 0.3306 | 1.3855 | 0.78 | 0.7488 | 0.2065 | 0.1001 | | 0.0842 | 142.0 | 1775 | 0.7789 | 0.78 | 0.3311 | 1.3855 | 0.78 | 0.7488 | 0.2067 | 0.1003 | | 0.0842 | 142.96 | 1787 | 0.7798 | 0.78 | 0.3314 | 1.3856 | 0.78 | 0.7488 | 0.2083 | 0.1010 | | 0.0842 | 144.0 | 1800 | 0.7799 | 0.78 | 0.3315 | 1.3914 | 0.78 | 0.7488 | 0.2161 | 0.1004 | | 0.0842 | 144.96 | 1812 | 0.7806 | 0.78 | 0.3317 | 1.3857 | 0.78 | 0.7488 | 0.2024 | 0.1008 | | 0.0842 | 146.0 | 1825 | 0.7817 | 0.78 | 0.3322 | 1.3947 | 0.78 | 0.7488 | 0.2043 | 0.1009 | | 0.0842 | 146.96 | 1837 | 0.7815 | 0.78 | 0.3324 | 1.3898 | 0.78 | 0.7488 | 0.2118 | 0.1006 | | 0.0842 | 148.0 | 1850 | 0.7820 | 0.78 | 0.3326 | 1.3874 | 0.78 | 0.7488 | 0.2114 | 0.1008 | | 0.0842 | 148.96 | 1862 | 0.7821 | 0.78 | 0.3327 | 1.4391 | 0.78 | 0.7488 | 0.2087 | 0.1006 | | 0.0842 | 150.0 | 1875 | 0.7816 | 0.78 | 0.3324 | 1.4410 | 0.78 | 0.7488 | 0.2160 | 0.1006 | | 0.0842 | 150.96 | 1887 | 0.7826 | 0.78 | 0.3328 | 1.3927 | 0.78 | 0.7488 | 0.2011 | 0.1007 | | 0.0842 | 152.0 | 1900 | 0.7834 | 0.78 | 0.3332 | 1.4411 | 0.78 | 0.7488 | 0.1994 | 0.1009 | | 0.0842 | 152.96 | 1912 | 0.7830 | 0.78 | 0.3331 | 1.4409 | 0.78 | 0.7488 | 0.1967 | 0.1008 | | 0.0842 | 154.0 | 1925 | 0.7825 | 0.78 | 0.3329 | 1.4412 | 0.78 | 0.7488 | 0.2070 | 0.1008 | | 0.0842 | 154.96 | 1937 | 0.7827 | 0.78 | 0.3332 | 1.4399 | 0.78 | 0.7488 | 0.2097 | 0.1007 | | 0.0842 | 156.0 | 1950 | 0.7822 | 0.78 | 0.3329 | 1.4391 | 0.78 | 0.7488 | 0.1986 | 0.1006 | | 0.0842 | 156.96 | 1962 | 0.7838 | 0.78 | 0.3335 | 1.4414 | 0.78 | 0.7488 | 0.1967 | 0.1007 | | 0.0842 | 158.0 | 1975 | 0.7845 | 0.78 | 0.3337 | 1.3899 | 0.78 | 0.7488 | 0.2087 | 0.1008 | | 0.0842 | 158.96 | 1987 | 0.7846 | 0.78 | 0.3340 | 1.4419 | 0.78 | 0.7488 | 0.2002 | 0.1007 | | 0.0731 | 160.0 | 2000 | 0.7853 | 0.78 | 0.3343 | 1.4419 | 0.78 | 0.7488 | 0.1967 | 0.1010 | | 0.0731 | 160.96 | 2012 | 0.7853 | 0.78 | 0.3343 | 1.4425 | 0.78 | 0.7488 | 0.1963 | 0.1012 | | 0.0731 | 162.0 | 2025 | 0.7846 | 0.78 | 0.3341 | 1.4411 | 0.78 | 0.7488 | 0.1998 | 0.1008 | | 0.0731 | 162.96 | 2037 | 0.7856 | 0.78 | 0.3345 | 1.4419 | 0.78 | 0.7488 | 0.2000 | 0.1011 | | 0.0731 | 164.0 | 2050 | 0.7863 | 0.78 | 0.3348 | 1.4425 | 0.78 | 0.7488 | 0.1991 | 0.1014 | | 0.0731 | 164.96 | 2062 | 0.7863 | 0.78 | 0.3349 | 1.4414 | 0.78 | 0.7488 | 0.1969 | 0.1013 | | 0.0731 | 166.0 | 2075 | 0.7865 | 0.78 | 0.3349 | 1.4425 | 0.78 | 0.7488 | 0.1994 | 0.1015 | | 0.0731 | 166.96 | 2087 | 0.7863 | 0.78 | 0.3349 | 1.4412 | 0.78 | 0.7488 | 0.1966 | 0.1015 | | 0.0731 | 168.0 | 2100 | 0.7870 | 0.78 | 0.3352 | 1.4431 | 0.78 | 0.7488 | 0.1944 | 0.1016 | | 0.0731 | 168.96 | 2112 | 0.7868 | 0.78 | 0.3351 | 1.4425 | 0.78 | 0.7488 | 0.1919 | 0.1017 | | 0.0731 | 170.0 | 2125 | 0.7873 | 0.78 | 0.3354 | 1.4424 | 0.78 | 0.7488 | 0.1965 | 0.1017 | | 0.0731 | 170.96 | 2137 | 0.7870 | 0.78 | 0.3352 | 1.4418 | 0.78 | 0.7488 | 0.2081 | 0.1014 | | 0.0731 | 172.0 | 2150 | 0.7879 | 0.78 | 0.3356 | 1.4431 | 0.78 | 0.7488 | 0.1922 | 0.1017 | | 0.0731 | 172.96 | 2162 | 0.7874 | 0.78 | 0.3355 | 1.4421 | 0.78 | 0.7488 | 0.1967 | 0.1015 | | 0.0731 | 174.0 | 2175 | 0.7872 | 0.78 | 0.3355 | 1.4418 | 0.78 | 0.7488 | 0.1965 | 0.1016 | | 0.0731 | 174.96 | 2187 | 0.7882 | 0.78 | 0.3358 | 1.4433 | 0.78 | 0.7488 | 0.1945 | 0.1017 | | 0.0731 | 176.0 | 2200 | 0.7882 | 0.78 | 0.3358 | 1.4423 | 0.78 | 0.7488 | 0.1969 | 0.1016 | | 0.0731 | 176.96 | 2212 | 0.7885 | 0.78 | 0.3361 | 1.4431 | 0.78 | 0.7488 | 0.1948 | 0.1017 | | 0.0731 | 178.0 | 2225 | 0.7883 | 0.78 | 0.3359 | 1.4428 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 178.96 | 2237 | 0.7882 | 0.78 | 0.3359 | 1.4426 | 0.78 | 0.7488 | 0.1920 | 0.1017 | | 0.0731 | 180.0 | 2250 | 0.7884 | 0.78 | 0.3360 | 1.4425 | 0.78 | 0.7488 | 0.2000 | 0.1016 | | 0.0731 | 180.96 | 2262 | 0.7886 | 0.78 | 0.3361 | 1.4431 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 182.0 | 2275 | 0.7888 | 0.78 | 0.3362 | 1.4428 | 0.78 | 0.7488 | 0.1977 | 0.1016 | | 0.0731 | 182.96 | 2287 | 0.7889 | 0.78 | 0.3362 | 1.4428 | 0.78 | 0.7488 | 0.1922 | 0.1017 | | 0.0731 | 184.0 | 2300 | 0.7889 | 0.78 | 0.3362 | 1.4431 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 184.96 | 2312 | 0.7889 | 0.78 | 0.3362 | 1.4427 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 186.0 | 2325 | 0.7889 | 0.78 | 0.3362 | 1.4431 | 0.78 | 0.7488 | 0.1922 | 0.1018 | | 0.0731 | 186.96 | 2337 | 0.7892 | 0.78 | 0.3364 | 1.4432 | 0.78 | 0.7488 | 0.1921 | 0.1017 | | 0.0731 | 188.0 | 2350 | 0.7890 | 0.78 | 0.3363 | 1.4427 | 0.78 | 0.7488 | 0.1921 | 0.1017 | | 0.0731 | 188.96 | 2362 | 0.7892 | 0.78 | 0.3364 | 1.4429 | 0.78 | 0.7488 | 0.1922 | 0.1017 | | 0.0731 | 190.0 | 2375 | 0.7893 | 0.78 | 0.3364 | 1.4433 | 0.78 | 0.7488 | 0.1945 | 0.1017 | | 0.0731 | 190.96 | 2387 | 0.7893 | 0.78 | 0.3364 | 1.4430 | 0.78 | 0.7488 | 0.1921 | 0.1017 | | 0.0731 | 192.0 | 2400 | 0.7893 | 0.78 | 0.3364 | 1.4430 | 0.78 | 0.7488 | 0.1922 | 0.1018 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

asrimanth/person-thumbs-up-lora-no-cap

asrimanth

2023-07-12T17:31:28Z

5

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-10T21:45:20Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - asrimanth/person-thumbs-up-lora-no-cap These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

zblaaa/t5-base-finetuned-ner_docred_30

zblaaa

2023-07-12T17:30:08Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-12T11:00:35Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-ner_docred_30 results: [] ---  # t5-base-finetuned-ner_docred_30 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1900 - Rouge1: 6.698 - Rouge2: 5.261 - Rougel: 6.6835 - Rougelsum: 6.6818 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 125 | 0.5156 | 6.5406 | 4.9855 | 6.4905 | 6.494 | 20.0 | | No log | 2.0 | 250 | 0.3949 | 6.5113 | 4.9122 | 6.4534 | 6.4453 | 20.0 | | No log | 3.0 | 375 | 0.3280 | 6.5165 | 4.9088 | 6.4537 | 6.451 | 20.0 | | 0.7311 | 4.0 | 500 | 0.2949 | 6.424 | 4.7298 | 6.3672 | 6.3627 | 20.0 | | 0.7311 | 5.0 | 625 | 0.2764 | 6.6189 | 5.1219 | 6.5651 | 6.5672 | 20.0 | | 0.7311 | 6.0 | 750 | 0.2633 | 6.628 | 5.1335 | 6.5664 | 6.5721 | 20.0 | | 0.7311 | 7.0 | 875 | 0.2547 | 6.5591 | 4.9979 | 6.5075 | 6.5057 | 20.0 | | 0.3331 | 8.0 | 1000 | 0.2482 | 6.6612 | 5.1918 | 6.5987 | 6.6068 | 20.0 | | 0.3331 | 9.0 | 1125 | 0.2413 | 6.6093 | 5.0954 | 6.5515 | 6.5553 | 20.0 | | 0.3331 | 10.0 | 1250 | 0.2357 | 6.6264 | 5.1201 | 6.5681 | 6.5723 | 20.0 | | 0.3331 | 11.0 | 1375 | 0.2300 | 6.6487 | 5.1525 | 6.6176 | 6.6177 | 20.0 | | 0.2788 | 12.0 | 1500 | 0.2226 | 6.6858 | 5.2325 | 6.6745 | 6.6762 | 20.0 | | 0.2788 | 13.0 | 1625 | 0.2166 | 6.6495 | 5.1531 | 6.6378 | 6.6377 | 20.0 | | 0.2788 | 14.0 | 1750 | 0.2108 | 6.6807 | 5.2212 | 6.6653 | 6.6664 | 20.0 | | 0.2788 | 15.0 | 1875 | 0.2068 | 6.6811 | 5.2248 | 6.6699 | 6.6697 | 20.0 | | 0.2435 | 16.0 | 2000 | 0.2030 | 6.6701 | 5.2077 | 6.652 | 6.6492 | 20.0 | | 0.2435 | 17.0 | 2125 | 0.1997 | 6.6845 | 5.2334 | 6.6647 | 6.6624 | 20.0 | | 0.2435 | 18.0 | 2250 | 0.1978 | 6.6762 | 5.2202 | 6.6571 | 6.6559 | 20.0 | | 0.2435 | 19.0 | 2375 | 0.1964 | 6.684 | 5.2358 | 6.6695 | 6.6683 | 20.0 | | 0.2188 | 20.0 | 2500 | 0.1957 | 6.6882 | 5.2426 | 6.675 | 6.6735 | 20.0 | | 0.2188 | 21.0 | 2625 | 0.1942 | 6.6882 | 5.2426 | 6.675 | 6.6735 | 20.0 | | 0.2188 | 22.0 | 2750 | 0.1932 | 6.6935 | 5.2513 | 6.6784 | 6.6762 | 20.0 | | 0.2188 | 23.0 | 2875 | 0.1924 | 6.6935 | 5.2513 | 6.6784 | 6.6762 | 20.0 | | 0.2052 | 24.0 | 3000 | 0.1918 | 6.6882 | 5.2426 | 6.675 | 6.6735 | 20.0 | | 0.2052 | 25.0 | 3125 | 0.1915 | 6.6935 | 5.2513 | 6.6784 | 6.6762 | 20.0 | | 0.2052 | 26.0 | 3250 | 0.1908 | 6.698 | 5.261 | 6.6835 | 6.6818 | 20.0 | | 0.2052 | 27.0 | 3375 | 0.1905 | 6.698 | 5.261 | 6.6835 | 6.6818 | 20.0 | | 0.1977 | 28.0 | 3500 | 0.1901 | 6.698 | 5.261 | 6.6835 | 6.6818 | 20.0 | | 0.1977 | 29.0 | 3625 | 0.1900 | 6.698 | 5.261 | 6.6835 | 6.6818 | 20.0 | | 0.1977 | 30.0 | 3750 | 0.1900 | 6.698 | 5.261 | 6.6835 | 6.6818 | 20.0 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.1.0.dev20230611+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

