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
zwtharry/Taxiiv3	zwtharry	2023-07-12T00:35:18Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T06:49:17Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxiiv3 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="zwtharry/Taxiiv3", 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"]) ```
WasuratS/distilhubert-finetuned-gtzan	WasuratS	2023-07-12T00:27:18Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-08T14:11:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set on best epoch: - Loss: 0.7305 - Accuracy: 0.9 ## Model description Distilhubert is distilled version of the [HuBERT](https://huggingface.co/docs/transformers/model_doc/hubert) and pretrained on data set with 16k frequency. <br/> Architecture of this model is CTC or Connectionist Temporal Classification is a technique that is used with encoder-only transformer. <br/> ## Training and evaluation data Training + Evaluation data set is GTZAN which is a popular dataset of 999 songs for music genre classification. <br/> Each song is a 30-second clip from one of 10 genres of music, spanning disco to metal.<br/> Train set is 899 songs and Evaluation set is 100 songs remainings. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1728 \| 1.0 \| 225 \| 2.0896 \| 0.42 \| \| 1.4211 \| 2.0 \| 450 \| 1.4951 \| 0.55 \| \| 1.2155 \| 3.0 \| 675 \| 1.0669 \| 0.72 \| \| 1.0175 \| 4.0 \| 900 \| 0.8862 \| 0.69 \| \| 0.3516 \| 5.0 \| 1125 \| 0.6265 \| 0.83 \| \| 0.6135 \| 6.0 \| 1350 \| 0.6485 \| 0.78 \| \| 0.0807 \| 7.0 \| 1575 \| 0.6567 \| 0.78 \| \| 0.0303 \| 8.0 \| 1800 \| 0.7615 \| 0.83 \| \| 0.2663 \| 9.0 \| 2025 \| 0.6612 \| 0.86 \| \| 0.0026 \| 10.0 \| 2250 \| 0.8354 \| 0.85 \| \| 0.0337 \| 11.0 \| 2475 \| 0.6768 \| 0.87 \| \| 0.0013 \| 12.0 \| 2700 \| 0.7718 \| 0.87 \| \| 0.001 \| 13.0 \| 2925 \| 0.7570 \| 0.88 \| \| 0.0008 \| 14.0 \| 3150 \| 0.8170 \| 0.89 \| \| 0.0006 \| 15.0 \| 3375 \| 0.7920 \| 0.89 \| \| 0.0005 \| 16.0 \| 3600 \| 0.9859 \| 0.83 \| \| 0.0004 \| 17.0 \| 3825 \| 0.8190 \| 0.9 \| \| 0.0003 \| 18.0 \| 4050 \| 0.7305 \| 0.9 \| \| 0.0003 \| 19.0 \| 4275 \| 0.8025 \| 0.88 \| \| 0.0002 \| 20.0 \| 4500 \| 0.8208 \| 0.87 \| \| 0.0003 \| 21.0 \| 4725 \| 0.7358 \| 0.88 \| \| 0.0002 \| 22.0 \| 4950 \| 0.8681 \| 0.87 \| \| 0.0002 \| 23.0 \| 5175 \| 0.7831 \| 0.9 \| \| 0.0003 \| 24.0 \| 5400 \| 0.8583 \| 0.88 \| \| 0.0002 \| 25.0 \| 5625 \| 0.8138 \| 0.88 \| \| 0.0002 \| 26.0 \| 5850 \| 0.7871 \| 0.89 \| \| 0.0002 \| 27.0 \| 6075 \| 0.8893 \| 0.88 \| \| 0.0002 \| 28.0 \| 6300 \| 0.8284 \| 0.89 \| \| 0.0001 \| 29.0 \| 6525 \| 0.8388 \| 0.89 \| \| 0.0001 \| 30.0 \| 6750 \| 0.8305 \| 0.9 \| \| 0.0001 \| 31.0 \| 6975 \| 0.8377 \| 0.88 \| \| 0.0153 \| 32.0 \| 7200 \| 0.8496 \| 0.88 \| \| 0.0001 \| 33.0 \| 7425 \| 0.8381 \| 0.88 \| \| 0.0001 \| 34.0 \| 7650 \| 0.8440 \| 0.88 \| \| 0.0001 \| 35.0 \| 7875 \| 0.8458 \| 0.88 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
macapa/emotion-text-classification	macapa	2023-07-12T00:24:51Z	0	0	fastai	[ "fastai", "region:us" ]	null	2023-07-12T00:24:34Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
yusha17/ppo-LunarLander-v2	yusha17	2023-07-12T00:14:17Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T02:56:40Z	--- 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: 315.81 +/- 9.19 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 ... ```
conorjudge/distilbert-base-uncased-finetuned-sprint-meds	conorjudge	2023-07-12T00:11:37Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-25T13:08:32Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-sprint-meds 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. --> # distilbert-base-uncased-finetuned-sprint-meds This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8427 - Accuracy: 0.8790 - F1: 0.8630 ## 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.8256 \| 1.0 \| 21 \| 1.9309 \| 0.6868 \| 0.5992 \| \| 1.7067 \| 2.0 \| 42 \| 1.8220 \| 0.6993 \| 0.6190 \| \| 1.5327 \| 3.0 \| 63 \| 1.7250 \| 0.7189 \| 0.6489 \| \| 1.4475 \| 4.0 \| 84 \| 1.6374 \| 0.7509 \| 0.6903 \| \| 1.3108 \| 5.0 \| 105 \| 1.5627 \| 0.7438 \| 0.6843 \| \| 1.1881 \| 6.0 \| 126 \| 1.4905 \| 0.7669 \| 0.7135 \| \| 1.1726 \| 7.0 \| 147 \| 1.4287 \| 0.7847 \| 0.7379 \| \| 1.0681 \| 8.0 \| 168 \| 1.3705 \| 0.7829 \| 0.7368 \| \| 0.9392 \| 9.0 \| 189 \| 1.3214 \| 0.7954 \| 0.7513 \| \| 0.9603 \| 10.0 \| 210 \| 1.2741 \| 0.8043 \| 0.7613 \| \| 0.8349 \| 11.0 \| 231 \| 1.2415 \| 0.8185 \| 0.7793 \| \| 0.8094 \| 12.0 \| 252 \| 1.2028 \| 0.8256 \| 0.7883 \| \| 0.787 \| 13.0 \| 273 \| 1.1673 \| 0.8310 \| 0.7951 \| \| 0.7128 \| 14.0 \| 294 \| 1.1412 \| 0.8381 \| 0.8056 \| \| 0.6821 \| 15.0 \| 315 \| 1.1091 \| 0.8399 \| 0.8074 \| \| 0.6177 \| 16.0 \| 336 \| 1.0906 \| 0.8399 \| 0.8098 \| \| 0.633 \| 17.0 \| 357 \| 1.0645 \| 0.8434 \| 0.8170 \| \| 0.5734 \| 18.0 \| 378 \| 1.0415 \| 0.8470 \| 0.8199 \| \| 0.5181 \| 19.0 \| 399 \| 1.0233 \| 0.8416 \| 0.8153 \| \| 0.4926 \| 20.0 \| 420 \| 1.0076 \| 0.8470 \| 0.8209 \| \| 0.4773 \| 21.0 \| 441 \| 0.9896 \| 0.8434 \| 0.8184 \| \| 0.4361 \| 22.0 \| 462 \| 0.9768 \| 0.8470 \| 0.8216 \| \| 0.4385 \| 23.0 \| 483 \| 0.9624 \| 0.8505 \| 0.8261 \| \| 0.3962 \| 24.0 \| 504 \| 0.9520 \| 0.8559 \| 0.8309 \| \| 0.392 \| 25.0 \| 525 \| 0.9392 \| 0.8577 \| 0.8339 \| \| 0.4095 \| 26.0 \| 546 \| 0.9331 \| 0.8577 \| 0.8359 \| \| 0.3389 \| 27.0 \| 567 \| 0.9242 \| 0.8577 \| 0.8348 \| \| 0.3296 \| 28.0 \| 588 \| 0.9117 \| 0.8577 \| 0.8344 \| \| 0.3527 \| 29.0 \| 609 \| 0.9026 \| 0.8665 \| 0.8465 \| \| 0.315 \| 30.0 \| 630 \| 0.9008 \| 0.8648 \| 0.8431 \| \| 0.2891 \| 31.0 \| 651 \| 0.8923 \| 0.8648 \| 0.8433 \| \| 0.3283 \| 32.0 \| 672 \| 0.8818 \| 0.8701 \| 0.8507 \| \| 0.2967 \| 33.0 \| 693 \| 0.8799 \| 0.8683 \| 0.8479 \| \| 0.2657 \| 34.0 \| 714 \| 0.8750 \| 0.8683 \| 0.8479 \| \| 0.3015 \| 35.0 \| 735 \| 0.8727 \| 0.8719 \| 0.8526 \| \| 0.2847 \| 36.0 \| 756 \| 0.8656 \| 0.8754 \| 0.8575 \| \| 0.2614 \| 37.0 \| 777 \| 0.8630 \| 0.8772 \| 0.8589 \| \| 0.26 \| 38.0 \| 798 \| 0.8604 \| 0.8754 \| 0.8598 \| \| 0.2557 \| 39.0 \| 819 \| 0.8588 \| 0.8772 \| 0.8612 \| \| 0.2389 \| 40.0 \| 840 \| 0.8562 \| 0.8790 \| 0.8619 \| \| 0.2464 \| 41.0 \| 861 \| 0.8529 \| 0.8790 \| 0.8615 \| \| 0.2304 \| 42.0 \| 882 \| 0.8529 \| 0.8772 \| 0.8613 \| \| 0.2356 \| 43.0 \| 903 \| 0.8514 \| 0.8790 \| 0.8636 \| \| 0.2291 \| 44.0 \| 924 \| 0.8479 \| 0.8790 \| 0.8631 \| \| 0.2323 \| 45.0 \| 945 \| 0.8457 \| 0.8790 \| 0.8631 \| \| 0.2281 \| 46.0 \| 966 \| 0.8454 \| 0.8790 \| 0.8638 \| \| 0.2163 \| 47.0 \| 987 \| 0.8432 \| 0.8790 \| 0.8633 \| \| 0.226 \| 48.0 \| 1008 \| 0.8433 \| 0.8790 \| 0.8631 \| \| 0.229 \| 49.0 \| 1029 \| 0.8431 \| 0.8790 \| 0.8631 \| \| 0.2388 \| 50.0 \| 1050 \| 0.8427 \| 0.8790 \| 0.8630 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
jordyvl/vit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone	jordyvl	2023-07-12T00:02:54Z	164	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T22:30:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone 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-small_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0689 - Accuracy: 0.6 - Brier Loss: 0.6433 - Nll: 2.4057 - F1 Micro: 0.6 - F1 Macro: 0.6101 - Ece: 0.3353 - Aurc: 0.1685 ## 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: 32 - eval_batch_size: 32 - 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 \| 25 \| 0.0859 \| 0.0675 \| 0.9373 \| 7.3238 \| 0.0675 \| 0.0163 \| 0.1099 \| 0.9351 \| \| No log \| 2.0 \| 50 \| 0.0810 \| 0.0675 \| 0.9372 \| 7.0436 \| 0.0675 \| 0.0153 \| 0.1067 \| 0.9365 \| \| No log \| 3.0 \| 75 \| 0.0804 \| 0.0725 \| 0.9368 \| 6.5507 \| 0.0725 \| 0.0268 \| 0.1041 \| 0.9438 \| \| No log \| 4.0 \| 100 \| 0.0800 \| 0.0725 \| 0.9362 \| 6.2816 \| 0.0725 \| 0.0293 \| 0.1056 \| 0.9404 \| \| No log \| 5.0 \| 125 \| 0.0797 \| 0.0775 \| 0.9352 \| 6.1624 \| 0.0775 \| 0.0225 \| 0.1125 \| 0.9037 \| \| No log \| 6.0 \| 150 \| 0.0793 \| 0.0875 \| 0.9337 \| 6.0364 \| 0.0875 \| 0.0376 \| 0.1173 \| 0.8572 \| \| No log \| 7.0 \| 175 \| 0.0788 \| 0.13 \| 0.9307 \| 4.5728 \| 0.13 \| 0.0918 \| 0.1430 \| 0.7693 \| \| No log \| 8.0 \| 200 \| 0.0781 \| 0.2325 \| 0.9246 \| 3.6321 \| 0.2325 \| 0.1958 \| 0.2225 \| 0.5621 \| \| No log \| 9.0 \| 225 \| 0.0770 \| 0.31 \| 0.9103 \| 3.3593 \| 0.31 \| 0.2693 \| 0.2782 \| 0.4570 \| \| No log \| 10.0 \| 250 \| 0.0755 \| 0.34 \| 0.8830 \| 2.9550 \| 0.34 \| 0.2911 \| 0.2951 \| 0.4131 \| \| No log \| 11.0 \| 275 \| 0.0740 \| 0.4075 \| 0.8559 \| 2.6844 \| 0.4075 \| 0.3802 \| 0.3347 \| 0.3241 \| \| No log \| 12.0 \| 300 \| 0.0730 \| 0.47 \| 0.8216 \| 2.7315 \| 0.47 \| 0.4439 \| 0.3582 \| 0.2707 \| \| No log \| 13.0 \| 325 \| 0.0720 \| 0.4925 \| 0.7913 \| 2.6641 \| 0.4925 \| 0.4606 \| 0.3561 \| 0.2588 \| \| No log \| 14.0 \| 350 \| 0.0717 \| 0.4725 \| 0.7854 \| 2.7229 \| 0.4725 \| 0.4565 \| 0.3296 \| 0.2732 \| \| No log \| 15.0 \| 375 \| 0.0708 \| 0.5125 \| 0.7515 \| 2.4866 \| 0.5125 \| 0.4890 \| 0.3445 \| 0.2379 \| \| No log \| 16.0 \| 400 \| 0.0704 \| 0.5375 \| 0.7424 \| 2.4355 \| 0.5375 \| 0.5131 \| 0.3525 \| 0.2259 \| \| No log \| 17.0 \| 425 \| 0.0702 \| 0.545 \| 0.7259 \| 2.5234 \| 0.545 \| 0.5227 \| 0.3427 \| 0.2199 \| \| No log \| 18.0 \| 450 \| 0.0696 \| 0.545 \| 0.7253 \| 2.5796 \| 0.545 \| 0.5318 \| 0.3471 \| 0.2118 \| \| No log \| 19.0 \| 475 \| 0.0697 \| 0.56 \| 0.7163 \| 2.3050 \| 0.56 \| 0.5547 \| 0.3494 \| 0.2048 \| \| 0.0745 \| 20.0 \| 500 \| 0.0692 \| 0.565 \| 0.7044 \| 2.4019 \| 0.565 \| 0.5669 \| 0.3598 \| 0.1869 \| \| 0.0745 \| 21.0 \| 525 \| 0.0690 \| 0.5775 \| 0.6983 \| 2.3271 \| 0.5775 \| 0.5805 \| 0.3615 \| 0.1906 \| \| 0.0745 \| 22.0 \| 550 \| 0.0689 \| 0.58 \| 0.6855 \| 2.2368 \| 0.58 \| 0.5808 \| 0.3572 \| 0.1851 \| \| 0.0745 \| 23.0 \| 575 \| 0.0690 \| 0.56 \| 0.6905 \| 2.4557 \| 0.56 \| 0.5709 \| 0.3387 \| 0.1925 \| \| 0.0745 \| 24.0 \| 600 \| 0.0688 \| 0.57 \| 0.6895 \| 2.3632 \| 0.57 \| 0.5736 \| 0.3516 \| 0.1912 \| \| 0.0745 \| 25.0 \| 625 \| 0.0686 \| 0.5775 \| 0.6826 \| 2.3272 \| 0.5775 \| 0.5838 \| 0.3376 \| 0.1802 \| \| 0.0745 \| 26.0 \| 650 \| 0.0689 \| 0.5625 \| 0.6886 \| 2.2696 \| 0.5625 \| 0.5754 \| 0.3445 \| 0.1917 \| \| 0.0745 \| 27.0 \| 675 \| 0.0687 \| 0.575 \| 0.6765 \| 2.3387 \| 0.575 \| 0.5800 \| 0.3511 \| 0.1861 \| \| 0.0745 \| 28.0 \| 700 \| 0.0689 \| 0.5775 \| 0.6785 \| 2.3039 \| 0.5775 \| 0.5821 \| 0.3546 \| 0.1860 \| \| 0.0745 \| 29.0 \| 725 \| 0.0685 \| 0.6 \| 0.6720 \| 2.4176 \| 0.6 \| 0.6013 \| 0.3606 \| 0.1750 \| \| 0.0745 \| 30.0 \| 750 \| 0.0685 \| 0.5925 \| 0.6690 \| 2.2827 \| 0.5925 \| 0.5962 \| 0.3646 \| 0.1750 \| \| 0.0745 \| 31.0 \| 775 \| 0.0685 \| 0.5825 \| 0.6682 \| 2.2957 \| 0.5825 \| 0.5885 \| 0.3476 \| 0.1771 \| \| 0.0745 \| 32.0 \| 800 \| 0.0687 \| 0.585 \| 0.6700 \| 2.2669 \| 0.585 \| 0.5914 \| 0.3428 \| 0.1797 \| \| 0.0745 \| 33.0 \| 825 \| 0.0685 \| 0.59 \| 0.6652 \| 2.3359 \| 0.59 \| 0.5927 \| 0.3429 \| 0.1775 \| \| 0.0745 \| 34.0 \| 850 \| 0.0686 \| 0.5825 \| 0.6717 \| 2.3900 \| 0.5825 \| 0.5919 \| 0.3453 \| 0.1790 \| \| 0.0745 \| 35.0 \| 875 \| 0.0685 \| 0.5875 \| 0.6721 \| 2.3131 \| 0.5875 \| 0.5932 \| 0.3579 \| 0.1799 \| \| 0.0745 \| 36.0 \| 900 \| 0.0686 \| 0.5925 \| 0.6625 \| 2.3435 \| 0.5925 \| 0.6005 \| 0.3441 \| 0.1728 \| \| 0.0745 \| 37.0 \| 925 \| 0.0685 \| 0.5875 \| 0.6649 \| 2.4475 \| 0.5875 \| 0.5885 \| 0.3550 \| 0.1756 \| \| 0.0745 \| 38.0 \| 950 \| 0.0685 \| 0.5925 \| 0.6607 \| 2.2842 \| 0.5925 \| 0.5962 \| 0.3410 \| 0.1732 \| \| 0.0745 \| 39.0 \| 975 \| 0.0685 \| 0.6 \| 0.6605 \| 2.2073 \| 0.6 \| 0.6083 \| 0.3414 \| 0.1708 \| \| 0.0599 \| 40.0 \| 1000 \| 0.0685 \| 0.575 \| 0.6578 \| 2.3075 \| 0.575 \| 0.5788 \| 0.3341 \| 0.1773 \| \| 0.0599 \| 41.0 \| 1025 \| 0.0685 \| 0.5975 \| 0.6598 \| 2.1562 \| 0.5975 \| 0.6067 \| 0.3462 \| 0.1685 \| \| 0.0599 \| 42.0 \| 1050 \| 0.0685 \| 0.5925 \| 0.6592 \| 2.3363 \| 0.5925 \| 0.5999 \| 0.3262 \| 0.1733 \| \| 0.0599 \| 43.0 \| 1075 \| 0.0683 \| 0.5925 \| 0.6545 \| 2.2970 \| 0.5925 \| 0.5975 \| 0.3413 \| 0.1741 \| \| 0.0599 \| 44.0 \| 1100 \| 0.0686 \| 0.5975 \| 0.6590 \| 2.2220 \| 0.5975 \| 0.6061 \| 0.3425 \| 0.1698 \| \| 0.0599 \| 45.0 \| 1125 \| 0.0684 \| 0.585 \| 0.6563 \| 2.2507 \| 0.585 \| 0.5876 \| 0.3214 \| 0.1795 \| \| 0.0599 \| 46.0 \| 1150 \| 0.0684 \| 0.5975 \| 0.6578 \| 2.2677 \| 0.5975 \| 0.6082 \| 0.3374 \| 0.1712 \| \| 0.0599 \| 47.0 \| 1175 \| 0.0684 \| 0.5925 \| 0.6531 \| 2.3091 \| 0.5925 \| 0.5974 \| 0.3362 \| 0.1716 \| \| 0.0599 \| 48.0 \| 1200 \| 0.0685 \| 0.5825 \| 0.6539 \| 2.3803 \| 0.5825 \| 0.5901 \| 0.3098 \| 0.1790 \| \| 0.0599 \| 49.0 \| 1225 \| 0.0685 \| 0.59 \| 0.6518 \| 2.1855 \| 0.59 \| 0.6001 \| 0.3229 \| 0.1759 \| \| 0.0599 \| 50.0 \| 1250 \| 0.0685 \| 0.595 \| 0.6513 \| 2.3357 \| 0.595 \| 0.6004 \| 0.3307 \| 0.1711 \| \| 0.0599 \| 51.0 \| 1275 \| 0.0684 \| 0.59 \| 0.6499 \| 2.3253 \| 0.59 \| 0.5968 \| 0.3298 \| 0.1708 \| \| 0.0599 \| 52.0 \| 1300 \| 0.0684 \| 0.61 \| 0.6500 \| 2.3352 \| 0.61 \| 0.6196 \| 0.3692 \| 0.1687 \| \| 0.0599 \| 53.0 \| 1325 \| 0.0685 \| 0.595 \| 0.6518 \| 2.2189 \| 0.595 \| 0.6036 \| 0.3278 \| 0.1735 \| \| 0.0599 \| 54.0 \| 1350 \| 0.0684 \| 0.6025 \| 0.6501 \| 2.3238 \| 0.6025 \| 0.6114 \| 0.3410 \| 0.1668 \| \| 0.0599 \| 55.0 \| 1375 \| 0.0684 \| 0.595 \| 0.6479 \| 2.2696 \| 0.595 \| 0.6022 \| 0.3341 \| 0.1719 \| \| 0.0599 \| 56.0 \| 1400 \| 0.0685 \| 0.595 \| 0.6496 \| 2.3172 \| 0.595 \| 0.6008 \| 0.3239 \| 0.1720 \| \| 0.0599 \| 57.0 \| 1425 \| 0.0684 \| 0.595 \| 0.6476 \| 2.2983 \| 0.595 \| 0.6023 \| 0.3310 \| 0.1667 \| \| 0.0599 \| 58.0 \| 1450 \| 0.0684 \| 0.605 \| 0.6483 \| 2.2607 \| 0.605 \| 0.6140 \| 0.3563 \| 0.1660 \| \| 0.0599 \| 59.0 \| 1475 \| 0.0685 \| 0.5975 \| 0.6491 \| 2.3956 \| 0.5975 \| 0.6091 \| 0.3222 \| 0.1691 \| \| 0.0576 \| 60.0 \| 1500 \| 0.0685 \| 0.5925 \| 0.6476 \| 2.2049 \| 0.5925 \| 0.6032 \| 0.3240 \| 0.1716 \| \| 0.0576 \| 61.0 \| 1525 \| 0.0685 \| 0.6 \| 0.6482 \| 2.3095 \| 0.6 \| 0.6068 \| 0.3276 \| 0.1703 \| \| 0.0576 \| 62.0 \| 1550 \| 0.0685 \| 0.6025 \| 0.6448 \| 2.2755 \| 0.6025 \| 0.6101 \| 0.3303 \| 0.1673 \| \| 0.0576 \| 63.0 \| 1575 \| 0.0685 \| 0.6 \| 0.6480 \| 2.3857 \| 0.6 \| 0.6078 \| 0.3358 \| 0.1687 \| \| 0.0576 \| 64.0 \| 1600 \| 0.0685 \| 0.59 \| 0.6465 \| 2.3280 \| 0.59 \| 0.5990 \| 0.3198 \| 0.1705 \| \| 0.0576 \| 65.0 \| 1625 \| 0.0684 \| 0.605 \| 0.6438 \| 2.3484 \| 0.605 \| 0.6125 \| 0.3346 \| 0.1651 \| \| 0.0576 \| 66.0 \| 1650 \| 0.0686 \| 0.6 \| 0.6462 \| 2.2443 \| 0.6 \| 0.6084 \| 0.3371 \| 0.1706 \| \| 0.0576 \| 67.0 \| 1675 \| 0.0685 \| 0.6025 \| 0.6449 \| 2.3717 \| 0.6025 \| 0.6115 \| 0.3317 \| 0.1674 \| \| 0.0576 \| 68.0 \| 1700 \| 0.0685 \| 0.595 \| 0.6449 \| 2.3396 \| 0.595 \| 0.6003 \| 0.3292 \| 0.1676 \| \| 0.0576 \| 69.0 \| 1725 \| 0.0686 \| 0.595 \| 0.6460 \| 2.3315 \| 0.595 \| 0.6047 \| 0.3339 \| 0.1683 \| \| 0.0576 \| 70.0 \| 1750 \| 0.0687 \| 0.5975 \| 0.6480 \| 2.3967 \| 0.5975 \| 0.6070 \| 0.3404 \| 0.1702 \| \| 0.0576 \| 71.0 \| 1775 \| 0.0686 \| 0.6 \| 0.6456 \| 2.3870 \| 0.6 \| 0.6095 \| 0.3215 \| 0.1689 \| \| 0.0576 \| 72.0 \| 1800 \| 0.0686 \| 0.59 \| 0.6455 \| 2.3966 \| 0.59 \| 0.5985 \| 0.3273 \| 0.1691 \| \| 0.0576 \| 73.0 \| 1825 \| 0.0686 \| 0.5875 \| 0.6472 \| 2.3619 \| 0.5875 \| 0.5975 \| 0.3465 \| 0.1711 \| \| 0.0576 \| 74.0 \| 1850 \| 0.0686 \| 0.595 \| 0.6436 \| 2.4181 \| 0.595 \| 0.6054 \| 0.3183 \| 0.1706 \| \| 0.0576 \| 75.0 \| 1875 \| 0.0686 \| 0.6 \| 0.6440 \| 2.4160 \| 0.6 \| 0.6077 \| 0.3285 \| 0.1677 \| \| 0.0576 \| 76.0 \| 1900 \| 0.0687 \| 0.6025 \| 0.6446 \| 2.4184 \| 0.6025 \| 0.6111 \| 0.3408 \| 0.1685 \| \| 0.0576 \| 77.0 \| 1925 \| 0.0686 \| 0.6025 \| 0.6440 \| 2.4208 \| 0.6025 \| 0.6111 \| 0.3323 \| 0.1670 \| \| 0.0576 \| 78.0 \| 1950 \| 0.0687 \| 0.5975 \| 0.6438 \| 2.4236 \| 0.5975 \| 0.6063 \| 0.3298 \| 0.1689 \| \| 0.0576 \| 79.0 \| 1975 \| 0.0687 \| 0.5975 \| 0.6438 \| 2.4521 \| 0.5975 \| 0.6057 \| 0.3328 \| 0.1692 \| \| 0.0565 \| 80.0 \| 2000 \| 0.0687 \| 0.6 \| 0.6448 \| 2.4213 \| 0.6 \| 0.6088 \| 0.3368 \| 0.1682 \| \| 0.0565 \| 81.0 \| 2025 \| 0.0688 \| 0.5975 \| 0.6444 \| 2.4257 \| 0.5975 \| 0.6076 \| 0.3179 \| 0.1681 \| \| 0.0565 \| 82.0 \| 2050 \| 0.0687 \| 0.6 \| 0.6446 \| 2.4225 \| 0.6 \| 0.6102 \| 0.3392 \| 0.1673 \| \| 0.0565 \| 83.0 \| 2075 \| 0.0687 \| 0.6 \| 0.6437 \| 2.4571 \| 0.6 \| 0.6091 \| 0.3281 \| 0.1681 \| \| 0.0565 \| 84.0 \| 2100 \| 0.0688 \| 0.595 \| 0.6439 \| 2.4360 \| 0.595 \| 0.6042 \| 0.3256 \| 0.1685 \| \| 0.0565 \| 85.0 \| 2125 \| 0.0688 \| 0.6 \| 0.6436 \| 2.4396 \| 0.6 \| 0.6104 \| 0.3318 \| 0.1683 \| \| 0.0565 \| 86.0 \| 2150 \| 0.0688 \| 0.6 \| 0.6434 \| 2.3977 \| 0.6 \| 0.6095 \| 0.3273 \| 0.1675 \| \| 0.0565 \| 87.0 \| 2175 \| 0.0688 \| 0.595 \| 0.6432 \| 2.4303 \| 0.595 \| 0.6053 \| 0.3146 \| 0.1687 \| \| 0.0565 \| 88.0 \| 2200 \| 0.0688 \| 0.5975 \| 0.6431 \| 2.4222 \| 0.5975 \| 0.6071 \| 0.3326 \| 0.1686 \| \| 0.0565 \| 89.0 \| 2225 \| 0.0688 \| 0.6 \| 0.6440 \| 2.4042 \| 0.6 \| 0.6108 \| 0.3303 \| 0.1678 \| \| 0.0565 \| 90.0 \| 2250 \| 0.0688 \| 0.6 \| 0.6433 \| 2.3998 \| 0.6 \| 0.6096 \| 0.3301 \| 0.1679 \| \| 0.0565 \| 91.0 \| 2275 \| 0.0689 \| 0.6 \| 0.6434 \| 2.4026 \| 0.6 \| 0.6108 \| 0.3362 \| 0.1680 \| \| 0.0565 \| 92.0 \| 2300 \| 0.0689 \| 0.5975 \| 0.6435 \| 2.4037 \| 0.5975 \| 0.6083 \| 0.3335 \| 0.1680 \| \| 0.0565 \| 93.0 \| 2325 \| 0.0689 \| 0.5975 \| 0.6434 \| 2.4060 \| 0.5975 \| 0.6077 \| 0.3344 \| 0.1679 \| \| 0.0565 \| 94.0 \| 2350 \| 0.0689 \| 0.6 \| 0.6433 \| 2.4024 \| 0.6 \| 0.6106 \| 0.3204 \| 0.1683 \| \| 0.0565 \| 95.0 \| 2375 \| 0.0689 \| 0.595 \| 0.6432 \| 2.4060 \| 0.595 \| 0.6052 \| 0.3423 \| 0.1684 \| \| 0.0565 \| 96.0 \| 2400 \| 0.0689 \| 0.6 \| 0.6432 \| 2.4044 \| 0.6 \| 0.6101 \| 0.3404 \| 0.1684 \| \| 0.0565 \| 97.0 \| 2425 \| 0.0689 \| 0.6 \| 0.6434 \| 2.4042 \| 0.6 \| 0.6101 \| 0.3349 \| 0.1683 \| \| 0.0565 \| 98.0 \| 2450 \| 0.0689 \| 0.6 \| 0.6432 \| 2.4055 \| 0.6 \| 0.6101 \| 0.3390 \| 0.1684 \| \| 0.0565 \| 99.0 \| 2475 \| 0.0689 \| 0.6 \| 0.6433 \| 2.4056 \| 0.6 \| 0.6101 \| 0.3393 \| 0.1685 \| \| 0.056 \| 100.0 \| 2500 \| 0.0689 \| 0.6 \| 0.6433 \| 2.4057 \| 0.6 \| 0.6101 \| 0.3353 \| 0.1685 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
ben-xl8/wmt22-cometkiwi-da	ben-xl8	2023-07-11T23:59:39Z	0	1	null	[ "translation", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	translation	2023-07-11T20:03:20Z	--- extra_gated_heading: Acknowledge license to accept the repository extra_gated_button_content: Acknowledge license pipeline_tag: translation language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: cc-by-nc-sa-4.0 --- This is a [COMET](https://github.com/Unbabel/COMET) quality estimation model by Unbabel: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation. # Paper [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.60) (Rei et al., WMT 2022) # License: cc-by-nc-sa-4.0 # Usage for Inference Endpoint ```python import json import requests API_URL = "" API_TOKEN="MY_API_KEY" headers = { "Authorization": f"Bearer {API_TOKEN}", "Content-Type": "application/json", } def query(url, headers, payload): data = json.dumps(payload) response = requests.request("POST", url, headers=headers, data=data) return json.loads(response.content.decode("utf-8")) payload = { "inputs": { "batch_size": 8, "workers": None, "data": [ { "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", }, ] } } scores = query(API_URL, headers, payload) ``` # Intended uses Unbabel's model is intented to be used for reference-free MT evaluation. Given a source text and its translation, outputs a single score between 0 and 1 where 1 represents a perfect translation. # Languages Covered: This model builds on top of InfoXLM which cover the following languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish. Thus, results for language pairs containing uncovered languages are unreliable!
KnutJaegersberg/wikipedia_categories_setfit	KnutJaegersberg	2023-07-11T23:52:45Z	2	1	setfit	[ "setfit", "pytorch", "bert", "feature-extraction", "sentence-similarity", "e5", "dataset:KnutJaegersberg/wikipedia_categories", "dataset:KnutJaegersberg/wikipedia_categories_labels", "license:mit", "region:us" ]	sentence-similarity	2023-07-11T11:01:19Z	--- pipeline_tag: sentence-similarity tags: - feature-extraction - sentence-similarity - setfit - e5 license: mit datasets: - KnutJaegersberg/wikipedia_categories - KnutJaegersberg/wikipedia_categories_labels --- This English model (e5-large as basis) predicts wikipedia categories (roundabout 37 labels). It is trained on the concatenation of the headlines of the lower level categories articles in few shot setting (i.e. 8 subcategories with their headline concatenations per level 2 category). Accuracy on test data split is 85 %. Note that these numbers are just an indicator that training worked, it will differ in production settings, which is why this classifier is meant for corpus exploration. Use the wikipedia_categories_labels dataset as key. from setfit import SetFitModel Download from Hub and run inference model = SetFitModel.from_pretrained("KnutJaegersberg/wikipedia_categories_setfit") Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
Evan-Lin/Bart-RL-many-entailment-keywordmax-attractive	Evan-Lin	2023-07-11T23:11:37Z	105	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-11T06:12:39Z	attractive 1 keyword 1/4 entailment 1 mul 10 normalization
sl8425/troubleshooting_steps_printer	sl8425	2023-07-11T23:07:39Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T20:49:26Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: sl8425/troubleshooting_steps_printer results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # sl8425/troubleshooting_steps_printer This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8644 - Validation Loss: 0.8744 - Train Accuracy: 0.7457 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 369, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.6729 \| 1.1343 \| 0.6428 \| 0 \| \| 1.0262 \| 0.9056 \| 0.7366 \| 1 \| \| 0.8644 \| 0.8744 \| 0.7457 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
talaugust/sci-writing-strategies	talaugust	2023-07-11T23:05:34Z	0	0	null	[ "region:us" ]	null	2023-07-11T22:42:06Z	RoBERTa Science writing strategy classifiers This is a finetuned BART Large model from the paper: "Writing Strategies for Science Communication: Data and Computational Analysis", By Tal August, Lauren Kim, Katharina Reinecke, and Noah A. Smith Published at the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020. Abstract: Communicating complex scientific ideas without misleading or overwhelming the public is challenging. While science communication guides exist, they rarely offer empirical evidence for how their strategies are used in practice. Writing strategies that can be automatically recognized could greatly support science communication efforts by enabling tools to detect and suggest strategies for writers. We compile a set of writing strategies drawn from a wide range of prescriptive sources and develop an annotation scheme allowing humans to recognize them. We collect a corpus of 128k science writing documents in English and annotate a subset of this corpus. We use the annotations to train transformer-based classifiers and measure the strategies’ use in the larger corpus. We find that the use of strategies, such as storytelling and emphasizing the most important findings, varies significantly across publications with different reader audiences. Description The model is finetuned on the task of identifying if a given sentence from a science news article is using a particular writing strategy (e.g., emphasizing the real world impact of the scientific findings). The intended use of this model is to identify common science communication writing strategies. The model is trained on annotated sentences drawn from science news articles. The URLs for the original news articles are at [https://github.com/talaugust/scientific-writing-strategies]. Biases & Limitations The goal of this model is to enable a wider audience of readers to understand and engage with scientific writing. A risk, though, is that such attempts might instead widen the gap to accessing scientific information. The texts in the datasets we train our models on are in General or Academic American. English. Many people, especially those who have been historically underrepresented in STEM disciplines and medicine, may not be comfortable with this dialect of English. This risks further alienating the readers we hope to serve. An important and exciting direction in NLP is making models more flexible to dialects and low-resource languages.
jovi848/autotrain-eng-ta-json-73876139369	jovi848	2023-07-11T23:05:03Z	106	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "translation", "unk", "dataset:jovi848/autotrain-data-eng-ta-json", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2023-07-11T22:16:50Z	--- tags: - autotrain - translation language: - unk - unk datasets: - jovi848/autotrain-data-eng-ta-json co2_eq_emissions: emissions: 33.5213011411702 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 73876139369 - CO2 Emissions (in grams): 33.5213 ## Validation Metrics - Loss: 0.000 - SacreBLEU: 0.001 - Gen len: 19.000
SHENMU007/neunit_BASE_V11.4	SHENMU007	2023-07-11T22:52:37Z	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	2023-07-11T20:08:34Z	--- 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
crowbarmassage/ppo-Pyramids	crowbarmassage	2023-07-11T22:48:42Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-11T22:48:40Z	--- 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: crowbarmassage/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
fgeyer/a2c-AntBulletEnv-v0	fgeyer	2023-07-11T22:40:23Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T22:24:37Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2384.01 +/- 64.45 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 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-small_tobacco3482_kd_NKD_t1.0_g1.5	jordyvl	2023-07-11T22:34:14Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-11T21:57:18Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_NKD_t1.0_g1.5 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-small_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9399 - Accuracy: 0.82 - Brier Loss: 0.3024 - Nll: 1.1952 - F1 Micro: 0.82 - F1 Macro: 0.7964 - Ece: 0.1494 - Aurc: 0.0548 ## 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 \| 4.9730 \| 0.205 \| 0.8886 \| 5.4337 \| 0.205 \| 0.1356 \| 0.2736 \| 0.7527 \| \| No log \| 2.0 \| 14 \| 4.6039 \| 0.355 \| 0.8122 \| 3.5880 \| 0.3550 \| 0.2120 \| 0.3197 \| 0.5132 \| \| No log \| 3.0 \| 21 \| 4.2754 \| 0.515 \| 0.7054 \| 2.0283 \| 0.515 \| 0.4046 \| 0.3275 \| 0.2966 \| \| No log \| 4.0 \| 28 \| 4.0263 \| 0.6 \| 0.5799 \| 1.5709 \| 0.6 \| 0.5157 \| 0.3062 \| 0.1890 \| \| No log \| 5.0 \| 35 \| 3.8749 \| 0.725 \| 0.4857 \| 1.5338 \| 0.7250 \| 0.6949 \| 0.3181 \| 0.1194 \| \| No log \| 6.0 \| 42 \| 3.7023 \| 0.765 \| 0.3925 \| 1.2926 \| 0.765 \| 0.6948 \| 0.2394 \| 0.0908 \| \| No log \| 7.0 \| 49 \| 3.7728 \| 0.78 \| 0.3668 \| 1.3007 \| 0.78 \| 0.7355 \| 0.2478 \| 0.0754 \| \| No log \| 8.0 \| 56 \| 3.7328 \| 0.785 \| 0.3459 \| 1.2487 \| 0.785 \| 0.7501 \| 0.2261 \| 0.0804 \| \| No log \| 9.0 \| 63 \| 3.7092 \| 0.77 \| 0.3289 \| 1.0921 \| 0.7700 \| 0.7672 \| 0.2019 \| 0.0767 \| \| No log \| 10.0 \| 70 \| 3.6273 \| 0.795 \| 0.3150 \| 1.0342 \| 0.795 \| 0.7690 \| 0.1927 \| 0.0716 \| \| No log \| 11.0 \| 77 \| 3.5677 \| 0.83 \| 0.2754 \| 1.3837 \| 0.83 \| 0.7933 \| 0.1697 \| 0.0532 \| \| No log \| 12.0 \| 84 \| 3.5668 \| 0.815 \| 0.2816 \| 1.1304 \| 0.815 \| 0.7934 \| 0.1563 \| 0.0617 \| \| No log \| 13.0 \| 91 \| 3.6080 \| 0.83 \| 0.2723 \| 0.9515 \| 0.83 \| 0.8088 \| 0.1648 \| 0.0543 \| \| No log \| 14.0 \| 98 \| 3.6095 \| 0.815 \| 0.3050 \| 1.2020 \| 0.815 \| 0.8207 \| 0.1523 \| 0.0633 \| \| No log \| 15.0 \| 105 \| 3.6685 \| 0.805 \| 0.3060 \| 1.2725 \| 0.805 \| 0.7920 \| 0.1618 \| 0.0676 \| \| No log \| 16.0 \| 112 \| 3.5523 \| 0.825 \| 0.2832 \| 0.9447 \| 0.825 \| 0.8163 \| 0.1614 \| 0.0569 \| \| No log \| 17.0 \| 119 \| 3.5294 \| 0.805 \| 0.2752 \| 0.9918 \| 0.805 \| 0.7636 \| 0.1537 \| 0.0549 \| \| No log \| 18.0 \| 126 \| 3.5382 \| 0.8 \| 0.2870 \| 1.2294 \| 0.8000 \| 0.7885 \| 0.1603 \| 0.0583 \| \| No log \| 19.0 \| 133 \| 3.5541 \| 0.82 \| 0.2905 \| 1.2181 \| 0.82 \| 0.8204 \| 0.1400 \| 0.0618 \| \| No log \| 20.0 \| 140 \| 3.4717 \| 0.835 \| 0.2606 \| 1.1119 \| 0.835 \| 0.8146 \| 0.1382 \| 0.0531 \| \| No log \| 21.0 \| 147 \| 3.6074 \| 0.79 \| 0.3099 \| 1.2144 \| 0.79 \| 0.7771 \| 0.1419 \| 0.0599 \| \| No log \| 22.0 \| 154 \| 3.5448 \| 0.805 \| 0.2868 \| 1.2075 \| 0.805 \| 0.7761 \| 0.1439 \| 0.0581 \| \| No log \| 23.0 \| 161 \| 3.6070 \| 0.805 \| 0.3057 \| 1.2908 \| 0.805 \| 0.7831 \| 0.1393 \| 0.0627 \| \| No log \| 24.0 \| 168 \| 3.5289 \| 0.81 \| 0.2716 \| 1.1844 \| 0.81 \| 0.7879 \| 0.1358 \| 0.0550 \| \| No log \| 25.0 \| 175 \| 3.5502 \| 0.82 \| 0.2827 \| 1.1141 \| 0.82 \| 0.7908 \| 0.1460 \| 0.0554 \| \| No log \| 26.0 \| 182 \| 3.5747 \| 0.82 \| 0.2829 \| 1.1727 \| 0.82 \| 0.8027 \| 0.1330 \| 0.0565 \| \| No log \| 27.0 \| 189 \| 3.6091 \| 0.83 \| 0.2787 \| 1.1040 \| 0.83 \| 0.8067 \| 0.1347 \| 0.0563 \| \| No log \| 28.0 \| 196 \| 3.5917 \| 0.82 \| 0.2837 \| 1.1775 \| 0.82 \| 0.7975 \| 0.1513 \| 0.0564 \| \| No log \| 29.0 \| 203 \| 3.6087 \| 0.815 \| 0.2875 \| 1.1448 \| 0.815 \| 0.7998 \| 0.1339 \| 0.0542 \| \| No log \| 30.0 \| 210 \| 3.6018 \| 0.815 \| 0.2819 \| 1.1613 \| 0.815 \| 0.8027 \| 0.1507 \| 0.0535 \| \| No log \| 31.0 \| 217 \| 3.6350 \| 0.815 \| 0.2845 \| 1.2278 \| 0.815 \| 0.7866 \| 0.1401 \| 0.0537 \| \| No log \| 32.0 \| 224 \| 3.6290 \| 0.82 \| 0.2815 \| 1.1528 \| 0.82 \| 0.7950 \| 0.1424 \| 0.0520 \| \| No log \| 33.0 \| 231 \| 3.6642 \| 0.815 \| 0.2865 \| 1.1504 \| 0.815 \| 0.7946 \| 0.1379 \| 0.0542 \| \| No log \| 34.0 \| 238 \| 3.6778 \| 0.815 \| 0.2929 \| 1.2116 \| 0.815 \| 0.7890 \| 0.1437 \| 0.0538 \| \| No log \| 35.0 \| 245 \| 3.6867 \| 0.82 \| 0.2869 \| 1.1547 \| 0.82 \| 0.7904 \| 0.1404 \| 0.0529 \| \| No log \| 36.0 \| 252 \| 3.6931 \| 0.795 \| 0.2946 \| 1.1478 \| 0.795 \| 0.7694 \| 0.1494 \| 0.0543 \| \| No log \| 37.0 \| 259 \| 3.7166 \| 0.82 \| 0.2921 \| 1.2109 \| 0.82 \| 0.7904 \| 0.1489 \| 0.0534 \| \| No log \| 38.0 \| 266 \| 3.7024 \| 0.81 \| 0.2889 \| 1.1516 \| 0.81 \| 0.7888 \| 0.1508 \| 0.0536 \| \| No log \| 39.0 \| 273 \| 3.7353 \| 0.81 \| 0.2943 \| 1.2088 \| 0.81 \| 0.7812 \| 0.1466 \| 0.0537 \| \| No log \| 40.0 \| 280 \| 3.7198 \| 0.82 \| 0.2891 \| 1.1515 \| 0.82 \| 0.8014 \| 0.1285 \| 0.0536 \| \| No log \| 41.0 \| 287 \| 3.7413 \| 0.815 \| 0.2899 \| 1.2124 \| 0.815 \| 0.7959 \| 0.1471 \| 0.0537 \| \| No log \| 42.0 \| 294 \| 3.7272 \| 0.82 \| 0.2896 \| 1.2071 \| 0.82 \| 0.8002 \| 0.1414 \| 0.0532 \| \| No log \| 43.0 \| 301 \| 3.7609 \| 0.815 \| 0.2925 \| 1.2100 \| 0.815 \| 0.7868 \| 0.1486 \| 0.0528 \| \| No log \| 44.0 \| 308 \| 3.7589 \| 0.815 \| 0.2922 \| 1.2074 \| 0.815 \| 0.7877 \| 0.1398 \| 0.0537 \| \| No log \| 45.0 \| 315 \| 3.7820 \| 0.815 \| 0.2961 \| 1.2078 \| 0.815 \| 0.7874 \| 0.1499 \| 0.0535 \| \| No log \| 46.0 \| 322 \| 3.7663 \| 0.82 \| 0.2926 \| 1.2053 \| 0.82 \| 0.8014 \| 0.1369 \| 0.0532 \| \| No log \| 47.0 \| 329 \| 3.7850 \| 0.82 \| 0.2944 \| 1.2079 \| 0.82 \| 0.7904 \| 0.1374 \| 0.0532 \| \| No log \| 48.0 \| 336 \| 3.7802 \| 0.82 \| 0.2935 \| 1.2025 \| 0.82 \| 0.7981 \| 0.1483 \| 0.0537 \| \| No log \| 49.0 \| 343 \| 3.7954 \| 0.82 \| 0.2937 \| 1.2068 \| 0.82 \| 0.7900 \| 0.1354 \| 0.0528 \| \| No log \| 50.0 \| 350 \| 3.7974 \| 0.815 \| 0.2954 \| 1.2020 \| 0.815 \| 0.7907 \| 0.1491 \| 0.0534 \| \| No log \| 51.0 \| 357 \| 3.8081 \| 0.815 \| 0.2965 \| 1.2035 \| 0.815 \| 0.7907 \| 0.1533 \| 0.0533 \| \| No log \| 52.0 \| 364 \| 3.8171 \| 0.815 \| 0.2982 \| 1.2033 \| 0.815 \| 0.7907 \| 0.1466 \| 0.0537 \| \| No log \| 53.0 \| 371 \| 3.8136 \| 0.815 \| 0.2961 \| 1.2035 \| 0.815 \| 0.7907 \| 0.1399 \| 0.0531 \| \| No log \| 54.0 \| 378 \| 3.8244 \| 0.815 \| 0.2977 \| 1.2024 \| 0.815 \| 0.7907 \| 0.1586 \| 0.0538 \| \| No log \| 55.0 \| 385 \| 3.8265 \| 0.815 \| 0.2963 \| 1.2004 \| 0.815 \| 0.7907 \| 0.1506 \| 0.0537 \| \| No log \| 56.0 \| 392 \| 3.8376 \| 0.82 \| 0.2980 \| 1.2011 \| 0.82 \| 0.7964 \| 0.1471 \| 0.0536 \| \| No log \| 57.0 \| 399 \| 3.8428 \| 0.82 \| 0.2982 \| 1.1994 \| 0.82 \| 0.7964 \| 0.1562 \| 0.0535 \| \| No log \| 58.0 \| 406 \| 3.8418 \| 0.82 \| 0.2973 \| 1.2004 \| 0.82 \| 0.7964 \| 0.1484 \| 0.0537 \| \| No log \| 59.0 \| 413 \| 3.8507 \| 0.82 \| 0.2984 \| 1.2009 \| 0.82 \| 0.7931 \| 0.1563 \| 0.0538 \| \| No log \| 60.0 \| 420 \| 3.8560 \| 0.82 \| 0.2989 \| 1.2001 \| 0.82 \| 0.7964 \| 0.1579 \| 0.0540 \| \| No log \| 61.0 \| 427 \| 3.8563 \| 0.82 \| 0.2974 \| 1.1997 \| 0.82 \| 0.7964 \| 0.1560 \| 0.0536 \| \| No log \| 62.0 \| 434 \| 3.8648 \| 0.815 \| 0.2986 \| 1.1995 \| 0.815 \| 0.7907 \| 0.1532 \| 0.0540 \| \| No log \| 63.0 \| 441 \| 3.8682 \| 0.82 \| 0.2991 \| 1.1991 \| 0.82 \| 0.7964 \| 0.1570 \| 0.0536 \| \| No log \| 64.0 \| 448 \| 3.8735 \| 0.82 \| 0.2989 \| 1.1984 \| 0.82 \| 0.7964 \| 0.1481 \| 0.0539 \| \| No log \| 65.0 \| 455 \| 3.8794 \| 0.82 \| 0.3000 \| 1.1981 \| 0.82 \| 0.7964 \| 0.1496 \| 0.0543 \| \| No log \| 66.0 \| 462 \| 3.8824 \| 0.82 \| 0.3002 \| 1.1980 \| 0.82 \| 0.7964 \| 0.1567 \| 0.0539 \| \| No log \| 67.0 \| 469 \| 3.8842 \| 0.82 \| 0.3005 \| 1.1983 \| 0.82 \| 0.7964 \| 0.1438 \| 0.0542 \| \| No log \| 68.0 \| 476 \| 3.8866 \| 0.82 \| 0.3001 \| 1.1978 \| 0.82 \| 0.7964 \| 0.1418 \| 0.0540 \| \| No log \| 69.0 \| 483 \| 3.8912 \| 0.82 \| 0.3003 \| 1.1977 \| 0.82 \| 0.7964 \| 0.1570 \| 0.0541 \| \| No log \| 70.0 \| 490 \| 3.8959 \| 0.82 \| 0.3008 \| 1.1971 \| 0.82 \| 0.7964 \| 0.1445 \| 0.0544 \| \| No log \| 71.0 \| 497 \| 3.8964 \| 0.82 \| 0.3002 \| 1.1977 \| 0.82 \| 0.7964 \| 0.1366 \| 0.0543 \| \| 3.4649 \| 72.0 \| 504 \| 3.9021 \| 0.82 \| 0.3009 \| 1.1969 \| 0.82 \| 0.7964 \| 0.1471 \| 0.0543 \| \| 3.4649 \| 73.0 \| 511 \| 3.9052 \| 0.82 \| 0.3015 \| 1.1976 \| 0.82 \| 0.7964 \| 0.1532 \| 0.0546 \| \| 3.4649 \| 74.0 \| 518 \| 3.9043 \| 0.82 \| 0.3002 \| 1.1973 \| 0.82 \| 0.7964 \| 0.1371 \| 0.0544 \| \| 3.4649 \| 75.0 \| 525 \| 3.9096 \| 0.82 \| 0.3004 \| 1.1966 \| 0.82 \| 0.7964 \| 0.1417 \| 0.0543 \| \| 3.4649 \| 76.0 \| 532 \| 3.9099 \| 0.82 \| 0.3010 \| 1.1965 \| 0.82 \| 0.7964 \| 0.1428 \| 0.0545 \| \| 3.4649 \| 77.0 \| 539 \| 3.9151 \| 0.82 \| 0.3016 \| 1.1963 \| 0.82 \| 0.7964 \| 0.1460 \| 0.0548 \| \| 3.4649 \| 78.0 \| 546 \| 3.9143 \| 0.82 \| 0.3010 \| 1.1970 \| 0.82 \| 0.7964 \| 0.1447 \| 0.0543 \| \| 3.4649 \| 79.0 \| 553 \| 3.9164 \| 0.82 \| 0.3014 \| 1.1966 \| 0.82 \| 0.7964 \| 0.1436 \| 0.0545 \| \| 3.4649 \| 80.0 \| 560 \| 3.9198 \| 0.82 \| 0.3018 \| 1.1965 \| 0.82 \| 0.7964 \| 0.1520 \| 0.0545 \| \| 3.4649 \| 81.0 \| 567 \| 3.9218 \| 0.82 \| 0.3015 \| 1.1959 \| 0.82 \| 0.7964 \| 0.1440 \| 0.0546 \| \| 3.4649 \| 82.0 \| 574 \| 3.9236 \| 0.82 \| 0.3018 \| 1.1961 \| 0.82 \| 0.7964 \| 0.1439 \| 0.0546 \| \| 3.4649 \| 83.0 \| 581 \| 3.9248 \| 0.82 \| 0.3017 \| 1.1959 \| 0.82 \| 0.7964 \| 0.1440 \| 0.0546 \| \| 3.4649 \| 84.0 \| 588 \| 3.9267 \| 0.82 \| 0.3018 \| 1.1958 \| 0.82 \| 0.7964 \| 0.1442 \| 0.0545 \| \| 3.4649 \| 85.0 \| 595 \| 3.9286 \| 0.82 \| 0.3019 \| 1.1959 \| 0.82 \| 0.7964 \| 0.1443 \| 0.0546 \| \| 3.4649 \| 86.0 \| 602 \| 3.9300 \| 0.82 \| 0.3020 \| 1.1958 \| 0.82 \| 0.7964 \| 0.1444 \| 0.0545 \| \| 3.4649 \| 87.0 \| 609 \| 3.9320 \| 0.82 \| 0.3022 \| 1.1956 \| 0.82 \| 0.7964 \| 0.1446 \| 0.0546 \| \| 3.4649 \| 88.0 \| 616 \| 3.9327 \| 0.82 \| 0.3022 \| 1.1957 \| 0.82 \| 0.7964 \| 0.1446 \| 0.0545 \| \| 3.4649 \| 89.0 \| 623 \| 3.9340 \| 0.82 \| 0.3022 \| 1.1955 \| 0.82 \| 0.7964 \| 0.1436 \| 0.0546 \| \| 3.4649 \| 90.0 \| 630 \| 3.9346 \| 0.82 \| 0.3022 \| 1.1956 \| 0.82 \| 0.7964 \| 0.1447 \| 0.0546 \| \| 3.4649 \| 91.0 \| 637 \| 3.9360 \| 0.82 \| 0.3023 \| 1.1953 \| 0.82 \| 0.7964 \| 0.1438 \| 0.0546 \| \| 3.4649 \| 92.0 \| 644 \| 3.9368 \| 0.82 \| 0.3023 \| 1.1954 \| 0.82 \| 0.7964 \| 0.1438 \| 0.0546 \| \| 3.4649 \| 93.0 \| 651 \| 3.9374 \| 0.82 \| 0.3023 \| 1.1954 \| 0.82 \| 0.7964 \| 0.1437 \| 0.0548 \| \| 3.4649 \| 94.0 \| 658 \| 3.9380 \| 0.82 \| 0.3023 \| 1.1953 \| 0.82 \| 0.7964 \| 0.1438 \| 0.0548 \| \| 3.4649 \| 95.0 \| 665 \| 3.9385 \| 0.82 \| 0.3023 \| 1.1953 \| 0.82 \| 0.7964 \| 0.1494 \| 0.0549 \| \| 3.4649 \| 96.0 \| 672 \| 3.9391 \| 0.82 \| 0.3024 \| 1.1952 \| 0.82 \| 0.7964 \| 0.1494 \| 0.0548 \| \| 3.4649 \| 97.0 \| 679 \| 3.9393 \| 0.82 \| 0.3024 \| 1.1952 \| 0.82 \| 0.7964 \| 0.1495 \| 0.0548 \| \| 3.4649 \| 98.0 \| 686 \| 3.9396 \| 0.82 \| 0.3024 \| 1.1952 \| 0.82 \| 0.7964 \| 0.1494 \| 0.0548 \| \| 3.4649 \| 99.0 \| 693 \| 3.9398 \| 0.82 \| 0.3024 \| 1.1952 \| 0.82 \| 0.7964 \| 0.1494 \| 0.0548 \| \| 3.4649 \| 100.0 \| 700 \| 3.9399 \| 0.82 \| 0.3024 \| 1.1952 \| 0.82 \| 0.7964 \| 0.1494 \| 0.0548 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
