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 2025-08-29 18:27:06	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 526 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-29 18:26:56	card stringlengths 11 1.01M
pmch/fgflex	pmch	2022-07-07T14:15:07Z	0	0	null	[ "region:us" ]	null	2022-07-07T10:53:17Z	# FgFlex: A flexible multitasking sequence-labeler for fine-grained sentiment analysis
cherrypaca/puppies_classify	cherrypaca	2022-07-07T13:25:43Z	81	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-07T13:25:31Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: puppies_classify results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9701492786407471 --- # puppies_classify Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### husky ![husky](images/husky.jpg) #### pomeranian ![pomeranian](images/pomeranian.jpg)
dminiotas05/distilbert-base-uncased-finetuned-ft500_6class600	dminiotas05	2022-07-07T13:23:59Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-07T12:40:35Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-ft500_6class600 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-ft500_6class600 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: 1.6317 - Accuracy: 0.35 - F1: 0.3327 ## 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.5717 \| 1.0 \| 188 \| 1.5375 \| 0.3067 \| 0.2820 \| \| 1.4338 \| 2.0 \| 376 \| 1.5354 \| 0.3207 \| 0.2824 \| \| 1.3516 \| 3.0 \| 564 \| 1.4852 \| 0.3573 \| 0.3287 \| \| 1.2722 \| 4.0 \| 752 \| 1.4997 \| 0.366 \| 0.3534 \| \| 1.1923 \| 5.0 \| 940 \| 1.5474 \| 0.362 \| 0.3454 \| \| 1.1156 \| 6.0 \| 1128 \| 1.5998 \| 0.3547 \| 0.3387 \| \| 1.0522 \| 7.0 \| 1316 \| 1.6154 \| 0.3473 \| 0.3316 \| \| 1.0148 \| 8.0 \| 1504 \| 1.6317 \| 0.35 \| 0.3327 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
kabelomalapane/En-Nso	kabelomalapane	2022-07-07T13:11:05Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-07-07T11:32:38Z	--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: En-Nso 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. --> # En-Nso This model is a fine-tuned version of [kabelomalapane/en_nso_ukuxhumana_model](https://huggingface.co/kabelomalapane/en_nso_ukuxhumana_model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9067 - Bleu: 23.5436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - 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 \| Bleu \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 1.0 \| 14 \| 3.7614 \| 8.0360 \| \| No log \| 2.0 \| 28 \| 3.3181 \| 20.7201 \| \| No log \| 3.0 \| 42 \| 3.1627 \| 21.5932 \| \| No log \| 4.0 \| 56 \| 3.0935 \| 22.0268 \| \| No log \| 5.0 \| 70 \| 3.0227 \| 21.0859 \| \| No log \| 6.0 \| 84 \| 2.9740 \| 21.6963 \| \| No log \| 7.0 \| 98 \| 2.9419 \| 23.2214 \| \| No log \| 8.0 \| 112 \| 2.9227 \| 24.4649 \| \| No log \| 9.0 \| 126 \| 2.9102 \| 23.5293 \| \| No log \| 10.0 \| 140 \| 2.9067 \| 23.5516 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07	Zengwei	2022-07-07T13:03:44Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-07-07T07:51:32Z	Introduction See https://github.com/k2-fsa/icefall/pull/330 and https://github.com/k2-fsa/icefall/pull/452 It has random combiner inside. Note: There is something wrong in the log file, which has been fixed in https://github.com/k2-fsa/icefall/pull/468.
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-B-2022-07-07	Zengwei	2022-07-07T12:34:11Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-07-07T09:00:28Z	Introduction See https://github.com/k2-fsa/icefall/pull/330 and https://github.com/k2-fsa/icefall/pull/452 It has random combiner inside.
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-07-07	Zengwei	2022-07-07T12:30:37Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-07-07T10:17:44Z	Introduction See https://github.com/k2-fsa/icefall/pull/330 and https://github.com/k2-fsa/icefall/pull/452 It has random combiner inside.