SHENMU007/neunit_BASE_V12.3

SHENMU007

2023-07-12T17:27:42Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-12T14:29:43Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

sl-alex/llama-7b-alpaca-stepwise-lora

sl-alex

2023-07-12T17:22:09Z

0

null

[ "en", "dataset:sl-alex/openai-prm800k-solutions-only", "license:apache-2.0", "region:us" ]

null

2023-07-12T15:00:40Z

--- license: apache-2.0 language: - en datasets: - sl-alex/openai-prm800k-solutions-only --- Finetunes Llama-7b+Alpaca to solve problems via stepwise reasoning (OpenAI [PRM800k dataset](https://github.com/openai/prm800k), or rather our postprocessed version, [`sl-alex/openai-prm800k-solutions-only`](https://huggingface.co/datasets/sl-alex/openai-prm800k-solutions-only)). ## Model description This is a fork of [`llama-7b`](https://huggingface.co/huggyllama/llama-7b) + [`tloen/alpaca-lora-7b`](https://huggingface.co/tloen/alpaca-lora-7b). That is: we loaded Llama-7b, we applied Alpaca LoRA, expanded vocabulary, then QLoRA 4-bit finetuned from there. Parts: - base model [`llama-7b`](https://huggingface.co/huggyllama/llama-7b) - LoRA 0 [`tloen/alpaca-lora-7b`](https://huggingface.co/tloen/alpaca-lora-7b) - LoRA 1 - `adapter_config.json` - `adapter_model.bin` - tokenizer (this is just [`llama-7b`](https://huggingface.co/huggyllama/llama-7b)'s tokenizer, plus a `[PAD]` token for which no embedding was learned. in practice you can inference a batch-of-1 using the original [`llama-7b`](https://huggingface.co/huggyllama/llama-7b) tokenizer instead) - `added_tokens.json` - `special_tokens_map.json` - `tokenizer.model` - `tokenizer_config.json` ## Training Trained using [`qlora.py`](https://github.com/scottlogic-alex/qlora/blob/stepwise/qlora.py) from our [`stepwise`](https://github.com/scottlogic-alex/qlora/tree/stepwise) branch of [qlora](https://github.com/artidoro/qlora). Known-good as of commit [`3a86919`](https://github.com/scottlogic-alex/qlora/blob/3a8691986b6718562bcd8e3522447b52842c1d9a/qlora.py). `python -m qlora --model_name_or_path huggyllama/llama-7b --lora_name_or_path tloen/alpaca-lora-7b --dataset prm800k-solutions --dataset_format prm800k-solutions --bf16 --max_memory_MB 24000 --use_bos_token_in_prompt --truncate_toward_center --source_max_len 184 --target_max_len 998 --gradient_accumulation_steps 4 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --learning_rate 0.0002 --run_name 13b_alpaca_special_tokens_long --report_to wandb --save_steps 64 --save_total_limit 3 --max_steps 1664 --evaluation_strategy steps --eval_steps 64 --generate_steps 16` ## Usage You can load using [`evaluate.py`](https://github.com/scottlogic-alex/qlora/blob/stepwise/evaluate.py#L209-L278) from our [`stepwise`](https://github.com/scottlogic-alex/qlora/tree/stepwise) branch of [qlora](https://github.com/artidoro/qlora). Known-good as of commit [`3a86919`](https://github.com/scottlogic-alex/qlora/blob/3a8691986b6718562bcd8e3522447b52842c1d9a/evaluate.py). ```bash git clone -b stepwise https://github.com/scottlogic-alex/qlora.git cd qlora python -m evaluate \ --model_name_or_path huggyllama/llama-7b \ --base_lora_model_name_or_path tloen/alpaca-lora-7b \ --tokenizer_model_name_or_path huggyllama/llama-7b \ --lora_model_name_or_path sl-alex/llama-7b-alpaca-stepwise-lora \ --use_bos_token_in_prompt \ --overrun_countermeasures False ``` ## Prompt style The model is finetuned for instruction-response. `prompt = '<s>Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nHow do I feed a family of 5 without ingredients?\n\n### Response:\n<|step_start|>'` ## Example output: `$ How do I drink a matcha latte?` `I’ve never drunk a matcha latte before, but I’ve seen people do it online. It looks like they dump the entire contents of a little teabag into a frothed milk drink and then mix it well. I think I could do that.<|step_end|><|step_start|>I’ll have to buy a matcha latte kit first, because I don’t know what they have in the teabags. I’ll see if my favorite coffee shop sells them.<|step_end|><|step_start|>I’ll also need a whisk or a mixer. I’ll look online for the best one. Which one do you recommend?<|step_end|><|step_start|>I’ll need to heat up the milk first. Which will you use -- the microwave or the stovetop?<|step_end|><|step_start|>I’ll warm up 16 ounces of milk in the microwave. For the stovetop, I’ll just put it on the burner for two minutes. I don’t want it to boil.` `$ What is the third Fibonacci number?` `I need to find the third Fibonacci number, which is the number after the second, which is F(2).<|step_end|><|step_start|>There are two ways to get the third Fibonacci number. I could either wait one more day and get F(3) using the Fibonacci sequence, or I could do something more clever and faster by using a computer. <|step_end|><|step_start|>Computers are great for mathematics, so I'll see if I can get F(3) quicker with a computer.<|step_end|><|step_start|>First, though, I'll explain what Fibonacci means. It's named after a 13th-century Italian accountant named Leonardo Fibonacci who created a sequence of numbers where each following number is the sum of the two previous ones. <|step_end|><|step_start|>The Fibonacci sequence begins 0, 1, 1, 2, 3, 5, 8, 13, 21, and so on. <|step_end|><|` `$ How do I make a cup of tea?` `First, I find a teaspoonful of loose-leaf Assam tea in a teapot. Then, I fill the teapot with water that has been warming up over a stovetop burner.<|step_end|><|step_start|>Once the water has reached a rolling boil, I pour it over the tea, cover the pot, and steam it for at least two minutes. Uncover the pot, remove the lid, and serve. That's how to make a cup of tea.` `<|response_start|>I've always thought Assam tea was an Indian tea, but this web site says it comes from the north-center part of Bangladesh. I wonder if there's a difference between Assam and Bangladesh tea.<|Response_End|><|step_start|>BANGLADESH tea has all the qualities of Assam, but the difference in the final cup lies in the aroma and the lingering aftertaste.` `<|step_end|><|response_start|>The Assam tea comes in very large, flat pieces of processed leaves. They are cut from the tree and rolled` `$ How do I open a jar?` `I need to find a jar opener at my school. I can't just use my mouth because it's too dangerous to choke on the lid, and it would ruin my school uniform. I could bring a bottle opener from home, but it might not work. I also can't use a knife, because that could get me into trouble with the headmaster. The solution is to ask a friendly student to open the jar for me.</|step_end|><|step_start|>I find a student who is willing to help. We both hold the jar with our hands on the inside and outside of the rim, and she uses her nails to pry the lid off. She has been doing this for years, and it looks easy, but I should be careful not to scratch myself.</|step_end|><|step_start|>We set the jar down and all the pieces are there: the lid, the jar, and the plastic bag. I can never open another jar without feeling proud of myself. My classmates are impressed by my skills as a consumer.</|step_end|><|step_start|>I should keep the lid as a souvenir, but`

henda/bert-multilingual-ner

henda

2023-07-12T17:17:04Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-12T08:39:41Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-multilingual-ner results: [] ---  # bert-multilingual-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0678 - Precision: 0.7057 - Recall: 0.7305 - F1: 0.7179 - Accuracy: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0946 | 0.06 | 10000 | 0.0974 | 0.5971 | 0.6368 | 0.6163 | 0.9612 | | 0.0935 | 0.12 | 20000 | 0.0892 | 0.6258 | 0.6633 | 0.6440 | 0.9652 | | 0.0829 | 0.17 | 30000 | 0.0913 | 0.6393 | 0.6557 | 0.6474 | 0.9653 | | 0.086 | 0.23 | 40000 | 0.0829 | 0.6868 | 0.6131 | 0.6478 | 0.9679 | | 0.0836 | 0.29 | 50000 | 0.0806 | 0.6450 | 0.7019 | 0.6722 | 0.9673 | | 0.0813 | 0.35 | 60000 | 0.0784 | 0.6718 | 0.6823 | 0.6770 | 0.9694 | | 0.0756 | 0.41 | 70000 | 0.0771 | 0.6559 | 0.7217 | 0.6872 | 0.9696 | | 0.0753 | 0.47 | 80000 | 0.0733 | 0.6944 | 0.6660 | 0.6799 | 0.9706 | | 0.0716 | 0.52 | 90000 | 0.0781 | 0.6698 | 0.7151 | 0.6917 | 0.9704 | | 0.0785 | 0.58 | 100000 | 0.0723 | 0.6936 | 0.6960 | 0.6948 | 0.9714 | | 0.0707 | 0.64 | 110000 | 0.0729 | 0.6943 | 0.7098 | 0.7020 | 0.9718 | | 0.0699 | 0.7 | 120000 | 0.0714 | 0.6928 | 0.7200 | 0.7061 | 0.9717 | | 0.0729 | 0.76 | 130000 | 0.0715 | 0.6887 | 0.7305 | 0.7090 | 0.9722 | | 0.0696 | 0.82 | 140000 | 0.0703 | 0.6826 | 0.7479 | 0.7137 | 0.9722 | | 0.0654 | 0.87 | 150000 | 0.0697 | 0.6851 | 0.7500 | 0.7161 | 0.9727 | | 0.0636 | 0.93 | 160000 | 0.0684 | 0.6968 | 0.7410 | 0.7183 | 0.9731 | | 0.0641 | 0.99 | 170000 | 0.0679 | 0.7047 | 0.7315 | 0.7179 | 0.9734 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3

asrimanth/person-thumbs-up-plain-lora

asrimanth

2023-07-12T17:12:05Z

2

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-11T22:22:40Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - asrimanth/person-thumbs-up-plain-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Custom dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

Winmodel/poca-SoccerTwos

Winmodel

2023-07-12T17:03:16Z

8

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2023-07-12T17:02:13Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Winmodel/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

jmarcolan/joao_funciona2

jmarcolan

2023-07-12T17:03:08Z

2

0

transformers

[ "transformers", "en", "endpoints_compatible", "region:us" ]

null

2023-07-12T16:50:45Z

--- language: en --- <p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p> **Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch** ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```

veluchs/q-FrozenLake-v1-4x4-noSlippery

veluchs

2023-07-12T16:47:45Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T16:47:43Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="veluchs/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Rui31415/ppo-Huggy

Rui31415

2023-07-12T16:26:21Z

25

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-07-12T16:26:17Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Rui31415/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Hinata197/videomae-base-finetuned-ucf101-subset

Hinata197

2023-07-12T16:20:32Z

59

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-07-12T16:02:00Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] ---  # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1006 - Accuracy: 0.9714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 600 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6882 | 0.25 | 150 | 1.3392 | 0.4714 | | 0.3556 | 1.25 | 300 | 0.5520 | 0.7857 | | 0.1375 | 2.25 | 450 | 0.2829 | 0.8857 | | 0.0361 | 3.25 | 600 | 0.1006 | 0.9714 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