0xMaka/based-bert-sc	0xMaka	2023-07-11T22:28:41Z	113	1	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "en", "dataset:0xMaka/trading-candles-subset-sc-format", "license:gpl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T17:56:55Z	--- datasets: - 0xMaka/trading-candles-subset-sc-format language: - en metrics: - accuracy - f1 widget: - text: 'identify candle: 17284.58,17264.41,17284.58,17264.41' example_title: Bear - text: 'identify candle: open: 17343.43, close: 17625.18, high: 17804.68, low: 17322.15' example_title: Bull license: gpl --- # Based Bert for sequence classification This model is a POC and shouldn't be used for any production task. ## Model description Based Bert SC is a text classification bot for binary classification of a trading candles opening and closing prices. ## Uses and limitations This model can reliably return the bullish or bearish status of a candle given the opening, closing, high and low, in a format shown. It will have trouble if the order of the numbers change (even if tags are included). ### How to use You can use this model directly with a pipeline ```python >>> from transformers import pipeline >>> pipe = pipeline("text-classification", model="0xMaka/based-bert-sc") >>> text = "identify candle: open: 21788.19, close: 21900, high: 21965.23, low: 21788.19" >>> pipe(text) [{'label': 'Bullish', 'score': 0.9999682903289795}] ``` ## Finetuning For parameters: https://github.com/0xMaka/based-bert-sc/blob/main/trainer.py This model was fine tuned on an RTX-3060-Mobile ``` // BUS_WIDTH = 192 // CLOCK_RATE = 1750 // DDR_MULTI = 8 // DDR6 // BWTheoretical = (((CLOCK_RATE * (10 ** 6)) * (BUS_WIDTH/8)) * DDR_MULI) / (10 ** 9) // BWTheoretical == 336 GB/s ``` Self-measured effective (GB/s): 316.280736
AACEE/textual_inversion_cat	AACEE	2023-07-11T22:10:09Z	4	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-08T07:11:18Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - AACEE/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
crowbarmassage/ppo-SnowballTarget	crowbarmassage	2023-07-11T22:08:09Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-11T22:08:08Z	--- 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: crowbarmassage/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kyungsukim-ai/distilbert-base-uncased-finetuned-squad	kyungsukim-ai	2023-07-11T22:05:45Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-05-09T23:24:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-squad 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. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ysmaicon/distilbert-base-uncased-finetuned-cola	ysmaicon	2023-07-11T22:04:26Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T21:15:40Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ysmaicon/distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ysmaicon/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1941 - Validation Loss: 0.5355 - Train Matthews Correlation: 0.5256 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5184 \| 0.4689 \| 0.4919 \| 0 \| \| 0.3229 \| 0.4772 \| 0.5191 \| 1 \| \| 0.1941 \| 0.5355 \| 0.5256 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
epicmobile181/huggingface_sequence_classification	epicmobile181	2023-07-11T22:03:01Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T18:00:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: huggingface_sequence_classification 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. --> # huggingface_sequence_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/vit-tiny_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone	jordyvl	2023-07-11T21:56:36Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-11T21:05:53Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone 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_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone 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: 0.0396 - Accuracy: 0.735 - Brier Loss: 0.7729 - Nll: 1.4473 - F1 Micro: 0.735 - F1 Macro: 0.6948 - Ece: 0.5886 - Aurc: 0.0947 ## 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: 32 - eval_batch_size: 32 - 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 \| 25 \| 0.0553 \| 0.085 \| 0.8991 \| 5.2518 \| 0.085 \| 0.0595 \| 0.1614 \| 0.8792 \| \| No log \| 2.0 \| 50 \| 0.0488 \| 0.035 \| 0.9007 \| 7.4288 \| 0.035 \| 0.0069 \| 0.1218 \| 0.9410 \| \| No log \| 3.0 \| 75 \| 0.0481 \| 0.045 \| 0.8999 \| 6.0525 \| 0.045 \| 0.0087 \| 0.1349 \| 0.9308 \| \| No log \| 4.0 \| 100 \| 0.0478 \| 0.05 \| 0.8991 \| 5.6444 \| 0.0500 \| 0.0149 \| 0.1378 \| 0.9211 \| \| No log \| 5.0 \| 125 \| 0.0475 \| 0.14 \| 0.8981 \| 5.8239 \| 0.14 \| 0.0863 \| 0.1987 \| 0.8452 \| \| No log \| 6.0 \| 150 \| 0.0471 \| 0.305 \| 0.8964 \| 5.7469 \| 0.305 \| 0.1652 \| 0.3097 \| 0.5016 \| \| No log \| 7.0 \| 175 \| 0.0466 \| 0.305 \| 0.8950 \| 4.9568 \| 0.305 \| 0.1899 \| 0.3035 \| 0.5165 \| \| No log \| 8.0 \| 200 \| 0.0461 \| 0.315 \| 0.8931 \| 4.8214 \| 0.315 \| 0.1811 \| 0.3152 \| 0.4687 \| \| No log \| 9.0 \| 225 \| 0.0455 \| 0.315 \| 0.8907 \| 4.7406 \| 0.315 \| 0.2028 \| 0.3225 \| 0.4671 \| \| No log \| 10.0 \| 250 \| 0.0449 \| 0.35 \| 0.8862 \| 4.7538 \| 0.35 \| 0.1972 \| 0.3364 \| 0.4263 \| \| No log \| 11.0 \| 275 \| 0.0443 \| 0.37 \| 0.8793 \| 4.6283 \| 0.37 \| 0.2106 \| 0.3455 \| 0.4084 \| \| No log \| 12.0 \| 300 \| 0.0438 \| 0.4 \| 0.8731 \| 3.9664 \| 0.4000 \| 0.2443 \| 0.3685 \| 0.3731 \| \| No log \| 13.0 \| 325 \| 0.0434 \| 0.425 \| 0.8628 \| 3.9702 \| 0.425 \| 0.2574 \| 0.3842 \| 0.3601 \| \| No log \| 14.0 \| 350 \| 0.0430 \| 0.465 \| 0.8586 \| 3.8630 \| 0.465 \| 0.3226 \| 0.4112 \| 0.3024 \| \| No log \| 15.0 \| 375 \| 0.0428 \| 0.46 \| 0.8488 \| 3.9046 \| 0.46 \| 0.2693 \| 0.4082 \| 0.2854 \| \| No log \| 16.0 \| 400 \| 0.0424 \| 0.475 \| 0.8430 \| 3.2916 \| 0.4750 \| 0.2802 \| 0.4183 \| 0.2626 \| \| No log \| 17.0 \| 425 \| 0.0421 \| 0.555 \| 0.8439 \| 2.7780 \| 0.555 \| 0.4109 \| 0.4760 \| 0.2123 \| \| No log \| 18.0 \| 450 \| 0.0418 \| 0.575 \| 0.8317 \| 2.8629 \| 0.575 \| 0.4399 \| 0.4869 \| 0.2123 \| \| No log \| 19.0 \| 475 \| 0.0415 \| 0.665 \| 0.8329 \| 2.5145 \| 0.665 \| 0.5077 \| 0.5655 \| 0.1361 \| \| 0.0491 \| 20.0 \| 500 \| 0.0412 \| 0.635 \| 0.8121 \| 2.7489 \| 0.635 \| 0.5155 \| 0.5235 \| 0.1686 \| \| 0.0491 \| 21.0 \| 525 \| 0.0410 \| 0.655 \| 0.8221 \| 1.7853 \| 0.655 \| 0.5182 \| 0.5509 \| 0.1545 \| \| 0.0491 \| 22.0 \| 550 \| 0.0406 \| 0.685 \| 0.8045 \| 1.5894 \| 0.685 \| 0.5486 \| 0.5627 \| 0.1305 \| \| 0.0491 \| 23.0 \| 575 \| 0.0405 \| 0.68 \| 0.7984 \| 1.7241 \| 0.68 \| 0.5489 \| 0.5545 \| 0.1296 \| \| 0.0491 \| 24.0 \| 600 \| 0.0402 \| 0.725 \| 0.7959 \| 1.5667 \| 0.7250 \| 0.6156 \| 0.5926 \| 0.1055 \| \| 0.0491 \| 25.0 \| 625 \| 0.0402 \| 0.68 \| 0.7927 \| 1.4334 \| 0.68 \| 0.5853 \| 0.5453 \| 0.1239 \| \| 0.0491 \| 26.0 \| 650 \| 0.0401 \| 0.705 \| 0.7808 \| 1.8114 \| 0.705 \| 0.5856 \| 0.5735 \| 0.1109 \| \| 0.0491 \| 27.0 \| 675 \| 0.0399 \| 0.71 \| 0.7859 \| 1.6101 \| 0.7100 \| 0.6176 \| 0.5679 \| 0.1034 \| \| 0.0491 \| 28.0 \| 700 \| 0.0399 \| 0.715 \| 0.7808 \| 1.3423 \| 0.715 \| 0.6612 \| 0.5582 \| 0.1130 \| \| 0.0491 \| 29.0 \| 725 \| 0.0398 \| 0.705 \| 0.7789 \| 1.3921 \| 0.705 \| 0.6477 \| 0.5615 \| 0.1175 \| \| 0.0491 \| 30.0 \| 750 \| 0.0397 \| 0.73 \| 0.7767 \| 1.5801 \| 0.7300 \| 0.6758 \| 0.5741 \| 0.1069 \| \| 0.0491 \| 31.0 \| 775 \| 0.0397 \| 0.72 \| 0.7774 \| 1.3193 \| 0.72 \| 0.6653 \| 0.5790 \| 0.1004 \| \| 0.0491 \| 32.0 \| 800 \| 0.0396 \| 0.745 \| 0.7729 \| 1.4864 \| 0.745 \| 0.6931 \| 0.5941 \| 0.0933 \| \| 0.0491 \| 33.0 \| 825 \| 0.0396 \| 0.74 \| 0.7736 \| 1.5161 \| 0.74 \| 0.6901 \| 0.5828 \| 0.0934 \| \| 0.0491 \| 34.0 \| 850 \| 0.0396 \| 0.745 \| 0.7754 \| 1.5432 \| 0.745 \| 0.6963 \| 0.5911 \| 0.0857 \| \| 0.0491 \| 35.0 \| 875 \| 0.0396 \| 0.74 \| 0.7744 \| 1.4773 \| 0.74 \| 0.6936 \| 0.5966 \| 0.0896 \| \| 0.0491 \| 36.0 \| 900 \| 0.0397 \| 0.715 \| 0.7762 \| 1.3769 \| 0.715 \| 0.6827 \| 0.5675 \| 0.1048 \| \| 0.0491 \| 37.0 \| 925 \| 0.0396 \| 0.72 \| 0.7744 \| 1.3882 \| 0.72 \| 0.6780 \| 0.5689 \| 0.0970 \| \| 0.0491 \| 38.0 \| 950 \| 0.0396 \| 0.72 \| 0.7762 \| 1.4098 \| 0.72 \| 0.6874 \| 0.5701 \| 0.1016 \| \| 0.0491 \| 39.0 \| 975 \| 0.0395 \| 0.74 \| 0.7728 \| 1.3890 \| 0.74 \| 0.6894 \| 0.5861 \| 0.0902 \| \| 0.0386 \| 40.0 \| 1000 \| 0.0396 \| 0.74 \| 0.7724 \| 1.5265 \| 0.74 \| 0.6936 \| 0.5906 \| 0.0881 \| \| 0.0386 \| 41.0 \| 1025 \| 0.0396 \| 0.725 \| 0.7730 \| 1.3516 \| 0.7250 \| 0.6768 \| 0.5784 \| 0.0942 \| \| 0.0386 \| 42.0 \| 1050 \| 0.0396 \| 0.73 \| 0.7728 \| 1.3633 \| 0.7300 \| 0.6847 \| 0.5899 \| 0.0945 \| \| 0.0386 \| 43.0 \| 1075 \| 0.0396 \| 0.735 \| 0.7730 \| 1.3670 \| 0.735 \| 0.6874 \| 0.5830 \| 0.0940 \| \| 0.0386 \| 44.0 \| 1100 \| 0.0395 \| 0.73 \| 0.7727 \| 1.4707 \| 0.7300 \| 0.6850 \| 0.5914 \| 0.0930 \| \| 0.0386 \| 45.0 \| 1125 \| 0.0396 \| 0.725 \| 0.7721 \| 1.4269 \| 0.7250 \| 0.6810 \| 0.5770 \| 0.0934 \| \| 0.0386 \| 46.0 \| 1150 \| 0.0396 \| 0.72 \| 0.7730 \| 1.3567 \| 0.72 \| 0.6793 \| 0.5717 \| 0.0976 \| \| 0.0386 \| 47.0 \| 1175 \| 0.0396 \| 0.715 \| 0.7731 \| 1.3708 \| 0.715 \| 0.6757 \| 0.5717 \| 0.0974 \| \| 0.0386 \| 48.0 \| 1200 \| 0.0396 \| 0.735 \| 0.7724 \| 1.4118 \| 0.735 \| 0.6874 \| 0.5791 \| 0.0923 \| \| 0.0386 \| 49.0 \| 1225 \| 0.0396 \| 0.72 \| 0.7729 \| 1.3647 \| 0.72 \| 0.6837 \| 0.5711 \| 0.0965 \| \| 0.0386 \| 50.0 \| 1250 \| 0.0396 \| 0.725 \| 0.7727 \| 1.3773 \| 0.7250 \| 0.6820 \| 0.5740 \| 0.0963 \| \| 0.0386 \| 51.0 \| 1275 \| 0.0396 \| 0.73 \| 0.7736 \| 1.3286 \| 0.7300 \| 0.6847 \| 0.5766 \| 0.0939 \| \| 0.0386 \| 52.0 \| 1300 \| 0.0396 \| 0.725 \| 0.7732 \| 1.3810 \| 0.7250 \| 0.6817 \| 0.5830 \| 0.0944 \| \| 0.0386 \| 53.0 \| 1325 \| 0.0396 \| 0.725 \| 0.7725 \| 1.3568 \| 0.7250 \| 0.6820 \| 0.5763 \| 0.0948 \| \| 0.0386 \| 54.0 \| 1350 \| 0.0396 \| 0.73 \| 0.7731 \| 1.3693 \| 0.7300 \| 0.6847 \| 0.5768 \| 0.0941 \| \| 0.0386 \| 55.0 \| 1375 \| 0.0396 \| 0.745 \| 0.7728 \| 1.3631 \| 0.745 \| 0.7112 \| 0.5842 \| 0.0928 \| \| 0.0386 \| 56.0 \| 1400 \| 0.0396 \| 0.715 \| 0.7731 \| 1.4175 \| 0.715 \| 0.6712 \| 0.5600 \| 0.0976 \| \| 0.0386 \| 57.0 \| 1425 \| 0.0396 \| 0.725 \| 0.7725 \| 1.3668 \| 0.7250 \| 0.6929 \| 0.5738 \| 0.0962 \| \| 0.0386 \| 58.0 \| 1450 \| 0.0396 \| 0.73 \| 0.7734 \| 1.3903 \| 0.7300 \| 0.6958 \| 0.5868 \| 0.0963 \| \| 0.0386 \| 59.0 \| 1475 \| 0.0396 \| 0.725 \| 0.7729 \| 1.4120 \| 0.7250 \| 0.6765 \| 0.5756 \| 0.0945 \| \| 0.0373 \| 60.0 \| 1500 \| 0.0396 \| 0.725 \| 0.7732 \| 1.3655 \| 0.7250 \| 0.6820 \| 0.5754 \| 0.0951 \| \| 0.0373 \| 61.0 \| 1525 \| 0.0396 \| 0.745 \| 0.7727 \| 1.3676 \| 0.745 \| 0.7038 \| 0.5913 \| 0.0921 \| \| 0.0373 \| 62.0 \| 1550 \| 0.0396 \| 0.72 \| 0.7729 \| 1.3629 \| 0.72 \| 0.6797 \| 0.5762 \| 0.0969 \| \| 0.0373 \| 63.0 \| 1575 \| 0.0396 \| 0.725 \| 0.7730 \| 1.4242 \| 0.7250 \| 0.6865 \| 0.5811 \| 0.0950 \| \| 0.0373 \| 64.0 \| 1600 \| 0.0396 \| 0.725 \| 0.7735 \| 1.3658 \| 0.7250 \| 0.6923 \| 0.5750 \| 0.0959 \| \| 0.0373 \| 65.0 \| 1625 \| 0.0396 \| 0.73 \| 0.7731 \| 1.4296 \| 0.7300 \| 0.6958 \| 0.5769 \| 0.0954 \| \| 0.0373 \| 66.0 \| 1650 \| 0.0396 \| 0.735 \| 0.7727 \| 1.4780 \| 0.735 \| 0.6980 \| 0.5851 \| 0.0938 \| \| 0.0373 \| 67.0 \| 1675 \| 0.0396 \| 0.725 \| 0.7725 \| 1.3669 \| 0.7250 \| 0.6824 \| 0.5715 \| 0.0938 \| \| 0.0373 \| 68.0 \| 1700 \| 0.0396 \| 0.725 \| 0.7730 \| 1.4327 \| 0.7250 \| 0.6804 \| 0.5741 \| 0.0940 \| \| 0.0373 \| 69.0 \| 1725 \| 0.0396 \| 0.73 \| 0.7728 \| 1.3811 \| 0.7300 \| 0.6961 \| 0.5806 \| 0.0963 \| \| 0.0373 \| 70.0 \| 1750 \| 0.0396 \| 0.735 \| 0.7727 \| 1.3812 \| 0.735 \| 0.7081 \| 0.5765 \| 0.0952 \| \| 0.0373 \| 71.0 \| 1775 \| 0.0396 \| 0.73 \| 0.7730 \| 1.4263 \| 0.7300 \| 0.6961 \| 0.5739 \| 0.0953 \| \| 0.0373 \| 72.0 \| 1800 \| 0.0396 \| 0.73 \| 0.7731 \| 1.4280 \| 0.7300 \| 0.6953 \| 0.5803 \| 0.0956 \| \| 0.0373 \| 73.0 \| 1825 \| 0.0396 \| 0.735 \| 0.7729 \| 1.3676 \| 0.735 \| 0.6988 \| 0.5889 \| 0.0953 \| \| 0.0373 \| 74.0 \| 1850 \| 0.0396 \| 0.735 \| 0.7727 \| 1.4358 \| 0.735 \| 0.6985 \| 0.5828 \| 0.0940 \| \| 0.0373 \| 75.0 \| 1875 \| 0.0396 \| 0.735 \| 0.7727 \| 1.4306 \| 0.735 \| 0.6965 \| 0.5786 \| 0.0940 \| \| 0.0373 \| 76.0 \| 1900 \| 0.0396 \| 0.73 \| 0.7729 \| 1.4343 \| 0.7300 \| 0.6957 \| 0.5802 \| 0.0958 \| \| 0.0373 \| 77.0 \| 1925 \| 0.0396 \| 0.73 \| 0.7726 \| 1.4259 \| 0.7300 \| 0.6961 \| 0.5795 \| 0.0962 \| \| 0.0373 \| 78.0 \| 1950 \| 0.0396 \| 0.74 \| 0.7731 \| 1.4246 \| 0.74 \| 0.7080 \| 0.5879 \| 0.0941 \| \| 0.0373 \| 79.0 \| 1975 \| 0.0396 \| 0.735 \| 0.7730 \| 1.4414 \| 0.735 \| 0.6980 \| 0.5914 \| 0.0945 \| \| 0.0372 \| 80.0 \| 2000 \| 0.0396 \| 0.74 \| 0.7727 \| 1.4285 \| 0.74 \| 0.7103 \| 0.5915 \| 0.0939 \| \| 0.0372 \| 81.0 \| 2025 \| 0.0396 \| 0.735 \| 0.7731 \| 1.4379 \| 0.735 \| 0.6980 \| 0.5826 \| 0.0942 \| \| 0.0372 \| 82.0 \| 2050 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4308 \| 0.735 \| 0.6963 \| 0.5827 \| 0.0942 \| \| 0.0372 \| 83.0 \| 2075 \| 0.0396 \| 0.735 \| 0.7728 \| 1.4329 \| 0.735 \| 0.6968 \| 0.5896 \| 0.0946 \| \| 0.0372 \| 84.0 \| 2100 \| 0.0396 \| 0.735 \| 0.7728 \| 1.4343 \| 0.735 \| 0.6948 \| 0.5889 \| 0.0947 \| \| 0.0372 \| 85.0 \| 2125 \| 0.0396 \| 0.735 \| 0.7727 \| 1.4320 \| 0.735 \| 0.6948 \| 0.5988 \| 0.0945 \| \| 0.0372 \| 86.0 \| 2150 \| 0.0396 \| 0.735 \| 0.7730 \| 1.4366 \| 0.735 \| 0.6963 \| 0.5883 \| 0.0949 \| \| 0.0372 \| 87.0 \| 2175 \| 0.0396 \| 0.73 \| 0.7728 \| 1.4825 \| 0.7300 \| 0.6888 \| 0.5878 \| 0.0945 \| \| 0.0372 \| 88.0 \| 2200 \| 0.0396 \| 0.735 \| 0.7731 \| 1.4339 \| 0.735 \| 0.6945 \| 0.5828 \| 0.0948 \| \| 0.0372 \| 89.0 \| 2225 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4383 \| 0.735 \| 0.6948 \| 0.5917 \| 0.0946 \| \| 0.0372 \| 90.0 \| 2250 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4471 \| 0.735 \| 0.6948 \| 0.5867 \| 0.0944 \| \| 0.0372 \| 91.0 \| 2275 \| 0.0396 \| 0.735 \| 0.7728 \| 1.4402 \| 0.735 \| 0.6948 \| 0.5892 \| 0.0946 \| \| 0.0372 \| 92.0 \| 2300 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4412 \| 0.735 \| 0.6948 \| 0.5952 \| 0.0948 \| \| 0.0372 \| 93.0 \| 2325 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4709 \| 0.735 \| 0.6948 \| 0.5917 \| 0.0948 \| \| 0.0372 \| 94.0 \| 2350 \| 0.0396 \| 0.735 \| 0.7728 \| 1.4413 \| 0.735 \| 0.6948 \| 0.5858 \| 0.0947 \| \| 0.0372 \| 95.0 \| 2375 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4422 \| 0.735 \| 0.6948 \| 0.5917 \| 0.0946 \| \| 0.0372 \| 96.0 \| 2400 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4527 \| 0.735 \| 0.6948 \| 0.5917 \| 0.0946 \| \| 0.0372 \| 97.0 \| 2425 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4441 \| 0.735 \| 0.6948 \| 0.5917 \| 0.0946 \| \| 0.0372 \| 98.0 \| 2450 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4423 \| 0.735 \| 0.6948 \| 0.5917 \| 0.0946 \| \| 0.0372 \| 99.0 \| 2475 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4457 \| 0.735 \| 0.6948 \| 0.5886 \| 0.0948 \| \| 0.0372 \| 100.0 \| 2500 \| 0.0396 \| 0.735 \| 0.7729 \| 1.4473 \| 0.735 \| 0.6948 \| 0.5886 \| 0.0947 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
a9i/scarlett-7b	a9i	2023-07-11T21:37:46Z	4	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text2text-generation", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-11T20:53:43Z	--- license: cc-by-nc-nd-4.0 language: - en pipeline_tag: text2text-generation ---
JuS2/ppo-Huggy	JuS2	2023-07-11T21:28:22Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T21:28:12Z	--- 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: JuS2/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ethannhzhouu/gpt2-generator	ethannhzhouu	2023-07-11T21:11:54Z	209	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	2023-07-11T21:11:25Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt2-generator 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-generator 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: 4.7855 ## 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 \| 5.3997 \| \| No log \| 2.0 \| 2 \| 4.9524 \| \| No log \| 3.0 \| 3 \| 4.7855 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Belphegor/dqn-SpaceInvadersNoFrameskip-v4	Belphegor	2023-07-11T21:11:25Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T21:10:44Z	--- 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: 581.50 +/- 92.36 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 Belphegor -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 Belphegor -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 Belphegor ``` ## 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'} ```
jordyvl/vit-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone	jordyvl	2023-07-11T21:05:03Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T22:41:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone 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-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0379 - Accuracy: 0.8 - Brier Loss: 0.6938 - Nll: 1.3290 - F1 Micro: 0.8000 - F1 Macro: 0.7859 - Ece: 0.5869 - Aurc: 0.0931 ## 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: 32 - eval_batch_size: 32 - 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 \| 25 \| 0.0506 \| 0.09 \| 0.8991 \| 6.5155 \| 0.09 \| 0.0484 \| 0.1622 \| 0.8986 \| \| No log \| 2.0 \| 50 \| 0.0468 \| 0.22 \| 0.8982 \| 4.6950 \| 0.22 \| 0.1025 \| 0.2491 \| 0.7656 \| \| No log \| 3.0 \| 75 \| 0.0463 \| 0.29 \| 0.8969 \| 3.3099 \| 0.29 \| 0.1676 \| 0.2924 \| 0.6888 \| \| No log \| 4.0 \| 100 \| 0.0459 \| 0.37 \| 0.8954 \| 3.2920 \| 0.37 \| 0.1891 \| 0.3517 \| 0.4208 \| \| No log \| 5.0 \| 125 \| 0.0455 \| 0.395 \| 0.8929 \| 3.2550 \| 0.395 \| 0.2299 \| 0.3759 \| 0.3617 \| \| No log \| 6.0 \| 150 \| 0.0449 \| 0.49 \| 0.8885 \| 2.9109 \| 0.49 \| 0.3135 \| 0.4396 \| 0.2804 \| \| No log \| 7.0 \| 175 \| 0.0441 \| 0.495 \| 0.8796 \| 2.8950 \| 0.495 \| 0.3248 \| 0.4360 \| 0.2721 \| \| No log \| 8.0 \| 200 \| 0.0430 \| 0.545 \| 0.8619 \| 2.5199 \| 0.545 \| 0.3771 \| 0.4777 \| 0.2129 \| \| No log \| 9.0 \| 225 \| 0.0418 \| 0.62 \| 0.8382 \| 2.2126 \| 0.62 \| 0.4291 \| 0.5298 \| 0.1659 \| \| No log \| 10.0 \| 250 \| 0.0409 \| 0.645 \| 0.8137 \| 2.2525 \| 0.645 \| 0.4947 \| 0.5293 \| 0.1552 \| \| No log \| 11.0 \| 275 \| 0.0401 \| 0.68 \| 0.7863 \| 2.4423 \| 0.68 \| 0.5145 \| 0.5433 \| 0.1215 \| \| No log \| 12.0 \| 300 \| 0.0392 \| 0.68 \| 0.7628 \| 1.9779 \| 0.68 \| 0.5373 \| 0.5402 \| 0.1172 \| \| No log \| 13.0 \| 325 \| 0.0385 \| 0.745 \| 0.7350 \| 1.8986 \| 0.745 \| 0.6126 \| 0.5806 \| 0.0843 \| \| No log \| 14.0 \| 350 \| 0.0384 \| 0.735 \| 0.7268 \| 1.9922 \| 0.735 \| 0.6451 \| 0.5466 \| 0.0997 \| \| No log \| 15.0 \| 375 \| 0.0381 \| 0.745 \| 0.7180 \| 1.6965 \| 0.745 \| 0.6627 \| 0.5586 \| 0.0761 \| \| No log \| 16.0 \| 400 \| 0.0377 \| 0.805 \| 0.7031 \| 1.2564 \| 0.805 \| 0.7353 \| 0.6034 \| 0.0713 \| \| No log \| 17.0 \| 425 \| 0.0389 \| 0.745 \| 0.7303 \| 1.5063 \| 0.745 \| 0.7192 \| 0.5779 \| 0.0705 \| \| No log \| 18.0 \| 450 \| 0.0387 \| 0.765 \| 0.7219 \| 1.5776 \| 0.765 \| 0.7703 \| 0.5815 \| 0.0923 \| \| No log \| 19.0 \| 475 \| 0.0383 \| 0.805 \| 0.7213 \| 1.3953 \| 0.805 \| 0.7906 \| 0.6159 \| 0.0667 \| \| 0.0432 \| 20.0 \| 500 \| 0.0377 \| 0.835 \| 0.6952 \| 1.3075 \| 0.835 \| 0.8271 \| 0.6116 \| 0.0799 \| \| 0.0432 \| 21.0 \| 525 \| 0.0381 \| 0.795 \| 0.7018 \| 1.6184 \| 0.795 \| 0.7723 \| 0.5851 \| 0.0880 \| \| 0.0432 \| 22.0 \| 550 \| 0.0378 \| 0.81 \| 0.6984 \| 1.4292 \| 0.81 \| 0.7950 \| 0.6103 \| 0.0673 \| \| 0.0432 \| 23.0 \| 575 \| 0.0380 \| 0.805 \| 0.6976 \| 1.4852 \| 0.805 \| 0.7951 \| 0.5942 \| 0.0808 \| \| 0.0432 \| 24.0 \| 600 \| 0.0377 \| 0.825 \| 0.6907 \| 1.4501 \| 0.825 \| 0.8103 \| 0.6020 \| 0.0774 \| \| 0.0432 \| 25.0 \| 625 \| 0.0377 \| 0.83 \| 0.6920 \| 1.4509 \| 0.83 \| 0.8148 \| 0.6038 \| 0.0759 \| \| 0.0432 \| 26.0 \| 650 \| 0.0377 \| 0.825 \| 0.6927 \| 1.4113 \| 0.825 \| 0.8114 \| 0.6072 \| 0.0765 \| \| 0.0432 \| 27.0 \| 675 \| 0.0377 \| 0.825 \| 0.6924 \| 1.4044 \| 0.825 \| 0.8114 \| 0.6057 \| 0.0757 \| \| 0.0432 \| 28.0 \| 700 \| 0.0377 \| 0.82 \| 0.6932 \| 1.4521 \| 0.82 \| 0.8061 \| 0.6017 \| 0.0815 \| \| 0.0432 \| 29.0 \| 725 \| 0.0377 \| 0.82 \| 0.6932 \| 1.3593 \| 0.82 \| 0.8080 \| 0.5983 \| 0.0794 \| \| 0.0432 \| 30.0 \| 750 \| 0.0377 \| 0.82 \| 0.6926 \| 1.3437 \| 0.82 \| 0.8069 \| 0.6042 \| 0.0819 \| \| 0.0432 \| 31.0 \| 775 \| 0.0377 \| 0.815 \| 0.6932 \| 1.3453 \| 0.815 \| 0.8027 \| 0.5988 \| 0.0815 \| \| 0.0432 \| 32.0 \| 800 \| 0.0377 \| 0.82 \| 0.6930 \| 1.3384 \| 0.82 \| 0.8029 \| 0.6044 \| 0.0855 \| \| 0.0432 \| 33.0 \| 825 \| 0.0377 \| 0.81 \| 0.6928 \| 1.3969 \| 0.81 \| 0.7927 \| 0.5929 \| 0.0835 \| \| 0.0432 \| 34.0 \| 850 \| 0.0378 \| 0.805 \| 0.6927 \| 1.3995 \| 0.805 \| 0.7886 \| 0.5961 \| 0.0855 \| \| 0.0432 \| 35.0 \| 875 \| 0.0377 \| 0.81 \| 0.6927 \| 1.3705 \| 0.81 \| 0.7979 \| 0.5910 \| 0.0887 \| \| 0.0432 \| 36.0 \| 900 \| 0.0378 \| 0.805 \| 0.6930 \| 1.3566 \| 0.805 \| 0.7886 \| 0.5850 \| 0.0817 \| \| 0.0432 \| 37.0 \| 925 \| 0.0377 \| 0.82 \| 0.6927 \| 1.3537 \| 0.82 \| 0.8022 \| 0.5936 \| 0.0847 \| \| 0.0432 \| 38.0 \| 950 \| 0.0377 \| 0.815 \| 0.6930 \| 1.3574 \| 0.815 \| 0.7978 \| 0.5976 \| 0.0854 \| \| 0.0432 \| 39.0 \| 975 \| 0.0377 \| 0.815 \| 0.6932 \| 1.4599 \| 0.815 \| 0.7978 \| 0.5955 \| 0.0864 \| \| 0.035 \| 40.0 \| 1000 \| 0.0377 \| 0.815 \| 0.6926 \| 1.4147 \| 0.815 \| 0.7978 \| 0.5990 \| 0.0869 \| \| 0.035 \| 41.0 \| 1025 \| 0.0377 \| 0.81 \| 0.6931 \| 1.4065 \| 0.81 \| 0.7943 \| 0.5966 \| 0.0844 \| \| 0.035 \| 42.0 \| 1050 \| 0.0378 \| 0.81 \| 0.6929 \| 1.4678 \| 0.81 \| 0.7961 \| 0.5902 \| 0.0891 \| \| 0.035 \| 43.0 \| 1075 \| 0.0378 \| 0.81 \| 0.6927 \| 1.4164 \| 0.81 \| 0.7971 \| 0.5951 \| 0.0897 \| \| 0.035 \| 44.0 \| 1100 \| 0.0378 \| 0.81 \| 0.6930 \| 1.4646 \| 0.81 \| 0.7961 \| 0.5948 \| 0.0875 \| \| 0.035 \| 45.0 \| 1125 \| 0.0378 \| 0.815 \| 0.6921 \| 1.4660 \| 0.815 \| 0.8004 \| 0.6024 \| 0.0895 \| \| 0.035 \| 46.0 \| 1150 \| 0.0378 \| 0.81 \| 0.6929 \| 1.4098 \| 0.81 \| 0.7961 \| 0.5987 \| 0.0831 \| \| 0.035 \| 47.0 \| 1175 \| 0.0378 \| 0.815 \| 0.6928 \| 1.4634 \| 0.815 \| 0.8004 \| 0.5963 \| 0.0911 \| \| 0.035 \| 48.0 \| 1200 \| 0.0378 \| 0.81 \| 0.6932 \| 1.4648 \| 0.81 \| 0.7961 \| 0.5841 \| 0.0877 \| \| 0.035 \| 49.0 \| 1225 \| 0.0378 \| 0.81 \| 0.6928 \| 1.4635 \| 0.81 \| 0.7961 \| 0.5955 \| 0.0898 \| \| 0.035 \| 50.0 \| 1250 \| 0.0378 \| 0.805 \| 0.6935 \| 1.4688 \| 0.805 \| 0.7882 \| 0.5795 \| 0.0902 \| \| 0.035 \| 51.0 \| 1275 \| 0.0378 \| 0.805 \| 0.6928 \| 1.4665 \| 0.805 \| 0.7882 \| 0.5848 \| 0.0916 \| \| 0.035 \| 52.0 \| 1300 \| 0.0378 \| 0.81 \| 0.6925 \| 1.4249 \| 0.81 \| 0.7961 \| 0.5869 \| 0.0926 \| \| 0.035 \| 53.0 \| 1325 \| 0.0378 \| 0.815 \| 0.6926 \| 1.4150 \| 0.815 \| 0.8021 \| 0.5934 \| 0.0913 \| \| 0.035 \| 54.0 \| 1350 \| 0.0378 \| 0.81 \| 0.6929 \| 1.4155 \| 0.81 \| 0.7961 \| 0.5943 \| 0.0913 \| \| 0.035 \| 55.0 \| 1375 \| 0.0378 \| 0.805 \| 0.6928 \| 1.4141 \| 0.805 \| 0.7882 \| 0.5934 \| 0.0964 \| \| 0.035 \| 56.0 \| 1400 \| 0.0378 \| 0.805 \| 0.6930 \| 1.4124 \| 0.805 \| 0.7882 \| 0.5926 \| 0.0958 \| \| 0.035 \| 57.0 \| 1425 \| 0.0378 \| 0.81 \| 0.6935 \| 1.4116 \| 0.81 \| 0.7934 \| 0.6002 \| 0.0895 \| \| 0.035 \| 58.0 \| 1450 \| 0.0378 \| 0.805 \| 0.6928 \| 1.4059 \| 0.805 \| 0.7882 \| 0.5890 \| 0.0937 \| \| 0.035 \| 59.0 \| 1475 \| 0.0378 \| 0.805 \| 0.6929 \| 1.4141 \| 0.805 \| 0.7882 \| 0.5918 \| 0.0967 \| \| 0.0348 \| 60.0 \| 1500 \| 0.0378 \| 0.81 \| 0.6935 \| 1.4086 \| 0.81 \| 0.7934 \| 0.5915 \| 0.0934 \| \| 0.0348 \| 61.0 \| 1525 \| 0.0378 \| 0.81 \| 0.6930 \| 1.4105 \| 0.81 \| 0.7941 \| 0.5954 \| 0.0961 \| \| 0.0348 \| 62.0 \| 1550 \| 0.0378 \| 0.81 \| 0.6933 \| 1.4166 \| 0.81 \| 0.7941 \| 0.5889 \| 0.0954 \| \| 0.0348 \| 63.0 \| 1575 \| 0.0378 \| 0.81 \| 0.6933 \| 1.4109 \| 0.81 \| 0.7934 \| 0.5963 \| 0.0975 \| \| 0.0348 \| 64.0 \| 1600 \| 0.0378 \| 0.81 \| 0.6932 \| 1.4131 \| 0.81 \| 0.7934 \| 0.5980 \| 0.0953 \| \| 0.0348 \| 65.0 \| 1625 \| 0.0378 \| 0.81 \| 0.6937 \| 1.4182 \| 0.81 \| 0.7934 \| 0.5956 \| 0.0970 \| \| 0.0348 \| 66.0 \| 1650 \| 0.0378 \| 0.805 \| 0.6933 \| 1.4125 \| 0.805 \| 0.7893 \| 0.5905 \| 0.0966 \| \| 0.0348 \| 67.0 \| 1675 \| 0.0378 \| 0.81 \| 0.6937 \| 1.4136 \| 0.81 \| 0.7934 \| 0.5965 \| 0.0975 \| \| 0.0348 \| 68.0 \| 1700 \| 0.0379 \| 0.81 \| 0.6935 \| 1.4137 \| 0.81 \| 0.7934 \| 0.5994 \| 0.0971 \| \| 0.0348 \| 69.0 \| 1725 \| 0.0378 \| 0.805 \| 0.6935 \| 1.4196 \| 0.805 \| 0.7893 \| 0.5913 \| 0.0946 \| \| 0.0348 \| 70.0 \| 1750 \| 0.0379 \| 0.805 \| 0.6933 \| 1.4129 \| 0.805 \| 0.7893 \| 0.5877 \| 0.0945 \| \| 0.0348 \| 71.0 \| 1775 \| 0.0379 \| 0.805 \| 0.6933 \| 1.4172 \| 0.805 \| 0.7893 \| 0.5921 \| 0.0951 \| \| 0.0348 \| 72.0 \| 1800 \| 0.0379 \| 0.805 \| 0.6931 \| 1.4136 \| 0.805 \| 0.7893 \| 0.5851 \| 0.0953 \| \| 0.0348 \| 73.0 \| 1825 \| 0.0379 \| 0.805 \| 0.6929 \| 1.4168 \| 0.805 \| 0.7893 \| 0.5846 \| 0.0971 \| \| 0.0348 \| 74.0 \| 1850 \| 0.0379 \| 0.805 \| 0.6939 \| 1.4185 \| 0.805 \| 0.7893 \| 0.5892 \| 0.0950 \| \| 0.0348 \| 75.0 \| 1875 \| 0.0379 \| 0.805 \| 0.6935 \| 1.4171 \| 0.805 \| 0.7893 \| 0.5946 \| 0.0938 \| \| 0.0348 \| 76.0 \| 1900 \| 0.0379 \| 0.805 \| 0.6934 \| 1.4217 \| 0.805 \| 0.7893 \| 0.5939 \| 0.0959 \| \| 0.0348 \| 77.0 \| 1925 \| 0.0379 \| 0.8 \| 0.6932 \| 1.4162 \| 0.8000 \| 0.7859 \| 0.5826 \| 0.0954 \| \| 0.0348 \| 78.0 \| 1950 \| 0.0379 \| 0.8 \| 0.6935 \| 1.4172 \| 0.8000 \| 0.7859 \| 0.5912 \| 0.0950 \| \| 0.0348 \| 79.0 \| 1975 \| 0.0379 \| 0.8 \| 0.6933 \| 1.4169 \| 0.8000 \| 0.7859 \| 0.5885 \| 0.0964 \| \| 0.0348 \| 80.0 \| 2000 \| 0.0379 \| 0.8 \| 0.6935 \| 1.4196 \| 0.8000 \| 0.7859 \| 0.5865 \| 0.0957 \| \| 0.0348 \| 81.0 \| 2025 \| 0.0379 \| 0.8 \| 0.6937 \| 1.4213 \| 0.8000 \| 0.7859 \| 0.5880 \| 0.0962 \| \| 0.0348 \| 82.0 \| 2050 \| 0.0379 \| 0.8 \| 0.6939 \| 1.4201 \| 0.8000 \| 0.7859 \| 0.5910 \| 0.0962 \| \| 0.0348 \| 83.0 \| 2075 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3762 \| 0.8000 \| 0.7859 \| 0.5883 \| 0.0945 \| \| 0.0348 \| 84.0 \| 2100 \| 0.0379 \| 0.8 \| 0.6938 \| 1.4218 \| 0.8000 \| 0.7859 \| 0.5947 \| 0.0950 \| \| 0.0348 \| 85.0 \| 2125 \| 0.0379 \| 0.8 \| 0.6935 \| 1.3657 \| 0.8000 \| 0.7859 \| 0.5857 \| 0.0912 \| \| 0.0348 \| 86.0 \| 2150 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3278 \| 0.8000 \| 0.7859 \| 0.5892 \| 0.0929 \| \| 0.0348 \| 87.0 \| 2175 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3831 \| 0.8000 \| 0.7859 \| 0.5856 \| 0.0946 \| \| 0.0348 \| 88.0 \| 2200 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3761 \| 0.8000 \| 0.7859 \| 0.5892 \| 0.0955 \| \| 0.0348 \| 89.0 \| 2225 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3296 \| 0.8000 \| 0.7859 \| 0.5870 \| 0.0947 \| \| 0.0348 \| 90.0 \| 2250 \| 0.0379 \| 0.8 \| 0.6939 \| 1.3667 \| 0.8000 \| 0.7859 \| 0.5909 \| 0.0926 \| \| 0.0348 \| 91.0 \| 2275 \| 0.0379 \| 0.8 \| 0.6940 \| 1.3346 \| 0.8000 \| 0.7859 \| 0.5906 \| 0.0930 \| \| 0.0348 \| 92.0 \| 2300 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3268 \| 0.8000 \| 0.7859 \| 0.5870 \| 0.0936 \| \| 0.0348 \| 93.0 \| 2325 \| 0.0379 \| 0.8 \| 0.6937 \| 1.3320 \| 0.8000 \| 0.7859 \| 0.5919 \| 0.0939 \| \| 0.0348 \| 94.0 \| 2350 \| 0.0379 \| 0.8 \| 0.6939 \| 1.3324 \| 0.8000 \| 0.7859 \| 0.5870 \| 0.0928 \| \| 0.0348 \| 95.0 \| 2375 \| 0.0379 \| 0.8 \| 0.6937 \| 1.3289 \| 0.8000 \| 0.7859 \| 0.5869 \| 0.0932 \| \| 0.0348 \| 96.0 \| 2400 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3264 \| 0.8000 \| 0.7859 \| 0.5869 \| 0.0931 \| \| 0.0348 \| 97.0 \| 2425 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3280 \| 0.8000 \| 0.7859 \| 0.5870 \| 0.0932 \| \| 0.0348 \| 98.0 \| 2450 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3297 \| 0.8000 \| 0.7859 \| 0.5869 \| 0.0930 \| \| 0.0348 \| 99.0 \| 2475 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3304 \| 0.8000 \| 0.7859 \| 0.5869 \| 0.0929 \| \| 0.0347 \| 100.0 \| 2500 \| 0.0379 \| 0.8 \| 0.6938 \| 1.3290 \| 0.8000 \| 0.7859 \| 0.5869 \| 0.0931 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
Jonathaniu/alpaca-breast-cancer-13b	Jonathaniu	2023-07-11T21:01:33Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-11T20:37:06Z	--- 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
davidmunechika/coreml-genshin-landscape-diffusion	davidmunechika	2023-07-11T21:01:14Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-30T17:28:52Z	--- license: creativeml-openrail-m ---
Finnfalter/ppo-LunarLander-v2	Finnfalter	2023-07-11T20:46:31Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T20:46:10Z	--- 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: 253.83 +/- 16.30 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 ... ```
ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30	ALM-AHME	2023-07-11T20:46:08Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-11T12:39:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Augmented-Final split: train args: Augmented-Final metrics: - name: Accuracy type: accuracy value: 0.9805094130675526 --- <!-- 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. --> # beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0474 - Accuracy: 0.9805 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - 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: 0.5 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2312 \| 0.99 \| 93 \| 0.1822 \| 0.9453 \| \| 0.3817 \| 1.99 \| 187 \| 0.2106 \| 0.9183 \| \| 0.2217 \| 3.0 \| 281 \| 0.1902 \| 0.9285 \| \| 0.1667 \| 4.0 \| 375 \| 0.1127 \| 0.9584 \| \| 0.0572 \| 4.96 \| 465 \| 0.0474 \| 0.9805 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
carova/q-FrozenLake-v1-4x4-noSlippery	carova	2023-07-11T20:42:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T20:42:14Z	--- 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="carova/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"]) ```