paola-md/recipe-roberta-is	paola-md	2022-07-07T11:53:27Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-07T08:40:25Z	--- license: mit tags: - generated_from_trainer model-index: - name: recipe-roberta-is 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. --> # recipe-roberta-is This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8382 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.334 \| 1.0 \| 961 \| 1.1217 \| \| 1.1638 \| 2.0 \| 1922 \| 1.0369 \| \| 1.0936 \| 3.0 \| 2883 \| 0.9922 \| \| 1.0503 \| 4.0 \| 3844 \| 0.9606 \| \| 1.0188 \| 5.0 \| 4805 \| 0.9314 \| \| 0.9953 \| 6.0 \| 5766 \| 0.9256 \| \| 0.9769 \| 7.0 \| 6727 \| 0.9109 \| \| 0.9599 \| 8.0 \| 7688 \| 0.8978 \| \| 0.9461 \| 9.0 \| 8649 \| 0.8813 \| \| 0.9377 \| 10.0 \| 9610 \| 0.8777 \| \| 0.9253 \| 11.0 \| 10571 \| 0.8755 \| \| 0.918 \| 12.0 \| 11532 \| 0.8601 \| \| 0.9112 \| 13.0 \| 12493 \| 0.8541 \| \| 0.9043 \| 14.0 \| 13454 \| 0.8548 \| \| 0.8984 \| 15.0 \| 14415 \| 0.8470 \| \| 0.8958 \| 16.0 \| 15376 \| 0.8412 \| \| 0.8914 \| 17.0 \| 16337 \| 0.8345 \| \| 0.8882 \| 18.0 \| 17298 \| 0.8353 \| \| 0.8871 \| 19.0 \| 18259 \| 0.8344 \| \| 0.8839 \| 20.0 \| 19220 \| 0.8382 \| ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
dminiotas05/distilbert-base-uncased-finetuned-ft500_6class	dminiotas05	2022-07-07T11:11:18Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-07T10:45:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-ft500_6class 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-ft500_6class 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: 1.5162 - Accuracy: 0.356 - F1: 0.3347 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.579 \| 1.0 \| 188 \| 1.5575 \| 0.2933 \| 0.2521 \| \| 1.4527 \| 2.0 \| 376 \| 1.5043 \| 0.3227 \| 0.2821 \| \| 1.3767 \| 3.0 \| 564 \| 1.4982 \| 0.34 \| 0.2938 \| \| 1.3122 \| 4.0 \| 752 \| 1.4784 \| 0.368 \| 0.3454 \| \| 1.2678 \| 5.0 \| 940 \| 1.5162 \| 0.356 \| 0.3347 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178	zhifei	2022-07-07T10:49:19Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "unk", "dataset:zhifei/autotrain-data-autotrain-chinese-title-summarization-9", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-07T10:48:04Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - zhifei/autotrain-data-autotrain-chinese-title-summarization-9 co2_eq_emissions: 1.565396518204961 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1101340178 - CO2 Emissions (in grams): 1.565396518204961 ## Validation Metrics - Loss: 0.00012778821110259742 - Rouge1: 29.2308 - Rouge2: 0.0 - RougeL: 29.2308 - RougeLsum: 29.2308 - Gen Len: 18.4462 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178 ```
Fulccrum/trainii_ac94u-label-classification	Fulccrum	2022-07-07T10:48:17Z	0	0	sklearn	[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]	tabular-classification	2022-07-07T10:48:16Z	--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on trainii_ac94u to apply classification on label Metrics of the best model: accuracy 0.361046 recall_macro 0.353192 precision_macro 0.240667 f1_macro 0.278231 Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64 See model plot below: <style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-9" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div> Disclaimer:* This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). Logs of training including the models tried in the process can be found in logs.txt
TestZee/t5-small-finetuned-custom-wion-test-BIG	TestZee	2022-07-07T10:31:54Z	5	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-07T10:30:30Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TestZee/t5-small-finetuned-custom-wion-test-BIG 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. --> # TestZee/t5-small-finetuned-custom-wion-test-BIG This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1165 - Validation Loss: 0.4609 - Epoch: 29 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.9622 \| 0.8875 \| 0 \| \| 1.9276 \| 0.8601 \| 1 \| \| 1.8301 \| 0.8342 \| 2 \| \| 1.7776 \| 0.8104 \| 3 \| \| 1.7345 \| 0.7878 \| 4 \| \| 1.7733 \| 0.7660 \| 5 \| \| 1.5626 \| 0.7448 \| 6 \| \| 1.6111 \| 0.7245 \| 7 \| \| 1.6754 \| 0.7050 \| 8 \| \| 1.5030 \| 0.6867 \| 9 \| \| 1.5101 \| 0.6696 \| 10 \| \| 1.4328 \| 0.6536 \| 11 \| \| 1.4311 \| 0.6383 \| 12 \| \| 1.3917 \| 0.6232 \| 13 \| \| 1.4102 \| 0.6071 \| 14 \| \| 1.3732 \| 0.5948 \| 15 \| \| 1.3468 \| 0.5828 \| 16 \| \| 1.2817 \| 0.5712 \| 17 \| \| 1.2920 \| 0.5600 \| 18 \| \| 1.2696 \| 0.5491 \| 19 \| \| 1.2552 \| 0.5385 \| 20 \| \| 1.1859 \| 0.5285 \| 21 \| \| 1.1995 \| 0.5188 \| 22 \| \| 1.1690 \| 0.5094 \| 23 \| \| 1.1678 \| 0.5003 \| 24 \| \| 1.1420 \| 0.4916 \| 25 \| \| 1.0959 \| 0.4830 \| 26 \| \| 1.0848 \| 0.4750 \| 27 \| \| 1.1248 \| 0.4677 \| 28 \| \| 1.1165 \| 0.4609 \| 29 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