0sunfire0/q-FrozenLake-v1-4x4-noSlippery

0sunfire0

2023-07-12T16:13:41Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T16:13:39Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="0sunfire0/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

YanJiangJerry/covid-augment-tweet-roberta-large-e4

YanJiangJerry

2023-07-12T16:11:00Z

116

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T14:31:14Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: covid-augment-tweet-roberta-large-e4 results: [] ---  # covid-augment-tweet-roberta-large-e4 This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5214 - Accuracy: 0.9510 - F1: 0.8707 - Precision: 0.8562 - Recall: 0.8858 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.1484 | 1.0 | 4089 | 0.4570 | 0.9265 | 0.8273 | 0.7358 | 0.9446 | | 0.0738 | 2.0 | 8178 | 0.4873 | 0.9368 | 0.8414 | 0.7903 | 0.8997 | | 0.0265 | 3.0 | 12267 | 0.4970 | 0.9484 | 0.8587 | 0.8773 | 0.8408 | | 0.0043 | 4.0 | 16356 | 0.5214 | 0.9510 | 0.8707 | 0.8562 | 0.8858 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

phoenix-glr/wav2vec2-base-finetuned-ks

phoenix-glr

2023-07-12T15:57:35Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-12T13:54:28Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks results: [] ---  # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0855 - Accuracy: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6422 | 1.0 | 399 | 0.5417 | 0.9622 | | 0.2815 | 2.0 | 798 | 0.1712 | 0.9771 | | 0.2034 | 3.0 | 1197 | 0.1125 | 0.9815 | | 0.1486 | 4.0 | 1597 | 0.0961 | 0.9809 | | 0.1262 | 5.0 | 1995 | 0.0855 | 0.9844 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

LarryAIDraw/brj-saori-V1

LarryAIDraw

2023-07-12T15:55:54Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-12T15:27:13Z

--- license: creativeml-openrail-m --- https://civitai.com/models/81104/saori-blue-archive

LarryAIDraw/silverwolf-000002

LarryAIDraw

2023-07-12T15:55:23Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-12T15:26:29Z

--- license: creativeml-openrail-m --- https://civitai.com/models/106319/lora-honkai-star-rail-silverwolf

LarryAIDraw/rezerowitches-25

LarryAIDraw

2023-07-12T15:54:25Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-12T15:23:05Z

--- license: creativeml-openrail-m --- https://civitai.com/models/107626/rezero-witches-pack-7-in-1-echidna-satella-minerva-daphne-typhon-carmilla-sekhmet

Ruxun/yolov8-player

Ruxun

2023-07-12T15:35:10Z

0

null

[ "region:us" ]

null

2023-06-05T21:17:20Z

Finetuned Model based on yolov8 for harvard basketball team player detection

bofenghuang/vigogne-7b-chat

bofenghuang

2023-07-12T15:33:38Z

1,478

4

transformers

[ "transformers", "pytorch", "llama", "text-generation", "LLM", "fr", "license:openrail", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-05-15T14:07:53Z

--- license: openrail language: - fr pipeline_tag: text-generation library_name: transformers tags: - llama - LLM inference: false --- <p align="center" width="100%"> <img src="https://huggingface.co/bofenghuang/vigogne-7b-chat/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;"> </p> # Vigogne-7B-Chat: A French Chat LLaMA Model Vigogne-7B-Chat is a LLaMA-7B model fine-tuned to conduct multi-turn dialogues in 🇫🇷 French between human user and AI assistant. For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne **Usage and License Notices**: Same as [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Vigogne is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from vigogne.preprocess import generate_inference_chat_prompt model_name_or_path = "bofenghuang/vigogne-7b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto") user_query = "Expliquez la différence entre DoS et phishing." prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer) input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device) input_length = input_ids.shape[1] generated_outputs = model.generate( input_ids=input_ids, generation_config=GenerationConfig( temperature=0.1, do_sample=True, repetition_penalty=1.0, max_new_tokens=512, ), return_dict_in_generate=True, ) generated_tokens = generated_outputs.sequences[0, input_length:] generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True) print(generated_text) ``` You can infer this model by using the following Google Colab Notebook. <a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> ## Limitations Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7