autopilot-ai/EthicalEye	autopilot-ai	2023-07-11T20:11:30Z	269	7	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "text-classification", "en", "fr", "hi", "gu", "bn", "ml", "mr", "pa", "it", "es", "kn", "as", "af", "ru", "ro", "sq", "ar", "am", "az", "bs", "bh", "bg", "bo", "ca", "ce", "zh", "cr", "hr", "cs", "da", "de", "nl", "el", "et", "eo", "fi", "fj", "fa", "gl", "ga", "ha", "ht", "he", "hu", "hy", "id", "is", "ja", "jv", "ka", "kk", "km", "ko", "ks", "ku", "ky", "la", "lb", "lt", "lv", "mk", "mn", "ms", "mi", "mt", "ne", "no", "or", "om", "ps", "pl", "pt", "qu", "sa", "sm", "gd", "sr", "sn", "sd", "si", "sk", "sl", "so", "su", "sw", "sv", "tg", "ta", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-01T10:36:08Z	--- license: apache-2.0 requirements: - sentencepiece: >- (if not installed install using `pip install sentencepiece`, and restart runtime) library_name: transformers pipeline_tag: text-classification language: - en - fr - hi - gu - bn - ml - mr - pa - it - es - kn - as - af - ru - ro - sq - ar - am - az - bs - bh - bg - bo - ca - ce - zh - cr - hr - cs - da - de - nl - el - et - eo - fi - fj - fa - gl - ga - ha - ht - he - hu - hy - id - is - ja - jv - ka - kk - km - ko - ks - ku - ky - la - lb - lt - lv - mk - mn - ms - mi - mt - ne - 'no' - or - om - ps - pl - pt - qu - sa - sm - gd - sr - sn - sd - si - sk - sl - so - su - sw - sv - tg - ta --- ## Details - Model Name: Ethical Eye - Description: Ethical Eye is an open-source AI model developed by AutopilotAI. It is designed to flag and analyze user-generated content for harmful or unethical behavior, providing a last layer of decision-making to assist AI systems in promoting ethical and moral actions. The model leverages advanced techniques such as text classification, toxicity analysis, and cross-lingual NLP to detect abuse, obscene language, and harmful or unethical comments in multiple languages. ## How to use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autopilot-ai/EthicalEye") model = AutoModelForSequenceClassification.from_pretrained("autopilot-ai/EthicalEye") ``` ## Intended Use - Primary Use Case: The Ethical Eye model is primarily intended to be used as a tool to flag or block users exhibiting harmful or unethical behavior on various platforms. It aims to assist developers, especially those with limited experience in NLP, in enforcing ethical standards and creating a safer environment for users. - User Expertise: The model is designed to be accessible to developers with various levels of NLP expertise, including those with limited experience in the field. - Limitations: While Ethical Eye provides valuable insights and analysis, it is essential to note that it should be used as an aid and not as the sole determinant of ethical decision-making. It may have limitations in understanding context-specific nuances and may require continuous improvement and customization for specific domains or languages. ## Model Details - Architecture: Ethical Eye is built using PyTorch and utilizes the Transformers library. It employs the XLM-Roberta architecture, which enables cross-lingual understanding and transfer learning. - Developed by: [Khush Patel](https://www.linkedin.com/in/khush-patel-kp/), [Jayveersinh Raj](https://www.linkedin.com/in/jayveersinh-raj-67694222a/) - License: The Ethical Eye model is released under the Apache 2.0 license, granting users the freedom to use, modify, and distribute the model according to the terms of the license. ## Use Cases - Content Moderation: Ethical Eye can be integrated into content moderation systems to automatically flag and block user-generated content that contains abusive language, hate speech, or other forms of harmful or unethical behavior. It helps platforms maintain a safe and respectful environment for their users. - Social Media Platforms: Social media platforms can utilize Ethical Eye to automatically detect and filter out toxic comments, obscenities, and offensive content in multiple languages. This helps to create a more positive and inclusive online community. - Chatbots and Virtual Assistants: By incorporating Ethical Eye into chatbots and virtual assistants, AI systems can ensure that their responses align with ethical guidelines. It helps prevent AI agents from engaging in inappropriate or offensive conversations with users. - Online Forums and Discussion Boards: Ethical Eye can be applied to online forums and discussion boards to monitor user interactions and identify potential instances of harassment, bullying, or unethical behavior. This allows moderators to take appropriate actions to maintain a healthy and respectful environment. - E-commerce Platforms: E-commerce platforms can utilize Ethical Eye to automatically identify and block reviews or comments that contain false information, spam, or unethical practices. This helps maintain the integrity of the platform and ensures honest and reliable user feedback. - Educational Platforms: Ethical Eye can be used in educational platforms to flag and address instances of cyberbullying, inappropriate language, or offensive content in student discussions and comments. It supports the creation of a safe and respectful learning environment. - AI Reinforcement Learning: The Ethical Eye model can serve as a critic in reinforcement learning scenarios, providing feedback on the ethical implications of actions taken by AI agents. It assists in developing AI systems that not only optimize for task performance but also align with ethical guidelines and societal norms. ## Considerations for Deployment - Hardware Requirements: The Ethical Eye model can be deployed on hardware configurations suitable for running deep learning models. Specific requirements may depend on the scale of deployment and the desired performance. - Dependencies: The model relies on PyTorch, Transformers, and XLM-Roberta libraries. Refer to the model documentation for specific version requirements. - Integration: Ethical Eye can be integrated into existing AI systems and platforms using the provided APIs and guidelines. Additional customization may be necessary to adapt the model to specific requirements. - Ethical and Legal Considerations: While Ethical Eye aims to promote ethical behavior, it is important to acknowledge that it may have limitations and biases inherent in its training data. Developers should exercise caution and consider the legal and ethical implications of relying solely on the model's outputs without human oversight.
BlueAvenir/model_growth_restructuring_V_1_0	BlueAvenir	2023-07-11T20:11:27Z	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	2023-07-11T20:10:59Z	--- 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 95 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": 95, "warmup_steps": 10, "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 -->
au2a/whisper-base-zh-20230711	au2a	2023-07-11T20:10:47Z	84	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:-", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-11T12:05:21Z	--- language: - zh license: apache-2.0 tags: - whisper - generated_from_trainer datasets: - '-' model-index: - name: whisper-base-zh-20230711 - au2a 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. --> # whisper-base-zh-20230711 - au2a This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the some hakka audio dataset. It achieves the following results on the evaluation set: - Loss: 0.4551 - Cer: 16.9978 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.4673 \| 0.65 \| 1000 \| 0.6526 \| 25.0548 \| \| 0.2203 \| 1.29 \| 2000 \| 0.4985 \| 19.8459 \| \| 0.1446 \| 1.94 \| 3000 \| 0.4557 \| 18.0026 \| \| 0.0956 \| 2.59 \| 4000 \| 0.4438 \| 16.9676 \| \| 0.0527 \| 3.24 \| 5000 \| 0.4450 \| 17.0998 \| \| 0.0423 \| 3.88 \| 6000 \| 0.4441 \| 17.7797 \| \| 0.027 \| 4.53 \| 7000 \| 0.4474 \| 16.9260 \| \| 0.0177 \| 5.18 \| 8000 \| 0.4515 \| 16.5861 \| \| 0.0165 \| 5.83 \| 9000 \| 0.4537 \| 16.8392 \| \| 0.0129 \| 6.47 \| 10000 \| 0.4551 \| 16.9978 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.11.0+cu113 - Datasets 2.13.1 - Tokenizers 0.13.3
datajanko/ppo-LunarLander-v2	datajanko	2023-07-11T20:08:45Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T20:08:25Z	--- 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: 268.35 +/- 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 ... ```
carova/ppo-Huggy	carova	2023-07-11T20:06:31Z	27	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T19:17:52Z	--- 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: carova/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
davidmunechika/coreml-future-diffusion	davidmunechika	2023-07-11T20:06:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T19:40:15Z	--- license: creativeml-openrail-m ---
BlueAvenir/model_it_recruit_V_0_1	BlueAvenir	2023-07-11T20:00:17Z	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	2023-07-11T19:59:50Z	--- 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 100 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": 100, "warmup_steps": 10, "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 -->
BlueAvenir/model_growth_restructuring_V_0_2	BlueAvenir	2023-07-11T19:51:05Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-07-11T19:50:43Z	--- 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 98 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": 98, "warmup_steps": 10, "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 -->
BlueAvenir/model_operations_V_0_2	BlueAvenir	2023-07-11T19:33:56Z	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	2023-07-11T19:33:15Z	--- 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 100 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": 100, "warmup_steps": 10, "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 -->
davidmunechika/oldjourney	davidmunechika	2023-07-11T19:29:28Z	31	0	diffusers	[ "diffusers", "Text-to-image", "Diffusers", "stable-diffusion", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-11T17:58:14Z	--- license: creativeml-openrail-m language: - en tags: - Text-to-image - Diffusers - stable-diffusion --- <b>Oldjourney</b> Oldjourney is a finetuned Stable Diffusion 2.1 model trained on images from Midjourney 3 using Dreambooth. That older version of Midjourney was often messy and imprecise, but had a great artistic style. These two versions of Oldjourney can recreate the essence of that art style with added details, precision, and quality. The two models, Oldjourney Ultra and Oldjourney Lite, are very similar, but they have different strengths. Ultra is better at people, while Lite is better at painterly style images. Use the keyword <b>Oldjourney</b> to trigger the style, and set the resolution to 768 x 768 or greater. Examples and sample prompts below. This is a model for Stable Diffusion 2.1, so make sure to download the yaml files. <b>Rendered with Oldjourney Lite</b> ![Oldjourney Lite.png](https://s3.amazonaws.com/moonup/production/uploads/1673363360976-6362b8dc2a84d82a8c91145c.png) <b>Rendered with Oldjourney Ultra</b> ![Oldjourney Ultra.png](https://s3.amazonaws.com/moonup/production/uploads/1673363412363-6362b8dc2a84d82a8c91145c.png) <b>Sample Prompts for Oldjourney Lite</b> <b>Sample 1</b> Oldjourney the legendary dream vortex and a dreamer, a boy laying on a bed in front of a vortex, ultrafine detailed painting, psychedelic art, watching the stars at night, pulled into the spiral vortex, videogame cover art, ... if only i could sleep, discord profile picture, time travel machine, photoshop render <b>Negative prompt:</b> pink, ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 810775161, Size: 768x768, Model: Oldjourney Lite, ENSD: 1</i> <b>Sample 2</b> Oldjourney an image of a wizard with a glowing staff turned to the side, black background, light art, full of colors and rich detail, color grunge, profile picture 1024px, glowing liquid, high detailed colors, colorful fire, an old man, blacklight, discord profile picture <b>Negative prompt:</b> ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2371590421, Size: 768x768, Model: Oldjourney Lite, ENSD: 1</i> <b>Sample 3</b> Oldjourney a dog with a tiny top hat and steampunk goggles on its head and a steampunk collar, matte painting, insanely detailed, ultrafine details, hyperrealism <b>Negative prompt:</b> (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 3142299054, Size: 768x768, Model: Oldjourney Lite, ENSD: 1</i> <b>Sample Prompts for Oldjourney Ultra</b> <b>Sample 4</b> Oldjourney A woman facing the camera dancing aura of cosmic energy vortex of sparkling blue sand and glowing embers ((grunge)) smoke magical eerie noir lighting stars in the sky ethereal dream sandman surreal rembrandt artstation dark atmosphere 8k highly detailed atmospheric <b>Negative prompt:</b> ugly, tiling, (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), out of frame, extra limbs, less than two arms, less than two legs, disfigured, deformed, body out of frame, blurry, (bad anatomy:1.2), blurred, grainy, cut off, draft, (overexposure:1.2), (high contrast:1.2),(cropped:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2676530026, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1</i> <b>Sample 5</b> Oldjourney your fate revealed inside a crystal ball, crystal ball with swirling otherworldly fog reveals your fate, insanely detailed masterpiece Trending on Artstation 8k ray traced volumetric lighting ambient occlusion ultrafine details digital art painting <b>Negative prompt:</b> ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2555061923, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1</i> <b>Sample 6</b> Oldjourney cosmic queen, ethereal woman with a crown on her head, head and shoulders portrait, fantasy art, star sky, star sky, face illuminated, sparkle, stars, cosmos, paticles <b>Negative prompt:</b> ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 868461039, Face restoration: GFPGAN, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1</i>
ontel/icaaalor	ontel	2023-07-11T19:26:09Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T19:24:54Z	--- license: creativeml-openrail-m ---
jakelcoop/Reinforce-pixelcopter	jakelcoop	2023-07-11T19:14:55Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T19:14:51Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 13.90 +/- 15.94 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
phatjk/bloomz-lora-vi-QA-NLLB-viquad	phatjk	2023-07-11T19:10:03Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-08T14:52:09Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
HiTZ/gpt2-eus-euscrawl	HiTZ	2023-07-11T18:54:30Z	169	1	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "eu", "dataset:HiTZ/euscrawl", "arxiv:1910.09700", "license:cc", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-02-20T15:35:58Z	--- license: cc datasets: - HiTZ/euscrawl language: - eu metrics: - perplexity library_name: transformers pipeline_tag: text-generation --- # Model Card for GPT2 Eus Euscrawl <!-- Provide a quick summary of what the model is/does. --> Pretrained GPT2 small model (124M parameters) on Basque language using a causal language modeling (CLM) objective. The English version of GPT2 was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> GPT-2 is a transformers model pretrained on a very large corpus of Basque data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the smallest version of GPT-2, with 124M parameters. - Developed by: [github.com/juletx](https://github.com/juletx) - Model type: GPT2 - Language(s) (NLP): Basque (eu) - License: cc ## Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [github.com/juletx/phd](https://github.com/juletx/phd) - 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. --> You can use this model directly with a pipeline for text generation. ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> You can also fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ## 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. --> The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## 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 Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) ``` # 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. --> EusCrawl (http://www.ixa.eus/euscrawl/) is a high-quality corpus for Basque comprising 12.5 million documents and 423 million tokens, totalling 2.1 GiB of uncompressed text. EusCrawl was built using ad-hoc scrapers to extract text from 33 Basque websites with high-quality content, resulting in cleaner text compared to general purpose approaches. [Dataset Card](https://huggingface.co/datasets/HiTZ/euscrawl) ## 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] The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,304. The inputs are sequences of 1024 consecutive tokens. ### Training Hyperparameters - Training regime: bf16 mixed precission <!--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: ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` 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]
niquito/falcon-7b-instruct-ft-adapters	niquito	2023-07-11T18:47:44Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-11T18:47:43Z	--- 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: True - bnb_4bit_compute_dtype: bfloat16 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
Winmodel/a2c-PandaReachDense-v2	Winmodel	2023-07-11T18:38:54Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T18:37:34Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.49 +/- 0.17 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
gerulata/slovakbert	gerulata	2023-07-11T18:36:33Z	4,830	19	transformers	[ "transformers", "pytorch", "tf", "safetensors", "roberta", "fill-mask", "SlovakBERT", "sk", "dataset:wikipedia", "dataset:opensubtitles", "dataset:oscar", "dataset:gerulatawebcrawl", "dataset:gerulatamonitoring", "dataset:blbec.online", "arxiv:2109.15254", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: sk tags: - SlovakBERT license: mit datasets: - wikipedia - opensubtitles - oscar - gerulatawebcrawl - gerulatamonitoring - blbec.online --- # SlovakBERT (base-sized model) SlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-sensitive: it makes a difference between slovensko and Slovensko. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. IMPORTANT: The model was not trained on the “ and ” (direct quote) character -> so before tokenizing the text, it is advised to replace all “ and ” (direct quote marks) with a single "(double quote marks). ### How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='gerulata/slovakbert') unmasker("Deti sa <mask> na ihrisku.") [{'sequence': 'Deti sa hrali na ihrisku.', 'score': 0.6355380415916443, 'token': 5949, 'token_str': ' hrali'}, {'sequence': 'Deti sa hrajú na ihrisku.', 'score': 0.14731724560260773, 'token': 9081, 'token_str': ' hrajú'}, {'sequence': 'Deti sa zahrali na ihrisku.', 'score': 0.05016357824206352, 'token': 32553, 'token_str': ' zahrali'}, {'sequence': 'Deti sa stretli na ihrisku.', 'score': 0.041727423667907715, 'token': 5964, 'token_str': ' stretli'}, {'sequence': 'Deti sa učia na ihrisku.', 'score': 0.01886524073779583, 'token': 18099, 'token_str': ' učia'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('gerulata/slovakbert') model = RobertaModel.from_pretrained('gerulata/slovakbert') text = "Text ktorý sa má embedovať." encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('gerulata/slovakbert') model = TFRobertaModel.from_pretrained('gerulata/slovakbert') text = "Text ktorý sa má embedovať." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` Or extract information from the model like this: ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='gerulata/slovakbert') unmasker("Slovenské národne povstanie sa uskutočnilo v roku <mask>.") [{'sequence': 'Slovenske narodne povstanie sa uskutočnilo v roku 1944.', 'score': 0.7383289933204651, 'token': 16621, 'token_str': ' 1944'},...] ``` # Training data The SlovakBERT model was pretrained on these datasets: - Wikipedia (326MB of text), - OpenSubtitles (415MB of text), - Oscar (4.6GB of text), - Gerulata WebCrawl (12.7GB of text) , - Gerulata Monitoring (214 MB of text), - blbec.online (4.5GB of text) The text was then processed with the following steps: - URL and email addresses were replaced with special tokens ("url", "email"). - Elongated interpunction was reduced (e.g. -- to -). - Markdown syntax was deleted. - All text content in braces f.g was eliminated to reduce the amount of markup and programming language text. We segmented the resulting corpus into sentences and removed duplicates to get 181.6M unique sentences. In total, the final corpus has 19.35GB of text. # Pretraining The model was trained in fairseq** on 4 x Nvidia A100 GPUs for 300K steps with a batch size of 512 and a sequence length of 512. The optimizer used is Adam with a learning rate of 5e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), a weight decay of 0.01, dropout rate 0.1, learning rate warmup for 10k steps and linear decay of the learning rate after. We used 16-bit float precision. ## About us <a href="https://www.gerulata.com/"> <img width="300px" src="https://www.gerulata.com/assets/images/Logo_Blue.svg"> </a> Gerulata Technologies is a tech company on a mission to provide tools for fighting disinformation and hostile propaganda. At Gerulata, we focus on providing state-of-the-art AI-powered tools that empower human analysts and provide them with the ability to make informed decisions. Our tools allow for the monitoring and analysis of online activity, as well as the detection and tracking of disinformation and hostile propaganda campaigns. With our products, our clients are better equipped to identify and respond to threats in real-time. ### BibTeX entry and citation info If you find our resource or paper is useful, please consider including the following citation in your paper. - https://arxiv.org/abs/2109.15254 ``` @misc{pikuliak2021slovakbert, title={SlovakBERT: Slovak Masked Language Model}, author={Matúš Pikuliak and Štefan Grivalský and Martin Konôpka and Miroslav Blšták and Martin Tamajka and Viktor Bachratý and Marián Šimko and Pavol Balážik and Michal Trnka and Filip Uhlárik}, year={2021}, eprint={2109.15254}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
SANTIAGo2005/ppo-LunarLander-v2	SANTIAGo2005	2023-07-11T18:21:31Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T18:17:32Z	--- 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: -116.26 +/- 67.01 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 ... ```
RogerB/afro-xlmr-large-finetuned-kintweets	RogerB	2023-07-11T18:13:40Z	98	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-11T18:07:30Z	--- license: mit tags: - generated_from_trainer model-index: - name: afro-xlmr-large-finetuned-kintweets 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. --> # afro-xlmr-large-finetuned-kintweets This model is a fine-tuned version of [Davlan/afro-xlmr-large](https://huggingface.co/Davlan/afro-xlmr-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7777 ## 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: 10 - eval_batch_size: 10 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9995 \| 1.0 \| 90 \| 1.6774 \| \| 1.9176 \| 2.0 \| 180 \| 1.6880 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Belphegor/Taxi-v3	Belphegor	2023-07-11T18:12:30Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T18:12:28Z	--- 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: 7.56 +/- 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="Belphegor/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"]) ```
Belphegor/q-FrozenLake-v1-4x4-noSlippery	Belphegor	2023-07-11T18:08:55Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T18:08:53Z	--- 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="Belphegor/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"]) ```
markjosims/wav2vec2-large-xls-r-300m-tr-colab	markjosims	2023-07-11T18:00:08Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-11T00:17:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-tr-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.37473189663977124 --- <!-- 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-large-xls-r-300m-tr-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4346 - Wer: 0.3747 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.9005 \| 4.26 \| 400 \| 0.6917 \| 0.7251 \| \| 0.4032 \| 8.51 \| 800 \| 0.4781 \| 0.5286 \| \| 0.1863 \| 12.77 \| 1200 \| 0.4682 \| 0.4690 \| \| 0.1323 \| 17.02 \| 1600 \| 0.4664 \| 0.4483 \| \| 0.1014 \| 21.28 \| 2000 \| 0.4500 \| 0.4124 \| \| 0.0749 \| 25.53 \| 2400 \| 0.4510 \| 0.3909 \| \| 0.0568 \| 29.79 \| 2800 \| 0.4346 \| 0.3747 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3
RogerB/KinyaBERT-small-finetuned-kintweets	RogerB	2023-07-11T17:52:20Z	115	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-11T17:51:35Z	--- tags: - generated_from_trainer model-index: - name: KinyaBERT-small-finetuned-kintweets 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. --> # KinyaBERT-small-finetuned-kintweets This model is a fine-tuned version of [jean-paul/KinyaBERT-small](https://huggingface.co/jean-paul/KinyaBERT-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3637 ## 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: 10 - eval_batch_size: 10 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.4855 \| 1.0 \| 90 \| 4.2106 \| \| 4.1658 \| 2.0 \| 180 \| 4.1444 \| \| 4.0402 \| 3.0 \| 270 \| 4.1616 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
swacks/ql-taxiv3	swacks	2023-07-11T17:41:32Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T17:41:30Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: ql-taxiv3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="swacks/ql-taxiv3", 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"]) ```
swacks/q-FrozenLake-v1-4x4-noSlippery	swacks	2023-07-11T17:39:19Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T17:39:16Z	--- 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="swacks/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"]) ```
Luke537/videomae-base-finetuned-ucf101-subset	Luke537	2023-07-11T17:30:17Z	59	0	transformers	[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-07-11T14:12:56Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer 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. ## 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: 8 - eval_batch_size: 8 - 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: 74 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cpu - Datasets 2.13.0 - Tokenizers 0.13.3
MaitreHibou/a2c-AntBulletEnv-v0	MaitreHibou	2023-07-11T17:25:59Z	1	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T17:24:54Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 732.68 +/- 43.01 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 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 ... ```
bhaskar-ruthvik/falcon7b-finance-tuned	bhaskar-ruthvik	2023-07-11T17:23:11Z	0	0	null	[ "region:us" ]	null	2023-07-11T14:32:51Z	# Falcon-7B Model Fine-Tuned on Finance Data This is a Falcon-7b model fine-tuned on finance data by using Parameter Efficient Fine-Tuning(PEFT) and Quantized Low-Rank Adapters. The data includes stock prices, transaction data, tweets about stocks and their sentiment analysis, frequently asked questions about the Finance industry. ## How was the data created? * The stock data was taken from the YFinance library which provides up-to-date stock prices. As the model cannot handle realtime data the last known stock prices will be dated 11-07-2023 which is the date of training of the model * The user transaction data was hand-crafted by a group member to look at the possibilities it would unlock without having to deal with the privacy concerns of using real user data * The tweets and their sentiments were taken from a kaggle dataset by Rutvik Nelluri * The faqs about finance were noted down through research on the internet and the prompts were framed using that data ## Why was the training data so small? * Despite the fact that the actual data collected was of a very large scale including atleast 5000 datapoints for the stocks and the tweets analysis, and that similar prompts for the transaction data and faqs could be generated using the OpenAI, the decision to stick with a much smaller subset of the data is to improve the training time on lower-end GPUs * This is also why PEFT and QLoRA have been used for the fine-tuning of the model which drastically reduce the trainable weights from 7 Billion to 432k which is significantly smaller ## How was the model trained? The model was trained by using the built-in transformers trainer with max_steps set to 140 which is approximately equal to 4 epochs of training. The final step training loss was 0.49. ## How to Run? 1. First run the cells to install all the libraries in their required versions (This code snippet uses a custom version of transformers but we can now use the official release): ``` python pip install -Uqqq pip --progress-bar off pip install -qqq bitsandbytes==0.39.0 --progress-bar off pip install -qqq torch==2.0.1 --progress-bar off pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc --progress-bar off pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71 --progress-bar off pip install -qqq datasets==2.12.0 --progress-bar off pip install -qqq loralib==0.1.1 --progress-bar off pip install -qqq einops==0.6.1 --progress-bar off ``` 2. Now import all the necessary libraries and set the default device to the gpu: ``` python import json import os from pprint import pprint import bitsandbytes as bnb import pandas as pd import torch import torch.nn as nn import transformers from datasets import load_dataset from huggingface_hub import notebook_login from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training, ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) os.environ['CUDA_VISIBLE_DEVICES'] = '0' ``` 3. Then load the model using 8-bit preferably if the hardware allows for it, to speed up the inference time: ``` python PEFT_MODEL = 'bhaskar-ruthvik/falcon7b-finance-tuned' config = PeftConfig.from_pretrained(PEFT_MODEL) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, return_dict = True, device_map = 'auto', trust_remote_code = True, load_in_8bit = True ) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model,PEFT_MODEL) ``` 4. Setup the generation configuration: ``` python generation_config = model.generation_config generation_config.max_new_tokens = 200 generation_config.temperature = 0.7 generation_config.top_p = 0.7 generation_config.num_return_sequences = 1 generation_config.pad_token_id = tokenizer.eos_token_id generation_config.eos_token_id = tokenizer.eos_token_id ``` 5. Now declare a function to generate prompts so the code can be reused ``` python def generate_response(question: str)->str: prompt = f""" <human>: {question} <bot>: """.strip() encoding = tokenizer(prompt,return_tensors='pt').to(device) with torch.inference_mode(): outputs = model.generate(input_ids=encoding.input_ids, attention_mask = encoding.attention_mask, generation_config = generation_config) response = tokenizer.decode(outputs[0],skip_special_tokens=True) assistant_start = "<bot>:" response_start = response.find(assistant_start) return response[response_start+ len(assistant_start) :].strip() ``` 6. Now provide the prompt to the model and wait for the inference (takes about 40 seconds): ``` python prompt = 'What is estate planning?' print('%.300s' % generate_response(prompt)) ```
Noureldin2303/ProvaImg	Noureldin2303	2023-07-11T17:22:17Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2023-07-11T17:22:17Z	--- license: bigcode-openrail-m ---
sanchit-gandhi/speecht5_tts_vox_nl	sanchit-gandhi	2023-07-11T17:21:55Z	94	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-11T17:19:50Z	--- license: mit tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] pipeline_tag: text-to-speech --- <!-- 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_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4587 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5225 \| 4.3 \| 1000 \| 0.4778 \| \| 0.5007 \| 8.61 \| 2000 \| 0.4656 \| \| 0.493 \| 12.91 \| 3000 \| 0.4602 \| \| 0.4902 \| 17.21 \| 4000 \| 0.4587 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
gokuls/sa_bert_12_layer_modified_complete_training_72	gokuls	2023-07-11T17:19:06Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T16:40:56Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: sa_bert_12_layer_modified_complete_training_72 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. --> # sa_bert_12_layer_modified_complete_training_72 This model is a fine-tuned version of [gokuls/sa_bert_12_layer_modified_complete_training_48](https://huggingface.co/gokuls/sa_bert_12_layer_modified_complete_training_48) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6236 - Accuracy: 0.5322 ## 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: 32 - eval_batch_size: 32 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.0311 \| 0.05 \| 10000 \| 2.8263 \| 0.5069 \| \| 2.8816 \| 0.11 \| 20000 \| 2.7833 \| 0.5126 \| \| 2.7734 \| 0.16 \| 30000 \| 2.7565 \| 0.5158 \| \| 2.7612 \| 0.22 \| 40000 \| 2.7284 \| 0.5196 \| \| 2.8843 \| 0.27 \| 50000 \| 2.7006 \| 0.5229 \| \| 2.7809 \| 0.33 \| 60000 \| 2.6765 \| 0.5254 \| \| 2.6683 \| 0.38 \| 70000 \| 2.6580 \| 0.5276 \| \| 2.7175 \| 0.44 \| 80000 \| 2.6270 \| 0.5316 \| \| 2.8903 \| 0.49 \| 90000 \| 2.6236 \| 0.5322 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
smerrill726/bank-sub	smerrill726	2023-07-11T17:15:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T17:15:35Z	--- license: creativeml-openrail-m ---
mark-oppenheim/Taxi-v3-V1	mark-oppenheim	2023-07-11T17:03:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T17:03:53Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-V1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.70 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="mark-oppenheim/Taxi-v3-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"]) ```
mark-oppenheim/q-FrozenLake-v1-4x4-Slippery	mark-oppenheim	2023-07-11T16:59:57Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T20:08:34Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.72 +/- 0.45 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="mark-oppenheim/q-FrozenLake-v1-4x4-Slippery", 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"]) ```
luzflavio/distilbert-base-uncased-finetuned-cola	luzflavio	2023-07-11T16:50:38Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T16:45:34Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: luzflavio/distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # luzflavio/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1975 - Validation Loss: 0.5266 - Train Matthews Correlation: 0.5279 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5218 \| 0.4601 \| 0.4776 \| 0 \| \| 0.3330 \| 0.4767 \| 0.5113 \| 1 \| \| 0.1975 \| 0.5266 \| 0.5279 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
d9021001/mms-1b-l1107-nan	d9021001	2023-07-11T16:49:35Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-11T15:49:29Z	--- tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: mms-1b-l1107-nan results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: nan-tw split: test args: nan-tw metrics: - name: Wer type: wer value: 1.005720823798627 --- <!-- 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. --> # mms-1b-l1107-nan This model was trained from scratch on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 1.5084 - Wer: 1.0057 ## 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: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 12.5725 \| 2.0 \| 100 \| 1.8002 \| 1.0 \| \| 1.5002 \| 4.0 \| 200 \| 1.5084 \| 1.0057 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
asenella/mmnist_JNFDccaconfig_resnet_seed_0_ratio_0_c	asenella	2023-07-11T16:47:28Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-11T16:47:19Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
SHENMU007/neunit_BASE_V11.2	SHENMU007	2023-07-11T16:45:51Z	74	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	2023-07-11T14:03:10Z	--- 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
vluz/MiniAsirraONNX	vluz	2023-07-11T16:42:29Z	0	0	null	[ "onnx", "license:cc0-1.0", "region:us" ]	null	2023-07-11T16:38:04Z	--- license: cc0-1.0 --- Very small onnx model, trained on Asirra 150 dataset, and intended as an example of Lobe beta It classifies input images as "Cat" or "Dog" Untested, do not use for production