jordyvl

2023-07-12T15:32:48Z

163

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-12T14:55:18Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7 results: [] ---  # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7789 - Accuracy: 0.565 - Brier Loss: 0.5798 - Nll: 2.3548 - F1 Micro: 0.565 - F1 Macro: 0.5569 - Ece: 0.1677 - Aurc: 0.2032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.7719 | 0.04 | 1.0712 | 7.3456 | 0.04 | 0.0315 | 0.2816 | 0.9543 | | No log | 2.0 | 14 | 3.7636 | 0.1 | 0.9486 | 5.7989 | 0.1000 | 0.0863 | 0.1625 | 0.8854 | | No log | 3.0 | 21 | 3.3871 | 0.235 | 0.8838 | 5.3315 | 0.235 | 0.1857 | 0.1589 | 0.6274 | | No log | 4.0 | 28 | 2.8780 | 0.2975 | 0.7900 | 3.5467 | 0.2975 | 0.2884 | 0.1672 | 0.4712 | | No log | 5.0 | 35 | 2.5223 | 0.3875 | 0.7164 | 3.0188 | 0.3875 | 0.3630 | 0.1596 | 0.3495 | | No log | 6.0 | 42 | 2.3634 | 0.43 | 0.6927 | 3.1378 | 0.4300 | 0.4031 | 0.1877 | 0.3055 | | No log | 7.0 | 49 | 2.3487 | 0.445 | 0.7009 | 3.0084 | 0.445 | 0.4123 | 0.2026 | 0.3003 | | No log | 8.0 | 56 | 2.2521 | 0.47 | 0.6857 | 2.8634 | 0.47 | 0.4607 | 0.1977 | 0.2918 | | No log | 9.0 | 63 | 2.3597 | 0.4775 | 0.6955 | 2.6768 | 0.4775 | 0.4642 | 0.2215 | 0.2787 | | No log | 10.0 | 70 | 2.3524 | 0.46 | 0.7130 | 2.7830 | 0.46 | 0.4441 | 0.2429 | 0.2941 | | No log | 11.0 | 77 | 2.4233 | 0.46 | 0.7108 | 3.0867 | 0.46 | 0.4418 | 0.2247 | 0.2828 | | No log | 12.0 | 84 | 2.2723 | 0.485 | 0.6901 | 2.7313 | 0.485 | 0.4742 | 0.2293 | 0.2774 | | No log | 13.0 | 91 | 2.2818 | 0.49 | 0.7007 | 2.5829 | 0.49 | 0.4859 | 0.2473 | 0.2791 | | No log | 14.0 | 98 | 2.1682 | 0.4975 | 0.6695 | 2.8292 | 0.4975 | 0.4815 | 0.2170 | 0.2653 | | No log | 15.0 | 105 | 2.0652 | 0.52 | 0.6520 | 2.4319 | 0.52 | 0.5120 | 0.2079 | 0.2617 | | No log | 16.0 | 112 | 2.0524 | 0.5225 | 0.6384 | 2.5273 | 0.5225 | 0.5128 | 0.1980 | 0.2391 | | No log | 17.0 | 119 | 2.1736 | 0.48 | 0.6765 | 2.4887 | 0.48 | 0.4809 | 0.2112 | 0.2834 | | No log | 18.0 | 126 | 2.0096 | 0.515 | 0.6496 | 2.4230 | 0.515 | 0.5215 | 0.1957 | 0.2513 | | No log | 19.0 | 133 | 2.0207 | 0.5175 | 0.6417 | 2.4591 | 0.5175 | 0.5104 | 0.2011 | 0.2526 | | No log | 20.0 | 140 | 1.9152 | 0.5425 | 0.6044 | 2.5069 | 0.5425 | 0.5405 | 0.1826 | 0.2147 | | No log | 21.0 | 147 | 1.9600 | 0.52 | 0.6332 | 2.5016 | 0.52 | 0.5204 | 0.1871 | 0.2427 | | No log | 22.0 | 154 | 1.9325 | 0.515 | 0.6226 | 2.4981 | 0.515 | 0.5078 | 0.1860 | 0.2323 | | No log | 23.0 | 161 | 1.9172 | 0.53 | 0.6144 | 2.4601 | 0.53 | 0.5221 | 0.1941 | 0.2311 | | No log | 24.0 | 168 | 1.8891 | 0.5425 | 0.6091 | 2.4653 | 0.5425 | 0.5399 | 0.1933 | 0.2260 | | No log | 25.0 | 175 | 1.9460 | 0.5175 | 0.6214 | 2.4785 | 0.5175 | 0.5168 | 0.1694 | 0.2335 | | No log | 26.0 | 182 | 1.9060 | 0.5525 | 0.5970 | 2.4789 | 0.5525 | 0.5430 | 0.1934 | 0.2137 | | No log | 27.0 | 189 | 1.9421 | 0.5375 | 0.6205 | 2.4814 | 0.5375 | 0.5313 | 0.2135 | 0.2402 | | No log | 28.0 | 196 | 2.0195 | 0.545 | 0.6187 | 2.5330 | 0.545 | 0.5256 | 0.1800 | 0.2360 | | No log | 29.0 | 203 | 1.9428 | 0.535 | 0.6167 | 2.4894 | 0.535 | 0.5111 | 0.1862 | 0.2322 | | No log | 30.0 | 210 | 1.8996 | 0.5225 | 0.6207 | 2.4810 | 0.5225 | 0.5137 | 0.1994 | 0.2330 | | No log | 31.0 | 217 | 1.8462 | 0.54 | 0.6119 | 2.4201 | 0.54 | 0.5341 | 0.1817 | 0.2190 | | No log | 32.0 | 224 | 1.8324 | 0.55 | 0.5988 | 2.4230 | 0.55 | 0.5427 | 0.1888 | 0.2171 | | No log | 33.0 | 231 | 1.8393 | 0.545 | 0.5977 | 2.3943 | 0.545 | 0.5339 | 0.1838 | 0.2172 | | No log | 34.0 | 238 | 1.8704 | 0.5475 | 0.6081 | 2.4488 | 0.5475 | 0.5427 | 0.1768 | 0.2200 | | No log | 35.0 | 245 | 1.8546 | 0.54 | 0.6022 | 2.3273 | 0.54 | 0.5316 | 0.1847 | 0.2226 | | No log | 36.0 | 252 | 1.8608 | 0.53 | 0.5972 | 2.5153 | 0.53 | 0.5139 | 0.1810 | 0.2202 | | No log | 37.0 | 259 | 1.8663 | 0.5325 | 0.6057 | 2.4642 | 0.5325 | 0.5243 | 0.1836 | 0.2205 | | No log | 38.0 | 266 | 1.8300 | 0.545 | 0.5954 | 2.5101 | 0.545 | 0.5418 | 0.1890 | 0.2141 | | No log | 39.0 | 273 | 1.8121 | 0.5625 | 0.5853 | 2.4397 | 0.5625 | 0.5550 | 0.1704 | 0.2110 | | No log | 40.0 | 280 | 1.7916 | 0.54 | 0.5884 | 2.3565 | 0.54 | 0.5361 | 0.1685 | 0.2135 | | No log | 41.0 | 287 | 1.8353 | 0.5575 | 0.5929 | 2.4252 | 0.5575 | 0.5451 | 0.1823 | 0.2116 | | No log | 42.0 | 294 | 1.7999 | 0.5675 | 0.5839 | 2.4820 | 0.5675 | 0.5631 | 0.1729 | 0.2045 | | No log | 43.0 | 301 | 1.8622 | 0.52 | 0.6106 | 2.4823 | 0.52 | 0.5028 | 0.1948 | 0.2270 | | No log | 44.0 | 308 | 1.7892 | 0.55 | 0.5892 | 2.3342 | 0.55 | 0.5450 | 0.1798 | 0.2126 | | No log | 45.0 | 315 | 1.7978 | 0.545 | 0.5868 | 2.4345 | 0.545 | 0.5439 | 0.1894 | 0.2094 | | No log | 46.0 | 322 | 1.7697 | 0.56 | 0.5772 | 2.4272 | 0.56 | 0.5585 | 0.1601 | 0.1997 | | No log | 47.0 | 329 | 1.7754 | 0.5475 | 0.5835 | 2.3977 | 0.5475 | 0.5438 | 0.1759 | 0.2059 | | No log | 48.0 | 336 | 1.7922 | 0.545 | 0.5929 | 2.4119 | 0.545 | 0.5390 | 0.1891 | 0.2131 | | No log | 49.0 | 343 | 1.8055 | 0.5625 | 0.5872 | 2.3654 | 0.5625 | 0.5497 | 0.1759 | 0.2073 | | No log | 50.0 | 350 | 1.7972 | 0.56 | 0.5894 | 2.3366 | 0.56 | 0.5487 | 0.1803 | 0.2083 | | No log | 51.0 | 357 | 1.7890 | 0.555 | 0.5815 | 2.3858 | 0.555 | 0.5501 | 0.1693 | 0.2067 | | No log | 52.0 | 364 | 1.7958 | 0.5475 | 0.5883 | 2.4244 | 0.5475 | 0.5355 | 0.1910 | 0.2105 | | No log | 53.0 | 371 | 1.7881 | 0.5675 | 0.5834 | 2.4135 | 0.5675 | 0.5603 | 0.1836 | 0.2028 | | No log | 54.0 | 378 | 1.7675 | 0.555 | 0.5766 | 2.4043 | 0.555 | 0.5563 | 0.1653 | 0.2047 | | No log | 55.0 | 385 | 1.7688 | 0.55 | 0.5843 | 2.3641 | 0.55 | 0.5505 | 0.1729 | 0.2092 | | No log | 56.0 | 392 | 1.7796 | 0.55 | 0.5861 | 2.3404 | 0.55 | 0.5458 | 0.1808 | 0.2114 | | No log | 57.0 | 399 | 1.7861 | 0.54 | 0.5885 | 2.3460 | 0.54 | 0.5323 | 0.1902 | 0.2073 | | No log | 58.0 | 406 | 1.7746 | 0.56 | 0.5818 | 2.3715 | 0.56 | 0.5557 | 0.1643 | 0.2034 | | No log | 59.0 | 413 | 1.7828 | 0.5575 | 0.5868 | 2.3086 | 0.5575 | 0.5526 | 0.1956 | 0.2088 | | No log | 60.0 | 420 | 1.7735 | 0.565 | 0.5825 | 2.3405 | 0.565 | 0.5619 | 0.1696 | 0.2058 | | No log | 61.0 | 427 | 1.7651 | 0.5675 | 0.5760 | 2.4771 | 0.5675 | 0.5636 | 0.1847 | 0.2027 | | No log | 62.0 | 434 | 1.7751 | 0.5575 | 0.5834 | 2.3727 | 0.5575 | 0.5524 | 0.1638 | 0.2052 | | No log | 63.0 | 441 | 1.7900 | 0.56 | 0.5834 | 2.3635 | 0.56 | 0.5502 | 0.1789 | 0.2061 | | No log | 64.0 | 448 | 1.7729 | 0.56 | 0.5821 | 2.3797 | 0.56 | 0.5554 | 0.1676 | 0.2046 | | No log | 65.0 | 455 | 1.7743 | 0.5625 | 0.5826 | 2.4174 | 0.5625 | 0.5581 | 0.1538 | 0.2052 | | No log | 66.0 | 462 | 1.7749 | 0.5625 | 0.5801 | 2.3799 | 0.5625 | 0.5592 | 0.1709 | 0.2036 | | No log | 67.0 | 469 | 1.7795 | 0.5625 | 0.5814 | 2.3169 | 0.5625 | 0.5533 | 0.1883 | 0.2037 | | No log | 68.0 | 476 | 1.7773 | 0.5675 | 0.5794 | 2.3588 | 0.5675 | 0.5622 | 0.1779 | 0.2013 | | No log | 69.0 | 483 | 1.7762 | 0.56 | 0.5793 | 2.3514 | 0.56 | 0.5566 | 0.1707 | 0.2039 | | No log | 70.0 | 490 | 1.7762 | 0.5625 | 0.5787 | 2.3620 | 0.5625 | 0.5529 | 0.1607 | 0.2017 | | No log | 71.0 | 497 | 1.7740 | 0.5675 | 0.5798 | 2.3235 | 0.5675 | 0.5612 | 0.1637 | 0.2046 | | 0.4215 | 72.0 | 504 | 1.7739 | 0.56 | 0.5790 | 2.3235 | 0.56 | 0.5542 | 0.1583 | 0.2023 | | 0.4215 | 73.0 | 511 | 1.7783 | 0.56 | 0.5806 | 2.4187 | 0.56 | 0.5545 | 0.1674 | 0.2040 | | 0.4215 | 74.0 | 518 | 1.7785 | 0.56 | 0.5805 | 2.3302 | 0.56 | 0.5544 | 0.1748 | 0.2033 | | 0.4215 | 75.0 | 525 | 1.7777 | 0.5625 | 0.5795 | 2.3321 | 0.5625 | 0.5548 | 0.1754 | 0.2029 | | 0.4215 | 76.0 | 532 | 1.7785 | 0.565 | 0.5799 | 2.3249 | 0.565 | 0.5586 | 0.1696 | 0.2023 | | 0.4215 | 77.0 | 539 | 1.7763 | 0.565 | 0.5790 | 2.3561 | 0.565 | 0.5573 | 0.1574 | 0.2022 | | 0.4215 | 78.0 | 546 | 1.7767 | 0.565 | 0.5790 | 2.3296 | 0.565 | 0.5572 | 0.1633 | 0.2024 | | 0.4215 | 79.0 | 553 | 1.7763 | 0.565 | 0.5790 | 2.3555 | 0.565 | 0.5580 | 0.1687 | 0.2016 | | 0.4215 | 80.0 | 560 | 1.7783 | 0.565 | 0.5800 | 2.3254 | 0.565 | 0.5576 | 0.1752 | 0.2032 | | 0.4215 | 81.0 | 567 | 1.7773 | 0.5675 | 0.5796 | 2.3530 | 0.5675 | 0.5605 | 0.1519 | 0.2023 | | 0.4215 | 82.0 | 574 | 1.7774 | 0.5625 | 0.5797 | 2.3253 | 0.5625 | 0.5549 | 0.1911 | 0.2028 | | 0.4215 | 83.0 | 581 | 1.7784 | 0.5625 | 0.5794 | 2.3554 | 0.5625 | 0.5544 | 0.1659 | 0.2030 | | 0.4215 | 84.0 | 588 | 1.7769 | 0.565 | 0.5793 | 2.3527 | 0.565 | 0.5585 | 0.1588 | 0.2024 | | 0.4215 | 85.0 | 595 | 1.7787 | 0.565 | 0.5799 | 2.3549 | 0.565 | 0.5576 | 0.1687 | 0.2032 | | 0.4215 | 86.0 | 602 | 1.7778 | 0.565 | 0.5795 | 2.3548 | 0.565 | 0.5574 | 0.1577 | 0.2029 | | 0.4215 | 87.0 | 609 | 1.7787 | 0.5625 | 0.5798 | 2.3545 | 0.5625 | 0.5549 | 0.1643 | 0.2032 | | 0.4215 | 88.0 | 616 | 1.7786 | 0.565 | 0.5796 | 2.3554 | 0.565 | 0.5574 | 0.1667 | 0.2031 | | 0.4215 | 89.0 | 623 | 1.7785 | 0.565 | 0.5799 | 2.3546 | 0.565 | 0.5574 | 0.1691 | 0.2032 | | 0.4215 | 90.0 | 630 | 1.7784 | 0.565 | 0.5797 | 2.3548 | 0.565 | 0.5574 | 0.1656 | 0.2031 | | 0.4215 | 91.0 | 637 | 1.7784 | 0.565 | 0.5797 | 2.3550 | 0.565 | 0.5569 | 0.1753 | 0.2032 | | 0.4215 | 92.0 | 644 | 1.7786 | 0.565 | 0.5797 | 2.3545 | 0.565 | 0.5574 | 0.1744 | 0.2030 | | 0.4215 | 93.0 | 651 | 1.7785 | 0.565 | 0.5797 | 2.3545 | 0.565 | 0.5574 | 0.1709 | 0.2031 | | 0.4215 | 94.0 | 658 | 1.7787 | 0.565 | 0.5797 | 2.3543 | 0.565 | 0.5574 | 0.1704 | 0.2032 | | 0.4215 | 95.0 | 665 | 1.7787 | 0.565 | 0.5798 | 2.3545 | 0.565 | 0.5574 | 0.1713 | 0.2032 | | 0.4215 | 96.0 | 672 | 1.7788 | 0.565 | 0.5798 | 2.3549 | 0.565 | 0.5569 | 0.1777 | 0.2031 | | 0.4215 | 97.0 | 679 | 1.7789 | 0.565 | 0.5798 | 2.3550 | 0.565 | 0.5569 | 0.1677 | 0.2032 | | 0.4215 | 98.0 | 686 | 1.7789 | 0.565 | 0.5798 | 2.3549 | 0.565 | 0.5569 | 0.1648 | 0.2032 | | 0.4215 | 99.0 | 693 | 1.7789 | 0.565 | 0.5798 | 2.3548 | 0.565 | 0.5569 | 0.1728 | 0.2032 | | 0.4215 | 100.0 | 700 | 1.7789 | 0.565 | 0.5798 | 2.3548 | 0.565 | 0.5569 | 0.1677 | 0.2032 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

banden/dqn-SpaceInvadersNoFrameskip-v1

banden

2023-07-12T15:31:37Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T15:30:56Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 517.00 +/- 204.56 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga banden -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga banden -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga banden ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

yashwantk/finetuning-sentiment-model-3000-samples

yashwantk

2023-07-12T15:26:18Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-30T05:27:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8633333333333333 - name: F1 type: f1 value: 0.8646864686468646 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3200 - Accuracy: 0.8633 - F1: 0.8647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Phips/dqn-BeamRiderNoFrameskip-v4

Phips

2023-07-12T15:26:02Z

4

0

stable-baselines3

[ "stable-baselines3", "BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T15:23:51Z

--- library_name: stable-baselines3 tags: - BeamRiderNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BeamRiderNoFrameskip-v4 type: BeamRiderNoFrameskip-v4 metrics: - type: mean_reward value: 4036.80 +/- 1681.31 name: mean_reward verified: false --- # **DQN** Agent playing **BeamRiderNoFrameskip-v4** This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga Phips -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga Phips -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga Phips ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ChaimaaBouafoud/arabicSent-ChamaBert

ChaimaaBouafoud

2023-07-12T15:24:41Z

106

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "ar", "fr", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T13:38:21Z

--- language: - ar - fr - en metrics: - accuracy - f1 - roc_auc pipeline_tag: text-classification --- # arabicSent-ChamaBert This model is a fine-tuned version of the <u> aubmindlab/bert-base-arabertv02-twitter </u> model on a webscraped dataset of Arabic comments. It has been trained specifically for sentiment classification tasks in Moroccan Arabic, covering both Standard Arabic and dialectal variations. The model's performance on the evaluation set is as follows: - Loss: 0.1626 - Accuracy: 0.9073 - F1: 0.9129 - Roc Auc: 0.9337 # Dataset The dataset used for training and evaluation consists of a collection of Moroccan Arabic comments specifically focused on sentiments towards the effects of vaccines. It contains a total of 81,971 comments, with sentiment labels of "Negative" and "Positive". The dataset provides ground truth annotations that enable the model to learn the association between the language used in comments and the corresponding sentiment expressed. The data collection process adhered to ethical considerations, respecting user privacy and complying with applicable data protection regulations. Measures were taken to ensure the anonymization of user identities and the removal of any personally identifiable information. # Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

jrosenzw/autotrain-diabetes-detection-74053139456

jrosenzw

2023-07-12T15:23:51Z

4

0

transformers

[ "transformers", "joblib", "xgboost", "autotrain", "tabular", "classification", "tabular-classification", "dataset:jrosenzw/autotrain-data-diabetes-detection", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

tabular-classification

2023-07-12T15:12:14Z

--- tags: - autotrain - tabular - classification - tabular-classification datasets: - jrosenzw/autotrain-data-diabetes-detection co2_eq_emissions: emissions: 0.8803900471704509 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 74053139456 - CO2 Emissions (in grams): 0.8804 ## Validation Metrics - Loss: 0.473 - Accuracy: 0.760 - Precision: 0.667 - Recall: 0.630 - AUC: 0.835 - F1: 0.648 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```

ynklab/XCodeBERT

ynklab

2023-07-12T15:17:51Z

105

1

transformers

[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "arxiv:2306.15604", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-29T14:01:20Z

--- license: mit --- [Paper on arXiv](https://arxiv.org/abs/2306.15604) ### Code Java/PHP/Python/Go ### Query En/Fr/Ja/Zh ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('ynklab/XCodeBERT') # Same as `xlm-roberta-base` model = AutoModel.from_pretrained('ynklab/XCodeBERT') ```

alujjdnd/10-Social-Dimensions

alujjdnd

2023-07-12T15:11:43Z

0

null

[ "license:cc-by-4.0", "region:us" ]

null

2023-06-12T11:19:49Z

--- license: cc-by-4.0 --- # 10 Social Dimensions A mirror of https://github.com/minjechoi/10dimensions/tree/master/weights - notably the Dropbox links ## Overview Contains pretrained weights for LSTM and BERT classifiers, for the 10 social dimensions. ### From Paper https://dl.acm.org/doi/10.1145/3366423.3380224 ### ACM Reference Format Minje Choi, Luca Maria Aiello, Krisztián Zsolt Varga, and Daniele Quercia. 2020. Ten Social Dimensions of Conversations and Relationships. In Proceedings of The Web Conference 2020 (WWW ’20), April 20–24, 2020, Taipei, Taiwan. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3366423.3380224

vineetsharma/q-Taxi-v3_million_steps

vineetsharma

2023-07-12T15:08:50Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T15:05:01Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3_million_steps results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="vineetsharma/q-Taxi-v3_million_steps", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

bofenghuang/vigogne-13b-chat

bofenghuang

2023-07-12T14:59:28Z

1,466

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "LLM", "fr", "license:openrail", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-07-12T13:59:04Z

--- license: openrail language: - fr pipeline_tag: text-generation library_name: transformers tags: - llama - LLM inference: false --- <p align="center" width="100%"> <img src="https://huggingface.co/bofenghuang/vigogne-13b-chat/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;"> </p> # Vigogne-13B-Chat: A French Chat LLaMA Model Vigogne-13B-Chat is a LLaMA-13B model fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant. For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne **Usage and License Notices**: Same as [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Vigogne is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from vigogne.preprocess import generate_inference_chat_prompt model_name_or_path = "bofenghuang/vigogne-13b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto") user_query = "Expliquez la différence entre DoS et phishing." prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer) input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device) input_length = input_ids.shape[1] generated_outputs = model.generate( input_ids=input_ids, generation_config=GenerationConfig( temperature=0.1, do_sample=True, repetition_penalty=1.0, max_new_tokens=512, ), return_dict_in_generate=True, ) generated_tokens = generated_outputs.sequences[0, input_length:] generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True) print(generated_text) ``` You can infer this model by using the following Google Colab Notebook. <a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> ## Limitations Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.

bardsai/finance-sentiment-zh-base

bardsai

2023-07-12T14:54:22Z

200

18

transformers

[ "transformers", "pytorch", "bert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "zh", "dataset:datasets/financial_phrasebank", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T14:41:06Z

--- language: zh tags: - text-classification - financial-sentiment-analysis - sentiment-analysis datasets: - datasets/financial_phrasebank metrics: - f1 - accuracy - precision - recall widget: - text: "净销售额增长30%，达到3600万欧元。" example_title: "Example 1" - text: "黑色星期五拉开帷幕。店内促销活动列表。" example_title: "Example 2" - text: "CDPROJEKT股价在WSE上市公司中跌幅最大。" example_title: "Example 3" --- # Finance Sentiment ZH (base) Finance Sentiment ZH (base) is a model based on [bert-base-chinese](https://huggingface.co/bert-base-chinese) for analyzing sentiment of Chinese financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu. The model will give you a three labels: positive, negative and neutral. ## How to use You can use this model directly with a pipeline for sentiment-analysis: ```python from transformers import pipeline nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-zh-base") nlp("净销售额增长30%，达到3600万欧元。") ``` ```bash [{'label': 'negative', 'score': 0.9999314546585083}] ``` ## Performance | Metric | Value | | --- | ----------- | | f1 macro | 0.966 | | precision macro | 0.963 | | recall macro | 0.969 | | accuracy | 0.973 | | samples per second | 135.2 | (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-07-12: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai

DipanAI/Low_falcan_7b

DipanAI

2023-07-12T14:49:33Z

0

null

[ "pytorch", "tensorboard", "generated_from_trainer", "region:us" ]

null

2023-07-12T14:00:09Z

--- tags: - generated_from_trainer model-index: - name: Low_falcan_7b results: [] ---  # Low_falcan_7b This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 800 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

bardsai/finance-sentiment-zh-fast

bardsai

2023-07-12T14:48:43Z

533

7

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "zh", "dataset:datasets/financial_phrasebank", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-12T14:21:24Z

--- language: zh tags: - text-classification - financial-sentiment-analysis - sentiment-analysis datasets: - datasets/financial_phrasebank metrics: - f1 - accuracy - precision - recall widget: - text: "净销售额增长30%，达到3600万欧元。" example_title: "Example 1" - text: "黑色星期五拉开帷幕。店内促销活动列表。" example_title: "Example 2" - text: "CDPROJEKT股价在WSE上市公司中跌幅最大。" example_title: "Example 3" --- # Finance Sentiment ZH (fast) Finance Sentiment ZH (fast) is a [distiluse](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1)-based model for analyzing sentiment of Chinese financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu. The model will give you a three labels: positive, negative and neutral. ## How to use You can use this model directly with a pipeline for sentiment-analysis: ```python from transformers import pipeline nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-zh-fast") nlp("净销售额增长30%，达到3600万欧元。") ``` ```bash [{'label': 'positive', 'score': 0.9996095299720764}] ``` ## Performance | Metric | Value | | --- | ----------- | | f1 macro | 0.953 | | precision macro | 0.959 | | recall macro | 0.949 | | accuracy | 0.961 | | samples per second | 264.6 | (The performance was evaluated on RTX 3090 gpu) ## Changelog - 2023-07-12: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: [bards.ai](https://bards.ai/) Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai

vineetsharma/q-Taxi-v3

vineetsharma

2023-07-12T14:35:12Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T14:35:11Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="vineetsharma/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

cenkersisman/chatbotgpt-turkish-latin

cenkersisman

2023-07-12T14:22:53Z

163

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "tr", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-02T04:19:39Z

--- widget: - text: '[Q] cengiz han kac yilinda dogmustur' example_title: Örnek 1 - text: '[Q] etimesgut havaalani nerede bulunmaktadir' example_title: Örnek 2 - text: '[Q] spartakus nereli bir gladyatordur' example_title: Örnek 3 - text: '[Q] ajda pekkan hangi sarkisiyla iyi cikis yakalamistir' example_title: Örnek 4 - text: '[Q] kobalt camlari kim tarafindan kesfedilmistir' example_title: Örnek 5 - text: '[Q] john turk ne zaman dogmustur' example_title: Örnek 6 - text: '[Q] tayvan ne zaman kurulmustur' example_title: Örnek 7 - text: '[Q] dunyanin her bolgesinden kutuphaneler hangi imkani verir' example_title: Örnek 8 - text: '[Q] gozun en distaki birinci tabakaya ne denir' example_title: Örnek 9 language: - tr --- # Model Card for Model ID  Bu model test amaçlı hazırlanmıştır ve fikir vermesi açısından geliştirilmiştir. Model için Vikipedi üzerinden üretilen 40 bin soru cevap GPT ile eğitilmiştir. Daha büyük veri setlerinde daha iyi sonuçlar alınabilir. ## Önemli Notlar * Inference için soruların başında [Q] kullanılmalıdır. * Sorular latin karakterlerden oluşmalıdır. ç ğ I ö ş ü gibi harfler içermemelidir. * Sorular küçük harflerden oluşmalıdır. Büyük harf veya sembol kullanımı farklı ve istenmeyen cevaplar üretecektir. ## Model Details ## Başlıklara göre en fazla soru cevap içeren konular aşağıdadır: * Cengiz Han: 665 adet * Futbol rekabetleri listesi: 409 adet * Lüleburgaz Muharebesi: 336 adet * I. Baybars: 263 adet * Sovyetler Birliği'nin askerî tarihi: 258 adet * Zümrüdüanka Yoldaşlığı: 245 adet * Gilles Deleuze: 208 adet * Ermenistan Sovyet Sosyalist Cumhuriyeti: 195 adet * Nâzım Hikmet: 173 adet * Hermann Göring: 169 adet * V. Leon: 163 adet * Gökhan Türkmen: 156 adet * Dumbledore'un Ordusu: 153 adet * Ajda Pekkan: 152 adet * Kırkpınar Yağlı Güreşleri: 152 adet * Mehdi Savaşı: 150 adet * İmamiye (Şiilik öğretisi): 149 adet * Rumyantsev Harekâtı: 145 adet * II. Dünya Savaşı tankları: 144 adet * Emîn: 142 adet * Boshin Savaşı: 137 adet * Wolfgang Amadeus Mozart: 136 adet * Faşizm: 134 adet * Kâzım Koyuncu: 133 adet * Suvorov Harekâtı: 128 adet * Mao Zedong: 127 adet * Mehdî (Abbâsî halifesi): 127 adet * Madagaskar: 124 adet * Oscar Niemeyer: 123 adet * Adolf Eichmann: 123 adet * Joachim von Ribbentrop: 121 adet * Crystal Palace FC: 119 adet * IV. Mihail: 111 adet * VI. Leon: 107 adet * Han Hanedanı: 105 adet * Portekiz coğrafi keşifleri: 104 adet * II. Nikiforos: 102 adet * III. Mihail: 98 adet * Fidel Castro: 96 adet * Tsushima Muharebesi: 94 adet * I. Basileios: 93 adet * VI. Konstantinos: 92 adet * Bijan Muharebesi: 91 adet * Otto Skorzeny: 90 adet * Antimon: 89 adet * Dijitalleştirme: 88 adet * Theofilos: 79 adet * Sovyet Hava Kuvvetleri: 78 adet * IV. Henry, Bölüm 1: 77 adet ### Model Description  - **Developed by:** Cenker Sisman - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model :** redrussianarmy/gpt2-turkish-cased ![Loss değerleri](https://huggingface.co/cenkersisman/chatbotgpt-turkish-cased/resolve/main/v.png) ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Sınırlamalar ve Önyargılar chatbotgpt-turkish, bir özyineli dil modeli olarak eğitildi. Bu, temel işlevinin bir metin dizisi alıp bir sonraki belirteci tahmin etmek olduğu anlamına gelir. Dil modelleri bunun dışında birçok görev için yaygın olarak kullanılsa da, bu çalışmayla ilgili birçok bilinmeyen bulunmaktadır. chatbotgpt-turkish, küfür, açık saçıklık ve aksi davranışlara yol açan metinleri içerdiği bilinen bir veri kümesi olan Pile üzerinde eğitildi. Kullanım durumunuza bağlı olarak, chatbotgpt-turkish toplumsal olarak kabul edilemez metinler üretebilir. Pile makalesinin 5. ve 6. bölümlerinde Pile'daki önyargıların daha ayrıntılı bir analizini bulabilirsiniz. Tüm dil modellerinde olduğu gibi, chatbotgpt-turkish'in belirli bir girişe nasıl yanıt vereceğini önceden tahmin etmek zordur ve uyarı olmaksızın saldırgan içerik ortaya çıkabilir. Sonuçları yayınlamadan önce insanların çıktıları denetlemesini veya filtrelemesini öneririz, hem istenmeyen içeriği sansürlemek hem de sonuçların kalitesini iyileştirmek için. ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model ```python """Inference""" from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel, GPT2TokenizerFast, GPT2Tokenizer def load_model(model_path): model = GPT2LMHeadModel.from_pretrained(model_path) return model def load_tokenizer(tokenizer_path): tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path) return tokenizer def generate_text(model_path, sequence, max_length): model = load_model(model_path) tokenizer = load_tokenizer(model_path) ids = tokenizer.encode(sequence, return_tensors='pt') outputs = model.generate( ids, do_sample=True, max_length=max_length, pad_token_id=model.config.eos_token_id, top_k=1, top_p=0.99, ) converted = tokenizer.convert_ids_to_tokens(outputs[0]) valid_tokens = [token if token is not None else '.' for token in converted] generated_text = tokenizer.convert_tokens_to_string(valid_tokens) print(generated_text) model2_path = "cenkersisman/chatbotgpt-turkish" sequence2 = "[Q] cengiz han kimdir" max_len = 120 generate_text(model2_path, sequence2, max_len) ``` ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