MaitreHibou/ppo-Pyramids	MaitreHibou	2023-07-11T16:28:23Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-11T16:28:19Z	--- 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: MaitreHibou/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
JTStephens/Reinforce-CartPoleV1	JTStephens	2023-07-11T16:23:23Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T16:23:15Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPoleV1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 416.60 +/- 38.82 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
Desainakut/YPKuatsi	Desainakut	2023-07-11T16:09:02Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T06:33:39Z	--- license: creativeml-openrail-m ---
1aurent/ppo-PyramidsRND	1aurent	2023-07-11T16:08:35Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-11T16:07:10Z	--- 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: 1aurent/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
SANTIAGo2005/ppo-Huggy	SANTIAGo2005	2023-07-11T16:07:41Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T16:07:36Z	--- 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: SANTIAGo2005/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
gokuls/sa_bert_12_layer_modified_complete_training_24	gokuls	2023-07-11T16:03:29Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T15:24:07Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: sa_bert_12_layer_modified_complete_training_24 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. --> # sa_bert_12_layer_modified_complete_training_24 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.7648 - Accuracy: 0.1722 ## 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: 32 - eval_batch_size: 32 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 6.5933 \| 0.05 \| 10000 \| 6.5711 \| 0.1226 \| \| 6.1523 \| 0.11 \| 20000 \| 6.3425 \| 0.1396 \| \| 6.1308 \| 0.16 \| 30000 \| 6.2468 \| 0.1444 \| \| 6.2297 \| 0.22 \| 40000 \| 6.1895 \| 0.1468 \| \| 6.1484 \| 0.27 \| 50000 \| 6.1483 \| 0.1487 \| \| 6.0591 \| 0.33 \| 60000 \| 6.1205 \| 0.1492 \| \| 6.0199 \| 0.38 \| 70000 \| 6.0862 \| 0.1501 \| \| 5.8666 \| 0.44 \| 80000 \| 5.8875 \| 0.1600 \| \| 5.9153 \| 0.49 \| 90000 \| 5.7648 \| 0.1722 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
MUNDOJU/ppo-Huggy	MUNDOJU	2023-07-11T16:02:28Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T16:02:25Z	--- 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: MUNDOJU/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
caballeroch/FakeNewsClassifierDistilBert-uncased	caballeroch	2023-07-11T16:01:26Z	106	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "dataset:liar", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T19:17:04Z	--- datasets: - liar metrics: - accuracy - f1 - precision - recall --- # Fake News Classifier - Finetuned: 'distilbert-base-uncased' #### LIAR Dataset * - This model is finetuned on a large dataset of hand-labeled short statements from politifact.com's API. - Data went through a series of text cleaning stages such as: 1. Lower-case standardization for improved 'uncased' model performance. 2. Mixed letter/digit word removal. 3. Stopword removal. 4. Extra space trimming. #### DistilBERT Uncased Tokenizer * - The text is tokenized using the 'distilbert-base-uncased' HuggingFace tokenizer. - For training, the text is cut to a block-size of 200. - Max length padding is used to maintain consistent input data shape. #### DistilBERT Uncased Model * - The model that is finetuned is the DistilBERT model, 'distilbert-base-uncased'**. - This is a small and fast text classifier, perfect for real-time inference! - 40% less parameters than the base BERT model. - 60% faster while preserving 95% performance of the base BERT model. - This model outperforms the finetuned 'distilbert-base-cased' by over 5% average F1-score. - This improvement comes mainly from the slower learning rate and improved data preprocessing. - These modifications allow for a smoother training curve and convergence.
caballeroch/FakeNewsClassifierDistilBert-cased	caballeroch	2023-07-11T16:00:55Z	104	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "dataset:liar", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-08T20:42:56Z	--- datasets: - liar metrics: - accuracy - f1 - precision - recall --- # Fake News Classifier - Finetuned: 'distilbert-base-cased' #### LIAR Dataset * - This model is finetuned on a large dataset of hand-labeled short statements from politifact.com's API. - Relevant columns of the data (speaker, statement, etc.) are concatenated and tokenized to create the model input. #### DistilBERT Cased Tokenizer * - The text is tokenized using the 'distilbert-base-cased' HuggingFace tokenizer. - For training, the text is cut to a block-size of 200. - Max length padding is used to maintain consistent input data shape. #### DistilBERT Cased Model * - The model that is finetuned is the DistilBERT model, 'distilbert-base-cased'. - This is a small and fast text classifier, perfect for real-time inference! - 40% less parameters than the base BERT model. - 60% faster while preserving 95% performance of the base BERT model. - The intuition for using the cased** model is to capture some patterns in the writing style (capitalization, punctuation). - This information may be relevant for detecting fake news sources. - Writing styles may be relevant (as we see in clickbait titles with capitalization). - This model performs well in flagging misinformation (fake news), especially if the format is similar to the training distribution. - Overall, the performance is worse than the finetuned 'distilbert-base-uncased,' as the training data is less clean.
fireday/ppo-Huggy	fireday	2023-07-11T16:00:33Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T16:00:29Z	--- 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: fireday/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Aceituna0813/ppo-Huggy	Aceituna0813	2023-07-11T15:57:29Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T15:57:16Z	--- 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: Aceituna0813/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Corran/all-mini-v2-L6-ft	Corran	2023-07-11T15:50:19Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-11T15:50:16Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # Corran/all-mini-v2-L6-ft 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("Corran/all-mini-v2-L6-ft") # 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} } ```
TheBloke/open-llama-7B-v2-open-instruct-GGML	TheBloke	2023-07-11T15:43:30Z	0	6	null	[ "license:other", "region:us" ]	null	2023-07-11T13:47:05Z	--- 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 --> # VMware's Open Llama 7B v2 Open Instruct GGML These files are GGML format model files for [VMware's Open Llama 7B v2 Open Instruct](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. These files 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/open-llama-7B-v2-open-instruct-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/open-llama-7B-v2-open-instruct-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| open-llama-7b-v2-open-instruct.ggmlv3.q2_K.bin \| q2_K \| 2 \| 2.87 GB\| 5.37 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| open-llama-7b-v2-open-instruct.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 3.60 GB\| 6.10 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| open-llama-7b-v2-open-instruct.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 3.28 GB\| 5.78 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| open-llama-7b-v2-open-instruct.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 2.95 GB\| 5.45 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| open-llama-7b-v2-open-instruct.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 3.79 GB\| 6.29 GB \| Original quant method, 4-bit. \| \| open-llama-7b-v2-open-instruct.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 4.21 GB\| 6.71 GB \| Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| open-llama-7b-v2-open-instruct.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 4.08 GB\| 6.58 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| open-llama-7b-v2-open-instruct.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 3.83 GB\| 6.33 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| open-llama-7b-v2-open-instruct.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 4.63 GB\| 7.13 GB \| Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| open-llama-7b-v2-open-instruct.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 5.06 GB\| 7.56 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| open-llama-7b-v2-open-instruct.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 4.78 GB\| 7.28 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| open-llama-7b-v2-open-instruct.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 4.65 GB\| 7.15 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| open-llama-7b-v2-open-instruct.ggmlv3.q6_K.bin \| q6_K \| 6 \| 5.53 GB\| 8.03 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| open-llama-7b-v2-open-instruct.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 7.16 GB\| 9.66 GB \| Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m open-llama-7b-v2-open-instruct.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- 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: VMware's Open Llama 7B v2 Open Instruct # VMware/open-llama-7B-v2-open-instruct Instruction-tuned version of the fully trained Open LLama 7B v2 model. The model is open for <b>COMMERCIAL USE</b>. <br> - This model performs better on code compared to v1 due to the improvements made on the base model by the openlm-research team. - The instruction model is trained on an improved instruction tuning dataset compared to v1 <b> NOTE </b> : The model was trained using the Alpaca prompt template <b> NOTE </b> : Fast tokenizer results in incorrect encoding, set the ```use_fast = False``` parameter, when instantiating the tokenizer ## License - <b>Commercially Viable </b> - Open-instruct-v1 - Mosaic/Dolly-HHRLHF + filtered OASST1 - cc by 3.0 Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples - ESNLI - MIT - ECQA - CDLA 1.0 - Sharing - Strategy - MIT - CREAK - MIT - gsmk8 - MIT - aqua - MIT - qasc - Apache 2.0 - Language Model, ([openlm-research/open_llama_v2_7b](https://huggingface.co/openlm-research/open_llama_v2_7b)) is under apache-2.0 ## Nomenclature - Model : Open-llama-v2 - Model Size: 7B parameters - Dataset: Open-instruct(oasst,dolly, hhrlhf) ## Use in Transformers ``` import os import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = 'VMware/open-llama-7b-open-instruct' tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential') prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" prompt = """What is attention mechanism of a transformer model? Write a python code to illustrate how attention works within a transformer model using numpy library. Donot use pytorch or tensorflow.""" inputt = prompt_template.format(instruction= prompt) input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda") output1 = model.generate(input_ids, max_length=512) input_length = input_ids.shape[1] output1 = output1[:, input_length:] output = tokenizer.decode(output1[0]) print(output) ''' Sure, I can help you with that! Attention mechanisms in transformer models are typically implemented using the attention mechanism in the self-attention layer. Self-attention allows the model to focus on different parts of the input sequence when processing it. This is achieved by computing a set of attention weights, which are used to weigh the contribution of each input element to the output. Here's an example code using NumPy to illustrate how attention works in a transformer model: ```python import numpy as np def attention_weights(query, key, value, mask): # Query, key, and value are input tensors. Mask is a tensor of zeros and ones that represents the attention mask. # It is used to prevent the model from attending to certain positions in the input sequence if they are not relevant. # The attention weights are the element-wise product of the query, key, and mask tensors. # The result is a tensor of the same shape as the query tensor. # Compute the dot product between the query tensor and the key tensor dot = np.matmul(query, key) # Compute the element-wise softmax of the dot product tensor exp_dot = np.exp(dot) # Multiply the dot product and the softmax of the dot product tensors weights = dot * exp_dot # Return the attention weights as a NumPy tensor return weights # Define the input sequence query = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) key = np.array([[0.1, 0.2], [0.3, 0.4]]) value = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) mask = np.array([[False, True, True], [False, True, True]]) # Compute the attention weights weights = attention_weights(query, key, value, mask) # Print the attention weights print(weights) ``` In this example, the `attention_weights` function takes as input the query tensor, key tensor, value tensor, and mask tensor. It computes the dot product between the query and key tensors using the `np.matmul` function, and then applies a softmax function using the `np.exp` function to the element-wise dot product tensor. It then multiplies the dot product and softmax tensors using the `np.matmul` function, and returns the result as a NumPy tensor. The `query`, `key`, and `value` tensors represent the input sequence to the transformer model. The `mask` tensor represents the attention mask, which is used to prevent the model from attending to certain positions in the input sequence if they are not relevant. The output of the `attention_weights` function is a NumPy tensor that represents the attention weights for the input sequence. These weights are used by the transformer model to weigh the contribution of each input element to the output. I hope this helps!</s> ''' ``` ## Finetuning details The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning) ## Evaluation <B>TODO</B>
parchiev/distilbert-base-uncased-finetuned-imdb	parchiev	2023-07-11T15:39:16Z	124	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-03T11:37:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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. --> # distilbert-base-uncased-finetuned-imdb 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: 2.4721 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7086 \| 1.0 \| 157 \| 2.4897 \| \| 2.5796 \| 2.0 \| 314 \| 2.4230 \| \| 2.5269 \| 3.0 \| 471 \| 2.4354 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
espnet/guangzhisun_librispeech100_asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix	espnet	2023-07-11T15:24:22Z	2	1	espnet	[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech_100", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2023-07-10T23:07:45Z	--- tags: - espnet - audio - automatic-speech-recognition language: en datasets: - librispeech_100 license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/guangzhisun_librispeech100_asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix` This model was trained by guangzhisun using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet pip install -e . cd egs2/librispeech_100/asr1_biasing ./run.sh --skip_data_prep false --skip_train true --download_model espnet/guangzhisun_librispeech100_asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix ``` # TCPGen in RNN-T # RESULTS ## Environments - date: `Wed Jul 5 02:01:19 BST 2023` - python version: `3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]` - espnet version: `espnet 202304` - pytorch version: `pytorch 2.0.1+cu117` - Git hash: `6f33b9d9a999d4cd7e9bc0dcfc0ba342bdff7c17` - Commit date: `Thu Jun 29 02:16:09 2023 +0100` ## exp/asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_asr_model_valid.loss.ave/dev_clean\|2703\|54402\|95.7\|3.9\|0.4\|0.6\|4.9\|48.0\| \|decode_asr_asr_model_valid.loss.ave/dev_other\|2864\|50948\|85.8\|12.6\|1.6\|1.9\|16.1\|77.0\| \|decode_asr_asr_model_valid.loss.ave/test_clean\|2620\|52576\|95.4\|4.1\|0.5\|0.7\|5.2\|49.9\| \|decode_asr_asr_model_valid.loss.ave/test_other\|2939\|52343\|86.0\|12.2\|1.7\|1.8\|15.8\|78.4\| \|decode_b20_nolm_avebest/test_clean\|2620\|52576\|0.0\|0.0\|100.0\|0.0\|100.0\|100.0\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_asr_model_valid.loss.ave/dev_clean\|2703\|288456\|98.4\|1.0\|0.7\|0.6\|2.3\|48.0\| \|decode_asr_asr_model_valid.loss.ave/dev_other\|2864\|265951\|93.3\|4.2\|2.5\|2.1\|8.8\|77.0\| \|decode_asr_asr_model_valid.loss.ave/test_clean\|2620\|281530\|98.3\|1.0\|0.7\|0.6\|2.3\|49.9\| \|decode_asr_asr_model_valid.loss.ave/test_other\|2939\|272758\|93.6\|3.8\|2.6\|1.9\|8.3\|78.4\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_asr_model_valid.loss.ave/dev_clean\|2703\|103998\|95.3\|3.5\|1.2\|0.6\|5.3\|48.0\| \|decode_asr_asr_model_valid.loss.ave/dev_other\|2864\|95172\|85.2\|11.8\|3.0\|2.5\|17.3\|77.0\| \|decode_asr_asr_model_valid.loss.ave/test_clean\|2620\|102045\|95.3\|3.4\|1.3\|0.6\|5.4\|49.9\| \|decode_asr_asr_model_valid.loss.ave/test_other\|2939\|98108\|85.5\|11.0\|3.5\|2.2\|16.7\|78.4\| ## ASR config <details><summary>expand</summary> ``` config: conf/train_rnnt.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix ngpu: 1 seed: 2022 num_workers: 8 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 70 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 6000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe600_spsuffix/train/speech_shape - exp/asr_stats_raw_en_bpe600_spsuffix/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe600_spsuffix/valid/speech_shape - exp/asr_stats_raw_en_bpe600_spsuffix/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] train_data_path_and_name_and_type: - - dump/raw/train_clean_100_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_clean_100_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} biasing: false deepbiasing: false biasinglist: '' battndim: 256 biasingsche: 0 bmaxlen: 100 bdrop: 0.0 biasingGNN: '' optim: adam optim_conf: lr: 0.002 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - THE▁ - C - AND▁ - S - OF▁ - S▁ - TO▁ - T - A▁ - G - I - ED▁ - E - RE - D - IN▁ - P - R - N - F - O - IN - B - T▁ - L - ING▁ - ▁ - W - I▁ - HE▁ - WAS▁ - A - THAT▁ - E▁ - IT▁ - AR - U - H - ES▁ - M - RI - '''' - HIS▁ - AN - D▁ - Y▁ - LY▁ - ON▁ - AS▁ - HAD▁ - WITH▁ - ST - Y - EN - HER▁ - YOU▁ - K - DE - AT▁ - FOR▁ - V - UN - TH - SE - RO - LI - LO - NOT▁ - TI - AL - BUT▁ - IS▁ - ER▁ - SI - OR - CH - ONE▁ - SHE▁ - OR▁ - ME▁ - BE▁ - K▁ - LA - LE - ALL▁ - HIM▁ - BE - CON - HO - PO - AT - THEY▁ - MY▁ - ME - 'ON' - BY▁ - AN▁ - VE▁ - DI - RA - AC - MA - HAVE▁ - SO▁ - WERE▁ - WHICH▁ - TED▁ - AL▁ - THIS▁ - FROM▁ - AD - SU - FI - AS - SAID▁ - ER - TH▁ - SE▁ - RY▁ - MO - EN▁ - FOR - HE - EX - NE - M▁ - VI - TS▁ - SH - BO - COM - PRO - EL - ARE▁ - FE - WE▁ - N▁ - NO▁ - ERS▁ - QU - THERE▁ - THEIR▁ - LE▁ - WHEN▁ - TE - TA - TY▁ - PER - THEM▁ - TER - WOULD▁ - OLD▁ - PA - CO - IR - IF▁ - WHO▁ - WHAT▁ - TER▁ - MAN▁ - ATION▁ - ST▁ - BEEN▁ - OUR▁ - CA - UP▁ - OUT▁ - PRE - AP - TION▁ - IT - FA - US - AM - VE - TUR - DO - PAR - PE - 'NO' - LU - THEN▁ - WI - SO - HI - P▁ - TO - COULD▁ - RE▁ - Z - WILL▁ - KING▁ - EAR▁ - DIS - EST▁ - LL▁ - SP - HA - ENCE▁ - TING▁ - IS - WE - DU - AND - MORE▁ - SOME▁ - US▁ - PI - ABLE▁ - NOW▁ - VERY▁ - GU - EM - ITY▁ - WA - H▁ - ATE▁ - LL - DO▁ - NA - DER - ANT▁ - LEA - PLA - BU - SA - CU - INTO▁ - OWN▁ - ET▁ - KE - PU - LITTLE▁ - MENT▁ - VER - TE▁ - DID▁ - LIKE▁ - IM - ABOUT▁ - OUR - TRA - TIME▁ - THAN▁ - YOUR▁ - RED▁ - MI - OTHER▁ - HU - ION▁ - ANCE▁ - STR - WELL▁ - W▁ - L▁ - ES - ANY▁ - ITS▁ - MIS - AB - AGE▁ - MAR - UPON▁ - OVER▁ - TU - DAY▁ - TEN - CH▁ - ALLY▁ - GRA - CAME▁ - MEN▁ - STO - LED▁ - AM▁ - GA - ONLY▁ - COME▁ - TWO▁ - UG - HOW▁ - VEN - INE▁ - NESS▁ - EL▁ - HAS▁ - BA - LONG▁ - AFTER▁ - IC▁ - WAY▁ - CAR - SC - HAR - MADE▁ - MIN - STE - BEFORE▁ - MOST▁ - ILL - FO - GE - DOWN▁ - DER▁ - BL - IONS▁ - SUCH▁ - THESE▁ - DE▁ - MEN - KED▁ - TRU - WHERE▁ - FUL▁ - BI - CAN▁ - SEE▁ - KNOW▁ - GO▁ - JE - GREAT▁ - LOW▁ - MUCH▁ - NEVER▁ - MISTER▁ - GOOD▁ - SHOULD▁ - EVEN▁ - ICE▁ - STA - LESS▁ - JO - BLE▁ - MUST▁ - AV - DA - ISH▁ - MON - TRI - KE▁ - BACK▁ - YING▁ - AIR▁ - AU - IOUS▁ - AGAIN▁ - MU - FIRST▁ - F▁ - GO - EVER▁ - VA - COR - OUS▁ - ATED▁ - COUNT - ROUND▁ - OVER - LING▁ - HERE▁ - HIMSELF▁ - SHED▁ - MIL - G▁ - THOUGH▁ - SIDE▁ - CL - MAY▁ - JUST▁ - WENT▁ - SAY▁ - NG▁ - PASS - HER - NED▁ - MIGHT▁ - FR - MAN - HOUSE▁ - JU - SON▁ - PEN - THROUGH▁ - EYES▁ - MAKE▁ - TOO▁ - THOUGHT▁ - WITHOUT▁ - THINK▁ - GEN - THOSE▁ - MANY▁ - SPEC - INTER - WHILE▁ - AWAY▁ - LIFE▁ - HEAD▁ - SUR - NTLY▁ - RIGHT▁ - DON - TAKE▁ - PORT - EVERY▁ - NIGHT▁ - WARD▁ - WAR - IMP - ALL - GET▁ - STILL▁ - BEING▁ - FOUND▁ - NOTHING▁ - LES▁ - LAST▁ - TURNED▁ - ILL▁ - YOUNG▁ - SURE▁ - INGS▁ - PEOPLE▁ - YET▁ - THREE▁ - FACE▁ - CUR - OFF▁ - ROOM▁ - OUT - ASKED▁ - SAW▁ - END▁ - FER - MISSUS▁ - EACH▁ - SAME▁ - SHA - SENT▁ - OUL - LET▁ - SOL - YOU - PLACE▁ - UNDER▁ - TOOK▁ - LIGHT▁ - LEFT▁ - PER▁ - PRESS - USE▁ - ANOTHER▁ - ONCE▁ - TELL▁ - SHALL▁ - 'OFF' - SEEMED▁ - ALWAYS▁ - NEW▁ - ATIONS▁ - J - CESS - USED▁ - WHY▁ - HEARD▁ - LOOKED▁ - GIVE▁ - PUT▁ - JA - BECAUSE▁ - THINGS▁ - BODY▁ - FATHER▁ - SOMETHING▁ - OWING▁ - LOOK▁ - ROW▁ - GOING▁ - MOTHER▁ - MIND▁ - WORK▁ - GOT▁ - CENT - HAVING▁ - SOON▁ - KNEW▁ - HEART▁ - FAR▁ - AGAINST▁ - WORLD▁ - FEW▁ - ICAL▁ - STOOD▁ - BEGAN▁ - SIR▁ - BETTER▁ - DOOR▁ - CALLED▁ - YEARS▁ - MOMENT▁ - ENOUGH▁ - WOMAN▁ - TOGETHER▁ - LIGHT - OWED▁ - READ▁ - WHOLE▁ - COURSE▁ - BETWEEN▁ - FELT▁ - LONG - HALF▁ - FULLY▁ - MORNING▁ - DENT - WOOD - HERSELF▁ - OLD - DAYS▁ - HOWEVER▁ - WATER▁ - WHITE▁ - PERHAPS▁ - REPLIED▁ - GIRL▁ - QUITE▁ - HUNDRED▁ - WORDS▁ - MYSELF▁ - VOICE▁ - EARLY▁ - OUGHT▁ - AIL▁ - WORD▁ - WHOM▁ - EITHER▁ - AMONG▁ - ENDED▁ - TAKEN▁ - UNTIL▁ - ANYTHING▁ - NEXT▁ - POSSIBLE▁ - KIND▁ - BROUGHT▁ - EAST▁ - LOOKING▁ - ROAD▁ - SMALL▁ - RATHER▁ - BELIEVE▁ - SINCE▁ - MONEY▁ - OPEN▁ - INDEED▁ - DOUBT - CERTAIN▁ - TWENTY▁ - MATTER▁ - HELD▁ - EXPECT - DIRECT - ANSWERED▁ - THERE - WHOSE▁ - SHIP▁ - HIGH▁ - THEMSELVES▁ - APPEARED▁ - BLACK▁ - NATURE▁ - BEHIND▁ - POWER▁ - IZED▁ - CHILD▁ - UNCLE▁ - DEATH▁ - KNOWN▁ - OFTEN▁ - LADY▁ - POSITION▁ - KEEP▁ - CHILDREN▁ - WIFE▁ - JOHN▁ - LARGE▁ - GIVEN▁ - EIGHT▁ - SHORT▁ - SAYS▁ - EVERYTHING▁ - GENERAL▁ - DOCTOR▁ - ABOVE▁ - HAPPY▁ - Q - X - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: joint_space_size: 320 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram600suffix/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe600_spsuffix/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.0 report_cer: false report_wer: false biasinglist: data/Blist/rareword_f15.txt bmaxlen: 500 bdrop: 0.0 battndim: 256 biasing: true biasingsche: 30 deepbiasing: true biasingGNN: gcn6 bpemodel: data/en_token_list/bpe_unigram600suffix/bpe.model preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 15 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 256 dropout: 0.1 dropout_embed: 0.2 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202304' distributed: false ``` </details> ### Citing TCPGen ```BibTex @INPROCEEDINGS{9687915, author={Sun, Guangzhi and Zhang, Chao and Woodland, Philip C.}, booktitle={2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, title={Tree-Constrained Pointer Generator for End-to-End Contextual Speech Recognition}, year={2021}, volume={}, number={}, pages={780-787}, doi={10.1109/ASRU51503.2021.9687915} } @inproceedings{Sun2022TreeconstrainedPG, title={Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition}, author={Guangzhi Sun and C. Zhang and Philip C. Woodland}, booktitle={Interspeech}, year={2022} } ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ```
vk21/a2c-PandaReachDense-v2-unit6	vk21	2023-07-11T15:23:31Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T15:06:09Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.39 +/- 0.17 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
grace-pro/afriberta-base-finetuned-igbo	grace-pro	2023-07-11T15:18:59Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-11T14:32:20Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-base-finetuned-igbo 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-base-finetuned-igbo This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2159 - Precision: 0.7242 - Recall: 0.5039 - F1: 0.5943 - Accuracy: 0.9367 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1989 \| 1.0 \| 2514 \| 0.2020 \| 0.7134 \| 0.4098 \| 0.5206 \| 0.9285 \| \| 0.1759 \| 2.0 \| 5028 \| 0.2125 \| 0.7383 \| 0.4263 \| 0.5405 \| 0.9315 \| \| 0.1417 \| 3.0 \| 7542 \| 0.2044 \| 0.7320 \| 0.4736 \| 0.5751 \| 0.9352 \| \| 0.1279 \| 4.0 \| 10056 \| 0.2066 \| 0.7341 \| 0.4884 \| 0.5866 \| 0.9363 \| \| 0.1132 \| 5.0 \| 12570 \| 0.2159 \| 0.7242 \| 0.5039 \| 0.5943 \| 0.9367 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ashnrk/textual_inversion_sealake	ashnrk	2023-07-11T15:04:24Z	14	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-11T14:02:17Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - ashnrk/textual_inversion_sealake These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1. You can find some example images in the following.
Belphegor/ppo-Huggy	Belphegor	2023-07-11T14:50:20Z	46	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T14:50:17Z	--- 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: Belphegor/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
turkish-nlp-suite/tr_vectors_web_lg	turkish-nlp-suite	2023-07-11T14:42:54Z	0	0	spacy	[ "spacy", "floret", "fasttext", "feature-extraction", "token-classification", "tr", "arxiv:1910.10683", "doi:10.57967/hf/0087", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-11-02T17:30:31Z	--- tags: - spacy - floret - fasttext - feature-extraction - token-classification language: - tr license: cc-by-sa-4.0 model-index: - name: tr_vectors_web_lg results: - task: name: NMT type: token-classification metrics: - name: Accuracy type: accuracy value: 0.1112 --- Medium sized Turkish Floret word vectors for spaCy. The vectors are trained on MC4 corpus using Floret with the following hyperparameters: ``` floret cbow -dim 300 --mode floret --bucket 200000 -minn 4 -maxn5 -minCount 100 -neg 10 -hashCount 2 -thread 12 -epoch 5 ``` Vector are published in Floret format. \| Feature \| Description \| \| --- \| --- \| \| Name \| `tr_vectors_web_lg` \| \| Version \| `1.0` \| \| Vectors \| 200000 keys (300 dimensions) \| \| Sources \| [MC4](https://arxiv.org/abs/1910.10683) \| \| License \| `cc-by-sa-4.0` \| \| Author \| [Duygu Altinok](https://www.onlyduygu.com/) \| --- If you'd like to use the vectors in your own work, please kindly cite the paper [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/): ``` @inproceedings{altinok-2023-diverse, title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish", author = "Altinok, Duygu", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.768", pages = "13739--13750", abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.", } ```
turkish-nlp-suite/tr_vectors_web_md	turkish-nlp-suite	2023-07-11T14:42:20Z	0	0	spacy	[ "spacy", "floret", "fasttext", "feature-extraction", "token-classification", "tr", "arxiv:1910.10683", "doi:10.57967/hf/0085", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-11-02T17:22:50Z	--- tags: - spacy - floret - fasttext - feature-extraction - token-classification language: - tr license: cc-by-sa-4.0 model-index: - name: tr_vectors_web_md results: - task: name: NMT type: token-classification metrics: - name: Accuracy type: accuracy value: 0.1112 --- Medium sized Turkish Floret word vectors for spaCy. The vectors are trained on MC4 corpus using Floret with the follwing hyperparameters: ``` floret cbow -dim 300 --mode floret --bucket 50000 -minn 4 -maxn5 -minCount 100 -neg 10 -hashCount 2 -thread 12 -epoch 5 ``` Vector are published in Floret format. \| Feature \| Description \| \| --- \| --- \| \| Name \| `tr_vectors_web_md` \| \| Version \| `1.0` \| \| Vectors \| 50000 keys (300 dimensions) \| \| Sources \| [MC4](https://arxiv.org/abs/1910.10683) \| \| License \| `cc-by-sa-4.0` \| \| Author \| [Duygu Altinok](https://www.onlyduygu.com/) \| --- If you'd like to use the vectors in your own work, please kindly cite the paper [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/): ``` @inproceedings{altinok-2023-diverse, title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish", author = "Altinok, Duygu", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.768", pages = "13739--13750", abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.", } ```
tyavika/lr1e5-layer2-bs16-Distil-CNN256LSTM128NoBi	tyavika	2023-07-11T14:42:14Z	76	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-11T11:07:20Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: lr1e5-layer2-bs16-Distil-CNN256LSTM128NoBi 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-layer2-bs16-Distil-CNN256LSTM128NoBi 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: 1.2859 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.928 \| 1.0 \| 3290 \| 1.5478 \| \| 1.1617 \| 2.0 \| 6580 \| 1.1964 \| \| 0.8463 \| 3.0 \| 9870 \| 1.2061 \| \| 0.6165 \| 4.0 \| 13160 \| 1.2859 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
CaliPanni/natcopeter	CaliPanni	2023-07-11T14:38:30Z	0	0	null	[ "region:us" ]	null	2023-07-11T14:02:57Z	NAT CO PETER OFFICIAL MODEL!!!!! (1.0)
gbellamy/rl_course_vizdoom_health_gathering_supreme	gbellamy	2023-07-11T14:31:42Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T14:31:32Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.87 +/- 4.95 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r gbellamy/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
matuane/distilbert-base-uncased-finetuned-cola	matuane	2023-07-11T14:22:57Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T03:58:34Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: matuane/distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # matuane/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1968 - Validation Loss: 0.5472 - Train Matthews Correlation: 0.5059 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5136 \| 0.4554 \| 0.4712 \| 0 \| \| 0.3229 \| 0.4651 \| 0.5136 \| 1 \| \| 0.1968 \| 0.5472 \| 0.5059 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
ericNguyen0132/roberta-large-Dep	ericNguyen0132	2023-07-11T14:20:36Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-02T12:57:45Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-large-Dep 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. --> # roberta-large-Dep This model is a fine-tuned version of [rafalposwiata/deproberta-large-depression](https://huggingface.co/rafalposwiata/deproberta-large-depression) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8107 - Accuracy: 0.8517 - F1: 0.9118 ## 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: 8 - eval_batch_size: 8 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 469 \| 0.3701 \| 0.87 \| 0.9264 \| \| 0.4293 \| 2.0 \| 938 \| 0.4385 \| 0.865 \| 0.9219 \| \| 0.3302 \| 3.0 \| 1407 \| 0.5293 \| 0.85 \| 0.9109 \| \| 0.2784 \| 4.0 \| 1876 \| 0.7077 \| 0.8517 \| 0.9118 \| \| 0.1914 \| 5.0 \| 2345 \| 0.8107 \| 0.8517 \| 0.9118 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
PeterBrendan/Adsdistilgpt2	PeterBrendan	2023-07-11T14:20:22Z	140	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-07T14:29:07Z	--- license: mit widget: - text: "Pizza" - text: "Nike Basketball" - text: "Used Porche" --- Model: distilgpt2 (GPT-2) Model name: Adsdistilgpt2 Model description: This is a fine-tuned version of the distilgpt2 model trained on a dataset of 10,000+ programmatic ad creatives. This model is designed to generate ad content given a product or a brand. For instance, when given the input "Nike Basketball", it will generate a sample ad and also suggest an ad size. The model's main purpose is to inspire ad creatives and provide a starting point for creating effective marketing content. Intended uses: This model is designed to be used as a starting point for creating ad creatives. You could use it in the early stages of your ad design process to generate creative ideas and inspiration. Limitations: This model has the potential to produce unusual or unexpected results, due to the varied and complex nature of advertising language. It should not be relied upon to produce perfect ad copy, but rather as a tool to inspire creative ideas. Also, the model might not have complete understanding of specific brand guidelines and may not adhere to them. How to use: You can use this model by providing a product or brand name as an input. For example: Nike Air Force Ones Training data: This model was trained on a dataset consisting of over 10,000 programmatic ad creatives, which included a variety of different product and brand advertisements. The data was collected from various ad platforms and represents a wide range of ad styles and formats. Training procedure: The model was fine-tuned using the distilgpt2 model with the aforementioned training data. The training loss was 0.16540415118743643. Evaluation results: As this model's primary objective is to generate creative ads, traditional evaluation metrics such as accuracy or F1 score are not applicable. However, the model's performance has been informally assessed based on the relevancy and creativity of the generated ads. Safety and bias considerations: This model shares the same safety and bias considerations as the distilgpt2 model. It may generate content that is offensive or inappropriate. Also, as the model is trained on data from the internet, it may reflect the biases present in those sources. Users should carefully review the generated ads to ensure they align with their brand's values and guidelines before using them. The model is not intended to replace the role of a human in creating ad copy, but rather to assist and provide inspiration.
parandhamuduchakali/bert-finetuned-ner	parandhamuduchakali	2023-07-11T14:20:04Z	61	0	transformers	[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-11T12:41:11Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: parandhamuduchakali/bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # parandhamuduchakali/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1736 - Validation Loss: 0.0682 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.1736 \| 0.0682 \| 0 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