SHENMU007/neunit_BASE_V12.2

SHENMU007

2023-07-12T14:17:28Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-12T11:35:11Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9

jordyvl

2023-07-12T14:10:56Z

163

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-12T13:24:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 results: [] ---  # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5908 - Accuracy: 0.54 - Brier Loss: 0.6121 - Nll: 2.4999 - F1 Micro: 0.54 - F1 Macro: 0.5334 - Ece: 0.1895 - Aurc: 0.2228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 3.9334 | 0.0425 | 1.0719 | 7.3494 | 0.0425 | 0.0341 | 0.2781 | 0.9557 | | No log | 2.0 | 14 | 3.0206 | 0.09 | 0.9526 | 5.9271 | 0.09 | 0.0682 | 0.1659 | 0.8896 | | No log | 3.0 | 21 | 2.6955 | 0.2275 | 0.8867 | 5.4041 | 0.2275 | 0.1804 | 0.1637 | 0.6351 | | No log | 4.0 | 28 | 2.3029 | 0.29 | 0.8005 | 3.5699 | 0.29 | 0.2727 | 0.1512 | 0.4877 | | No log | 5.0 | 35 | 2.0293 | 0.37 | 0.7219 | 2.9856 | 0.37 | 0.3519 | 0.1495 | 0.3632 | | No log | 6.0 | 42 | 1.9194 | 0.44 | 0.6926 | 2.9746 | 0.44 | 0.4240 | 0.1778 | 0.3091 | | No log | 7.0 | 49 | 1.9640 | 0.4675 | 0.7000 | 3.0857 | 0.4675 | 0.4390 | 0.2009 | 0.3004 | | No log | 8.0 | 56 | 1.9049 | 0.4625 | 0.7003 | 2.8472 | 0.4625 | 0.4602 | 0.2053 | 0.3051 | | No log | 9.0 | 63 | 2.0561 | 0.4675 | 0.7168 | 2.8229 | 0.4675 | 0.4501 | 0.2288 | 0.2994 | | No log | 10.0 | 70 | 2.1002 | 0.45 | 0.7433 | 2.7915 | 0.45 | 0.4234 | 0.2691 | 0.3022 | | No log | 11.0 | 77 | 2.2528 | 0.4525 | 0.7686 | 3.0103 | 0.4525 | 0.4320 | 0.2921 | 0.3183 | | No log | 12.0 | 84 | 2.1190 | 0.475 | 0.7427 | 2.6715 | 0.4750 | 0.4660 | 0.2832 | 0.3077 | | No log | 13.0 | 91 | 2.3102 | 0.445 | 0.7825 | 2.9698 | 0.445 | 0.4252 | 0.3093 | 0.3100 | | No log | 14.0 | 98 | 2.3501 | 0.42 | 0.8145 | 2.7585 | 0.4200 | 0.4248 | 0.3206 | 0.3662 | | No log | 15.0 | 105 | 2.2402 | 0.495 | 0.7423 | 3.0313 | 0.495 | 0.4702 | 0.2692 | 0.2818 | | No log | 16.0 | 112 | 2.2266 | 0.49 | 0.7349 | 2.8824 | 0.49 | 0.4714 | 0.2763 | 0.2895 | | No log | 17.0 | 119 | 2.2989 | 0.4725 | 0.7509 | 3.0951 | 0.4725 | 0.4499 | 0.2863 | 0.2855 | | No log | 18.0 | 126 | 2.1355 | 0.47 | 0.7322 | 2.9349 | 0.47 | 0.4616 | 0.2725 | 0.2845 | | No log | 19.0 | 133 | 2.0965 | 0.505 | 0.7067 | 2.8254 | 0.505 | 0.4956 | 0.2523 | 0.2757 | | No log | 20.0 | 140 | 2.1961 | 0.485 | 0.7358 | 3.1604 | 0.485 | 0.4567 | 0.2825 | 0.2841 | | No log | 21.0 | 147 | 2.1287 | 0.5025 | 0.7247 | 2.5998 | 0.5025 | 0.5074 | 0.2703 | 0.3064 | | No log | 22.0 | 154 | 2.2280 | 0.4675 | 0.7760 | 2.8571 | 0.4675 | 0.4636 | 0.2911 | 0.3232 | | No log | 23.0 | 161 | 1.9649 | 0.5025 | 0.6828 | 2.8224 | 0.5025 | 0.4970 | 0.2410 | 0.2633 | | No log | 24.0 | 168 | 1.9361 | 0.5125 | 0.6780 | 2.7309 | 0.5125 | 0.5035 | 0.2326 | 0.2553 | | No log | 25.0 | 175 | 2.0161 | 0.5 | 0.6980 | 2.9958 | 0.5 | 0.4912 | 0.2580 | 0.2556 | | No log | 26.0 | 182 | 1.8763 | 0.5025 | 0.6624 | 2.8291 | 0.5025 | 0.4952 | 0.2305 | 0.2431 | | No log | 27.0 | 189 | 1.9057 | 0.525 | 0.6793 | 2.5627 | 0.525 | 0.5174 | 0.2161 | 0.2634 | | No log | 28.0 | 196 | 1.8529 | 0.52 | 0.6683 | 2.7191 | 0.52 | 0.5132 | 0.2375 | 0.2535 | | No log | 29.0 | 203 | 1.9603 | 0.5125 | 0.6831 | 2.7822 | 0.5125 | 0.5076 | 0.2395 | 0.2657 | | No log | 30.0 | 210 | 1.8247 | 0.52 | 0.6533 | 2.8547 | 0.52 | 0.5058 | 0.2080 | 0.2426 | | No log | 31.0 | 217 | 1.8275 | 0.5125 | 0.6547 | 2.6194 | 0.5125 | 0.5032 | 0.2208 | 0.2488 | | No log | 32.0 | 224 | 1.8003 | 0.52 | 0.6455 | 2.6138 | 0.52 | 0.5124 | 0.2302 | 0.2370 | | No log | 33.0 | 231 | 1.8714 | 0.505 | 0.6694 | 2.6643 | 0.505 | 0.4970 | 0.2195 | 0.2553 | | No log | 34.0 | 238 | 1.8018 | 0.5075 | 0.6659 | 2.5423 | 0.5075 | 0.4978 | 0.2241 | 0.2515 | | No log | 35.0 | 245 | 1.7844 | 0.5225 | 0.6503 | 2.6100 | 0.5225 | 0.5181 | 0.2181 | 0.2435 | | No log | 36.0 | 252 | 1.8321 | 0.5225 | 0.6674 | 2.7821 | 0.5225 | 0.5020 | 0.2285 | 0.2462 | | No log | 37.0 | 259 | 1.7859 | 0.4975 | 0.6725 | 2.6066 | 0.4975 | 0.4974 | 0.2351 | 0.2627 | | No log | 38.0 | 266 | 1.7790 | 0.5125 | 0.6595 | 2.6983 | 0.5125 | 0.5023 | 0.2172 | 0.2497 | | No log | 39.0 | 273 | 1.6989 | 0.5225 | 0.6401 | 2.6743 | 0.5225 | 0.5151 | 0.2100 | 0.2407 | | No log | 40.0 | 280 | 1.7568 | 0.52 | 0.6488 | 2.5294 | 0.52 | 0.5132 | 0.2208 | 0.2442 | | No log | 41.0 | 287 | 1.6896 | 0.5275 | 0.6362 | 2.5489 | 0.5275 | 0.5141 | 0.2045 | 0.2323 | | No log | 42.0 | 294 | 1.7193 | 0.5275 | 0.6517 | 2.5525 | 0.5275 | 0.5232 | 0.1986 | 0.2467 | | No log | 43.0 | 301 | 1.7199 | 0.535 | 0.6403 | 2.5974 | 0.535 | 0.5279 | 0.2104 | 0.2432 | | No log | 44.0 | 308 | 1.6594 | 0.5375 | 0.6330 | 2.4854 | 0.5375 | 0.5316 | 0.2015 | 0.2321 | | No log | 45.0 | 315 | 1.6543 | 0.5275 | 0.6239 | 2.4955 | 0.5275 | 0.5223 | 0.2144 | 0.2308 | | No log | 46.0 | 322 | 1.6490 | 0.5425 | 0.6262 | 2.5215 | 0.5425 | 0.5358 | 0.2104 | 0.2273 | | No log | 47.0 | 329 | 1.6570 | 0.54 | 0.6233 | 2.5454 | 0.54 | 0.5380 | 0.2047 | 0.2301 | | No log | 48.0 | 336 | 1.6359 | 0.5375 | 0.6218 | 2.5546 | 0.5375 | 0.5320 | 0.2171 | 0.2257 | | No log | 49.0 | 343 | 1.6320 | 0.55 | 0.6214 | 2.4958 | 0.55 | 0.5452 | 0.2014 | 0.2267 | | No log | 50.0 | 350 | 1.6230 | 0.53 | 0.6208 | 2.4979 | 0.53 | 0.5243 | 0.2017 | 0.2315 | | No log | 51.0 | 357 | 1.6374 | 0.535 | 0.6257 | 2.4644 | 0.535 | 0.5293 | 0.2038 | 0.2286 | | No log | 52.0 | 364 | 1.6190 | 0.5375 | 0.6199 | 2.5279 | 0.5375 | 0.5310 | 0.1855 | 0.2290 | | No log | 53.0 | 371 | 1.6155 | 0.5475 | 0.6158 | 2.4738 | 0.5475 | 0.5435 | 0.1913 | 0.2239 | | No log | 54.0 | 378 | 1.6131 | 0.5425 | 0.6184 | 2.4982 | 0.5425 | 0.5377 | 0.1969 | 0.2248 | | No log | 55.0 | 385 | 1.6035 | 0.545 | 0.6138 | 2.4690 | 0.545 | 0.5406 | 0.2164 | 0.2223 | | No log | 56.0 | 392 | 1.5990 | 0.54 | 0.6153 | 2.4701 | 0.54 | 0.5356 | 0.2019 | 0.2249 | | No log | 57.0 | 399 | 1.6024 | 0.5425 | 0.6153 | 2.4626 | 0.5425 | 0.5375 | 0.1826 | 0.2237 | | No log | 58.0 | 406 | 1.5935 | 0.545 | 0.6141 | 2.4390 | 0.545 | 0.5415 | 0.1933 | 0.2238 | | No log | 59.0 | 413 | 1.6016 | 0.545 | 0.6137 | 2.4640 | 0.545 | 0.5401 | 0.2021 | 0.2230 | | No log | 60.0 | 420 | 1.5976 | 0.54 | 0.6146 | 2.4618 | 0.54 | 0.5355 | 0.1912 | 0.2245 | | No log | 61.0 | 427 | 1.5984 | 0.545 | 0.6133 | 2.4683 | 0.545 | 0.5408 | 0.1971 | 0.2228 | | No log | 62.0 | 434 | 1.5941 | 0.54 | 0.6131 | 2.4639 | 0.54 | 0.5358 | 0.1898 | 0.2236 | | No log | 63.0 | 441 | 1.5936 | 0.545 | 0.6123 | 2.4689 | 0.545 | 0.5404 | 0.1953 | 0.2222 | | No log | 64.0 | 448 | 1.5970 | 0.5425 | 0.6138 | 2.4647 | 0.5425 | 0.5384 | 0.2015 | 0.2238 | | No log | 65.0 | 455 | 1.5943 | 0.545 | 0.6130 | 2.4963 | 0.545 | 0.5400 | 0.1979 | 0.2229 | | No log | 66.0 | 462 | 1.5936 | 0.545 | 0.6127 | 2.4977 | 0.545 | 0.5400 | 0.1933 | 0.2229 | | No log | 67.0 | 469 | 1.5928 | 0.5425 | 0.6127 | 2.4965 | 0.5425 | 0.5381 | 0.1976 | 0.2233 | | No log | 68.0 | 476 | 1.5946 | 0.5425 | 0.6128 | 2.4768 | 0.5425 | 0.5383 | 0.2149 | 0.2233 | | No log | 69.0 | 483 | 1.5924 | 0.54 | 0.6126 | 2.4946 | 0.54 | 0.5356 | 0.2094 | 0.2233 | | No log | 70.0 | 490 | 1.5921 | 0.54 | 0.6120 | 2.4964 | 0.54 | 0.5356 | 0.1801 | 0.2230 | | No log | 71.0 | 497 | 1.5926 | 0.54 | 0.6126 | 2.4955 | 0.54 | 0.5356 | 0.2039 | 0.2235 | | 0.3138 | 72.0 | 504 | 1.5916 | 0.5425 | 0.6121 | 2.4964 | 0.5425 | 0.5366 | 0.1898 | 0.2229 | | 0.3138 | 73.0 | 511 | 1.5917 | 0.54 | 0.6119 | 2.4966 | 0.54 | 0.5356 | 0.2039 | 0.2231 | | 0.3138 | 74.0 | 518 | 1.5918 | 0.54 | 0.6123 | 2.4964 | 0.54 | 0.5351 | 0.2035 | 0.2229 | | 0.3138 | 75.0 | 525 | 1.5912 | 0.54 | 0.6118 | 2.4975 | 0.54 | 0.5351 | 0.2059 | 0.2228 | | 0.3138 | 76.0 | 532 | 1.5918 | 0.54 | 0.6124 | 2.4965 | 0.54 | 0.5351 | 0.1971 | 0.2231 | | 0.3138 | 77.0 | 539 | 1.5919 | 0.5425 | 0.6120 | 2.4974 | 0.5425 | 0.5358 | 0.2087 | 0.2227 | | 0.3138 | 78.0 | 546 | 1.5903 | 0.54 | 0.6118 | 2.4978 | 0.54 | 0.5341 | 0.2169 | 0.2228 | | 0.3138 | 79.0 | 553 | 1.5922 | 0.54 | 0.6124 | 2.4976 | 0.54 | 0.5351 | 0.2109 | 0.2234 | | 0.3138 | 80.0 | 560 | 1.5914 | 0.54 | 0.6122 | 2.4983 | 0.54 | 0.5345 | 0.2041 | 0.2228 | | 0.3138 | 81.0 | 567 | 1.5907 | 0.54 | 0.6119 | 2.4981 | 0.54 | 0.5345 | 0.2128 | 0.2226 | | 0.3138 | 82.0 | 574 | 1.5921 | 0.5425 | 0.6124 | 2.4986 | 0.5425 | 0.5362 | 0.2084 | 0.2227 | | 0.3138 | 83.0 | 581 | 1.5918 | 0.5425 | 0.6125 | 2.4987 | 0.5425 | 0.5362 | 0.2038 | 0.2230 | | 0.3138 | 84.0 | 588 | 1.5902 | 0.54 | 0.6120 | 2.4989 | 0.54 | 0.5345 | 0.2043 | 0.2226 | | 0.3138 | 85.0 | 595 | 1.5919 | 0.5425 | 0.6124 | 2.4988 | 0.5425 | 0.5360 | 0.1998 | 0.2228 | | 0.3138 | 86.0 | 602 | 1.5916 | 0.5425 | 0.6124 | 2.4990 | 0.5425 | 0.5362 | 0.2079 | 0.2227 | | 0.3138 | 87.0 | 609 | 1.5906 | 0.54 | 0.6120 | 2.4990 | 0.54 | 0.5345 | 0.2037 | 0.2227 | | 0.3138 | 88.0 | 616 | 1.5908 | 0.54 | 0.6120 | 2.4989 | 0.54 | 0.5345 | 0.2091 | 0.2230 | | 0.3138 | 89.0 | 623 | 1.5909 | 0.54 | 0.6120 | 2.4995 | 0.54 | 0.5344 | 0.2113 | 0.2228 | | 0.3138 | 90.0 | 630 | 1.5906 | 0.54 | 0.6119 | 2.4996 | 0.54 | 0.5345 | 0.1969 | 0.2228 | | 0.3138 | 91.0 | 637 | 1.5911 | 0.5425 | 0.6121 | 2.4999 | 0.5425 | 0.5360 | 0.1954 | 0.2226 | | 0.3138 | 92.0 | 644 | 1.5909 | 0.54 | 0.6121 | 2.4994 | 0.54 | 0.5344 | 0.1928 | 0.2228 | | 0.3138 | 93.0 | 651 | 1.5907 | 0.5425 | 0.6121 | 2.4999 | 0.5425 | 0.5360 | 0.2034 | 0.2225 | | 0.3138 | 94.0 | 658 | 1.5910 | 0.5425 | 0.6122 | 2.4996 | 0.5425 | 0.5360 | 0.1974 | 0.2227 | | 0.3138 | 95.0 | 665 | 1.5909 | 0.5375 | 0.6121 | 2.4995 | 0.5375 | 0.5319 | 0.1990 | 0.2230 | | 0.3138 | 96.0 | 672 | 1.5907 | 0.5375 | 0.6120 | 2.4997 | 0.5375 | 0.5318 | 0.1980 | 0.2229 | | 0.3138 | 97.0 | 679 | 1.5907 | 0.54 | 0.6120 | 2.4998 | 0.54 | 0.5344 | 0.1900 | 0.2228 | | 0.3138 | 98.0 | 686 | 1.5907 | 0.5425 | 0.6120 | 2.4999 | 0.5425 | 0.5362 | 0.1899 | 0.2226 | | 0.3138 | 99.0 | 693 | 1.5908 | 0.54 | 0.6121 | 2.4999 | 0.54 | 0.5334 | 0.1936 | 0.2228 | | 0.3138 | 100.0 | 700 | 1.5908 | 0.54 | 0.6121 | 2.4999 | 0.54 | 0.5334 | 0.1895 | 0.2228 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

YojitShinde/Reinforce-CartPole-v1

YojitShinde

2023-07-12T14:06:04Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T14:05:54Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

fireday/ppo-LunarLander-v2

fireday

2023-07-12T14:05:26Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T14:05:07Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 291.16 +/- 20.30 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

malditoguisante/ppo-LunarLander-v2

malditoguisante

2023-07-12T14:04:14Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T14:03:55Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 272.05 +/- 16.08 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

aalloott/ppo-LunarLander-v2

aalloott

2023-07-12T14:01:04Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T14:00:45Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 250.52 +/- 23.91 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

alexbrandsen/bert-base-german-cased-archaeo-NER

alexbrandsen

2023-07-12T13:59:45Z

121

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "Archaeology", "Named Entity Recognition", "NER", "de", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-12T13:22:47Z

--- license: cc0-1.0 language: - de tags: - Archaeology - Named Entity Recognition - NER metrics: - f1 --- # bert-base-german-cased-archaeo-NER A German BERT model for Named Entity Recognition in the Archaeology domain This is the bert-base-german-cased-archaeo model finetuned for NER, targeting the following entities: - Time periods (PER) - Places (LOC) - Artefacts (ART) - Contexts (CON) - Materials (MAT) - Species (SPE)

MUNDOJU/ppo-LunarLander-v2

MUNDOJU

2023-07-12T13:55:46Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T13:55:27Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.13 +/- 13.96 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Mateo2008/ppo-LunarLander-v2

Mateo2008

2023-07-12T13:54:02Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T13:53:38Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.21 +/- 22.06 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

PisoF/ppo-LunarLander-v2

PisoF

2023-07-12T13:53:40Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-12T13:53:19Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.80 +/- 20.96 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Samuel1234/ppo-LunarLander-v2

Samuel1234

2023-07-12T13:52:00Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-11T18:25:26Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.25 +/- 19.97 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Aceituna0813/ppo-LunarLander-v2

Aceituna0813

2023-07-12T13:47:32Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-11T18:22:43Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.22 +/- 34.96 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7