zwtharry/Taxiiv3

zwtharry

2023-07-12T00:35:18Z

0

null

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

reinforcement-learning

2023-07-11T06:49:17Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxiiv3 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="zwtharry/Taxiiv3", 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"]) ```

WasuratS/distilhubert-finetuned-gtzan

WasuratS

2023-07-12T00:27:18Z

12

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-08T14:11:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: [] ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set on best epoch: - Loss: 0.7305 - Accuracy: 0.9 ## Model description Distilhubert is distilled version of the [HuBERT](https://huggingface.co/docs/transformers/model_doc/hubert) and pretrained on data set with 16k frequency. Architecture of this model is CTC or Connectionist Temporal Classification is a technique that is used with encoder-only transformer. ## Training and evaluation data Training + Evaluation data set is GTZAN which is a popular dataset of 999 songs for music genre classification. Each song is a 30-second clip from one of 10 genres of music, spanning disco to metal. Train set is 899 songs and Evaluation set is 100 songs remainings. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1728 | 1.0 | 225 | 2.0896 | 0.42 | | 1.4211 | 2.0 | 450 | 1.4951 | 0.55 | | 1.2155 | 3.0 | 675 | 1.0669 | 0.72 | | 1.0175 | 4.0 | 900 | 0.8862 | 0.69 | | 0.3516 | 5.0 | 1125 | 0.6265 | 0.83 | | 0.6135 | 6.0 | 1350 | 0.6485 | 0.78 | | 0.0807 | 7.0 | 1575 | 0.6567 | 0.78 | | 0.0303 | 8.0 | 1800 | 0.7615 | 0.83 | | 0.2663 | 9.0 | 2025 | 0.6612 | 0.86 | | 0.0026 | 10.0 | 2250 | 0.8354 | 0.85 | | 0.0337 | 11.0 | 2475 | 0.6768 | 0.87 | | 0.0013 | 12.0 | 2700 | 0.7718 | 0.87 | | 0.001 | 13.0 | 2925 | 0.7570 | 0.88 | | 0.0008 | 14.0 | 3150 | 0.8170 | 0.89 | | 0.0006 | 15.0 | 3375 | 0.7920 | 0.89 | | 0.0005 | 16.0 | 3600 | 0.9859 | 0.83 | | 0.0004 | 17.0 | 3825 | 0.8190 | 0.9 | | 0.0003 | 18.0 | 4050 | 0.7305 | 0.9 | | 0.0003 | 19.0 | 4275 | 0.8025 | 0.88 | | 0.0002 | 20.0 | 4500 | 0.8208 | 0.87 | | 0.0003 | 21.0 | 4725 | 0.7358 | 0.88 | | 0.0002 | 22.0 | 4950 | 0.8681 | 0.87 | | 0.0002 | 23.0 | 5175 | 0.7831 | 0.9 | | 0.0003 | 24.0 | 5400 | 0.8583 | 0.88 | | 0.0002 | 25.0 | 5625 | 0.8138 | 0.88 | | 0.0002 | 26.0 | 5850 | 0.7871 | 0.89 | | 0.0002 | 27.0 | 6075 | 0.8893 | 0.88 | | 0.0002 | 28.0 | 6300 | 0.8284 | 0.89 | | 0.0001 | 29.0 | 6525 | 0.8388 | 0.89 | | 0.0001 | 30.0 | 6750 | 0.8305 | 0.9 | | 0.0001 | 31.0 | 6975 | 0.8377 | 0.88 | | 0.0153 | 32.0 | 7200 | 0.8496 | 0.88 | | 0.0001 | 33.0 | 7425 | 0.8381 | 0.88 | | 0.0001 | 34.0 | 7650 | 0.8440 | 0.88 | | 0.0001 | 35.0 | 7875 | 0.8458 | 0.88 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

macapa/emotion-text-classification

macapa

2023-07-12T00:24:51Z

0

fastai

[ "fastai", "region:us" ]

null

2023-07-12T00:24:34Z

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

yusha17/ppo-LunarLander-v2

yusha17

2023-07-12T00:14:17Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-11T02:56:40Z

--- 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: 315.81 +/- 9.19 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 ... ```

conorjudge/distilbert-base-uncased-finetuned-sprint-meds

conorjudge

2023-07-12T00:11:37Z

105

0

transformers

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

text-classification

2023-04-25T13:08:32Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-sprint-meds results: [] ---  # distilbert-base-uncased-finetuned-sprint-meds This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8427 - Accuracy: 0.8790 - F1: 0.8630 ## 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.8256 | 1.0 | 21 | 1.9309 | 0.6868 | 0.5992 | | 1.7067 | 2.0 | 42 | 1.8220 | 0.6993 | 0.6190 | | 1.5327 | 3.0 | 63 | 1.7250 | 0.7189 | 0.6489 | | 1.4475 | 4.0 | 84 | 1.6374 | 0.7509 | 0.6903 | | 1.3108 | 5.0 | 105 | 1.5627 | 0.7438 | 0.6843 | | 1.1881 | 6.0 | 126 | 1.4905 | 0.7669 | 0.7135 | | 1.1726 | 7.0 | 147 | 1.4287 | 0.7847 | 0.7379 | | 1.0681 | 8.0 | 168 | 1.3705 | 0.7829 | 0.7368 | | 0.9392 | 9.0 | 189 | 1.3214 | 0.7954 | 0.7513 | | 0.9603 | 10.0 | 210 | 1.2741 | 0.8043 | 0.7613 | | 0.8349 | 11.0 | 231 | 1.2415 | 0.8185 | 0.7793 | | 0.8094 | 12.0 | 252 | 1.2028 | 0.8256 | 0.7883 | | 0.787 | 13.0 | 273 | 1.1673 | 0.8310 | 0.7951 | | 0.7128 | 14.0 | 294 | 1.1412 | 0.8381 | 0.8056 | | 0.6821 | 15.0 | 315 | 1.1091 | 0.8399 | 0.8074 | | 0.6177 | 16.0 | 336 | 1.0906 | 0.8399 | 0.8098 | | 0.633 | 17.0 | 357 | 1.0645 | 0.8434 | 0.8170 | | 0.5734 | 18.0 | 378 | 1.0415 | 0.8470 | 0.8199 | | 0.5181 | 19.0 | 399 | 1.0233 | 0.8416 | 0.8153 | | 0.4926 | 20.0 | 420 | 1.0076 | 0.8470 | 0.8209 | | 0.4773 | 21.0 | 441 | 0.9896 | 0.8434 | 0.8184 | | 0.4361 | 22.0 | 462 | 0.9768 | 0.8470 | 0.8216 | | 0.4385 | 23.0 | 483 | 0.9624 | 0.8505 | 0.8261 | | 0.3962 | 24.0 | 504 | 0.9520 | 0.8559 | 0.8309 | | 0.392 | 25.0 | 525 | 0.9392 | 0.8577 | 0.8339 | | 0.4095 | 26.0 | 546 | 0.9331 | 0.8577 | 0.8359 | | 0.3389 | 27.0 | 567 | 0.9242 | 0.8577 | 0.8348 | | 0.3296 | 28.0 | 588 | 0.9117 | 0.8577 | 0.8344 | | 0.3527 | 29.0 | 609 | 0.9026 | 0.8665 | 0.8465 | | 0.315 | 30.0 | 630 | 0.9008 | 0.8648 | 0.8431 | | 0.2891 | 31.0 | 651 | 0.8923 | 0.8648 | 0.8433 | | 0.3283 | 32.0 | 672 | 0.8818 | 0.8701 | 0.8507 | | 0.2967 | 33.0 | 693 | 0.8799 | 0.8683 | 0.8479 | | 0.2657 | 34.0 | 714 | 0.8750 | 0.8683 | 0.8479 | | 0.3015 | 35.0 | 735 | 0.8727 | 0.8719 | 0.8526 | | 0.2847 | 36.0 | 756 | 0.8656 | 0.8754 | 0.8575 | | 0.2614 | 37.0 | 777 | 0.8630 | 0.8772 | 0.8589 | | 0.26 | 38.0 | 798 | 0.8604 | 0.8754 | 0.8598 | | 0.2557 | 39.0 | 819 | 0.8588 | 0.8772 | 0.8612 | | 0.2389 | 40.0 | 840 | 0.8562 | 0.8790 | 0.8619 | | 0.2464 | 41.0 | 861 | 0.8529 | 0.8790 | 0.8615 | | 0.2304 | 42.0 | 882 | 0.8529 | 0.8772 | 0.8613 | | 0.2356 | 43.0 | 903 | 0.8514 | 0.8790 | 0.8636 | | 0.2291 | 44.0 | 924 | 0.8479 | 0.8790 | 0.8631 | | 0.2323 | 45.0 | 945 | 0.8457 | 0.8790 | 0.8631 | | 0.2281 | 46.0 | 966 | 0.8454 | 0.8790 | 0.8638 | | 0.2163 | 47.0 | 987 | 0.8432 | 0.8790 | 0.8633 | | 0.226 | 48.0 | 1008 | 0.8433 | 0.8790 | 0.8631 | | 0.229 | 49.0 | 1029 | 0.8431 | 0.8790 | 0.8631 | | 0.2388 | 50.0 | 1050 | 0.8427 | 0.8790 | 0.8630 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

jordyvl/vit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone

jordyvl

2023-07-12T00:02:54Z

164

0

transformers

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

image-classification

2023-07-10T22:30:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone results: [] ---  # vit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0689 - Accuracy: 0.6 - Brier Loss: 0.6433 - Nll: 2.4057 - F1 Micro: 0.6 - F1 Macro: 0.6101 - Ece: 0.3353 - Aurc: 0.1685 ## 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: 32 - eval_batch_size: 32 - 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 | 25 | 0.0859 | 0.0675 | 0.9373 | 7.3238 | 0.0675 | 0.0163 | 0.1099 | 0.9351 | | No log | 2.0 | 50 | 0.0810 | 0.0675 | 0.9372 | 7.0436 | 0.0675 | 0.0153 | 0.1067 | 0.9365 | | No log | 3.0 | 75 | 0.0804 | 0.0725 | 0.9368 | 6.5507 | 0.0725 | 0.0268 | 0.1041 | 0.9438 | | No log | 4.0 | 100 | 0.0800 | 0.0725 | 0.9362 | 6.2816 | 0.0725 | 0.0293 | 0.1056 | 0.9404 | | No log | 5.0 | 125 | 0.0797 | 0.0775 | 0.9352 | 6.1624 | 0.0775 | 0.0225 | 0.1125 | 0.9037 | | No log | 6.0 | 150 | 0.0793 | 0.0875 | 0.9337 | 6.0364 | 0.0875 | 0.0376 | 0.1173 | 0.8572 | | No log | 7.0 | 175 | 0.0788 | 0.13 | 0.9307 | 4.5728 | 0.13 | 0.0918 | 0.1430 | 0.7693 | | No log | 8.0 | 200 | 0.0781 | 0.2325 | 0.9246 | 3.6321 | 0.2325 | 0.1958 | 0.2225 | 0.5621 | | No log | 9.0 | 225 | 0.0770 | 0.31 | 0.9103 | 3.3593 | 0.31 | 0.2693 | 0.2782 | 0.4570 | | No log | 10.0 | 250 | 0.0755 | 0.34 | 0.8830 | 2.9550 | 0.34 | 0.2911 | 0.2951 | 0.4131 | | No log | 11.0 | 275 | 0.0740 | 0.4075 | 0.8559 | 2.6844 | 0.4075 | 0.3802 | 0.3347 | 0.3241 | | No log | 12.0 | 300 | 0.0730 | 0.47 | 0.8216 | 2.7315 | 0.47 | 0.4439 | 0.3582 | 0.2707 | | No log | 13.0 | 325 | 0.0720 | 0.4925 | 0.7913 | 2.6641 | 0.4925 | 0.4606 | 0.3561 | 0.2588 | | No log | 14.0 | 350 | 0.0717 | 0.4725 | 0.7854 | 2.7229 | 0.4725 | 0.4565 | 0.3296 | 0.2732 | | No log | 15.0 | 375 | 0.0708 | 0.5125 | 0.7515 | 2.4866 | 0.5125 | 0.4890 | 0.3445 | 0.2379 | | No log | 16.0 | 400 | 0.0704 | 0.5375 | 0.7424 | 2.4355 | 0.5375 | 0.5131 | 0.3525 | 0.2259 | | No log | 17.0 | 425 | 0.0702 | 0.545 | 0.7259 | 2.5234 | 0.545 | 0.5227 | 0.3427 | 0.2199 | | No log | 18.0 | 450 | 0.0696 | 0.545 | 0.7253 | 2.5796 | 0.545 | 0.5318 | 0.3471 | 0.2118 | | No log | 19.0 | 475 | 0.0697 | 0.56 | 0.7163 | 2.3050 | 0.56 | 0.5547 | 0.3494 | 0.2048 | | 0.0745 | 20.0 | 500 | 0.0692 | 0.565 | 0.7044 | 2.4019 | 0.565 | 0.5669 | 0.3598 | 0.1869 | | 0.0745 | 21.0 | 525 | 0.0690 | 0.5775 | 0.6983 | 2.3271 | 0.5775 | 0.5805 | 0.3615 | 0.1906 | | 0.0745 | 22.0 | 550 | 0.0689 | 0.58 | 0.6855 | 2.2368 | 0.58 | 0.5808 | 0.3572 | 0.1851 | | 0.0745 | 23.0 | 575 | 0.0690 | 0.56 | 0.6905 | 2.4557 | 0.56 | 0.5709 | 0.3387 | 0.1925 | | 0.0745 | 24.0 | 600 | 0.0688 | 0.57 | 0.6895 | 2.3632 | 0.57 | 0.5736 | 0.3516 | 0.1912 | | 0.0745 | 25.0 | 625 | 0.0686 | 0.5775 | 0.6826 | 2.3272 | 0.5775 | 0.5838 | 0.3376 | 0.1802 | | 0.0745 | 26.0 | 650 | 0.0689 | 0.5625 | 0.6886 | 2.2696 | 0.5625 | 0.5754 | 0.3445 | 0.1917 | | 0.0745 | 27.0 | 675 | 0.0687 | 0.575 | 0.6765 | 2.3387 | 0.575 | 0.5800 | 0.3511 | 0.1861 | | 0.0745 | 28.0 | 700 | 0.0689 | 0.5775 | 0.6785 | 2.3039 | 0.5775 | 0.5821 | 0.3546 | 0.1860 | | 0.0745 | 29.0 | 725 | 0.0685 | 0.6 | 0.6720 | 2.4176 | 0.6 | 0.6013 | 0.3606 | 0.1750 | | 0.0745 | 30.0 | 750 | 0.0685 | 0.5925 | 0.6690 | 2.2827 | 0.5925 | 0.5962 | 0.3646 | 0.1750 | | 0.0745 | 31.0 | 775 | 0.0685 | 0.5825 | 0.6682 | 2.2957 | 0.5825 | 0.5885 | 0.3476 | 0.1771 | | 0.0745 | 32.0 | 800 | 0.0687 | 0.585 | 0.6700 | 2.2669 | 0.585 | 0.5914 | 0.3428 | 0.1797 | | 0.0745 | 33.0 | 825 | 0.0685 | 0.59 | 0.6652 | 2.3359 | 0.59 | 0.5927 | 0.3429 | 0.1775 | | 0.0745 | 34.0 | 850 | 0.0686 | 0.5825 | 0.6717 | 2.3900 | 0.5825 | 0.5919 | 0.3453 | 0.1790 | | 0.0745 | 35.0 | 875 | 0.0685 | 0.5875 | 0.6721 | 2.3131 | 0.5875 | 0.5932 | 0.3579 | 0.1799 | | 0.0745 | 36.0 | 900 | 0.0686 | 0.5925 | 0.6625 | 2.3435 | 0.5925 | 0.6005 | 0.3441 | 0.1728 | | 0.0745 | 37.0 | 925 | 0.0685 | 0.5875 | 0.6649 | 2.4475 | 0.5875 | 0.5885 | 0.3550 | 0.1756 | | 0.0745 | 38.0 | 950 | 0.0685 | 0.5925 | 0.6607 | 2.2842 | 0.5925 | 0.5962 | 0.3410 | 0.1732 | | 0.0745 | 39.0 | 975 | 0.0685 | 0.6 | 0.6605 | 2.2073 | 0.6 | 0.6083 | 0.3414 | 0.1708 | | 0.0599 | 40.0 | 1000 | 0.0685 | 0.575 | 0.6578 | 2.3075 | 0.575 | 0.5788 | 0.3341 | 0.1773 | | 0.0599 | 41.0 | 1025 | 0.0685 | 0.5975 | 0.6598 | 2.1562 | 0.5975 | 0.6067 | 0.3462 | 0.1685 | | 0.0599 | 42.0 | 1050 | 0.0685 | 0.5925 | 0.6592 | 2.3363 | 0.5925 | 0.5999 | 0.3262 | 0.1733 | | 0.0599 | 43.0 | 1075 | 0.0683 | 0.5925 | 0.6545 | 2.2970 | 0.5925 | 0.5975 | 0.3413 | 0.1741 | | 0.0599 | 44.0 | 1100 | 0.0686 | 0.5975 | 0.6590 | 2.2220 | 0.5975 | 0.6061 | 0.3425 | 0.1698 | | 0.0599 | 45.0 | 1125 | 0.0684 | 0.585 | 0.6563 | 2.2507 | 0.585 | 0.5876 | 0.3214 | 0.1795 | | 0.0599 | 46.0 | 1150 | 0.0684 | 0.5975 | 0.6578 | 2.2677 | 0.5975 | 0.6082 | 0.3374 | 0.1712 | | 0.0599 | 47.0 | 1175 | 0.0684 | 0.5925 | 0.6531 | 2.3091 | 0.5925 | 0.5974 | 0.3362 | 0.1716 | | 0.0599 | 48.0 | 1200 | 0.0685 | 0.5825 | 0.6539 | 2.3803 | 0.5825 | 0.5901 | 0.3098 | 0.1790 | | 0.0599 | 49.0 | 1225 | 0.0685 | 0.59 | 0.6518 | 2.1855 | 0.59 | 0.6001 | 0.3229 | 0.1759 | | 0.0599 | 50.0 | 1250 | 0.0685 | 0.595 | 0.6513 | 2.3357 | 0.595 | 0.6004 | 0.3307 | 0.1711 | | 0.0599 | 51.0 | 1275 | 0.0684 | 0.59 | 0.6499 | 2.3253 | 0.59 | 0.5968 | 0.3298 | 0.1708 | | 0.0599 | 52.0 | 1300 | 0.0684 | 0.61 | 0.6500 | 2.3352 | 0.61 | 0.6196 | 0.3692 | 0.1687 | | 0.0599 | 53.0 | 1325 | 0.0685 | 0.595 | 0.6518 | 2.2189 | 0.595 | 0.6036 | 0.3278 | 0.1735 | | 0.0599 | 54.0 | 1350 | 0.0684 | 0.6025 | 0.6501 | 2.3238 | 0.6025 | 0.6114 | 0.3410 | 0.1668 | | 0.0599 | 55.0 | 1375 | 0.0684 | 0.595 | 0.6479 | 2.2696 | 0.595 | 0.6022 | 0.3341 | 0.1719 | | 0.0599 | 56.0 | 1400 | 0.0685 | 0.595 | 0.6496 | 2.3172 | 0.595 | 0.6008 | 0.3239 | 0.1720 | | 0.0599 | 57.0 | 1425 | 0.0684 | 0.595 | 0.6476 | 2.2983 | 0.595 | 0.6023 | 0.3310 | 0.1667 | | 0.0599 | 58.0 | 1450 | 0.0684 | 0.605 | 0.6483 | 2.2607 | 0.605 | 0.6140 | 0.3563 | 0.1660 | | 0.0599 | 59.0 | 1475 | 0.0685 | 0.5975 | 0.6491 | 2.3956 | 0.5975 | 0.6091 | 0.3222 | 0.1691 | | 0.0576 | 60.0 | 1500 | 0.0685 | 0.5925 | 0.6476 | 2.2049 | 0.5925 | 0.6032 | 0.3240 | 0.1716 | | 0.0576 | 61.0 | 1525 | 0.0685 | 0.6 | 0.6482 | 2.3095 | 0.6 | 0.6068 | 0.3276 | 0.1703 | | 0.0576 | 62.0 | 1550 | 0.0685 | 0.6025 | 0.6448 | 2.2755 | 0.6025 | 0.6101 | 0.3303 | 0.1673 | | 0.0576 | 63.0 | 1575 | 0.0685 | 0.6 | 0.6480 | 2.3857 | 0.6 | 0.6078 | 0.3358 | 0.1687 | | 0.0576 | 64.0 | 1600 | 0.0685 | 0.59 | 0.6465 | 2.3280 | 0.59 | 0.5990 | 0.3198 | 0.1705 | | 0.0576 | 65.0 | 1625 | 0.0684 | 0.605 | 0.6438 | 2.3484 | 0.605 | 0.6125 | 0.3346 | 0.1651 | | 0.0576 | 66.0 | 1650 | 0.0686 | 0.6 | 0.6462 | 2.2443 | 0.6 | 0.6084 | 0.3371 | 0.1706 | | 0.0576 | 67.0 | 1675 | 0.0685 | 0.6025 | 0.6449 | 2.3717 | 0.6025 | 0.6115 | 0.3317 | 0.1674 | | 0.0576 | 68.0 | 1700 | 0.0685 | 0.595 | 0.6449 | 2.3396 | 0.595 | 0.6003 | 0.3292 | 0.1676 | | 0.0576 | 69.0 | 1725 | 0.0686 | 0.595 | 0.6460 | 2.3315 | 0.595 | 0.6047 | 0.3339 | 0.1683 | | 0.0576 | 70.0 | 1750 | 0.0687 | 0.5975 | 0.6480 | 2.3967 | 0.5975 | 0.6070 | 0.3404 | 0.1702 | | 0.0576 | 71.0 | 1775 | 0.0686 | 0.6 | 0.6456 | 2.3870 | 0.6 | 0.6095 | 0.3215 | 0.1689 | | 0.0576 | 72.0 | 1800 | 0.0686 | 0.59 | 0.6455 | 2.3966 | 0.59 | 0.5985 | 0.3273 | 0.1691 | | 0.0576 | 73.0 | 1825 | 0.0686 | 0.5875 | 0.6472 | 2.3619 | 0.5875 | 0.5975 | 0.3465 | 0.1711 | | 0.0576 | 74.0 | 1850 | 0.0686 | 0.595 | 0.6436 | 2.4181 | 0.595 | 0.6054 | 0.3183 | 0.1706 | | 0.0576 | 75.0 | 1875 | 0.0686 | 0.6 | 0.6440 | 2.4160 | 0.6 | 0.6077 | 0.3285 | 0.1677 | | 0.0576 | 76.0 | 1900 | 0.0687 | 0.6025 | 0.6446 | 2.4184 | 0.6025 | 0.6111 | 0.3408 | 0.1685 | | 0.0576 | 77.0 | 1925 | 0.0686 | 0.6025 | 0.6440 | 2.4208 | 0.6025 | 0.6111 | 0.3323 | 0.1670 | | 0.0576 | 78.0 | 1950 | 0.0687 | 0.5975 | 0.6438 | 2.4236 | 0.5975 | 0.6063 | 0.3298 | 0.1689 | | 0.0576 | 79.0 | 1975 | 0.0687 | 0.5975 | 0.6438 | 2.4521 | 0.5975 | 0.6057 | 0.3328 | 0.1692 | | 0.0565 | 80.0 | 2000 | 0.0687 | 0.6 | 0.6448 | 2.4213 | 0.6 | 0.6088 | 0.3368 | 0.1682 | | 0.0565 | 81.0 | 2025 | 0.0688 | 0.5975 | 0.6444 | 2.4257 | 0.5975 | 0.6076 | 0.3179 | 0.1681 | | 0.0565 | 82.0 | 2050 | 0.0687 | 0.6 | 0.6446 | 2.4225 | 0.6 | 0.6102 | 0.3392 | 0.1673 | | 0.0565 | 83.0 | 2075 | 0.0687 | 0.6 | 0.6437 | 2.4571 | 0.6 | 0.6091 | 0.3281 | 0.1681 | | 0.0565 | 84.0 | 2100 | 0.0688 | 0.595 | 0.6439 | 2.4360 | 0.595 | 0.6042 | 0.3256 | 0.1685 | | 0.0565 | 85.0 | 2125 | 0.0688 | 0.6 | 0.6436 | 2.4396 | 0.6 | 0.6104 | 0.3318 | 0.1683 | | 0.0565 | 86.0 | 2150 | 0.0688 | 0.6 | 0.6434 | 2.3977 | 0.6 | 0.6095 | 0.3273 | 0.1675 | | 0.0565 | 87.0 | 2175 | 0.0688 | 0.595 | 0.6432 | 2.4303 | 0.595 | 0.6053 | 0.3146 | 0.1687 | | 0.0565 | 88.0 | 2200 | 0.0688 | 0.5975 | 0.6431 | 2.4222 | 0.5975 | 0.6071 | 0.3326 | 0.1686 | | 0.0565 | 89.0 | 2225 | 0.0688 | 0.6 | 0.6440 | 2.4042 | 0.6 | 0.6108 | 0.3303 | 0.1678 | | 0.0565 | 90.0 | 2250 | 0.0688 | 0.6 | 0.6433 | 2.3998 | 0.6 | 0.6096 | 0.3301 | 0.1679 | | 0.0565 | 91.0 | 2275 | 0.0689 | 0.6 | 0.6434 | 2.4026 | 0.6 | 0.6108 | 0.3362 | 0.1680 | | 0.0565 | 92.0 | 2300 | 0.0689 | 0.5975 | 0.6435 | 2.4037 | 0.5975 | 0.6083 | 0.3335 | 0.1680 | | 0.0565 | 93.0 | 2325 | 0.0689 | 0.5975 | 0.6434 | 2.4060 | 0.5975 | 0.6077 | 0.3344 | 0.1679 | | 0.0565 | 94.0 | 2350 | 0.0689 | 0.6 | 0.6433 | 2.4024 | 0.6 | 0.6106 | 0.3204 | 0.1683 | | 0.0565 | 95.0 | 2375 | 0.0689 | 0.595 | 0.6432 | 2.4060 | 0.595 | 0.6052 | 0.3423 | 0.1684 | | 0.0565 | 96.0 | 2400 | 0.0689 | 0.6 | 0.6432 | 2.4044 | 0.6 | 0.6101 | 0.3404 | 0.1684 | | 0.0565 | 97.0 | 2425 | 0.0689 | 0.6 | 0.6434 | 2.4042 | 0.6 | 0.6101 | 0.3349 | 0.1683 | | 0.0565 | 98.0 | 2450 | 0.0689 | 0.6 | 0.6432 | 2.4055 | 0.6 | 0.6101 | 0.3390 | 0.1684 | | 0.0565 | 99.0 | 2475 | 0.0689 | 0.6 | 0.6433 | 2.4056 | 0.6 | 0.6101 | 0.3393 | 0.1685 | | 0.056 | 100.0 | 2500 | 0.0689 | 0.6 | 0.6433 | 2.4057 | 0.6 | 0.6101 | 0.3353 | 0.1685 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

ben-xl8/wmt22-cometkiwi-da

ben-xl8

2023-07-11T23:59:39Z

0

1

null

[ "translation", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]

translation

2023-07-11T20:03:20Z

--- extra_gated_heading: Acknowledge license to accept the repository extra_gated_button_content: Acknowledge license pipeline_tag: translation language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: cc-by-nc-sa-4.0 --- This is a [COMET](https://github.com/Unbabel/COMET) quality estimation model by Unbabel: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation. # Paper [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.60) (Rei et al., WMT 2022) # License: cc-by-nc-sa-4.0 # Usage for Inference Endpoint ```python import json import requests API_URL = "" API_TOKEN="MY_API_KEY" headers = { "Authorization": f"Bearer {API_TOKEN}", "Content-Type": "application/json", } def query(url, headers, payload): data = json.dumps(payload) response = requests.request("POST", url, headers=headers, data=data) return json.loads(response.content.decode("utf-8")) payload = { "inputs": { "batch_size": 8, "workers": None, "data": [ { "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", },{ "src": "Youll be picking fruit and generally helping us do all the usual farm work", "mt": "당신은 과일을 따기도 하고 대체로 우리가 하는 일상적인 농장 일을 돕게 될 겁니다", }, ] } } scores = query(API_URL, headers, payload) ``` # Intended uses Unbabel's model is intented to be used for **reference-free MT evaluation**. Given a source text and its translation, outputs a single score between 0 and 1 where 1 represents a perfect translation. # Languages Covered: This model builds on top of InfoXLM which cover the following languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish. Thus, results for language pairs containing uncovered languages are unreliable!

KnutJaegersberg/wikipedia_categories_setfit

KnutJaegersberg

2023-07-11T23:52:45Z

2

1

setfit

[ "setfit", "pytorch", "bert", "feature-extraction", "sentence-similarity", "e5", "dataset:KnutJaegersberg/wikipedia_categories", "dataset:KnutJaegersberg/wikipedia_categories_labels", "license:mit", "region:us" ]

sentence-similarity

2023-07-11T11:01:19Z

--- pipeline_tag: sentence-similarity tags: - feature-extraction - sentence-similarity - setfit - e5 license: mit datasets: - KnutJaegersberg/wikipedia_categories - KnutJaegersberg/wikipedia_categories_labels --- This English model (e5-large as basis) predicts wikipedia categories (roundabout 37 labels). It is trained on the concatenation of the headlines of the lower level categories articles in few shot setting (i.e. 8 subcategories with their headline concatenations per level 2 category). Accuracy on test data split is 85 %. Note that these numbers are just an indicator that training worked, it will differ in production settings, which is why this classifier is meant for corpus exploration. Use the wikipedia_categories_labels dataset as key. from setfit import SetFitModel Download from Hub and run inference model = SetFitModel.from_pretrained("KnutJaegersberg/wikipedia_categories_setfit") Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])

Evan-Lin/Bart-RL-many-entailment-keywordmax-attractive

Evan-Lin

2023-07-11T23:11:37Z

105

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-11T06:12:39Z

attractive 1 keyword 1/4 entailment 1 mul 10 normalization

sl8425/troubleshooting_steps_printer

sl8425

2023-07-11T23:07:39Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-11T20:49:26Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: sl8425/troubleshooting_steps_printer results: [] ---  # sl8425/troubleshooting_steps_printer This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8644 - Validation Loss: 0.8744 - Train Accuracy: 0.7457 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 369, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 1.6729 | 1.1343 | 0.6428 | 0 | | 1.0262 | 0.9056 | 0.7366 | 1 | | 0.8644 | 0.8744 | 0.7457 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

talaugust/sci-writing-strategies

talaugust

2023-07-11T23:05:34Z

0

null

[ "region:us" ]

null

2023-07-11T22:42:06Z

RoBERTa Science writing strategy classifiers This is a finetuned BART Large model from the paper: "Writing Strategies for Science Communication: Data and Computational Analysis", By Tal August, Lauren Kim, Katharina Reinecke, and Noah A. Smith Published at the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020. Abstract: Communicating complex scientific ideas without misleading or overwhelming the public is challenging. While science communication guides exist, they rarely offer empirical evidence for how their strategies are used in practice. Writing strategies that can be automatically recognized could greatly support science communication efforts by enabling tools to detect and suggest strategies for writers. We compile a set of writing strategies drawn from a wide range of prescriptive sources and develop an annotation scheme allowing humans to recognize them. We collect a corpus of 128k science writing documents in English and annotate a subset of this corpus. We use the annotations to train transformer-based classifiers and measure the strategies’ use in the larger corpus. We find that the use of strategies, such as storytelling and emphasizing the most important findings, varies significantly across publications with different reader audiences. Description The model is finetuned on the task of identifying if a given sentence from a science news article is using a particular writing strategy (e.g., emphasizing the real world impact of the scientific findings). The intended use of this model is to identify common science communication writing strategies. The model is trained on annotated sentences drawn from science news articles. The URLs for the original news articles are at [https://github.com/talaugust/scientific-writing-strategies]. Biases & Limitations The goal of this model is to enable a wider audience of readers to understand and engage with scientific writing. A risk, though, is that such attempts might instead widen the gap to accessing scientific information. The texts in the datasets we train our models on are in General or Academic American. English. Many people, especially those who have been historically underrepresented in STEM disciplines and medicine, may not be comfortable with this dialect of English. This risks further alienating the readers we hope to serve. An important and exciting direction in NLP is making models more flexible to dialects and low-resource languages.

jovi848/autotrain-eng-ta-json-73876139369

jovi848

2023-07-11T23:05:03Z

106

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "translation", "unk", "dataset:jovi848/autotrain-data-eng-ta-json", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

translation

2023-07-11T22:16:50Z

--- tags: - autotrain - translation language: - unk - unk datasets: - jovi848/autotrain-data-eng-ta-json co2_eq_emissions: emissions: 33.5213011411702 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 73876139369 - CO2 Emissions (in grams): 33.5213 ## Validation Metrics - Loss: 0.000 - SacreBLEU: 0.001 - Gen len: 19.000