jordyvl

2023-07-12T13:24:05Z

163

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-12T12:43:57Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 results: [] ---  # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5923 - Accuracy: 0.57 - Brier Loss: 0.5750 - Nll: 2.3088 - F1 Micro: 0.57 - F1 Macro: 0.5661 - Ece: 0.1722 - Aurc: 0.2058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.3150 | 0.0425 | 1.0714 | 7.3695 | 0.0425 | 0.0339 | 0.2866 | 0.9549 | | No log | 2.0 | 14 | 3.3516 | 0.1 | 0.9507 | 5.8558 | 0.1000 | 0.0866 | 0.1647 | 0.8831 | | No log | 3.0 | 21 | 2.9890 | 0.225 | 0.8838 | 5.3580 | 0.225 | 0.1805 | 0.1576 | 0.6316 | | No log | 4.0 | 28 | 2.5376 | 0.29 | 0.7946 | 3.5543 | 0.29 | 0.2749 | 0.1832 | 0.4807 | | No log | 5.0 | 35 | 2.2193 | 0.3875 | 0.7186 | 3.0794 | 0.3875 | 0.3677 | 0.1577 | 0.3531 | | No log | 6.0 | 42 | 2.0818 | 0.43 | 0.6905 | 2.9853 | 0.4300 | 0.4165 | 0.1668 | 0.3056 | | No log | 7.0 | 49 | 2.1032 | 0.45 | 0.7019 | 3.1044 | 0.45 | 0.4208 | 0.2121 | 0.2997 | | No log | 8.0 | 56 | 2.0360 | 0.455 | 0.6994 | 2.8491 | 0.455 | 0.4492 | 0.2131 | 0.3026 | | No log | 9.0 | 63 | 2.1719 | 0.475 | 0.7092 | 2.7831 | 0.4750 | 0.4549 | 0.2332 | 0.2870 | | No log | 10.0 | 70 | 2.1820 | 0.4525 | 0.7393 | 2.8185 | 0.4525 | 0.4318 | 0.2813 | 0.2994 | | No log | 11.0 | 77 | 2.2659 | 0.4475 | 0.7485 | 2.8020 | 0.4475 | 0.4227 | 0.2677 | 0.3046 | | No log | 12.0 | 84 | 2.1798 | 0.4575 | 0.7325 | 2.6772 | 0.4575 | 0.4555 | 0.2738 | 0.3081 | | No log | 13.0 | 91 | 2.3031 | 0.465 | 0.7431 | 2.8956 | 0.465 | 0.4390 | 0.2771 | 0.2945 | | No log | 14.0 | 98 | 2.0867 | 0.49 | 0.7048 | 2.5312 | 0.49 | 0.4823 | 0.2528 | 0.2921 | | No log | 15.0 | 105 | 2.1671 | 0.49 | 0.7218 | 2.7779 | 0.49 | 0.4749 | 0.2396 | 0.2877 | | No log | 16.0 | 112 | 2.0091 | 0.485 | 0.6857 | 2.7234 | 0.485 | 0.4608 | 0.2493 | 0.2577 | | No log | 17.0 | 119 | 1.9494 | 0.515 | 0.6714 | 2.4841 | 0.515 | 0.5072 | 0.2380 | 0.2614 | | No log | 18.0 | 126 | 1.9132 | 0.505 | 0.6665 | 2.4777 | 0.505 | 0.4945 | 0.2206 | 0.2622 | | No log | 19.0 | 133 | 2.0539 | 0.505 | 0.6776 | 2.7885 | 0.505 | 0.4986 | 0.2209 | 0.2724 | | No log | 20.0 | 140 | 1.9533 | 0.5125 | 0.6666 | 2.7287 | 0.5125 | 0.5044 | 0.2385 | 0.2645 | | No log | 21.0 | 147 | 1.9038 | 0.525 | 0.6365 | 2.8345 | 0.525 | 0.5099 | 0.2021 | 0.2290 | | No log | 22.0 | 154 | 1.8525 | 0.5075 | 0.6448 | 2.6337 | 0.5075 | 0.4958 | 0.2083 | 0.2494 | | No log | 23.0 | 161 | 1.7880 | 0.51 | 0.6386 | 2.4856 | 0.51 | 0.5078 | 0.2186 | 0.2478 | | No log | 24.0 | 168 | 1.8363 | 0.505 | 0.6456 | 2.5075 | 0.505 | 0.4966 | 0.1962 | 0.2399 | | No log | 25.0 | 175 | 1.9655 | 0.4725 | 0.6864 | 2.6331 | 0.4725 | 0.4608 | 0.2291 | 0.2669 | | No log | 26.0 | 182 | 1.8660 | 0.5175 | 0.6547 | 2.5404 | 0.5175 | 0.5076 | 0.2252 | 0.2489 | | No log | 27.0 | 189 | 1.8693 | 0.525 | 0.6446 | 2.6230 | 0.525 | 0.5145 | 0.2047 | 0.2540 | | No log | 28.0 | 196 | 1.8113 | 0.51 | 0.6407 | 2.4380 | 0.51 | 0.4978 | 0.2030 | 0.2454 | | No log | 29.0 | 203 | 1.8313 | 0.53 | 0.6445 | 2.4777 | 0.53 | 0.5284 | 0.2071 | 0.2575 | | No log | 30.0 | 210 | 1.7310 | 0.5425 | 0.6197 | 2.4559 | 0.5425 | 0.5384 | 0.1869 | 0.2367 | | No log | 31.0 | 217 | 1.8023 | 0.5325 | 0.6351 | 2.5026 | 0.5325 | 0.5216 | 0.2081 | 0.2496 | | No log | 32.0 | 224 | 1.7652 | 0.5325 | 0.6186 | 2.4794 | 0.5325 | 0.5145 | 0.1715 | 0.2338 | | No log | 33.0 | 231 | 1.7583 | 0.525 | 0.6363 | 2.4889 | 0.525 | 0.5275 | 0.1984 | 0.2463 | | No log | 34.0 | 238 | 1.7552 | 0.5475 | 0.6164 | 2.4291 | 0.5475 | 0.5305 | 0.2026 | 0.2377 | | No log | 35.0 | 245 | 1.6839 | 0.5375 | 0.6085 | 2.5915 | 0.5375 | 0.5253 | 0.1828 | 0.2214 | | No log | 36.0 | 252 | 1.7480 | 0.5425 | 0.6104 | 2.5809 | 0.5425 | 0.5366 | 0.1716 | 0.2232 | | No log | 37.0 | 259 | 1.7534 | 0.525 | 0.6225 | 2.3614 | 0.525 | 0.5183 | 0.1930 | 0.2249 | | No log | 38.0 | 266 | 1.7484 | 0.5425 | 0.6125 | 2.5714 | 0.5425 | 0.5282 | 0.1792 | 0.2272 | | No log | 39.0 | 273 | 1.7073 | 0.55 | 0.6172 | 2.4200 | 0.55 | 0.5370 | 0.1902 | 0.2314 | | No log | 40.0 | 280 | 1.7303 | 0.55 | 0.6134 | 2.4829 | 0.55 | 0.5394 | 0.1916 | 0.2324 | | No log | 41.0 | 287 | 1.6684 | 0.54 | 0.6060 | 2.4632 | 0.54 | 0.5350 | 0.2028 | 0.2251 | | No log | 42.0 | 294 | 1.7171 | 0.5375 | 0.6055 | 2.4705 | 0.5375 | 0.5213 | 0.1776 | 0.2262 | | No log | 43.0 | 301 | 1.6493 | 0.545 | 0.5991 | 2.5207 | 0.545 | 0.5412 | 0.1779 | 0.2214 | | No log | 44.0 | 308 | 1.6548 | 0.5625 | 0.5920 | 2.4810 | 0.5625 | 0.5568 | 0.1892 | 0.2182 | | No log | 45.0 | 315 | 1.6392 | 0.565 | 0.5943 | 2.3771 | 0.565 | 0.5586 | 0.2165 | 0.2162 | | No log | 46.0 | 322 | 1.6923 | 0.5225 | 0.6159 | 2.3661 | 0.5225 | 0.5158 | 0.1775 | 0.2400 | | No log | 47.0 | 329 | 1.6266 | 0.5525 | 0.5827 | 2.4385 | 0.5525 | 0.5468 | 0.1845 | 0.2100 | | No log | 48.0 | 336 | 1.6804 | 0.55 | 0.6019 | 2.3884 | 0.55 | 0.5481 | 0.1895 | 0.2291 | | No log | 49.0 | 343 | 1.6202 | 0.5725 | 0.5847 | 2.4882 | 0.5725 | 0.5596 | 0.1642 | 0.2125 | | No log | 50.0 | 350 | 1.6222 | 0.54 | 0.5882 | 2.4144 | 0.54 | 0.5311 | 0.1830 | 0.2226 | | No log | 51.0 | 357 | 1.6119 | 0.5775 | 0.5794 | 2.4063 | 0.5775 | 0.5731 | 0.1647 | 0.2019 | | No log | 52.0 | 364 | 1.5958 | 0.57 | 0.5757 | 2.3342 | 0.57 | 0.5642 | 0.1778 | 0.2094 | | No log | 53.0 | 371 | 1.6206 | 0.545 | 0.5913 | 2.3884 | 0.545 | 0.5365 | 0.1799 | 0.2187 | | No log | 54.0 | 378 | 1.5982 | 0.5675 | 0.5745 | 2.4276 | 0.5675 | 0.5640 | 0.1746 | 0.2050 | | No log | 55.0 | 385 | 1.6258 | 0.5525 | 0.5856 | 2.4005 | 0.5525 | 0.5373 | 0.1890 | 0.2124 | | No log | 56.0 | 392 | 1.5763 | 0.57 | 0.5744 | 2.4477 | 0.57 | 0.5729 | 0.1651 | 0.2081 | | No log | 57.0 | 399 | 1.6249 | 0.5525 | 0.5861 | 2.3791 | 0.5525 | 0.5432 | 0.1531 | 0.2114 | | No log | 58.0 | 406 | 1.6240 | 0.5775 | 0.5791 | 2.4540 | 0.5775 | 0.5730 | 0.1582 | 0.2054 | | No log | 59.0 | 413 | 1.6149 | 0.545 | 0.5851 | 2.3134 | 0.545 | 0.5395 | 0.1870 | 0.2137 | | No log | 60.0 | 420 | 1.6163 | 0.5775 | 0.5792 | 2.3778 | 0.5775 | 0.5708 | 0.1762 | 0.2076 | | No log | 61.0 | 427 | 1.6132 | 0.5575 | 0.5868 | 2.3759 | 0.5575 | 0.5530 | 0.1842 | 0.2159 | | No log | 62.0 | 434 | 1.5940 | 0.5725 | 0.5756 | 2.3394 | 0.5725 | 0.5731 | 0.2102 | 0.2054 | | No log | 63.0 | 441 | 1.6167 | 0.56 | 0.5841 | 2.4117 | 0.56 | 0.5541 | 0.1806 | 0.2160 | | No log | 64.0 | 448 | 1.5988 | 0.57 | 0.5775 | 2.3388 | 0.57 | 0.5667 | 0.1680 | 0.2064 | | No log | 65.0 | 455 | 1.5893 | 0.5725 | 0.5752 | 2.4281 | 0.5725 | 0.5695 | 0.1624 | 0.2050 | | No log | 66.0 | 462 | 1.5975 | 0.5725 | 0.5737 | 2.3760 | 0.5725 | 0.5662 | 0.1733 | 0.2026 | | No log | 67.0 | 469 | 1.5903 | 0.57 | 0.5772 | 2.2921 | 0.57 | 0.5675 | 0.1888 | 0.2112 | | No log | 68.0 | 476 | 1.5878 | 0.575 | 0.5730 | 2.3676 | 0.575 | 0.5706 | 0.1683 | 0.2039 | | No log | 69.0 | 483 | 1.5950 | 0.57 | 0.5775 | 2.3006 | 0.57 | 0.5641 | 0.1639 | 0.2076 | | No log | 70.0 | 490 | 1.5916 | 0.58 | 0.5728 | 2.3424 | 0.58 | 0.5769 | 0.1714 | 0.2026 | | No log | 71.0 | 497 | 1.5960 | 0.5675 | 0.5784 | 2.3057 | 0.5675 | 0.5624 | 0.1600 | 0.2073 | | 0.3705 | 72.0 | 504 | 1.5907 | 0.575 | 0.5755 | 2.3322 | 0.575 | 0.5723 | 0.1578 | 0.2066 | | 0.3705 | 73.0 | 511 | 1.5918 | 0.5675 | 0.5762 | 2.3182 | 0.5675 | 0.5605 | 0.1942 | 0.2071 | | 0.3705 | 74.0 | 518 | 1.5894 | 0.585 | 0.5747 | 2.3335 | 0.585 | 0.5818 | 0.1739 | 0.2035 | | 0.3705 | 75.0 | 525 | 1.5878 | 0.565 | 0.5750 | 2.3019 | 0.565 | 0.5607 | 0.1649 | 0.2060 | | 0.3705 | 76.0 | 532 | 1.5923 | 0.575 | 0.5742 | 2.3376 | 0.575 | 0.5699 | 0.1779 | 0.2048 | | 0.3705 | 77.0 | 539 | 1.5891 | 0.565 | 0.5760 | 2.2978 | 0.565 | 0.5616 | 0.1691 | 0.2066 | | 0.3705 | 78.0 | 546 | 1.5896 | 0.575 | 0.5738 | 2.3748 | 0.575 | 0.5703 | 0.1733 | 0.2048 | | 0.3705 | 79.0 | 553 | 1.5901 | 0.5675 | 0.5757 | 2.3039 | 0.5675 | 0.5634 | 0.1710 | 0.2064 | | 0.3705 | 80.0 | 560 | 1.5906 | 0.57 | 0.5746 | 2.3125 | 0.57 | 0.5657 | 0.1692 | 0.2054 | | 0.3705 | 81.0 | 567 | 1.5907 | 0.57 | 0.5751 | 2.3097 | 0.57 | 0.5659 | 0.1600 | 0.2047 | | 0.3705 | 82.0 | 574 | 1.5902 | 0.57 | 0.5746 | 2.3072 | 0.57 | 0.5657 | 0.1797 | 0.2055 | | 0.3705 | 83.0 | 581 | 1.5906 | 0.5725 | 0.5746 | 2.3145 | 0.5725 | 0.5681 | 0.1547 | 0.2050 | | 0.3705 | 84.0 | 588 | 1.5909 | 0.5725 | 0.5750 | 2.3057 | 0.5725 | 0.5684 | 0.1746 | 0.2055 | | 0.3705 | 85.0 | 595 | 1.5906 | 0.57 | 0.5746 | 2.3098 | 0.57 | 0.5661 | 0.1721 | 0.2054 | | 0.3705 | 86.0 | 602 | 1.5916 | 0.57 | 0.5749 | 2.3093 | 0.57 | 0.5661 | 0.1659 | 0.2058 | | 0.3705 | 87.0 | 609 | 1.5913 | 0.57 | 0.5748 | 2.3084 | 0.57 | 0.5661 | 0.1631 | 0.2058 | | 0.3705 | 88.0 | 616 | 1.5918 | 0.57 | 0.5749 | 2.3082 | 0.57 | 0.5661 | 0.1652 | 0.2058 | | 0.3705 | 89.0 | 623 | 1.5919 | 0.57 | 0.5750 | 2.3084 | 0.57 | 0.5661 | 0.1658 | 0.2059 | | 0.3705 | 90.0 | 630 | 1.5918 | 0.5725 | 0.5749 | 2.3087 | 0.5725 | 0.5685 | 0.1650 | 0.2056 | | 0.3705 | 91.0 | 637 | 1.5921 | 0.57 | 0.5750 | 2.3076 | 0.57 | 0.5661 | 0.1549 | 0.2059 | | 0.3705 | 92.0 | 644 | 1.5920 | 0.57 | 0.5750 | 2.3079 | 0.57 | 0.5661 | 0.1581 | 0.2058 | | 0.3705 | 93.0 | 651 | 1.5917 | 0.57 | 0.5749 | 2.3080 | 0.57 | 0.5661 | 0.1680 | 0.2057 | | 0.3705 | 94.0 | 658 | 1.5923 | 0.57 | 0.5750 | 2.3083 | 0.57 | 0.5661 | 0.1643 | 0.2058 | | 0.3705 | 95.0 | 665 | 1.5924 | 0.57 | 0.5751 | 2.3085 | 0.57 | 0.5661 | 0.1543 | 0.2059 | | 0.3705 | 96.0 | 672 | 1.5922 | 0.57 | 0.5750 | 2.3085 | 0.57 | 0.5661 | 0.1530 | 0.2058 | | 0.3705 | 97.0 | 679 | 1.5923 | 0.57 | 0.5750 | 2.3088 | 0.57 | 0.5661 | 0.1688 | 0.2058 | | 0.3705 | 98.0 | 686 | 1.5923 | 0.57 | 0.5749 | 2.3089 | 0.57 | 0.5661 | 0.1733 | 0.2058 | | 0.3705 | 99.0 | 693 | 1.5923 | 0.57 | 0.5750 | 2.3088 | 0.57 | 0.5661 | 0.1735 | 0.2058 | | 0.3705 | 100.0 | 700 | 1.5923 | 0.57 | 0.5750 | 2.3088 | 0.57 | 0.5661 | 0.1722 | 0.2058 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2