SHENMU007/neunit_BASE_V11.4

SHENMU007

2023-07-11T22:52:37Z

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

2023-07-11T20:08:34Z

--- 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

crowbarmassage/ppo-Pyramids

crowbarmassage

2023-07-11T22:48:42Z

1

0

ml-agents

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

reinforcement-learning

2023-07-11T22:48:40Z

--- 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: crowbarmassage/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

fgeyer/a2c-AntBulletEnv-v0

fgeyer

2023-07-11T22:40:23Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-11T22:24:37Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2384.01 +/- 64.45 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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-small_tobacco3482_kd_NKD_t1.0_g1.5

jordyvl

2023-07-11T22:34:14Z

163

0

transformers

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

image-classification

2023-07-11T21:57:18Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_NKD_t1.0_g1.5 results: [] ---  # vit-small_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9399 - Accuracy: 0.82 - Brier Loss: 0.3024 - Nll: 1.1952 - F1 Micro: 0.82 - F1 Macro: 0.7964 - Ece: 0.1494 - Aurc: 0.0548 ## 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 | 4.9730 | 0.205 | 0.8886 | 5.4337 | 0.205 | 0.1356 | 0.2736 | 0.7527 | | No log | 2.0 | 14 | 4.6039 | 0.355 | 0.8122 | 3.5880 | 0.3550 | 0.2120 | 0.3197 | 0.5132 | | No log | 3.0 | 21 | 4.2754 | 0.515 | 0.7054 | 2.0283 | 0.515 | 0.4046 | 0.3275 | 0.2966 | | No log | 4.0 | 28 | 4.0263 | 0.6 | 0.5799 | 1.5709 | 0.6 | 0.5157 | 0.3062 | 0.1890 | | No log | 5.0 | 35 | 3.8749 | 0.725 | 0.4857 | 1.5338 | 0.7250 | 0.6949 | 0.3181 | 0.1194 | | No log | 6.0 | 42 | 3.7023 | 0.765 | 0.3925 | 1.2926 | 0.765 | 0.6948 | 0.2394 | 0.0908 | | No log | 7.0 | 49 | 3.7728 | 0.78 | 0.3668 | 1.3007 | 0.78 | 0.7355 | 0.2478 | 0.0754 | | No log | 8.0 | 56 | 3.7328 | 0.785 | 0.3459 | 1.2487 | 0.785 | 0.7501 | 0.2261 | 0.0804 | | No log | 9.0 | 63 | 3.7092 | 0.77 | 0.3289 | 1.0921 | 0.7700 | 0.7672 | 0.2019 | 0.0767 | | No log | 10.0 | 70 | 3.6273 | 0.795 | 0.3150 | 1.0342 | 0.795 | 0.7690 | 0.1927 | 0.0716 | | No log | 11.0 | 77 | 3.5677 | 0.83 | 0.2754 | 1.3837 | 0.83 | 0.7933 | 0.1697 | 0.0532 | | No log | 12.0 | 84 | 3.5668 | 0.815 | 0.2816 | 1.1304 | 0.815 | 0.7934 | 0.1563 | 0.0617 | | No log | 13.0 | 91 | 3.6080 | 0.83 | 0.2723 | 0.9515 | 0.83 | 0.8088 | 0.1648 | 0.0543 | | No log | 14.0 | 98 | 3.6095 | 0.815 | 0.3050 | 1.2020 | 0.815 | 0.8207 | 0.1523 | 0.0633 | | No log | 15.0 | 105 | 3.6685 | 0.805 | 0.3060 | 1.2725 | 0.805 | 0.7920 | 0.1618 | 0.0676 | | No log | 16.0 | 112 | 3.5523 | 0.825 | 0.2832 | 0.9447 | 0.825 | 0.8163 | 0.1614 | 0.0569 | | No log | 17.0 | 119 | 3.5294 | 0.805 | 0.2752 | 0.9918 | 0.805 | 0.7636 | 0.1537 | 0.0549 | | No log | 18.0 | 126 | 3.5382 | 0.8 | 0.2870 | 1.2294 | 0.8000 | 0.7885 | 0.1603 | 0.0583 | | No log | 19.0 | 133 | 3.5541 | 0.82 | 0.2905 | 1.2181 | 0.82 | 0.8204 | 0.1400 | 0.0618 | | No log | 20.0 | 140 | 3.4717 | 0.835 | 0.2606 | 1.1119 | 0.835 | 0.8146 | 0.1382 | 0.0531 | | No log | 21.0 | 147 | 3.6074 | 0.79 | 0.3099 | 1.2144 | 0.79 | 0.7771 | 0.1419 | 0.0599 | | No log | 22.0 | 154 | 3.5448 | 0.805 | 0.2868 | 1.2075 | 0.805 | 0.7761 | 0.1439 | 0.0581 | | No log | 23.0 | 161 | 3.6070 | 0.805 | 0.3057 | 1.2908 | 0.805 | 0.7831 | 0.1393 | 0.0627 | | No log | 24.0 | 168 | 3.5289 | 0.81 | 0.2716 | 1.1844 | 0.81 | 0.7879 | 0.1358 | 0.0550 | | No log | 25.0 | 175 | 3.5502 | 0.82 | 0.2827 | 1.1141 | 0.82 | 0.7908 | 0.1460 | 0.0554 | | No log | 26.0 | 182 | 3.5747 | 0.82 | 0.2829 | 1.1727 | 0.82 | 0.8027 | 0.1330 | 0.0565 | | No log | 27.0 | 189 | 3.6091 | 0.83 | 0.2787 | 1.1040 | 0.83 | 0.8067 | 0.1347 | 0.0563 | | No log | 28.0 | 196 | 3.5917 | 0.82 | 0.2837 | 1.1775 | 0.82 | 0.7975 | 0.1513 | 0.0564 | | No log | 29.0 | 203 | 3.6087 | 0.815 | 0.2875 | 1.1448 | 0.815 | 0.7998 | 0.1339 | 0.0542 | | No log | 30.0 | 210 | 3.6018 | 0.815 | 0.2819 | 1.1613 | 0.815 | 0.8027 | 0.1507 | 0.0535 | | No log | 31.0 | 217 | 3.6350 | 0.815 | 0.2845 | 1.2278 | 0.815 | 0.7866 | 0.1401 | 0.0537 | | No log | 32.0 | 224 | 3.6290 | 0.82 | 0.2815 | 1.1528 | 0.82 | 0.7950 | 0.1424 | 0.0520 | | No log | 33.0 | 231 | 3.6642 | 0.815 | 0.2865 | 1.1504 | 0.815 | 0.7946 | 0.1379 | 0.0542 | | No log | 34.0 | 238 | 3.6778 | 0.815 | 0.2929 | 1.2116 | 0.815 | 0.7890 | 0.1437 | 0.0538 | | No log | 35.0 | 245 | 3.6867 | 0.82 | 0.2869 | 1.1547 | 0.82 | 0.7904 | 0.1404 | 0.0529 | | No log | 36.0 | 252 | 3.6931 | 0.795 | 0.2946 | 1.1478 | 0.795 | 0.7694 | 0.1494 | 0.0543 | | No log | 37.0 | 259 | 3.7166 | 0.82 | 0.2921 | 1.2109 | 0.82 | 0.7904 | 0.1489 | 0.0534 | | No log | 38.0 | 266 | 3.7024 | 0.81 | 0.2889 | 1.1516 | 0.81 | 0.7888 | 0.1508 | 0.0536 | | No log | 39.0 | 273 | 3.7353 | 0.81 | 0.2943 | 1.2088 | 0.81 | 0.7812 | 0.1466 | 0.0537 | | No log | 40.0 | 280 | 3.7198 | 0.82 | 0.2891 | 1.1515 | 0.82 | 0.8014 | 0.1285 | 0.0536 | | No log | 41.0 | 287 | 3.7413 | 0.815 | 0.2899 | 1.2124 | 0.815 | 0.7959 | 0.1471 | 0.0537 | | No log | 42.0 | 294 | 3.7272 | 0.82 | 0.2896 | 1.2071 | 0.82 | 0.8002 | 0.1414 | 0.0532 | | No log | 43.0 | 301 | 3.7609 | 0.815 | 0.2925 | 1.2100 | 0.815 | 0.7868 | 0.1486 | 0.0528 | | No log | 44.0 | 308 | 3.7589 | 0.815 | 0.2922 | 1.2074 | 0.815 | 0.7877 | 0.1398 | 0.0537 | | No log | 45.0 | 315 | 3.7820 | 0.815 | 0.2961 | 1.2078 | 0.815 | 0.7874 | 0.1499 | 0.0535 | | No log | 46.0 | 322 | 3.7663 | 0.82 | 0.2926 | 1.2053 | 0.82 | 0.8014 | 0.1369 | 0.0532 | | No log | 47.0 | 329 | 3.7850 | 0.82 | 0.2944 | 1.2079 | 0.82 | 0.7904 | 0.1374 | 0.0532 | | No log | 48.0 | 336 | 3.7802 | 0.82 | 0.2935 | 1.2025 | 0.82 | 0.7981 | 0.1483 | 0.0537 | | No log | 49.0 | 343 | 3.7954 | 0.82 | 0.2937 | 1.2068 | 0.82 | 0.7900 | 0.1354 | 0.0528 | | No log | 50.0 | 350 | 3.7974 | 0.815 | 0.2954 | 1.2020 | 0.815 | 0.7907 | 0.1491 | 0.0534 | | No log | 51.0 | 357 | 3.8081 | 0.815 | 0.2965 | 1.2035 | 0.815 | 0.7907 | 0.1533 | 0.0533 | | No log | 52.0 | 364 | 3.8171 | 0.815 | 0.2982 | 1.2033 | 0.815 | 0.7907 | 0.1466 | 0.0537 | | No log | 53.0 | 371 | 3.8136 | 0.815 | 0.2961 | 1.2035 | 0.815 | 0.7907 | 0.1399 | 0.0531 | | No log | 54.0 | 378 | 3.8244 | 0.815 | 0.2977 | 1.2024 | 0.815 | 0.7907 | 0.1586 | 0.0538 | | No log | 55.0 | 385 | 3.8265 | 0.815 | 0.2963 | 1.2004 | 0.815 | 0.7907 | 0.1506 | 0.0537 | | No log | 56.0 | 392 | 3.8376 | 0.82 | 0.2980 | 1.2011 | 0.82 | 0.7964 | 0.1471 | 0.0536 | | No log | 57.0 | 399 | 3.8428 | 0.82 | 0.2982 | 1.1994 | 0.82 | 0.7964 | 0.1562 | 0.0535 | | No log | 58.0 | 406 | 3.8418 | 0.82 | 0.2973 | 1.2004 | 0.82 | 0.7964 | 0.1484 | 0.0537 | | No log | 59.0 | 413 | 3.8507 | 0.82 | 0.2984 | 1.2009 | 0.82 | 0.7931 | 0.1563 | 0.0538 | | No log | 60.0 | 420 | 3.8560 | 0.82 | 0.2989 | 1.2001 | 0.82 | 0.7964 | 0.1579 | 0.0540 | | No log | 61.0 | 427 | 3.8563 | 0.82 | 0.2974 | 1.1997 | 0.82 | 0.7964 | 0.1560 | 0.0536 | | No log | 62.0 | 434 | 3.8648 | 0.815 | 0.2986 | 1.1995 | 0.815 | 0.7907 | 0.1532 | 0.0540 | | No log | 63.0 | 441 | 3.8682 | 0.82 | 0.2991 | 1.1991 | 0.82 | 0.7964 | 0.1570 | 0.0536 | | No log | 64.0 | 448 | 3.8735 | 0.82 | 0.2989 | 1.1984 | 0.82 | 0.7964 | 0.1481 | 0.0539 | | No log | 65.0 | 455 | 3.8794 | 0.82 | 0.3000 | 1.1981 | 0.82 | 0.7964 | 0.1496 | 0.0543 | | No log | 66.0 | 462 | 3.8824 | 0.82 | 0.3002 | 1.1980 | 0.82 | 0.7964 | 0.1567 | 0.0539 | | No log | 67.0 | 469 | 3.8842 | 0.82 | 0.3005 | 1.1983 | 0.82 | 0.7964 | 0.1438 | 0.0542 | | No log | 68.0 | 476 | 3.8866 | 0.82 | 0.3001 | 1.1978 | 0.82 | 0.7964 | 0.1418 | 0.0540 | | No log | 69.0 | 483 | 3.8912 | 0.82 | 0.3003 | 1.1977 | 0.82 | 0.7964 | 0.1570 | 0.0541 | | No log | 70.0 | 490 | 3.8959 | 0.82 | 0.3008 | 1.1971 | 0.82 | 0.7964 | 0.1445 | 0.0544 | | No log | 71.0 | 497 | 3.8964 | 0.82 | 0.3002 | 1.1977 | 0.82 | 0.7964 | 0.1366 | 0.0543 | | 3.4649 | 72.0 | 504 | 3.9021 | 0.82 | 0.3009 | 1.1969 | 0.82 | 0.7964 | 0.1471 | 0.0543 | | 3.4649 | 73.0 | 511 | 3.9052 | 0.82 | 0.3015 | 1.1976 | 0.82 | 0.7964 | 0.1532 | 0.0546 | | 3.4649 | 74.0 | 518 | 3.9043 | 0.82 | 0.3002 | 1.1973 | 0.82 | 0.7964 | 0.1371 | 0.0544 | | 3.4649 | 75.0 | 525 | 3.9096 | 0.82 | 0.3004 | 1.1966 | 0.82 | 0.7964 | 0.1417 | 0.0543 | | 3.4649 | 76.0 | 532 | 3.9099 | 0.82 | 0.3010 | 1.1965 | 0.82 | 0.7964 | 0.1428 | 0.0545 | | 3.4649 | 77.0 | 539 | 3.9151 | 0.82 | 0.3016 | 1.1963 | 0.82 | 0.7964 | 0.1460 | 0.0548 | | 3.4649 | 78.0 | 546 | 3.9143 | 0.82 | 0.3010 | 1.1970 | 0.82 | 0.7964 | 0.1447 | 0.0543 | | 3.4649 | 79.0 | 553 | 3.9164 | 0.82 | 0.3014 | 1.1966 | 0.82 | 0.7964 | 0.1436 | 0.0545 | | 3.4649 | 80.0 | 560 | 3.9198 | 0.82 | 0.3018 | 1.1965 | 0.82 | 0.7964 | 0.1520 | 0.0545 | | 3.4649 | 81.0 | 567 | 3.9218 | 0.82 | 0.3015 | 1.1959 | 0.82 | 0.7964 | 0.1440 | 0.0546 | | 3.4649 | 82.0 | 574 | 3.9236 | 0.82 | 0.3018 | 1.1961 | 0.82 | 0.7964 | 0.1439 | 0.0546 | | 3.4649 | 83.0 | 581 | 3.9248 | 0.82 | 0.3017 | 1.1959 | 0.82 | 0.7964 | 0.1440 | 0.0546 | | 3.4649 | 84.0 | 588 | 3.9267 | 0.82 | 0.3018 | 1.1958 | 0.82 | 0.7964 | 0.1442 | 0.0545 | | 3.4649 | 85.0 | 595 | 3.9286 | 0.82 | 0.3019 | 1.1959 | 0.82 | 0.7964 | 0.1443 | 0.0546 | | 3.4649 | 86.0 | 602 | 3.9300 | 0.82 | 0.3020 | 1.1958 | 0.82 | 0.7964 | 0.1444 | 0.0545 | | 3.4649 | 87.0 | 609 | 3.9320 | 0.82 | 0.3022 | 1.1956 | 0.82 | 0.7964 | 0.1446 | 0.0546 | | 3.4649 | 88.0 | 616 | 3.9327 | 0.82 | 0.3022 | 1.1957 | 0.82 | 0.7964 | 0.1446 | 0.0545 | | 3.4649 | 89.0 | 623 | 3.9340 | 0.82 | 0.3022 | 1.1955 | 0.82 | 0.7964 | 0.1436 | 0.0546 | | 3.4649 | 90.0 | 630 | 3.9346 | 0.82 | 0.3022 | 1.1956 | 0.82 | 0.7964 | 0.1447 | 0.0546 | | 3.4649 | 91.0 | 637 | 3.9360 | 0.82 | 0.3023 | 1.1953 | 0.82 | 0.7964 | 0.1438 | 0.0546 | | 3.4649 | 92.0 | 644 | 3.9368 | 0.82 | 0.3023 | 1.1954 | 0.82 | 0.7964 | 0.1438 | 0.0546 | | 3.4649 | 93.0 | 651 | 3.9374 | 0.82 | 0.3023 | 1.1954 | 0.82 | 0.7964 | 0.1437 | 0.0548 | | 3.4649 | 94.0 | 658 | 3.9380 | 0.82 | 0.3023 | 1.1953 | 0.82 | 0.7964 | 0.1438 | 0.0548 | | 3.4649 | 95.0 | 665 | 3.9385 | 0.82 | 0.3023 | 1.1953 | 0.82 | 0.7964 | 0.1494 | 0.0549 | | 3.4649 | 96.0 | 672 | 3.9391 | 0.82 | 0.3024 | 1.1952 | 0.82 | 0.7964 | 0.1494 | 0.0548 | | 3.4649 | 97.0 | 679 | 3.9393 | 0.82 | 0.3024 | 1.1952 | 0.82 | 0.7964 | 0.1495 | 0.0548 | | 3.4649 | 98.0 | 686 | 3.9396 | 0.82 | 0.3024 | 1.1952 | 0.82 | 0.7964 | 0.1494 | 0.0548 | | 3.4649 | 99.0 | 693 | 3.9398 | 0.82 | 0.3024 | 1.1952 | 0.82 | 0.7964 | 0.1494 | 0.0548 | | 3.4649 | 100.0 | 700 | 3.9399 | 0.82 | 0.3024 | 1.1952 | 0.82 | 0.7964 | 0.1494 | 0.0548 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

0xMaka/based-bert-sc

0xMaka

2023-07-11T22:28:41Z

113

1

transformers

[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "en", "dataset:0xMaka/trading-candles-subset-sc-format", "license:gpl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-11T17:56:55Z

--- datasets: - 0xMaka/trading-candles-subset-sc-format language: - en metrics: - accuracy - f1 widget: - text: 'identify candle: 17284.58,17264.41,17284.58,17264.41' example_title: Bear - text: 'identify candle: open: 17343.43, close: 17625.18, high: 17804.68, low: 17322.15' example_title: Bull license: gpl --- # Based Bert for sequence classification This model is a POC and shouldn't be used for any production task. ## Model description Based Bert SC is a text classification bot for binary classification of a trading candles opening and closing prices. ## Uses and limitations This model can reliably return the bullish or bearish status of a candle given the opening, closing, high and low, in a format shown. It will have trouble if the order of the numbers change (even if tags are included). ### How to use You can use this model directly with a pipeline ```python >>> from transformers import pipeline >>> pipe = pipeline("text-classification", model="0xMaka/based-bert-sc") >>> text = "identify candle: open: 21788.19, close: 21900, high: 21965.23, low: 21788.19" >>> pipe(text) [{'label': 'Bullish', 'score': 0.9999682903289795}] ``` ## Finetuning For parameters: https://github.com/0xMaka/based-bert-sc/blob/main/trainer.py This model was fine tuned on an RTX-3060-Mobile ``` // BUS_WIDTH = 192 // CLOCK_RATE = 1750 // DDR_MULTI = 8 // DDR6 // BWTheoretical = (((CLOCK_RATE * (10 ** 6)) * (BUS_WIDTH/8)) * DDR_MULI) / (10 ** 9) // BWTheoretical == 336 GB/s ``` Self-measured effective (GB/s): 316.280736

AACEE/textual_inversion_cat

AACEE

2023-07-11T22:10:09Z

4

0

diffusers

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

text-to-image

2023-07-08T07:11:18Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - AACEE/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

crowbarmassage/ppo-SnowballTarget

crowbarmassage

2023-07-11T22:08:09Z

1

0

ml-agents

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

reinforcement-learning

2023-07-11T22:08:08Z

--- 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: crowbarmassage/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

kyungsukim-ai/distilbert-base-uncased-finetuned-squad

kyungsukim-ai

2023-07-11T22:05:45Z

117

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-05-09T23:24:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ysmaicon/distilbert-base-uncased-finetuned-cola

ysmaicon

2023-07-11T22:04:26Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-11T21:15:40Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ysmaicon/distilbert-base-uncased-finetuned-cola results: [] ---  # ysmaicon/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1941 - Validation Loss: 0.5355 - Train Matthews Correlation: 0.5256 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5184 | 0.4689 | 0.4919 | 0 | | 0.3229 | 0.4772 | 0.5191 | 1 | | 0.1941 | 0.5355 | 0.5256 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

epicmobile181/huggingface_sequence_classification

epicmobile181

2023-07-11T22:03:01Z

107

0

transformers

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

text-classification

2023-07-11T18:00:57Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: huggingface_sequence_classification results: [] ---  # huggingface_sequence_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-tiny_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone

jordyvl

2023-07-11T21:56:36Z

163

0

transformers

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

image-classification

2023-07-11T21:05:53Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone results: [] ---  # vit-tiny_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone 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: 0.0396 - Accuracy: 0.735 - Brier Loss: 0.7729 - Nll: 1.4473 - F1 Micro: 0.735 - F1 Macro: 0.6948 - Ece: 0.5886 - Aurc: 0.0947 ## 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: 32 - eval_batch_size: 32 - 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 | 25 | 0.0553 | 0.085 | 0.8991 | 5.2518 | 0.085 | 0.0595 | 0.1614 | 0.8792 | | No log | 2.0 | 50 | 0.0488 | 0.035 | 0.9007 | 7.4288 | 0.035 | 0.0069 | 0.1218 | 0.9410 | | No log | 3.0 | 75 | 0.0481 | 0.045 | 0.8999 | 6.0525 | 0.045 | 0.0087 | 0.1349 | 0.9308 | | No log | 4.0 | 100 | 0.0478 | 0.05 | 0.8991 | 5.6444 | 0.0500 | 0.0149 | 0.1378 | 0.9211 | | No log | 5.0 | 125 | 0.0475 | 0.14 | 0.8981 | 5.8239 | 0.14 | 0.0863 | 0.1987 | 0.8452 | | No log | 6.0 | 150 | 0.0471 | 0.305 | 0.8964 | 5.7469 | 0.305 | 0.1652 | 0.3097 | 0.5016 | | No log | 7.0 | 175 | 0.0466 | 0.305 | 0.8950 | 4.9568 | 0.305 | 0.1899 | 0.3035 | 0.5165 | | No log | 8.0 | 200 | 0.0461 | 0.315 | 0.8931 | 4.8214 | 0.315 | 0.1811 | 0.3152 | 0.4687 | | No log | 9.0 | 225 | 0.0455 | 0.315 | 0.8907 | 4.7406 | 0.315 | 0.2028 | 0.3225 | 0.4671 | | No log | 10.0 | 250 | 0.0449 | 0.35 | 0.8862 | 4.7538 | 0.35 | 0.1972 | 0.3364 | 0.4263 | | No log | 11.0 | 275 | 0.0443 | 0.37 | 0.8793 | 4.6283 | 0.37 | 0.2106 | 0.3455 | 0.4084 | | No log | 12.0 | 300 | 0.0438 | 0.4 | 0.8731 | 3.9664 | 0.4000 | 0.2443 | 0.3685 | 0.3731 | | No log | 13.0 | 325 | 0.0434 | 0.425 | 0.8628 | 3.9702 | 0.425 | 0.2574 | 0.3842 | 0.3601 | | No log | 14.0 | 350 | 0.0430 | 0.465 | 0.8586 | 3.8630 | 0.465 | 0.3226 | 0.4112 | 0.3024 | | No log | 15.0 | 375 | 0.0428 | 0.46 | 0.8488 | 3.9046 | 0.46 | 0.2693 | 0.4082 | 0.2854 | | No log | 16.0 | 400 | 0.0424 | 0.475 | 0.8430 | 3.2916 | 0.4750 | 0.2802 | 0.4183 | 0.2626 | | No log | 17.0 | 425 | 0.0421 | 0.555 | 0.8439 | 2.7780 | 0.555 | 0.4109 | 0.4760 | 0.2123 | | No log | 18.0 | 450 | 0.0418 | 0.575 | 0.8317 | 2.8629 | 0.575 | 0.4399 | 0.4869 | 0.2123 | | No log | 19.0 | 475 | 0.0415 | 0.665 | 0.8329 | 2.5145 | 0.665 | 0.5077 | 0.5655 | 0.1361 | | 0.0491 | 20.0 | 500 | 0.0412 | 0.635 | 0.8121 | 2.7489 | 0.635 | 0.5155 | 0.5235 | 0.1686 | | 0.0491 | 21.0 | 525 | 0.0410 | 0.655 | 0.8221 | 1.7853 | 0.655 | 0.5182 | 0.5509 | 0.1545 | | 0.0491 | 22.0 | 550 | 0.0406 | 0.685 | 0.8045 | 1.5894 | 0.685 | 0.5486 | 0.5627 | 0.1305 | | 0.0491 | 23.0 | 575 | 0.0405 | 0.68 | 0.7984 | 1.7241 | 0.68 | 0.5489 | 0.5545 | 0.1296 | | 0.0491 | 24.0 | 600 | 0.0402 | 0.725 | 0.7959 | 1.5667 | 0.7250 | 0.6156 | 0.5926 | 0.1055 | | 0.0491 | 25.0 | 625 | 0.0402 | 0.68 | 0.7927 | 1.4334 | 0.68 | 0.5853 | 0.5453 | 0.1239 | | 0.0491 | 26.0 | 650 | 0.0401 | 0.705 | 0.7808 | 1.8114 | 0.705 | 0.5856 | 0.5735 | 0.1109 | | 0.0491 | 27.0 | 675 | 0.0399 | 0.71 | 0.7859 | 1.6101 | 0.7100 | 0.6176 | 0.5679 | 0.1034 | | 0.0491 | 28.0 | 700 | 0.0399 | 0.715 | 0.7808 | 1.3423 | 0.715 | 0.6612 | 0.5582 | 0.1130 | | 0.0491 | 29.0 | 725 | 0.0398 | 0.705 | 0.7789 | 1.3921 | 0.705 | 0.6477 | 0.5615 | 0.1175 | | 0.0491 | 30.0 | 750 | 0.0397 | 0.73 | 0.7767 | 1.5801 | 0.7300 | 0.6758 | 0.5741 | 0.1069 | | 0.0491 | 31.0 | 775 | 0.0397 | 0.72 | 0.7774 | 1.3193 | 0.72 | 0.6653 | 0.5790 | 0.1004 | | 0.0491 | 32.0 | 800 | 0.0396 | 0.745 | 0.7729 | 1.4864 | 0.745 | 0.6931 | 0.5941 | 0.0933 | | 0.0491 | 33.0 | 825 | 0.0396 | 0.74 | 0.7736 | 1.5161 | 0.74 | 0.6901 | 0.5828 | 0.0934 | | 0.0491 | 34.0 | 850 | 0.0396 | 0.745 | 0.7754 | 1.5432 | 0.745 | 0.6963 | 0.5911 | 0.0857 | | 0.0491 | 35.0 | 875 | 0.0396 | 0.74 | 0.7744 | 1.4773 | 0.74 | 0.6936 | 0.5966 | 0.0896 | | 0.0491 | 36.0 | 900 | 0.0397 | 0.715 | 0.7762 | 1.3769 | 0.715 | 0.6827 | 0.5675 | 0.1048 | | 0.0491 | 37.0 | 925 | 0.0396 | 0.72 | 0.7744 | 1.3882 | 0.72 | 0.6780 | 0.5689 | 0.0970 | | 0.0491 | 38.0 | 950 | 0.0396 | 0.72 | 0.7762 | 1.4098 | 0.72 | 0.6874 | 0.5701 | 0.1016 | | 0.0491 | 39.0 | 975 | 0.0395 | 0.74 | 0.7728 | 1.3890 | 0.74 | 0.6894 | 0.5861 | 0.0902 | | 0.0386 | 40.0 | 1000 | 0.0396 | 0.74 | 0.7724 | 1.5265 | 0.74 | 0.6936 | 0.5906 | 0.0881 | | 0.0386 | 41.0 | 1025 | 0.0396 | 0.725 | 0.7730 | 1.3516 | 0.7250 | 0.6768 | 0.5784 | 0.0942 | | 0.0386 | 42.0 | 1050 | 0.0396 | 0.73 | 0.7728 | 1.3633 | 0.7300 | 0.6847 | 0.5899 | 0.0945 | | 0.0386 | 43.0 | 1075 | 0.0396 | 0.735 | 0.7730 | 1.3670 | 0.735 | 0.6874 | 0.5830 | 0.0940 | | 0.0386 | 44.0 | 1100 | 0.0395 | 0.73 | 0.7727 | 1.4707 | 0.7300 | 0.6850 | 0.5914 | 0.0930 | | 0.0386 | 45.0 | 1125 | 0.0396 | 0.725 | 0.7721 | 1.4269 | 0.7250 | 0.6810 | 0.5770 | 0.0934 | | 0.0386 | 46.0 | 1150 | 0.0396 | 0.72 | 0.7730 | 1.3567 | 0.72 | 0.6793 | 0.5717 | 0.0976 | | 0.0386 | 47.0 | 1175 | 0.0396 | 0.715 | 0.7731 | 1.3708 | 0.715 | 0.6757 | 0.5717 | 0.0974 | | 0.0386 | 48.0 | 1200 | 0.0396 | 0.735 | 0.7724 | 1.4118 | 0.735 | 0.6874 | 0.5791 | 0.0923 | | 0.0386 | 49.0 | 1225 | 0.0396 | 0.72 | 0.7729 | 1.3647 | 0.72 | 0.6837 | 0.5711 | 0.0965 | | 0.0386 | 50.0 | 1250 | 0.0396 | 0.725 | 0.7727 | 1.3773 | 0.7250 | 0.6820 | 0.5740 | 0.0963 | | 0.0386 | 51.0 | 1275 | 0.0396 | 0.73 | 0.7736 | 1.3286 | 0.7300 | 0.6847 | 0.5766 | 0.0939 | | 0.0386 | 52.0 | 1300 | 0.0396 | 0.725 | 0.7732 | 1.3810 | 0.7250 | 0.6817 | 0.5830 | 0.0944 | | 0.0386 | 53.0 | 1325 | 0.0396 | 0.725 | 0.7725 | 1.3568 | 0.7250 | 0.6820 | 0.5763 | 0.0948 | | 0.0386 | 54.0 | 1350 | 0.0396 | 0.73 | 0.7731 | 1.3693 | 0.7300 | 0.6847 | 0.5768 | 0.0941 | | 0.0386 | 55.0 | 1375 | 0.0396 | 0.745 | 0.7728 | 1.3631 | 0.745 | 0.7112 | 0.5842 | 0.0928 | | 0.0386 | 56.0 | 1400 | 0.0396 | 0.715 | 0.7731 | 1.4175 | 0.715 | 0.6712 | 0.5600 | 0.0976 | | 0.0386 | 57.0 | 1425 | 0.0396 | 0.725 | 0.7725 | 1.3668 | 0.7250 | 0.6929 | 0.5738 | 0.0962 | | 0.0386 | 58.0 | 1450 | 0.0396 | 0.73 | 0.7734 | 1.3903 | 0.7300 | 0.6958 | 0.5868 | 0.0963 | | 0.0386 | 59.0 | 1475 | 0.0396 | 0.725 | 0.7729 | 1.4120 | 0.7250 | 0.6765 | 0.5756 | 0.0945 | | 0.0373 | 60.0 | 1500 | 0.0396 | 0.725 | 0.7732 | 1.3655 | 0.7250 | 0.6820 | 0.5754 | 0.0951 | | 0.0373 | 61.0 | 1525 | 0.0396 | 0.745 | 0.7727 | 1.3676 | 0.745 | 0.7038 | 0.5913 | 0.0921 | | 0.0373 | 62.0 | 1550 | 0.0396 | 0.72 | 0.7729 | 1.3629 | 0.72 | 0.6797 | 0.5762 | 0.0969 | | 0.0373 | 63.0 | 1575 | 0.0396 | 0.725 | 0.7730 | 1.4242 | 0.7250 | 0.6865 | 0.5811 | 0.0950 | | 0.0373 | 64.0 | 1600 | 0.0396 | 0.725 | 0.7735 | 1.3658 | 0.7250 | 0.6923 | 0.5750 | 0.0959 | | 0.0373 | 65.0 | 1625 | 0.0396 | 0.73 | 0.7731 | 1.4296 | 0.7300 | 0.6958 | 0.5769 | 0.0954 | | 0.0373 | 66.0 | 1650 | 0.0396 | 0.735 | 0.7727 | 1.4780 | 0.735 | 0.6980 | 0.5851 | 0.0938 | | 0.0373 | 67.0 | 1675 | 0.0396 | 0.725 | 0.7725 | 1.3669 | 0.7250 | 0.6824 | 0.5715 | 0.0938 | | 0.0373 | 68.0 | 1700 | 0.0396 | 0.725 | 0.7730 | 1.4327 | 0.7250 | 0.6804 | 0.5741 | 0.0940 | | 0.0373 | 69.0 | 1725 | 0.0396 | 0.73 | 0.7728 | 1.3811 | 0.7300 | 0.6961 | 0.5806 | 0.0963 | | 0.0373 | 70.0 | 1750 | 0.0396 | 0.735 | 0.7727 | 1.3812 | 0.735 | 0.7081 | 0.5765 | 0.0952 | | 0.0373 | 71.0 | 1775 | 0.0396 | 0.73 | 0.7730 | 1.4263 | 0.7300 | 0.6961 | 0.5739 | 0.0953 | | 0.0373 | 72.0 | 1800 | 0.0396 | 0.73 | 0.7731 | 1.4280 | 0.7300 | 0.6953 | 0.5803 | 0.0956 | | 0.0373 | 73.0 | 1825 | 0.0396 | 0.735 | 0.7729 | 1.3676 | 0.735 | 0.6988 | 0.5889 | 0.0953 | | 0.0373 | 74.0 | 1850 | 0.0396 | 0.735 | 0.7727 | 1.4358 | 0.735 | 0.6985 | 0.5828 | 0.0940 | | 0.0373 | 75.0 | 1875 | 0.0396 | 0.735 | 0.7727 | 1.4306 | 0.735 | 0.6965 | 0.5786 | 0.0940 | | 0.0373 | 76.0 | 1900 | 0.0396 | 0.73 | 0.7729 | 1.4343 | 0.7300 | 0.6957 | 0.5802 | 0.0958 | | 0.0373 | 77.0 | 1925 | 0.0396 | 0.73 | 0.7726 | 1.4259 | 0.7300 | 0.6961 | 0.5795 | 0.0962 | | 0.0373 | 78.0 | 1950 | 0.0396 | 0.74 | 0.7731 | 1.4246 | 0.74 | 0.7080 | 0.5879 | 0.0941 | | 0.0373 | 79.0 | 1975 | 0.0396 | 0.735 | 0.7730 | 1.4414 | 0.735 | 0.6980 | 0.5914 | 0.0945 | | 0.0372 | 80.0 | 2000 | 0.0396 | 0.74 | 0.7727 | 1.4285 | 0.74 | 0.7103 | 0.5915 | 0.0939 | | 0.0372 | 81.0 | 2025 | 0.0396 | 0.735 | 0.7731 | 1.4379 | 0.735 | 0.6980 | 0.5826 | 0.0942 | | 0.0372 | 82.0 | 2050 | 0.0396 | 0.735 | 0.7729 | 1.4308 | 0.735 | 0.6963 | 0.5827 | 0.0942 | | 0.0372 | 83.0 | 2075 | 0.0396 | 0.735 | 0.7728 | 1.4329 | 0.735 | 0.6968 | 0.5896 | 0.0946 | | 0.0372 | 84.0 | 2100 | 0.0396 | 0.735 | 0.7728 | 1.4343 | 0.735 | 0.6948 | 0.5889 | 0.0947 | | 0.0372 | 85.0 | 2125 | 0.0396 | 0.735 | 0.7727 | 1.4320 | 0.735 | 0.6948 | 0.5988 | 0.0945 | | 0.0372 | 86.0 | 2150 | 0.0396 | 0.735 | 0.7730 | 1.4366 | 0.735 | 0.6963 | 0.5883 | 0.0949 | | 0.0372 | 87.0 | 2175 | 0.0396 | 0.73 | 0.7728 | 1.4825 | 0.7300 | 0.6888 | 0.5878 | 0.0945 | | 0.0372 | 88.0 | 2200 | 0.0396 | 0.735 | 0.7731 | 1.4339 | 0.735 | 0.6945 | 0.5828 | 0.0948 | | 0.0372 | 89.0 | 2225 | 0.0396 | 0.735 | 0.7729 | 1.4383 | 0.735 | 0.6948 | 0.5917 | 0.0946 | | 0.0372 | 90.0 | 2250 | 0.0396 | 0.735 | 0.7729 | 1.4471 | 0.735 | 0.6948 | 0.5867 | 0.0944 | | 0.0372 | 91.0 | 2275 | 0.0396 | 0.735 | 0.7728 | 1.4402 | 0.735 | 0.6948 | 0.5892 | 0.0946 | | 0.0372 | 92.0 | 2300 | 0.0396 | 0.735 | 0.7729 | 1.4412 | 0.735 | 0.6948 | 0.5952 | 0.0948 | | 0.0372 | 93.0 | 2325 | 0.0396 | 0.735 | 0.7729 | 1.4709 | 0.735 | 0.6948 | 0.5917 | 0.0948 | | 0.0372 | 94.0 | 2350 | 0.0396 | 0.735 | 0.7728 | 1.4413 | 0.735 | 0.6948 | 0.5858 | 0.0947 | | 0.0372 | 95.0 | 2375 | 0.0396 | 0.735 | 0.7729 | 1.4422 | 0.735 | 0.6948 | 0.5917 | 0.0946 | | 0.0372 | 96.0 | 2400 | 0.0396 | 0.735 | 0.7729 | 1.4527 | 0.735 | 0.6948 | 0.5917 | 0.0946 | | 0.0372 | 97.0 | 2425 | 0.0396 | 0.735 | 0.7729 | 1.4441 | 0.735 | 0.6948 | 0.5917 | 0.0946 | | 0.0372 | 98.0 | 2450 | 0.0396 | 0.735 | 0.7729 | 1.4423 | 0.735 | 0.6948 | 0.5917 | 0.0946 | | 0.0372 | 99.0 | 2475 | 0.0396 | 0.735 | 0.7729 | 1.4457 | 0.735 | 0.6948 | 0.5886 | 0.0948 | | 0.0372 | 100.0 | 2500 | 0.0396 | 0.735 | 0.7729 | 1.4473 | 0.735 | 0.6948 | 0.5886 | 0.0947 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

a9i/scarlett-7b

a9i

2023-07-11T21:37:46Z

4

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "text2text-generation", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-11T20:53:43Z

--- license: cc-by-nc-nd-4.0 language: - en pipeline_tag: text2text-generation ---

JuS2/ppo-Huggy

JuS2

2023-07-11T21:28:22Z

0

ml-agents

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

reinforcement-learning

2023-07-11T21:28:12Z

--- 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: JuS2/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ethannhzhouu/gpt2-generator

ethannhzhouu

2023-07-11T21:11:54Z

209

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

2023-07-11T21:11:25Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt2-generator results: [] ---  # gpt2-generator 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: 4.7855 ## 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 | 5.3997 | | No log | 2.0 | 2 | 4.9524 | | No log | 3.0 | 3 | 4.7855 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Belphegor/dqn-SpaceInvadersNoFrameskip-v4

Belphegor

2023-07-11T21:11:25Z

0

stable-baselines3

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

reinforcement-learning

2023-07-11T21:10:44Z

--- 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: 581.50 +/- 92.36 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 Belphegor -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 Belphegor -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 Belphegor ``` ## 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'} ```

jordyvl/vit-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone

jordyvl

2023-07-11T21:05:03Z

163

0

transformers

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

image-classification

2023-07-10T22:41:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone results: [] ---  # vit-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0379 - Accuracy: 0.8 - Brier Loss: 0.6938 - Nll: 1.3290 - F1 Micro: 0.8000 - F1 Macro: 0.7859 - Ece: 0.5869 - Aurc: 0.0931 ## 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: 32 - eval_batch_size: 32 - 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 | 25 | 0.0506 | 0.09 | 0.8991 | 6.5155 | 0.09 | 0.0484 | 0.1622 | 0.8986 | | No log | 2.0 | 50 | 0.0468 | 0.22 | 0.8982 | 4.6950 | 0.22 | 0.1025 | 0.2491 | 0.7656 | | No log | 3.0 | 75 | 0.0463 | 0.29 | 0.8969 | 3.3099 | 0.29 | 0.1676 | 0.2924 | 0.6888 | | No log | 4.0 | 100 | 0.0459 | 0.37 | 0.8954 | 3.2920 | 0.37 | 0.1891 | 0.3517 | 0.4208 | | No log | 5.0 | 125 | 0.0455 | 0.395 | 0.8929 | 3.2550 | 0.395 | 0.2299 | 0.3759 | 0.3617 | | No log | 6.0 | 150 | 0.0449 | 0.49 | 0.8885 | 2.9109 | 0.49 | 0.3135 | 0.4396 | 0.2804 | | No log | 7.0 | 175 | 0.0441 | 0.495 | 0.8796 | 2.8950 | 0.495 | 0.3248 | 0.4360 | 0.2721 | | No log | 8.0 | 200 | 0.0430 | 0.545 | 0.8619 | 2.5199 | 0.545 | 0.3771 | 0.4777 | 0.2129 | | No log | 9.0 | 225 | 0.0418 | 0.62 | 0.8382 | 2.2126 | 0.62 | 0.4291 | 0.5298 | 0.1659 | | No log | 10.0 | 250 | 0.0409 | 0.645 | 0.8137 | 2.2525 | 0.645 | 0.4947 | 0.5293 | 0.1552 | | No log | 11.0 | 275 | 0.0401 | 0.68 | 0.7863 | 2.4423 | 0.68 | 0.5145 | 0.5433 | 0.1215 | | No log | 12.0 | 300 | 0.0392 | 0.68 | 0.7628 | 1.9779 | 0.68 | 0.5373 | 0.5402 | 0.1172 | | No log | 13.0 | 325 | 0.0385 | 0.745 | 0.7350 | 1.8986 | 0.745 | 0.6126 | 0.5806 | 0.0843 | | No log | 14.0 | 350 | 0.0384 | 0.735 | 0.7268 | 1.9922 | 0.735 | 0.6451 | 0.5466 | 0.0997 | | No log | 15.0 | 375 | 0.0381 | 0.745 | 0.7180 | 1.6965 | 0.745 | 0.6627 | 0.5586 | 0.0761 | | No log | 16.0 | 400 | 0.0377 | 0.805 | 0.7031 | 1.2564 | 0.805 | 0.7353 | 0.6034 | 0.0713 | | No log | 17.0 | 425 | 0.0389 | 0.745 | 0.7303 | 1.5063 | 0.745 | 0.7192 | 0.5779 | 0.0705 | | No log | 18.0 | 450 | 0.0387 | 0.765 | 0.7219 | 1.5776 | 0.765 | 0.7703 | 0.5815 | 0.0923 | | No log | 19.0 | 475 | 0.0383 | 0.805 | 0.7213 | 1.3953 | 0.805 | 0.7906 | 0.6159 | 0.0667 | | 0.0432 | 20.0 | 500 | 0.0377 | 0.835 | 0.6952 | 1.3075 | 0.835 | 0.8271 | 0.6116 | 0.0799 | | 0.0432 | 21.0 | 525 | 0.0381 | 0.795 | 0.7018 | 1.6184 | 0.795 | 0.7723 | 0.5851 | 0.0880 | | 0.0432 | 22.0 | 550 | 0.0378 | 0.81 | 0.6984 | 1.4292 | 0.81 | 0.7950 | 0.6103 | 0.0673 | | 0.0432 | 23.0 | 575 | 0.0380 | 0.805 | 0.6976 | 1.4852 | 0.805 | 0.7951 | 0.5942 | 0.0808 | | 0.0432 | 24.0 | 600 | 0.0377 | 0.825 | 0.6907 | 1.4501 | 0.825 | 0.8103 | 0.6020 | 0.0774 | | 0.0432 | 25.0 | 625 | 0.0377 | 0.83 | 0.6920 | 1.4509 | 0.83 | 0.8148 | 0.6038 | 0.0759 | | 0.0432 | 26.0 | 650 | 0.0377 | 0.825 | 0.6927 | 1.4113 | 0.825 | 0.8114 | 0.6072 | 0.0765 | | 0.0432 | 27.0 | 675 | 0.0377 | 0.825 | 0.6924 | 1.4044 | 0.825 | 0.8114 | 0.6057 | 0.0757 | | 0.0432 | 28.0 | 700 | 0.0377 | 0.82 | 0.6932 | 1.4521 | 0.82 | 0.8061 | 0.6017 | 0.0815 | | 0.0432 | 29.0 | 725 | 0.0377 | 0.82 | 0.6932 | 1.3593 | 0.82 | 0.8080 | 0.5983 | 0.0794 | | 0.0432 | 30.0 | 750 | 0.0377 | 0.82 | 0.6926 | 1.3437 | 0.82 | 0.8069 | 0.6042 | 0.0819 | | 0.0432 | 31.0 | 775 | 0.0377 | 0.815 | 0.6932 | 1.3453 | 0.815 | 0.8027 | 0.5988 | 0.0815 | | 0.0432 | 32.0 | 800 | 0.0377 | 0.82 | 0.6930 | 1.3384 | 0.82 | 0.8029 | 0.6044 | 0.0855 | | 0.0432 | 33.0 | 825 | 0.0377 | 0.81 | 0.6928 | 1.3969 | 0.81 | 0.7927 | 0.5929 | 0.0835 | | 0.0432 | 34.0 | 850 | 0.0378 | 0.805 | 0.6927 | 1.3995 | 0.805 | 0.7886 | 0.5961 | 0.0855 | | 0.0432 | 35.0 | 875 | 0.0377 | 0.81 | 0.6927 | 1.3705 | 0.81 | 0.7979 | 0.5910 | 0.0887 | | 0.0432 | 36.0 | 900 | 0.0378 | 0.805 | 0.6930 | 1.3566 | 0.805 | 0.7886 | 0.5850 | 0.0817 | | 0.0432 | 37.0 | 925 | 0.0377 | 0.82 | 0.6927 | 1.3537 | 0.82 | 0.8022 | 0.5936 | 0.0847 | | 0.0432 | 38.0 | 950 | 0.0377 | 0.815 | 0.6930 | 1.3574 | 0.815 | 0.7978 | 0.5976 | 0.0854 | | 0.0432 | 39.0 | 975 | 0.0377 | 0.815 | 0.6932 | 1.4599 | 0.815 | 0.7978 | 0.5955 | 0.0864 | | 0.035 | 40.0 | 1000 | 0.0377 | 0.815 | 0.6926 | 1.4147 | 0.815 | 0.7978 | 0.5990 | 0.0869 | | 0.035 | 41.0 | 1025 | 0.0377 | 0.81 | 0.6931 | 1.4065 | 0.81 | 0.7943 | 0.5966 | 0.0844 | | 0.035 | 42.0 | 1050 | 0.0378 | 0.81 | 0.6929 | 1.4678 | 0.81 | 0.7961 | 0.5902 | 0.0891 | | 0.035 | 43.0 | 1075 | 0.0378 | 0.81 | 0.6927 | 1.4164 | 0.81 | 0.7971 | 0.5951 | 0.0897 | | 0.035 | 44.0 | 1100 | 0.0378 | 0.81 | 0.6930 | 1.4646 | 0.81 | 0.7961 | 0.5948 | 0.0875 | | 0.035 | 45.0 | 1125 | 0.0378 | 0.815 | 0.6921 | 1.4660 | 0.815 | 0.8004 | 0.6024 | 0.0895 | | 0.035 | 46.0 | 1150 | 0.0378 | 0.81 | 0.6929 | 1.4098 | 0.81 | 0.7961 | 0.5987 | 0.0831 | | 0.035 | 47.0 | 1175 | 0.0378 | 0.815 | 0.6928 | 1.4634 | 0.815 | 0.8004 | 0.5963 | 0.0911 | | 0.035 | 48.0 | 1200 | 0.0378 | 0.81 | 0.6932 | 1.4648 | 0.81 | 0.7961 | 0.5841 | 0.0877 | | 0.035 | 49.0 | 1225 | 0.0378 | 0.81 | 0.6928 | 1.4635 | 0.81 | 0.7961 | 0.5955 | 0.0898 | | 0.035 | 50.0 | 1250 | 0.0378 | 0.805 | 0.6935 | 1.4688 | 0.805 | 0.7882 | 0.5795 | 0.0902 | | 0.035 | 51.0 | 1275 | 0.0378 | 0.805 | 0.6928 | 1.4665 | 0.805 | 0.7882 | 0.5848 | 0.0916 | | 0.035 | 52.0 | 1300 | 0.0378 | 0.81 | 0.6925 | 1.4249 | 0.81 | 0.7961 | 0.5869 | 0.0926 | | 0.035 | 53.0 | 1325 | 0.0378 | 0.815 | 0.6926 | 1.4150 | 0.815 | 0.8021 | 0.5934 | 0.0913 | | 0.035 | 54.0 | 1350 | 0.0378 | 0.81 | 0.6929 | 1.4155 | 0.81 | 0.7961 | 0.5943 | 0.0913 | | 0.035 | 55.0 | 1375 | 0.0378 | 0.805 | 0.6928 | 1.4141 | 0.805 | 0.7882 | 0.5934 | 0.0964 | | 0.035 | 56.0 | 1400 | 0.0378 | 0.805 | 0.6930 | 1.4124 | 0.805 | 0.7882 | 0.5926 | 0.0958 | | 0.035 | 57.0 | 1425 | 0.0378 | 0.81 | 0.6935 | 1.4116 | 0.81 | 0.7934 | 0.6002 | 0.0895 | | 0.035 | 58.0 | 1450 | 0.0378 | 0.805 | 0.6928 | 1.4059 | 0.805 | 0.7882 | 0.5890 | 0.0937 | | 0.035 | 59.0 | 1475 | 0.0378 | 0.805 | 0.6929 | 1.4141 | 0.805 | 0.7882 | 0.5918 | 0.0967 | | 0.0348 | 60.0 | 1500 | 0.0378 | 0.81 | 0.6935 | 1.4086 | 0.81 | 0.7934 | 0.5915 | 0.0934 | | 0.0348 | 61.0 | 1525 | 0.0378 | 0.81 | 0.6930 | 1.4105 | 0.81 | 0.7941 | 0.5954 | 0.0961 | | 0.0348 | 62.0 | 1550 | 0.0378 | 0.81 | 0.6933 | 1.4166 | 0.81 | 0.7941 | 0.5889 | 0.0954 | | 0.0348 | 63.0 | 1575 | 0.0378 | 0.81 | 0.6933 | 1.4109 | 0.81 | 0.7934 | 0.5963 | 0.0975 | | 0.0348 | 64.0 | 1600 | 0.0378 | 0.81 | 0.6932 | 1.4131 | 0.81 | 0.7934 | 0.5980 | 0.0953 | | 0.0348 | 65.0 | 1625 | 0.0378 | 0.81 | 0.6937 | 1.4182 | 0.81 | 0.7934 | 0.5956 | 0.0970 | | 0.0348 | 66.0 | 1650 | 0.0378 | 0.805 | 0.6933 | 1.4125 | 0.805 | 0.7893 | 0.5905 | 0.0966 | | 0.0348 | 67.0 | 1675 | 0.0378 | 0.81 | 0.6937 | 1.4136 | 0.81 | 0.7934 | 0.5965 | 0.0975 | | 0.0348 | 68.0 | 1700 | 0.0379 | 0.81 | 0.6935 | 1.4137 | 0.81 | 0.7934 | 0.5994 | 0.0971 | | 0.0348 | 69.0 | 1725 | 0.0378 | 0.805 | 0.6935 | 1.4196 | 0.805 | 0.7893 | 0.5913 | 0.0946 | | 0.0348 | 70.0 | 1750 | 0.0379 | 0.805 | 0.6933 | 1.4129 | 0.805 | 0.7893 | 0.5877 | 0.0945 | | 0.0348 | 71.0 | 1775 | 0.0379 | 0.805 | 0.6933 | 1.4172 | 0.805 | 0.7893 | 0.5921 | 0.0951 | | 0.0348 | 72.0 | 1800 | 0.0379 | 0.805 | 0.6931 | 1.4136 | 0.805 | 0.7893 | 0.5851 | 0.0953 | | 0.0348 | 73.0 | 1825 | 0.0379 | 0.805 | 0.6929 | 1.4168 | 0.805 | 0.7893 | 0.5846 | 0.0971 | | 0.0348 | 74.0 | 1850 | 0.0379 | 0.805 | 0.6939 | 1.4185 | 0.805 | 0.7893 | 0.5892 | 0.0950 | | 0.0348 | 75.0 | 1875 | 0.0379 | 0.805 | 0.6935 | 1.4171 | 0.805 | 0.7893 | 0.5946 | 0.0938 | | 0.0348 | 76.0 | 1900 | 0.0379 | 0.805 | 0.6934 | 1.4217 | 0.805 | 0.7893 | 0.5939 | 0.0959 | | 0.0348 | 77.0 | 1925 | 0.0379 | 0.8 | 0.6932 | 1.4162 | 0.8000 | 0.7859 | 0.5826 | 0.0954 | | 0.0348 | 78.0 | 1950 | 0.0379 | 0.8 | 0.6935 | 1.4172 | 0.8000 | 0.7859 | 0.5912 | 0.0950 | | 0.0348 | 79.0 | 1975 | 0.0379 | 0.8 | 0.6933 | 1.4169 | 0.8000 | 0.7859 | 0.5885 | 0.0964 | | 0.0348 | 80.0 | 2000 | 0.0379 | 0.8 | 0.6935 | 1.4196 | 0.8000 | 0.7859 | 0.5865 | 0.0957 | | 0.0348 | 81.0 | 2025 | 0.0379 | 0.8 | 0.6937 | 1.4213 | 0.8000 | 0.7859 | 0.5880 | 0.0962 | | 0.0348 | 82.0 | 2050 | 0.0379 | 0.8 | 0.6939 | 1.4201 | 0.8000 | 0.7859 | 0.5910 | 0.0962 | | 0.0348 | 83.0 | 2075 | 0.0379 | 0.8 | 0.6938 | 1.3762 | 0.8000 | 0.7859 | 0.5883 | 0.0945 | | 0.0348 | 84.0 | 2100 | 0.0379 | 0.8 | 0.6938 | 1.4218 | 0.8000 | 0.7859 | 0.5947 | 0.0950 | | 0.0348 | 85.0 | 2125 | 0.0379 | 0.8 | 0.6935 | 1.3657 | 0.8000 | 0.7859 | 0.5857 | 0.0912 | | 0.0348 | 86.0 | 2150 | 0.0379 | 0.8 | 0.6938 | 1.3278 | 0.8000 | 0.7859 | 0.5892 | 0.0929 | | 0.0348 | 87.0 | 2175 | 0.0379 | 0.8 | 0.6938 | 1.3831 | 0.8000 | 0.7859 | 0.5856 | 0.0946 | | 0.0348 | 88.0 | 2200 | 0.0379 | 0.8 | 0.6938 | 1.3761 | 0.8000 | 0.7859 | 0.5892 | 0.0955 | | 0.0348 | 89.0 | 2225 | 0.0379 | 0.8 | 0.6938 | 1.3296 | 0.8000 | 0.7859 | 0.5870 | 0.0947 | | 0.0348 | 90.0 | 2250 | 0.0379 | 0.8 | 0.6939 | 1.3667 | 0.8000 | 0.7859 | 0.5909 | 0.0926 | | 0.0348 | 91.0 | 2275 | 0.0379 | 0.8 | 0.6940 | 1.3346 | 0.8000 | 0.7859 | 0.5906 | 0.0930 | | 0.0348 | 92.0 | 2300 | 0.0379 | 0.8 | 0.6938 | 1.3268 | 0.8000 | 0.7859 | 0.5870 | 0.0936 | | 0.0348 | 93.0 | 2325 | 0.0379 | 0.8 | 0.6937 | 1.3320 | 0.8000 | 0.7859 | 0.5919 | 0.0939 | | 0.0348 | 94.0 | 2350 | 0.0379 | 0.8 | 0.6939 | 1.3324 | 0.8000 | 0.7859 | 0.5870 | 0.0928 | | 0.0348 | 95.0 | 2375 | 0.0379 | 0.8 | 0.6937 | 1.3289 | 0.8000 | 0.7859 | 0.5869 | 0.0932 | | 0.0348 | 96.0 | 2400 | 0.0379 | 0.8 | 0.6938 | 1.3264 | 0.8000 | 0.7859 | 0.5869 | 0.0931 | | 0.0348 | 97.0 | 2425 | 0.0379 | 0.8 | 0.6938 | 1.3280 | 0.8000 | 0.7859 | 0.5870 | 0.0932 | | 0.0348 | 98.0 | 2450 | 0.0379 | 0.8 | 0.6938 | 1.3297 | 0.8000 | 0.7859 | 0.5869 | 0.0930 | | 0.0348 | 99.0 | 2475 | 0.0379 | 0.8 | 0.6938 | 1.3304 | 0.8000 | 0.7859 | 0.5869 | 0.0929 | | 0.0347 | 100.0 | 2500 | 0.0379 | 0.8 | 0.6938 | 1.3290 | 0.8000 | 0.7859 | 0.5869 | 0.0931 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

Jonathaniu/alpaca-breast-cancer-13b

Jonathaniu

2023-07-11T21:01:33Z

0

peft

[ "peft", "region:us" ]

null

2023-07-11T20:37:06Z

--- 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

davidmunechika/coreml-genshin-landscape-diffusion

davidmunechika

2023-07-11T21:01:14Z

0

1

null

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

null

2023-06-30T17:28:52Z

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

Finnfalter/ppo-LunarLander-v2

Finnfalter

2023-07-11T20:46:31Z

2

0

stable-baselines3

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

reinforcement-learning

2023-07-11T20:46:10Z

--- 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: 253.83 +/- 16.30 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 ... ```

ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30

ALM-AHME

2023-07-11T20:46:08Z

5

1

transformers

[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-11T12:39:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Augmented-Final split: train args: Augmented-Final metrics: - name: Accuracy type: accuracy value: 0.9805094130675526 ---  # beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0474 - Accuracy: 0.9805 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - 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: 0.5 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2312 | 0.99 | 93 | 0.1822 | 0.9453 | | 0.3817 | 1.99 | 187 | 0.2106 | 0.9183 | | 0.2217 | 3.0 | 281 | 0.1902 | 0.9285 | | 0.1667 | 4.0 | 375 | 0.1127 | 0.9584 | | 0.0572 | 4.96 | 465 | 0.0474 | 0.9805 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

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

carova

2023-07-11T20:42:18Z

0

null

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

reinforcement-learning

2023-07-11T20:42:14Z

--- 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="carova/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"]) ```

autopilot-ai/EthicalEye

autopilot-ai

2023-07-11T20:11:30Z

269

7

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "text-classification", "en", "fr", "hi", "gu", "bn", "ml", "mr", "pa", "it", "es", "kn", "as", "af", "ru", "ro", "sq", "ar", "am", "az", "bs", "bh", "bg", "bo", "ca", "ce", "zh", "cr", "hr", "cs", "da", "de", "nl", "el", "et", "eo", "fi", "fj", "fa", "gl", "ga", "ha", "ht", "he", "hu", "hy", "id", "is", "ja", "jv", "ka", "kk", "km", "ko", "ks", "ku", "ky", "la", "lb", "lt", "lv", "mk", "mn", "ms", "mi", "mt", "ne", "no", "or", "om", "ps", "pl", "pt", "qu", "sa", "sm", "gd", "sr", "sn", "sd", "si", "sk", "sl", "so", "su", "sw", "sv", "tg", "ta", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-01T10:36:08Z

--- license: apache-2.0 requirements: - sentencepiece: >- (if not installed install using `pip install sentencepiece`, and restart runtime) library_name: transformers pipeline_tag: text-classification language: - en - fr - hi - gu - bn - ml - mr - pa - it - es - kn - as - af - ru - ro - sq - ar - am - az - bs - bh - bg - bo - ca - ce - zh - cr - hr - cs - da - de - nl - el - et - eo - fi - fj - fa - gl - ga - ha - ht - he - hu - hy - id - is - ja - jv - ka - kk - km - ko - ks - ku - ky - la - lb - lt - lv - mk - mn - ms - mi - mt - ne - 'no' - or - om - ps - pl - pt - qu - sa - sm - gd - sr - sn - sd - si - sk - sl - so - su - sw - sv - tg - ta --- ## Details - Model Name: Ethical Eye - Description: Ethical Eye is an open-source AI model developed by AutopilotAI. It is designed to flag and analyze user-generated content for harmful or unethical behavior, providing a last layer of decision-making to assist AI systems in promoting ethical and moral actions. The model leverages advanced techniques such as text classification, toxicity analysis, and cross-lingual NLP to detect abuse, obscene language, and harmful or unethical comments in multiple languages. ## How to use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autopilot-ai/EthicalEye") model = AutoModelForSequenceClassification.from_pretrained("autopilot-ai/EthicalEye") ``` ## Intended Use - Primary Use Case: The Ethical Eye model is primarily intended to be used as a tool to flag or block users exhibiting harmful or unethical behavior on various platforms. It aims to assist developers, especially those with limited experience in NLP, in enforcing ethical standards and creating a safer environment for users. - User Expertise: The model is designed to be accessible to developers with various levels of NLP expertise, including those with limited experience in the field. - Limitations: While Ethical Eye provides valuable insights and analysis, it is essential to note that it should be used as an aid and not as the sole determinant of ethical decision-making. It may have limitations in understanding context-specific nuances and may require continuous improvement and customization for specific domains or languages. ## Model Details - Architecture: Ethical Eye is built using PyTorch and utilizes the Transformers library. It employs the XLM-Roberta architecture, which enables cross-lingual understanding and transfer learning. - Developed by: [Khush Patel](https://www.linkedin.com/in/khush-patel-kp/), [Jayveersinh Raj](https://www.linkedin.com/in/jayveersinh-raj-67694222a/) - License: The Ethical Eye model is released under the Apache 2.0 license, granting users the freedom to use, modify, and distribute the model according to the terms of the license. ## Use Cases - Content Moderation: Ethical Eye can be integrated into content moderation systems to automatically flag and block user-generated content that contains abusive language, hate speech, or other forms of harmful or unethical behavior. It helps platforms maintain a safe and respectful environment for their users. - Social Media Platforms: Social media platforms can utilize Ethical Eye to automatically detect and filter out toxic comments, obscenities, and offensive content in multiple languages. This helps to create a more positive and inclusive online community. - Chatbots and Virtual Assistants: By incorporating Ethical Eye into chatbots and virtual assistants, AI systems can ensure that their responses align with ethical guidelines. It helps prevent AI agents from engaging in inappropriate or offensive conversations with users. - Online Forums and Discussion Boards: Ethical Eye can be applied to online forums and discussion boards to monitor user interactions and identify potential instances of harassment, bullying, or unethical behavior. This allows moderators to take appropriate actions to maintain a healthy and respectful environment. - E-commerce Platforms: E-commerce platforms can utilize Ethical Eye to automatically identify and block reviews or comments that contain false information, spam, or unethical practices. This helps maintain the integrity of the platform and ensures honest and reliable user feedback. - Educational Platforms: Ethical Eye can be used in educational platforms to flag and address instances of cyberbullying, inappropriate language, or offensive content in student discussions and comments. It supports the creation of a safe and respectful learning environment. - AI Reinforcement Learning: The Ethical Eye model can serve as a critic in reinforcement learning scenarios, providing feedback on the ethical implications of actions taken by AI agents. It assists in developing AI systems that not only optimize for task performance but also align with ethical guidelines and societal norms. ## Considerations for Deployment - Hardware Requirements: The Ethical Eye model can be deployed on hardware configurations suitable for running deep learning models. Specific requirements may depend on the scale of deployment and the desired performance. - Dependencies: The model relies on PyTorch, Transformers, and XLM-Roberta libraries. Refer to the model documentation for specific version requirements. - Integration: Ethical Eye can be integrated into existing AI systems and platforms using the provided APIs and guidelines. Additional customization may be necessary to adapt the model to specific requirements. - Ethical and Legal Considerations: While Ethical Eye aims to promote ethical behavior, it is important to acknowledge that it may have limitations and biases inherent in its training data. Developers should exercise caution and consider the legal and ethical implications of relying solely on the model's outputs without human oversight.

BlueAvenir/model_growth_restructuring_V_1_0

BlueAvenir

2023-07-11T20:11:27Z

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

2023-07-11T20:10:59Z

--- 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 95 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": 95, "warmup_steps": 10, "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

au2a/whisper-base-zh-20230711

au2a

2023-07-11T20:10:47Z

84

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:-", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-11T12:05:21Z

--- language: - zh license: apache-2.0 tags: - whisper - generated_from_trainer datasets: - '-' model-index: - name: whisper-base-zh-20230711 - au2a results: [] ---  # whisper-base-zh-20230711 - au2a This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the some hakka audio dataset. It achieves the following results on the evaluation set: - Loss: 0.4551 - Cer: 16.9978 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.4673 | 0.65 | 1000 | 0.6526 | 25.0548 | | 0.2203 | 1.29 | 2000 | 0.4985 | 19.8459 | | 0.1446 | 1.94 | 3000 | 0.4557 | 18.0026 | | 0.0956 | 2.59 | 4000 | 0.4438 | 16.9676 | | 0.0527 | 3.24 | 5000 | 0.4450 | 17.0998 | | 0.0423 | 3.88 | 6000 | 0.4441 | 17.7797 | | 0.027 | 4.53 | 7000 | 0.4474 | 16.9260 | | 0.0177 | 5.18 | 8000 | 0.4515 | 16.5861 | | 0.0165 | 5.83 | 9000 | 0.4537 | 16.8392 | | 0.0129 | 6.47 | 10000 | 0.4551 | 16.9978 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.11.0+cu113 - Datasets 2.13.1 - Tokenizers 0.13.3

datajanko/ppo-LunarLander-v2

datajanko

2023-07-11T20:08:45Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-11T20:08:25Z

--- 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: 268.35 +/- 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 ... ```

carova/ppo-Huggy

carova

2023-07-11T20:06:31Z

27

0

ml-agents

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

reinforcement-learning

2023-07-11T19:17:52Z

--- 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: carova/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

davidmunechika/coreml-future-diffusion

davidmunechika

2023-07-11T20:06:24Z

0

null

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

null

2023-07-11T19:40:15Z

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

BlueAvenir/model_it_recruit_V_0_1

BlueAvenir

2023-07-11T20:00:17Z

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

2023-07-11T19:59:50Z

--- 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 100 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": 100, "warmup_steps": 10, "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

BlueAvenir/model_growth_restructuring_V_0_2

BlueAvenir

2023-07-11T19:51:05Z

4

0

sentence-transformers

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

sentence-similarity

2023-07-11T19:50:43Z

--- 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 98 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": 98, "warmup_steps": 10, "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

BlueAvenir/model_operations_V_0_2

BlueAvenir

2023-07-11T19:33:56Z

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

2023-07-11T19:33:15Z

--- 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 100 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": 100, "warmup_steps": 10, "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

davidmunechika/oldjourney

davidmunechika

2023-07-11T19:29:28Z

31

0

diffusers

[ "diffusers", "Text-to-image", "Diffusers", "stable-diffusion", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-11T17:58:14Z

--- license: creativeml-openrail-m language: - en tags: - Text-to-image - Diffusers - stable-diffusion --- Oldjourney Oldjourney is a finetuned Stable Diffusion 2.1 model trained on images from Midjourney 3 using Dreambooth. That older version of Midjourney was often messy and imprecise, but had a great artistic style. These two versions of Oldjourney can recreate the essence of that art style with added details, precision, and quality. The two models, Oldjourney Ultra and Oldjourney Lite, are very similar, but they have different strengths. Ultra is better at people, while Lite is better at painterly style images. Use the keyword Oldjourney to trigger the style, and set the resolution to 768 x 768 or greater. Examples and sample prompts below. This is a model for Stable Diffusion 2.1, so make sure to download the yaml files. Rendered with Oldjourney Lite ![Oldjourney Lite.png](https://s3.amazonaws.com/moonup/production/uploads/1673363360976-6362b8dc2a84d82a8c91145c.png) Rendered with Oldjourney Ultra ![Oldjourney Ultra.png](https://s3.amazonaws.com/moonup/production/uploads/1673363412363-6362b8dc2a84d82a8c91145c.png) Sample Prompts for Oldjourney Lite Sample 1 Oldjourney the legendary dream vortex and a dreamer, a boy laying on a bed in front of a vortex, ultrafine detailed painting, psychedelic art, watching the stars at night, pulled into the spiral vortex, videogame cover art, ... if only i could sleep, discord profile picture, time travel machine, photoshop render Negative prompt: pink, ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 810775161, Size: 768x768, Model: Oldjourney Lite, ENSD: 1 Sample 2 Oldjourney an image of a wizard with a glowing staff turned to the side, black background, light art, full of colors and rich detail, color grunge, profile picture 1024px, glowing liquid, high detailed colors, colorful fire, an old man, blacklight, discord profile picture Negative prompt: ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2371590421, Size: 768x768, Model: Oldjourney Lite, ENSD: 1 Sample 3 Oldjourney a dog with a tiny top hat and steampunk goggles on its head and a steampunk collar, matte painting, insanely detailed, ultrafine details, hyperrealism Negative prompt: (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 3142299054, Size: 768x768, Model: Oldjourney Lite, ENSD: 1 Sample Prompts for Oldjourney Ultra Sample 4 Oldjourney A woman facing the camera dancing aura of cosmic energy vortex of sparkling blue sand and glowing embers ((grunge)) smoke magical eerie noir lighting stars in the sky ethereal dream sandman surreal rembrandt artstation dark atmosphere 8k highly detailed atmospheric Negative prompt: ugly, tiling, (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), out of frame, extra limbs, less than two arms, less than two legs, disfigured, deformed, body out of frame, blurry, (bad anatomy:1.2), blurred, grainy, cut off, draft, (overexposure:1.2), (high contrast:1.2),(cropped:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2676530026, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1 Sample 5 Oldjourney your fate revealed inside a crystal ball, crystal ball with swirling otherworldly fog reveals your fate, insanely detailed masterpiece Trending on Artstation 8k ray traced volumetric lighting ambient occlusion ultrafine details digital art painting Negative prompt: ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2555061923, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1 Sample 6 Oldjourney cosmic queen, ethereal woman with a crown on her head, head and shoulders portrait, fantasy art, star sky, star sky, face illuminated, sparkle, stars, cosmos, paticles Negative prompt: ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 868461039, Face restoration: GFPGAN, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1

ontel/icaaalor

ontel

2023-07-11T19:26:09Z

0

null

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

null

2023-07-11T19:24:54Z

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

jakelcoop/Reinforce-pixelcopter

jakelcoop

2023-07-11T19:14:55Z

0

null

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

reinforcement-learning

2023-07-11T19:14:51Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 13.90 +/- 15.94 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

phatjk/bloomz-lora-vi-QA-NLLB-viquad

phatjk

2023-07-11T19:10:03Z

4

0

peft

[ "peft", "region:us" ]

null

2023-07-08T14:52:09Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

HiTZ/gpt2-eus-euscrawl

HiTZ

2023-07-11T18:54:30Z

169

1

transformers

[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "eu", "dataset:HiTZ/euscrawl", "arxiv:1910.09700", "license:cc", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-02-20T15:35:58Z

--- license: cc datasets: - HiTZ/euscrawl language: - eu metrics: - perplexity library_name: transformers pipeline_tag: text-generation --- # Model Card for GPT2 Eus Euscrawl  Pretrained GPT2 small model (124M parameters) on Basque language using a causal language modeling (CLM) objective. The English version of GPT2 was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. # Model Details ## Model Description  GPT-2 is a transformers model pretrained on a very large corpus of Basque data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the **smallest** version of GPT-2, with 124M parameters. - **Developed by:** [github.com/juletx](https://github.com/juletx) - **Model type:** GPT2 - **Language(s) (NLP):** Basque (eu) - **License:** cc ## Model Sources [optional]  - **Repository:** [github.com/juletx/phd](https://github.com/juletx/phd) - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses  ## Direct Use  You can use this model directly with a pipeline for text generation. ## Downstream Use [optional]  You can also fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## 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. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` # Training Details ## Training Data  EusCrawl (http://www.ixa.eus/euscrawl/) is a high-quality corpus for Basque comprising 12.5 million documents and 423 million tokens, totalling 2.1 GiB of uncompressed text. EusCrawl was built using ad-hoc scrapers to extract text from 33 Basque websites with high-quality content, resulting in cleaner text compared to general purpose approaches. [Dataset Card](https://huggingface.co/datasets/HiTZ/euscrawl) ## Training Procedure  ### Preprocessing [optional] The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,304. The inputs are sequences of 1024 consecutive tokens. ### Training Hyperparameters - **Training regime:** bf16 mixed precission  ### 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:** ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` **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]

niquito/falcon-7b-instruct-ft-adapters

niquito

2023-07-11T18:47:44Z

0

peft

[ "peft", "region:us" ]

null

2023-07-11T18:47:43Z

--- 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: True - bnb_4bit_compute_dtype: bfloat16 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0

Winmodel/a2c-PandaReachDense-v2

Winmodel

2023-07-11T18:38:54Z

0

stable-baselines3

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

reinforcement-learning

2023-07-11T18:37:34Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.49 +/- 0.17 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

gerulata/slovakbert

gerulata

2023-07-11T18:36:33Z

4,830

19

transformers

[ "transformers", "pytorch", "tf", "safetensors", "roberta", "fill-mask", "SlovakBERT", "sk", "dataset:wikipedia", "dataset:opensubtitles", "dataset:oscar", "dataset:gerulatawebcrawl", "dataset:gerulatamonitoring", "dataset:blbec.online", "arxiv:2109.15254", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: sk tags: - SlovakBERT license: mit datasets: - wikipedia - opensubtitles - oscar - gerulatawebcrawl - gerulatamonitoring - blbec.online --- # SlovakBERT (base-sized model) SlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-sensitive: it makes a difference between slovensko and Slovensko. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. **IMPORTANT**: The model was not trained on the “ and ” (direct quote) character -> so before tokenizing the text, it is advised to replace all “ and ” (direct quote marks) with a single "(double quote marks). ### How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='gerulata/slovakbert') unmasker("Deti sa <mask> na ihrisku.") [{'sequence': 'Deti sa hrali na ihrisku.', 'score': 0.6355380415916443, 'token': 5949, 'token_str': ' hrali'}, {'sequence': 'Deti sa hrajú na ihrisku.', 'score': 0.14731724560260773, 'token': 9081, 'token_str': ' hrajú'}, {'sequence': 'Deti sa zahrali na ihrisku.', 'score': 0.05016357824206352, 'token': 32553, 'token_str': ' zahrali'}, {'sequence': 'Deti sa stretli na ihrisku.', 'score': 0.041727423667907715, 'token': 5964, 'token_str': ' stretli'}, {'sequence': 'Deti sa učia na ihrisku.', 'score': 0.01886524073779583, 'token': 18099, 'token_str': ' učia'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('gerulata/slovakbert') model = RobertaModel.from_pretrained('gerulata/slovakbert') text = "Text ktorý sa má embedovať." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('gerulata/slovakbert') model = TFRobertaModel.from_pretrained('gerulata/slovakbert') text = "Text ktorý sa má embedovať." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` Or extract information from the model like this: ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='gerulata/slovakbert') unmasker("Slovenské národne povstanie sa uskutočnilo v roku <mask>.") [{'sequence': 'Slovenske narodne povstanie sa uskutočnilo v roku 1944.', 'score': 0.7383289933204651, 'token': 16621, 'token_str': ' 1944'},...] ``` # Training data The SlovakBERT model was pretrained on these datasets: - Wikipedia (326MB of text), - OpenSubtitles (415MB of text), - Oscar (4.6GB of text), - Gerulata WebCrawl (12.7GB of text) , - Gerulata Monitoring (214 MB of text), - blbec.online (4.5GB of text) The text was then processed with the following steps: - URL and email addresses were replaced with special tokens ("url", "email"). - Elongated interpunction was reduced (e.g. -- to -). - Markdown syntax was deleted. - All text content in braces f.g was eliminated to reduce the amount of markup and programming language text. We segmented the resulting corpus into sentences and removed duplicates to get 181.6M unique sentences. In total, the final corpus has 19.35GB of text. # Pretraining The model was trained in **fairseq** on 4 x Nvidia A100 GPUs for 300K steps with a batch size of 512 and a sequence length of 512. The optimizer used is Adam with a learning rate of 5e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), a weight decay of 0.01, dropout rate 0.1, learning rate warmup for 10k steps and linear decay of the learning rate after. We used 16-bit float precision. ## About us <a href="https://www.gerulata.com/"> <img width="300px" src="https://www.gerulata.com/assets/images/Logo_Blue.svg"> </a> Gerulata Technologies is a tech company on a mission to provide tools for fighting disinformation and hostile propaganda. At Gerulata, we focus on providing state-of-the-art AI-powered tools that empower human analysts and provide them with the ability to make informed decisions. Our tools allow for the monitoring and analysis of online activity, as well as the detection and tracking of disinformation and hostile propaganda campaigns. With our products, our clients are better equipped to identify and respond to threats in real-time. ### BibTeX entry and citation info If you find our resource or paper is useful, please consider including the following citation in your paper. - https://arxiv.org/abs/2109.15254 ``` @misc{pikuliak2021slovakbert, title={SlovakBERT: Slovak Masked Language Model}, author={Matúš Pikuliak and Štefan Grivalský and Martin Konôpka and Miroslav Blšták and Martin Tamajka and Viktor Bachratý and Marián Šimko and Pavol Balážik and Michal Trnka and Filip Uhlárik}, year={2021}, eprint={2109.15254}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

SANTIAGo2005/ppo-LunarLander-v2

SANTIAGo2005

2023-07-11T18:21:31Z

0

stable-baselines3

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

reinforcement-learning

2023-07-11T18:17:32Z

--- 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: -116.26 +/- 67.01 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 ... ```

RogerB/afro-xlmr-large-finetuned-kintweets

RogerB

2023-07-11T18:13:40Z

98

0

transformers

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

fill-mask

2023-07-11T18:07:30Z

--- license: mit tags: - generated_from_trainer model-index: - name: afro-xlmr-large-finetuned-kintweets results: [] ---  # afro-xlmr-large-finetuned-kintweets This model is a fine-tuned version of [Davlan/afro-xlmr-large](https://huggingface.co/Davlan/afro-xlmr-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7777 ## 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: 10 - eval_batch_size: 10 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.9995 | 1.0 | 90 | 1.6774 | | 1.9176 | 2.0 | 180 | 1.6880 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Belphegor/Taxi-v3

Belphegor

2023-07-11T18:12:30Z

0

null

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

reinforcement-learning

2023-07-11T18:12:28Z

--- 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: 7.56 +/- 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="Belphegor/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"]) ```

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

Belphegor

2023-07-11T18:08:55Z

0

null

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

reinforcement-learning

2023-07-11T18:08:53Z

--- 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="Belphegor/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"]) ```

markjosims/wav2vec2-large-xls-r-300m-tr-colab

markjosims

2023-07-11T18:00:08Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-11T00:17:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-tr-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.37473189663977124 ---  # wav2vec2-large-xls-r-300m-tr-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4346 - Wer: 0.3747 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9005 | 4.26 | 400 | 0.6917 | 0.7251 | | 0.4032 | 8.51 | 800 | 0.4781 | 0.5286 | | 0.1863 | 12.77 | 1200 | 0.4682 | 0.4690 | | 0.1323 | 17.02 | 1600 | 0.4664 | 0.4483 | | 0.1014 | 21.28 | 2000 | 0.4500 | 0.4124 | | 0.0749 | 25.53 | 2400 | 0.4510 | 0.3909 | | 0.0568 | 29.79 | 2800 | 0.4346 | 0.3747 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3

RogerB/KinyaBERT-small-finetuned-kintweets

RogerB

2023-07-11T17:52:20Z

115

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-11T17:51:35Z

--- tags: - generated_from_trainer model-index: - name: KinyaBERT-small-finetuned-kintweets results: [] ---  # KinyaBERT-small-finetuned-kintweets This model is a fine-tuned version of [jean-paul/KinyaBERT-small](https://huggingface.co/jean-paul/KinyaBERT-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3637 ## 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: 10 - eval_batch_size: 10 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 4.4855 | 1.0 | 90 | 4.2106 | | 4.1658 | 2.0 | 180 | 4.1444 | | 4.0402 | 3.0 | 270 | 4.1616 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

swacks/ql-taxiv3

swacks

2023-07-11T17:41:32Z

0

null

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

reinforcement-learning

2023-07-11T17:41:30Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: ql-taxiv3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="swacks/ql-taxiv3", 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"]) ```

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

swacks

2023-07-11T17:39:19Z

0

null

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

reinforcement-learning

2023-07-11T17:39:16Z

--- 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="swacks/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"]) ```

Luke537/videomae-base-finetuned-ucf101-subset

Luke537

2023-07-11T17:30:17Z

59

0

transformers

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

video-classification

2023-07-11T14:12:56Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer 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. ## 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: 8 - eval_batch_size: 8 - 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: 74 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cpu - Datasets 2.13.0 - Tokenizers 0.13.3

MaitreHibou/a2c-AntBulletEnv-v0

MaitreHibou

2023-07-11T17:25:59Z

1

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-11T17:24:54Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 732.68 +/- 43.01 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

bhaskar-ruthvik/falcon7b-finance-tuned

bhaskar-ruthvik

2023-07-11T17:23:11Z

0

null

[ "region:us" ]

null

2023-07-11T14:32:51Z

# Falcon-7B Model Fine-Tuned on Finance Data This is a Falcon-7b model fine-tuned on finance data by using Parameter Efficient Fine-Tuning(PEFT) and Quantized Low-Rank Adapters. The data includes stock prices, transaction data, tweets about stocks and their sentiment analysis, frequently asked questions about the Finance industry. ## How was the data created? * The stock data was taken from the YFinance library which provides up-to-date stock prices. As the model cannot handle realtime data the last known stock prices will be dated 11-07-2023 which is the date of training of the model * The user transaction data was hand-crafted by a group member to look at the possibilities it would unlock without having to deal with the privacy concerns of using real user data * The tweets and their sentiments were taken from a kaggle dataset by Rutvik Nelluri * The faqs about finance were noted down through research on the internet and the prompts were framed using that data ## Why was the training data so small? * Despite the fact that the actual data collected was of a very large scale including atleast 5000 datapoints for the stocks and the tweets analysis, and that similar prompts for the transaction data and faqs could be generated using the OpenAI, the decision to stick with a much smaller subset of the data is to improve the training time on lower-end GPUs * This is also why PEFT and QLoRA have been used for the fine-tuning of the model which drastically reduce the trainable weights from 7 Billion to 432k which is significantly smaller ## How was the model trained? The model was trained by using the built-in transformers trainer with max_steps set to 140 which is approximately equal to 4 epochs of training. The final step training loss was 0.49. ## How to Run? 1. First run the cells to install all the libraries in their required versions (This code snippet uses a custom version of transformers but we can now use the official release): ``` python pip install -Uqqq pip --progress-bar off pip install -qqq bitsandbytes==0.39.0 --progress-bar off pip install -qqq torch==2.0.1 --progress-bar off pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc --progress-bar off pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71 --progress-bar off pip install -qqq datasets==2.12.0 --progress-bar off pip install -qqq loralib==0.1.1 --progress-bar off pip install -qqq einops==0.6.1 --progress-bar off ``` 2. Now import all the necessary libraries and set the default device to the gpu: ``` python import json import os from pprint import pprint import bitsandbytes as bnb import pandas as pd import torch import torch.nn as nn import transformers from datasets import load_dataset from huggingface_hub import notebook_login from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training, ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) os.environ['CUDA_VISIBLE_DEVICES'] = '0' ``` 3. Then load the model using 8-bit preferably if the hardware allows for it, to speed up the inference time: ``` python PEFT_MODEL = 'bhaskar-ruthvik/falcon7b-finance-tuned' config = PeftConfig.from_pretrained(PEFT_MODEL) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, return_dict = True, device_map = 'auto', trust_remote_code = True, load_in_8bit = True ) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model,PEFT_MODEL) ``` 4. Setup the generation configuration: ``` python generation_config = model.generation_config generation_config.max_new_tokens = 200 generation_config.temperature = 0.7 generation_config.top_p = 0.7 generation_config.num_return_sequences = 1 generation_config.pad_token_id = tokenizer.eos_token_id generation_config.eos_token_id = tokenizer.eos_token_id ``` 5. Now declare a function to generate prompts so the code can be reused ``` python def generate_response(question: str)->str: prompt = f""" <human>: {question} <bot>: """.strip() encoding = tokenizer(prompt,return_tensors='pt').to(device) with torch.inference_mode(): outputs = model.generate(input_ids=encoding.input_ids, attention_mask = encoding.attention_mask, generation_config = generation_config) response = tokenizer.decode(outputs[0],skip_special_tokens=True) assistant_start = "<bot>:" response_start = response.find(assistant_start) return response[response_start+ len(assistant_start) :].strip() ``` 6. Now provide the prompt to the model and wait for the inference (takes about 40 seconds): ``` python prompt = 'What is estate planning?' print('%.300s' % generate_response(prompt)) ```

Noureldin2303/ProvaImg

Noureldin2303

2023-07-11T17:22:17Z

0

null

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

null

2023-07-11T17:22:17Z

--- license: bigcode-openrail-m ---

sanchit-gandhi/speecht5_tts_vox_nl

sanchit-gandhi

2023-07-11T17:21:55Z

94

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-11T17:19:50Z

--- license: mit tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] pipeline_tag: text-to-speech ---  # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4587 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5225 | 4.3 | 1000 | 0.4778 | | 0.5007 | 8.61 | 2000 | 0.4656 | | 0.493 | 12.91 | 3000 | 0.4602 | | 0.4902 | 17.21 | 4000 | 0.4587 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

gokuls/sa_bert_12_layer_modified_complete_training_72

gokuls

2023-07-11T17:19:06Z

47

0

transformers

[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-10T16:40:56Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: sa_bert_12_layer_modified_complete_training_72 results: [] ---  # sa_bert_12_layer_modified_complete_training_72 This model is a fine-tuned version of [gokuls/sa_bert_12_layer_modified_complete_training_48](https://huggingface.co/gokuls/sa_bert_12_layer_modified_complete_training_48) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6236 - Accuracy: 0.5322 ## 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: 32 - eval_batch_size: 32 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.0311 | 0.05 | 10000 | 2.8263 | 0.5069 | | 2.8816 | 0.11 | 20000 | 2.7833 | 0.5126 | | 2.7734 | 0.16 | 30000 | 2.7565 | 0.5158 | | 2.7612 | 0.22 | 40000 | 2.7284 | 0.5196 | | 2.8843 | 0.27 | 50000 | 2.7006 | 0.5229 | | 2.7809 | 0.33 | 60000 | 2.6765 | 0.5254 | | 2.6683 | 0.38 | 70000 | 2.6580 | 0.5276 | | 2.7175 | 0.44 | 80000 | 2.6270 | 0.5316 | | 2.8903 | 0.49 | 90000 | 2.6236 | 0.5322 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3

smerrill726/bank-sub

smerrill726

2023-07-11T17:15:35Z

0

null

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

null

2023-07-11T17:15:35Z

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

mark-oppenheim/Taxi-v3-V1

mark-oppenheim

2023-07-11T17:03:55Z

0

null

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

reinforcement-learning

2023-07-11T17:03:53Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-V1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.70 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="mark-oppenheim/Taxi-v3-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"]) ```

mark-oppenheim/q-FrozenLake-v1-4x4-Slippery

mark-oppenheim

2023-07-11T16:59:57Z

0

null

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

reinforcement-learning

2023-07-10T20:08:34Z

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.72 +/- 0.45 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="mark-oppenheim/q-FrozenLake-v1-4x4-Slippery", 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"]) ```

luzflavio/distilbert-base-uncased-finetuned-cola

luzflavio

2023-07-11T16:50:38Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-11T16:45:34Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: luzflavio/distilbert-base-uncased-finetuned-cola results: [] ---  # luzflavio/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1975 - Validation Loss: 0.5266 - Train Matthews Correlation: 0.5279 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5218 | 0.4601 | 0.4776 | 0 | | 0.3330 | 0.4767 | 0.5113 | 1 | | 0.1975 | 0.5266 | 0.5279 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

d9021001/mms-1b-l1107-nan

d9021001

2023-07-11T16:49:35Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-11T15:49:29Z

--- tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: mms-1b-l1107-nan results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: nan-tw split: test args: nan-tw metrics: - name: Wer type: wer value: 1.005720823798627 ---  # mms-1b-l1107-nan This model was trained from scratch on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 1.5084 - Wer: 1.0057 ## 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: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.5725 | 2.0 | 100 | 1.8002 | 1.0 | | 1.5002 | 4.0 | 200 | 1.5084 | 1.0057 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

asenella/mmnist_JNFDccaconfig_resnet_seed_0_ratio_0_c

asenella

2023-07-11T16:47:28Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-07-11T16:47:19Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

SHENMU007/neunit_BASE_V11.2

SHENMU007

2023-07-11T16:45:51Z

74

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

2023-07-11T14:03:10Z

--- 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

vluz/MiniAsirraONNX

vluz

2023-07-11T16:42:29Z

0

null

[ "onnx", "license:cc0-1.0", "region:us" ]

null

2023-07-11T16:38:04Z

--- license: cc0-1.0 --- Very small onnx model, trained on Asirra 150 dataset, and intended as an example of Lobe beta It classifies input images as "Cat" or "Dog" Untested, do not use for production

MaitreHibou/ppo-Pyramids

MaitreHibou

2023-07-11T16:28:23Z

0

ml-agents

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

reinforcement-learning

2023-07-11T16:28:19Z

--- 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: MaitreHibou/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

JTStephens/Reinforce-CartPoleV1

JTStephens

2023-07-11T16:23:23Z

0

null

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

reinforcement-learning

2023-07-11T16:23:15Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPoleV1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 416.60 +/- 38.82 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

Desainakut/YPKuatsi

Desainakut

2023-07-11T16:09:02Z

0

null

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

null

2023-07-11T06:33:39Z

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

1aurent/ppo-PyramidsRND

1aurent

2023-07-11T16:08:35Z

5

0

ml-agents

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

reinforcement-learning

2023-07-11T16:07:10Z

--- 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: 1aurent/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

SANTIAGo2005/ppo-Huggy

SANTIAGo2005

2023-07-11T16:07:41Z

0

ml-agents

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

reinforcement-learning

2023-07-11T16:07:36Z

--- 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: SANTIAGo2005/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

gokuls/sa_bert_12_layer_modified_complete_training_24

gokuls

2023-07-11T16:03:29Z

47

0

transformers

[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-10T15:24:07Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: sa_bert_12_layer_modified_complete_training_24 results: [] ---  # sa_bert_12_layer_modified_complete_training_24 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.7648 - Accuracy: 0.1722 ## 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: 32 - eval_batch_size: 32 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 6.5933 | 0.05 | 10000 | 6.5711 | 0.1226 | | 6.1523 | 0.11 | 20000 | 6.3425 | 0.1396 | | 6.1308 | 0.16 | 30000 | 6.2468 | 0.1444 | | 6.2297 | 0.22 | 40000 | 6.1895 | 0.1468 | | 6.1484 | 0.27 | 50000 | 6.1483 | 0.1487 | | 6.0591 | 0.33 | 60000 | 6.1205 | 0.1492 | | 6.0199 | 0.38 | 70000 | 6.0862 | 0.1501 | | 5.8666 | 0.44 | 80000 | 5.8875 | 0.1600 | | 5.9153 | 0.49 | 90000 | 5.7648 | 0.1722 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3

MUNDOJU/ppo-Huggy

MUNDOJU

2023-07-11T16:02:28Z

4

0

ml-agents

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

reinforcement-learning

2023-07-11T16:02:25Z

--- 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: MUNDOJU/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

caballeroch/FakeNewsClassifierDistilBert-uncased

caballeroch

2023-07-11T16:01:26Z

106

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "dataset:liar", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-10T19:17:04Z

--- datasets: - liar metrics: - accuracy - f1 - precision - recall --- # Fake News Classifier - Finetuned: 'distilbert-base-uncased' #### **LIAR Dataset** *** - This model is finetuned on a large dataset of hand-labeled short statements from politifact.com's API. - Data went through a series of text cleaning stages such as: 1. Lower-case standardization for improved 'uncased' model performance. 2. Mixed letter/digit word removal. 3. Stopword removal. 4. Extra space trimming. #### **DistilBERT Uncased Tokenizer** *** - The text is tokenized using the **'distilbert-base-uncased'** HuggingFace tokenizer. - For training, the text is cut to a block-size of 200. - Max length padding is used to maintain consistent input data shape. #### **DistilBERT Uncased Model** *** - The model that is finetuned is the DistilBERT model, **'distilbert-base-uncased'**. - This is a small and fast text classifier, perfect for real-time inference! - 40% less parameters than the base BERT model. - 60% faster while preserving 95% performance of the base BERT model. - This model outperforms the finetuned 'distilbert-base-cased' by over 5% average F1-score. - This improvement comes mainly from the slower learning rate and improved data preprocessing. - These modifications allow for a smoother training curve and convergence.

caballeroch/FakeNewsClassifierDistilBert-cased

caballeroch

2023-07-11T16:00:55Z

104

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "dataset:liar", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-08T20:42:56Z

--- datasets: - liar metrics: - accuracy - f1 - precision - recall --- # Fake News Classifier - Finetuned: 'distilbert-base-cased' #### **LIAR Dataset** *** - This model is finetuned on a large dataset of hand-labeled short statements from politifact.com's API. - Relevant columns of the data (speaker, statement, etc.) are concatenated and tokenized to create the model input. #### **DistilBERT Cased Tokenizer** *** - The text is tokenized using the **'distilbert-base-cased'** HuggingFace tokenizer. - For training, the text is cut to a block-size of 200. - Max length padding is used to maintain consistent input data shape. #### **DistilBERT Cased Model** *** - The model that is finetuned is the DistilBERT model, **'distilbert-base-cased'**. - This is a small and fast text classifier, perfect for real-time inference! - 40% less parameters than the base BERT model. - 60% faster while preserving 95% performance of the base BERT model. - The intuition for using the ***cased*** model is to capture some patterns in the writing style (capitalization, punctuation). - This information may be relevant for detecting fake news sources. - Writing styles may be relevant (as we see in clickbait titles with capitalization). - This model performs well in flagging misinformation (fake news), especially if the format is similar to the training distribution. - Overall, the performance is worse than the finetuned 'distilbert-base-uncased,' as the training data is less clean.

fireday/ppo-Huggy

fireday

2023-07-11T16:00:33Z

0

ml-agents

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

reinforcement-learning

2023-07-11T16:00:29Z

--- 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: fireday/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Aceituna0813/ppo-Huggy

Aceituna0813

2023-07-11T15:57:29Z

1

0

ml-agents

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

reinforcement-learning

2023-07-11T15:57:16Z

--- 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: Aceituna0813/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Corran/all-mini-v2-L6-ft

Corran

2023-07-11T15:50:19Z

3

0

sentence-transformers

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

text-classification

2023-07-11T15:50:16Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # Corran/all-mini-v2-L6-ft 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("Corran/all-mini-v2-L6-ft") # 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} } ```

TheBloke/open-llama-7B-v2-open-instruct-GGML

TheBloke

2023-07-11T15:43:30Z

0

6

null

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

null

2023-07-11T13:47:05Z

--- 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;"> <a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a> </div> </div>  # VMware's Open Llama 7B v2 Open Instruct GGML These files are GGML format model files for [VMware's Open Llama 7B v2 Open Instruct](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. These files 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/open-llama-7B-v2-open-instruct-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/open-llama-7B-v2-open-instruct-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details>  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | open-llama-7b-v2-open-instruct.ggmlv3.q2_K.bin | q2_K | 2 | 2.87 GB| 5.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | open-llama-7b-v2-open-instruct.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 3.60 GB| 6.10 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | open-llama-7b-v2-open-instruct.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 3.28 GB| 5.78 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | open-llama-7b-v2-open-instruct.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 2.95 GB| 5.45 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | open-llama-7b-v2-open-instruct.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB| 6.29 GB | Original quant method, 4-bit. | | open-llama-7b-v2-open-instruct.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB| 6.71 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | open-llama-7b-v2-open-instruct.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 4.08 GB| 6.58 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | open-llama-7b-v2-open-instruct.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 3.83 GB| 6.33 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | open-llama-7b-v2-open-instruct.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB| 7.13 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | open-llama-7b-v2-open-instruct.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB| 7.56 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | open-llama-7b-v2-open-instruct.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 4.78 GB| 7.28 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | open-llama-7b-v2-open-instruct.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 4.65 GB| 7.15 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | open-llama-7b-v2-open-instruct.ggmlv3.q6_K.bin | q6_K | 6 | 5.53 GB| 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | open-llama-7b-v2-open-instruct.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB| 9.66 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m open-llama-7b-v2-open-instruct.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## 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: VMware's Open Llama 7B v2 Open Instruct # VMware/open-llama-7B-v2-open-instruct Instruction-tuned version of the fully trained Open LLama 7B v2 model. The model is open for COMMERCIAL USE. - This model performs better on code compared to v1 due to the improvements made on the base model by the openlm-research team. - The instruction model is trained on an improved instruction tuning dataset compared to v1 NOTE : The model was trained using the Alpaca prompt template NOTE : Fast tokenizer results in incorrect encoding, set the ```use_fast = False``` parameter, when instantiating the tokenizer ## License - Commercially Viable - Open-instruct-v1 - Mosaic/Dolly-HHRLHF + filtered OASST1 - cc by 3.0 Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples - ESNLI - MIT - ECQA - CDLA 1.0 - Sharing - Strategy - MIT - CREAK - MIT - gsmk8 - MIT - aqua - MIT - qasc - Apache 2.0 - Language Model, ([openlm-research/open_llama_v2_7b](https://huggingface.co/openlm-research/open_llama_v2_7b)) is under apache-2.0 ## Nomenclature - Model : Open-llama-v2 - Model Size: 7B parameters - Dataset: Open-instruct(oasst,dolly, hhrlhf) ## Use in Transformers ``` import os import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = 'VMware/open-llama-7b-open-instruct' tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential') prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" prompt = """What is attention mechanism of a transformer model? Write a python code to illustrate how attention works within a transformer model using numpy library. Donot use pytorch or tensorflow.""" inputt = prompt_template.format(instruction= prompt) input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda") output1 = model.generate(input_ids, max_length=512) input_length = input_ids.shape[1] output1 = output1[:, input_length:] output = tokenizer.decode(output1[0]) print(output) ''' Sure, I can help you with that! Attention mechanisms in transformer models are typically implemented using the attention mechanism in the self-attention layer. Self-attention allows the model to focus on different parts of the input sequence when processing it. This is achieved by computing a set of attention weights, which are used to weigh the contribution of each input element to the output. Here's an example code using NumPy to illustrate how attention works in a transformer model: ```python import numpy as np def attention_weights(query, key, value, mask): # Query, key, and value are input tensors. Mask is a tensor of zeros and ones that represents the attention mask. # It is used to prevent the model from attending to certain positions in the input sequence if they are not relevant. # The attention weights are the element-wise product of the query, key, and mask tensors. # The result is a tensor of the same shape as the query tensor. # Compute the dot product between the query tensor and the key tensor dot = np.matmul(query, key) # Compute the element-wise softmax of the dot product tensor exp_dot = np.exp(dot) # Multiply the dot product and the softmax of the dot product tensors weights = dot * exp_dot # Return the attention weights as a NumPy tensor return weights # Define the input sequence query = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) key = np.array([[0.1, 0.2], [0.3, 0.4]]) value = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) mask = np.array([[False, True, True], [False, True, True]]) # Compute the attention weights weights = attention_weights(query, key, value, mask) # Print the attention weights print(weights) ``` In this example, the `attention_weights` function takes as input the query tensor, key tensor, value tensor, and mask tensor. It computes the dot product between the query and key tensors using the `np.matmul` function, and then applies a softmax function using the `np.exp` function to the element-wise dot product tensor. It then multiplies the dot product and softmax tensors using the `np.matmul` function, and returns the result as a NumPy tensor. The `query`, `key`, and `value` tensors represent the input sequence to the transformer model. The `mask` tensor represents the attention mask, which is used to prevent the model from attending to certain positions in the input sequence if they are not relevant. The output of the `attention_weights` function is a NumPy tensor that represents the attention weights for the input sequence. These weights are used by the transformer model to weigh the contribution of each input element to the output. I hope this helps!</s> ''' ``` ## Finetuning details The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning) ## Evaluation TODO

parchiev/distilbert-base-uncased-finetuned-imdb

parchiev

2023-07-11T15:39:16Z

124

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-03T11:37:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb 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: 2.4721 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4897 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

espnet/guangzhisun_librispeech100_asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix

espnet

2023-07-11T15:24:22Z

2

1

espnet

[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech_100", "license:cc-by-4.0", "region:us" ]

automatic-speech-recognition

2023-07-10T23:07:45Z

--- tags: - espnet - audio - automatic-speech-recognition language: en datasets: - librispeech_100 license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/guangzhisun_librispeech100_asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix` This model was trained by guangzhisun using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet pip install -e . cd egs2/librispeech_100/asr1_biasing ./run.sh --skip_data_prep false --skip_train true --download_model espnet/guangzhisun_librispeech100_asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix ``` # TCPGen in RNN-T # RESULTS ## Environments - date: `Wed Jul 5 02:01:19 BST 2023` - python version: `3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]` - espnet version: `espnet 202304` - pytorch version: `pytorch 2.0.1+cu117` - Git hash: `6f33b9d9a999d4cd7e9bc0dcfc0ba342bdff7c17` - Commit date: `Thu Jun 29 02:16:09 2023 +0100` ## exp/asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/dev_clean|2703|54402|95.7|3.9|0.4|0.6|4.9|48.0| |decode_asr_asr_model_valid.loss.ave/dev_other|2864|50948|85.8|12.6|1.6|1.9|16.1|77.0| |decode_asr_asr_model_valid.loss.ave/test_clean|2620|52576|95.4|4.1|0.5|0.7|5.2|49.9| |decode_asr_asr_model_valid.loss.ave/test_other|2939|52343|86.0|12.2|1.7|1.8|15.8|78.4| |decode_b20_nolm_avebest/test_clean|2620|52576|0.0|0.0|100.0|0.0|100.0|100.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/dev_clean|2703|288456|98.4|1.0|0.7|0.6|2.3|48.0| |decode_asr_asr_model_valid.loss.ave/dev_other|2864|265951|93.3|4.2|2.5|2.1|8.8|77.0| |decode_asr_asr_model_valid.loss.ave/test_clean|2620|281530|98.3|1.0|0.7|0.6|2.3|49.9| |decode_asr_asr_model_valid.loss.ave/test_other|2939|272758|93.6|3.8|2.6|1.9|8.3|78.4| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/dev_clean|2703|103998|95.3|3.5|1.2|0.6|5.3|48.0| |decode_asr_asr_model_valid.loss.ave/dev_other|2864|95172|85.2|11.8|3.0|2.5|17.3|77.0| |decode_asr_asr_model_valid.loss.ave/test_clean|2620|102045|95.3|3.4|1.3|0.6|5.4|49.9| |decode_asr_asr_model_valid.loss.ave/test_other|2939|98108|85.5|11.0|3.5|2.2|16.7|78.4| ## ASR config <details><summary>expand</summary> ``` config: conf/train_rnnt.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer_transducer_tcpgen500_deep_sche30_GCN6L_rep_suffix ngpu: 1 seed: 2022 num_workers: 8 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 70 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 6000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe600_spsuffix/train/speech_shape - exp/asr_stats_raw_en_bpe600_spsuffix/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe600_spsuffix/valid/speech_shape - exp/asr_stats_raw_en_bpe600_spsuffix/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] train_data_path_and_name_and_type: - - dump/raw/train_clean_100_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_clean_100_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} biasing: false deepbiasing: false biasinglist: '' battndim: 256 biasingsche: 0 bmaxlen: 100 bdrop: 0.0 biasingGNN: '' optim: adam optim_conf: lr: 0.002 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - THE▁ - C - AND▁ - S - OF▁ - S▁ - TO▁ - T - A▁ - G - I - ED▁ - E - RE - D - IN▁ - P - R - N - F - O - IN - B - T▁ - L - ING▁ - ▁ - W - I▁ - HE▁ - WAS▁ - A - THAT▁ - E▁ - IT▁ - AR - U - H - ES▁ - M - RI - '''' - HIS▁ - AN - D▁ - Y▁ - LY▁ - ON▁ - AS▁ - HAD▁ - WITH▁ - ST - Y - EN - HER▁ - YOU▁ - K - DE - AT▁ - FOR▁ - V - UN - TH - SE - RO - LI - LO - NOT▁ - TI - AL - BUT▁ - IS▁ - ER▁ - SI - OR - CH - ONE▁ - SHE▁ - OR▁ - ME▁ - BE▁ - K▁ - LA - LE - ALL▁ - HIM▁ - BE - CON - HO - PO - AT - THEY▁ - MY▁ - ME - 'ON' - BY▁ - AN▁ - VE▁ - DI - RA - AC - MA - HAVE▁ - SO▁ - WERE▁ - WHICH▁ - TED▁ - AL▁ - THIS▁ - FROM▁ - AD - SU - FI - AS - SAID▁ - ER - TH▁ - SE▁ - RY▁ - MO - EN▁ - FOR - HE - EX - NE - M▁ - VI - TS▁ - SH - BO - COM - PRO - EL - ARE▁ - FE - WE▁ - N▁ - NO▁ - ERS▁ - QU - THERE▁ - THEIR▁ - LE▁ - WHEN▁ - TE - TA - TY▁ - PER - THEM▁ - TER - WOULD▁ - OLD▁ - PA - CO - IR - IF▁ - WHO▁ - WHAT▁ - TER▁ - MAN▁ - ATION▁ - ST▁ - BEEN▁ - OUR▁ - CA - UP▁ - OUT▁ - PRE - AP - TION▁ - IT - FA - US - AM - VE - TUR - DO - PAR - PE - 'NO' - LU - THEN▁ - WI - SO - HI - P▁ - TO - COULD▁ - RE▁ - Z - WILL▁ - KING▁ - EAR▁ - DIS - EST▁ - LL▁ - SP - HA - ENCE▁ - TING▁ - IS - WE - DU - AND - MORE▁ - SOME▁ - US▁ - PI - ABLE▁ - NOW▁ - VERY▁ - GU - EM - ITY▁ - WA - H▁ - ATE▁ - LL - DO▁ - NA - DER - ANT▁ - LEA - PLA - BU - SA - CU - INTO▁ - OWN▁ - ET▁ - KE - PU - LITTLE▁ - MENT▁ - VER - TE▁ - DID▁ - LIKE▁ - IM - ABOUT▁ - OUR - TRA - TIME▁ - THAN▁ - YOUR▁ - RED▁ - MI - OTHER▁ - HU - ION▁ - ANCE▁ - STR - WELL▁ - W▁ - L▁ - ES - ANY▁ - ITS▁ - MIS - AB - AGE▁ - MAR - UPON▁ - OVER▁ - TU - DAY▁ - TEN - CH▁ - ALLY▁ - GRA - CAME▁ - MEN▁ - STO - LED▁ - AM▁ - GA - ONLY▁ - COME▁ - TWO▁ - UG - HOW▁ - VEN - INE▁ - NESS▁ - EL▁ - HAS▁ - BA - LONG▁ - AFTER▁ - IC▁ - WAY▁ - CAR - SC - HAR - MADE▁ - MIN - STE - BEFORE▁ - MOST▁ - ILL - FO - GE - DOWN▁ - DER▁ - BL - IONS▁ - SUCH▁ - THESE▁ - DE▁ - MEN - KED▁ - TRU - WHERE▁ - FUL▁ - BI - CAN▁ - SEE▁ - KNOW▁ - GO▁ - JE - GREAT▁ - LOW▁ - MUCH▁ - NEVER▁ - MISTER▁ - GOOD▁ - SHOULD▁ - EVEN▁ - ICE▁ - STA - LESS▁ - JO - BLE▁ - MUST▁ - AV - DA - ISH▁ - MON - TRI - KE▁ - BACK▁ - YING▁ - AIR▁ - AU - IOUS▁ - AGAIN▁ - MU - FIRST▁ - F▁ - GO - EVER▁ - VA - COR - OUS▁ - ATED▁ - COUNT - ROUND▁ - OVER - LING▁ - HERE▁ - HIMSELF▁ - SHED▁ - MIL - G▁ - THOUGH▁ - SIDE▁ - CL - MAY▁ - JUST▁ - WENT▁ - SAY▁ - NG▁ - PASS - HER - NED▁ - MIGHT▁ - FR - MAN - HOUSE▁ - JU - SON▁ - PEN - THROUGH▁ - EYES▁ - MAKE▁ - TOO▁ - THOUGHT▁ - WITHOUT▁ - THINK▁ - GEN - THOSE▁ - MANY▁ - SPEC - INTER - WHILE▁ - AWAY▁ - LIFE▁ - HEAD▁ - SUR - NTLY▁ - RIGHT▁ - DON - TAKE▁ - PORT - EVERY▁ - NIGHT▁ - WARD▁ - WAR - IMP - ALL - GET▁ - STILL▁ - BEING▁ - FOUND▁ - NOTHING▁ - LES▁ - LAST▁ - TURNED▁ - ILL▁ - YOUNG▁ - SURE▁ - INGS▁ - PEOPLE▁ - YET▁ - THREE▁ - FACE▁ - CUR - OFF▁ - ROOM▁ - OUT - ASKED▁ - SAW▁ - END▁ - FER - MISSUS▁ - EACH▁ - SAME▁ - SHA - SENT▁ - OUL - LET▁ - SOL - YOU - PLACE▁ - UNDER▁ - TOOK▁ - LIGHT▁ - LEFT▁ - PER▁ - PRESS - USE▁ - ANOTHER▁ - ONCE▁ - TELL▁ - SHALL▁ - 'OFF' - SEEMED▁ - ALWAYS▁ - NEW▁ - ATIONS▁ - J - CESS - USED▁ - WHY▁ - HEARD▁ - LOOKED▁ - GIVE▁ - PUT▁ - JA - BECAUSE▁ - THINGS▁ - BODY▁ - FATHER▁ - SOMETHING▁ - OWING▁ - LOOK▁ - ROW▁ - GOING▁ - MOTHER▁ - MIND▁ - WORK▁ - GOT▁ - CENT - HAVING▁ - SOON▁ - KNEW▁ - HEART▁ - FAR▁ - AGAINST▁ - WORLD▁ - FEW▁ - ICAL▁ - STOOD▁ - BEGAN▁ - SIR▁ - BETTER▁ - DOOR▁ - CALLED▁ - YEARS▁ - MOMENT▁ - ENOUGH▁ - WOMAN▁ - TOGETHER▁ - LIGHT - OWED▁ - READ▁ - WHOLE▁ - COURSE▁ - BETWEEN▁ - FELT▁ - LONG - HALF▁ - FULLY▁ - MORNING▁ - DENT - WOOD - HERSELF▁ - OLD - DAYS▁ - HOWEVER▁ - WATER▁ - WHITE▁ - PERHAPS▁ - REPLIED▁ - GIRL▁ - QUITE▁ - HUNDRED▁ - WORDS▁ - MYSELF▁ - VOICE▁ - EARLY▁ - OUGHT▁ - AIL▁ - WORD▁ - WHOM▁ - EITHER▁ - AMONG▁ - ENDED▁ - TAKEN▁ - UNTIL▁ - ANYTHING▁ - NEXT▁ - POSSIBLE▁ - KIND▁ - BROUGHT▁ - EAST▁ - LOOKING▁ - ROAD▁ - SMALL▁ - RATHER▁ - BELIEVE▁ - SINCE▁ - MONEY▁ - OPEN▁ - INDEED▁ - DOUBT - CERTAIN▁ - TWENTY▁ - MATTER▁ - HELD▁ - EXPECT - DIRECT - ANSWERED▁ - THERE - WHOSE▁ - SHIP▁ - HIGH▁ - THEMSELVES▁ - APPEARED▁ - BLACK▁ - NATURE▁ - BEHIND▁ - POWER▁ - IZED▁ - CHILD▁ - UNCLE▁ - DEATH▁ - KNOWN▁ - OFTEN▁ - LADY▁ - POSITION▁ - KEEP▁ - CHILDREN▁ - WIFE▁ - JOHN▁ - LARGE▁ - GIVEN▁ - EIGHT▁ - SHORT▁ - SAYS▁ - EVERYTHING▁ - GENERAL▁ - DOCTOR▁ - ABOVE▁ - HAPPY▁ - Q - X - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: joint_space_size: 320 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram600suffix/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe600_spsuffix/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.0 report_cer: false report_wer: false biasinglist: data/Blist/rareword_f15.txt bmaxlen: 500 bdrop: 0.0 battndim: 256 biasing: true biasingsche: 30 deepbiasing: true biasingGNN: gcn6 bpemodel: data/en_token_list/bpe_unigram600suffix/bpe.model preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 15 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 256 dropout: 0.1 dropout_embed: 0.2 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202304' distributed: false ``` </details> ### Citing TCPGen ```BibTex @INPROCEEDINGS{9687915, author={Sun, Guangzhi and Zhang, Chao and Woodland, Philip C.}, booktitle={2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, title={Tree-Constrained Pointer Generator for End-to-End Contextual Speech Recognition}, year={2021}, volume={}, number={}, pages={780-787}, doi={10.1109/ASRU51503.2021.9687915} } @inproceedings{Sun2022TreeconstrainedPG, title={Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition}, author={Guangzhi Sun and C. Zhang and Philip C. Woodland}, booktitle={Interspeech}, year={2022} } ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ```

vk21/a2c-PandaReachDense-v2-unit6

vk21

2023-07-11T15:23:31Z

0

stable-baselines3

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

reinforcement-learning

2023-07-11T15:06:09Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.39 +/- 0.17 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

grace-pro/afriberta-base-finetuned-igbo

grace-pro

2023-07-11T15:18:59Z

103

0

transformers

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

token-classification

2023-07-11T14:32:20Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-base-finetuned-igbo results: [] ---  # afriberta-base-finetuned-igbo This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2159 - Precision: 0.7242 - Recall: 0.5039 - F1: 0.5943 - Accuracy: 0.9367 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1989 | 1.0 | 2514 | 0.2020 | 0.7134 | 0.4098 | 0.5206 | 0.9285 | | 0.1759 | 2.0 | 5028 | 0.2125 | 0.7383 | 0.4263 | 0.5405 | 0.9315 | | 0.1417 | 3.0 | 7542 | 0.2044 | 0.7320 | 0.4736 | 0.5751 | 0.9352 | | 0.1279 | 4.0 | 10056 | 0.2066 | 0.7341 | 0.4884 | 0.5866 | 0.9363 | | 0.1132 | 5.0 | 12570 | 0.2159 | 0.7242 | 0.5039 | 0.5943 | 0.9367 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ashnrk/textual_inversion_sealake

ashnrk

2023-07-11T15:04:24Z

14

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-11T14:02:17Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - ashnrk/textual_inversion_sealake These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1. You can find some example images in the following.

Belphegor/ppo-Huggy

Belphegor

2023-07-11T14:50:20Z

46

0

ml-agents

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

reinforcement-learning

2023-07-11T14:50:17Z

--- 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: Belphegor/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

turkish-nlp-suite/tr_vectors_web_lg

turkish-nlp-suite

2023-07-11T14:42:54Z

0

spacy

[ "spacy", "floret", "fasttext", "feature-extraction", "token-classification", "tr", "arxiv:1910.10683", "doi:10.57967/hf/0087", "license:cc-by-sa-4.0", "model-index", "region:us" ]

token-classification

2022-11-02T17:30:31Z

--- tags: - spacy - floret - fasttext - feature-extraction - token-classification language: - tr license: cc-by-sa-4.0 model-index: - name: tr_vectors_web_lg results: - task: name: NMT type: token-classification metrics: - name: Accuracy type: accuracy value: 0.1112 --- Medium sized Turkish Floret word vectors for spaCy. The vectors are trained on MC4 corpus using Floret with the following hyperparameters: ``` floret cbow -dim 300 --mode floret --bucket 200000 -minn 4 -maxn5 -minCount 100 -neg 10 -hashCount 2 -thread 12 -epoch 5 ``` Vector are published in Floret format. | Feature | Description | | --- | --- | | **Name** | `tr_vectors_web_lg` | | **Version** | `1.0` | | **Vectors** | 200000 keys (300 dimensions) | | **Sources** | [MC4](https://arxiv.org/abs/1910.10683) | | **License** | `cc-by-sa-4.0` | | **Author** | [Duygu Altinok](https://www.onlyduygu.com/) | --- If you'd like to use the vectors in your own work, please kindly cite the paper [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/): ``` @inproceedings{altinok-2023-diverse, title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish", author = "Altinok, Duygu", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.768", pages = "13739--13750", abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.", } ```

turkish-nlp-suite/tr_vectors_web_md

turkish-nlp-suite

2023-07-11T14:42:20Z

0

spacy

[ "spacy", "floret", "fasttext", "feature-extraction", "token-classification", "tr", "arxiv:1910.10683", "doi:10.57967/hf/0085", "license:cc-by-sa-4.0", "model-index", "region:us" ]

token-classification

2022-11-02T17:22:50Z

--- tags: - spacy - floret - fasttext - feature-extraction - token-classification language: - tr license: cc-by-sa-4.0 model-index: - name: tr_vectors_web_md results: - task: name: NMT type: token-classification metrics: - name: Accuracy type: accuracy value: 0.1112 --- Medium sized Turkish Floret word vectors for spaCy. The vectors are trained on MC4 corpus using Floret with the follwing hyperparameters: ``` floret cbow -dim 300 --mode floret --bucket 50000 -minn 4 -maxn5 -minCount 100 -neg 10 -hashCount 2 -thread 12 -epoch 5 ``` Vector are published in Floret format. | Feature | Description | | --- | --- | | **Name** | `tr_vectors_web_md` | | **Version** | `1.0` | | **Vectors** | 50000 keys (300 dimensions) | | **Sources** | [MC4](https://arxiv.org/abs/1910.10683) | | **License** | `cc-by-sa-4.0` | | **Author** | [Duygu Altinok](https://www.onlyduygu.com/) | --- If you'd like to use the vectors in your own work, please kindly cite the paper [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/): ``` @inproceedings{altinok-2023-diverse, title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish", author = "Altinok, Duygu", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.768", pages = "13739--13750", abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.", } ```

tyavika/lr1e5-layer2-bs16-Distil-CNN256LSTM128NoBi

tyavika

2023-07-11T14:42:14Z

76

0

transformers

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

question-answering

2023-07-11T11:07:20Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: lr1e5-layer2-bs16-Distil-CNN256LSTM128NoBi results: [] ---  # lr1e5-layer2-bs16-Distil-CNN256LSTM128NoBi 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: 1.2859 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.928 | 1.0 | 3290 | 1.5478 | | 1.1617 | 2.0 | 6580 | 1.1964 | | 0.8463 | 3.0 | 9870 | 1.2061 | | 0.6165 | 4.0 | 13160 | 1.2859 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

CaliPanni/natcopeter

CaliPanni

2023-07-11T14:38:30Z

0

null

[ "region:us" ]

null

2023-07-11T14:02:57Z

NAT CO PETER OFFICIAL MODEL!!!!! (1.0)

gbellamy/rl_course_vizdoom_health_gathering_supreme

gbellamy

2023-07-11T14:31:42Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-11T14:31:32Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.87 +/- 4.95 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r gbellamy/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

matuane/distilbert-base-uncased-finetuned-cola

matuane

2023-07-11T14:22:57Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-11T03:58:34Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: matuane/distilbert-base-uncased-finetuned-cola results: [] ---  # matuane/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1968 - Validation Loss: 0.5472 - Train Matthews Correlation: 0.5059 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5136 | 0.4554 | 0.4712 | 0 | | 0.3229 | 0.4651 | 0.5136 | 1 | | 0.1968 | 0.5472 | 0.5059 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

ericNguyen0132/roberta-large-Dep

ericNguyen0132

2023-07-11T14:20:36Z

3

0

transformers

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

text-classification

2023-07-02T12:57:45Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-large-Dep results: [] ---  # roberta-large-Dep This model is a fine-tuned version of [rafalposwiata/deproberta-large-depression](https://huggingface.co/rafalposwiata/deproberta-large-depression) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8107 - Accuracy: 0.8517 - F1: 0.9118 ## 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: 8 - eval_batch_size: 8 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 469 | 0.3701 | 0.87 | 0.9264 | | 0.4293 | 2.0 | 938 | 0.4385 | 0.865 | 0.9219 | | 0.3302 | 3.0 | 1407 | 0.5293 | 0.85 | 0.9109 | | 0.2784 | 4.0 | 1876 | 0.7077 | 0.8517 | 0.9118 | | 0.1914 | 5.0 | 2345 | 0.8107 | 0.8517 | 0.9118 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

PeterBrendan/Adsdistilgpt2

PeterBrendan

2023-07-11T14:20:22Z

140

1

transformers

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

text-generation

2023-07-07T14:29:07Z

--- license: mit widget: - text: "Pizza" - text: "Nike Basketball" - text: "Used Porche" --- **Model:** distilgpt2 (GPT-2) **Model name:** Adsdistilgpt2 **Model description:** This is a fine-tuned version of the distilgpt2 model trained on a dataset of 10,000+ programmatic ad creatives. This model is designed to generate ad content given a product or a brand. For instance, when given the input "Nike Basketball", it will generate a sample ad and also suggest an ad size. The model's main purpose is to inspire ad creatives and provide a starting point for creating effective marketing content. **Intended uses:** This model is designed to be used as a starting point for creating ad creatives. You could use it in the early stages of your ad design process to generate creative ideas and inspiration. **Limitations:** This model has the potential to produce unusual or unexpected results, due to the varied and complex nature of advertising language. It should not be relied upon to produce perfect ad copy, but rather as a tool to inspire creative ideas. Also, the model might not have complete understanding of specific brand guidelines and may not adhere to them. **How to use:** You can use this model by providing a product or brand name as an input. For example: *Nike Air Force Ones* **Training data:** This model was trained on a dataset consisting of over 10,000 programmatic ad creatives, which included a variety of different product and brand advertisements. The data was collected from various ad platforms and represents a wide range of ad styles and formats. **Training procedure:** The model was fine-tuned using the distilgpt2 model with the aforementioned training data. The training loss was 0.16540415118743643. **Evaluation results:** As this model's primary objective is to generate creative ads, traditional evaluation metrics such as accuracy or F1 score are not applicable. However, the model's performance has been informally assessed based on the relevancy and creativity of the generated ads. **Safety and bias considerations:** This model shares the same safety and bias considerations as the distilgpt2 model. It may generate content that is offensive or inappropriate. Also, as the model is trained on data from the internet, it may reflect the biases present in those sources. Users should carefully review the generated ads to ensure they align with their brand's values and guidelines before using them. The model is not intended to replace the role of a human in creating ad copy, but rather to assist and provide inspiration.

parandhamuduchakali/bert-finetuned-ner

parandhamuduchakali

2023-07-11T14:20:04Z

61

0

transformers

[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-11T12:41:11Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: parandhamuduchakali/bert-finetuned-ner results: [] ---  # parandhamuduchakali/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1736 - Validation Loss: 0.0682 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1736 | 0.0682 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3