hugginglearners/malayalam-blurr-xlm-roberta-base	hugginglearners	2022-07-07T10:17:28Z	0	2	fastai	[ "fastai", "text-generation", "ml", "dataset:rajeshradhakrishnan/malayalam_wiki", "region:us" ]	text-generation	2022-07-06T11:10:26Z	--- tags: - fastai - text-generation language: ml widget: - text: "ഓഹരി വിപണി തകരുമ്പോള്‍ നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം" example_title: "Malayalam Casual Language Model" datasets: - rajeshradhakrishnan/malayalam_wiki --- # Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress) [![മലയാളം: notebook](https://img.shields.io/badge/മലയാളം%20-notebook-green.svg)](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/main/ml/malayalam_blurr_xlm_roberta_base.ipynb) --- # malayalam-blurr-xlm-roberta-base (base-sized model) malayalam-blurr-xlm-roberta-base model is pre-trained on [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) using the library [blurr](https://ohmeow.github.io/blurr/) Language Model using fastai x huggingface frameworks. Ref: [Causal Language Modeling](https://ohmeow.github.io/blurr/text-modeling-language-modeling.html#Causal-language-modeling). ## Usage ``` !pip install -Uqq huggingface_hub["fastai"] ohmeow-blurr from huggingface_hub import from_pretrained_fastai learner = from_pretrained_fastai(repo_id) learner.blurr_generate("ബ്‌ളൂർ പഠിക്കാൻ വളെരെ എളുപ്പമാണ് എന്തുകൊണ്ട് എന്നാൽ", max_length=50, do_sample=True, top_k=25) ``` ## Intended uses & limitations It's not fine tuned to the state of the art accuracy ## Training and evaluation data [Wiki 2020 Malayalam Dataset ](https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_wiki)
osanseviero/ppo-LunarLander-v10	osanseviero	2022-07-07T09:42:36Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-07T09:38:00Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -574.85 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
huggingtweets/marsajal	huggingtweets	2022-07-07T09:42:16Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/marsajal/1657186931820/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1463196823728771079/wZc0m7cd_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ajeng🦦</div> <div style="text-align: center; font-size: 14px;">@marsajal</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ajeng🦦. \| Data \| ajeng🦦 \| \| --- \| --- \| \| Tweets downloaded \| 214 \| \| Retweets \| 37 \| \| Short tweets \| 41 \| \| Tweets kept \| 136 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kdiymty/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @marsajal's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lfk0v9ey) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lfk0v9ey/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/marsajal') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
osanseviero/ppo-LunarLander-v9	osanseviero	2022-07-07T09:37:00Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-07T09:36:34Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -30.40 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
osanseviero/ppo-LunarLander-v6	osanseviero	2022-07-07T09:29:20Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-07T09:07:08Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -443.18 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53	gary109	2022-07-07T09:10:42Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-07-01T03:42:00Z	--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 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. --> # ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset. It achieves the following results on the evaluation set: - Loss: 0.8797 - Wer: 0.5513 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.9613 \| 1.0 \| 2309 \| 1.0171 \| 0.7271 \| \| 0.8254 \| 2.0 \| 4618 \| 0.9771 \| 0.6650 \| \| 0.7406 \| 3.0 \| 6927 \| 0.9174 \| 0.6420 \| \| 0.74 \| 4.0 \| 9236 \| 0.9551 \| 0.6371 \| \| 0.5855 \| 5.0 \| 11545 \| 0.9262 \| 0.6453 \| \| 0.5536 \| 6.0 \| 13854 \| 0.9056 \| 0.5894 \| \| 0.505 \| 7.0 \| 16163 \| 0.9166 \| 0.6029 \| \| 0.449 \| 8.0 \| 18472 \| 0.8816 \| 0.5873 \| \| 0.4219 \| 9.0 \| 20781 \| 0.8970 \| 0.5589 \| \| 0.5764 \| 10.0 \| 23090 \| 0.9189 \| 0.5649 \| \| 0.5075 \| 11.0 \| 25399 \| 0.8797 \| 0.5513 \| \| 0.4366 \| 12.0 \| 27708 \| 0.9011 \| 0.5567 \| \| 0.4915 \| 13.0 \| 30017 \| 0.9248 \| 0.5455 \| \| 0.3554 \| 14.0 \| 32326 \| 0.9309 \| 0.5374 \| \| 0.3975 \| 15.0 \| 34635 \| 0.9103 \| 0.5259 \| \| 0.4119 \| 16.0 \| 36944 \| 0.9402 \| 0.5290 \| \| 0.267 \| 17.0 \| 39253 \| 0.9479 \| 0.5115 \| \| 0.3107 \| 18.0 \| 41562 \| 0.9428 \| 0.5099 \| \| 0.2684 \| 19.0 \| 43871 \| 0.9508 \| 0.5133 \| \| 0.2125 \| 20.0 \| 46180 \| 0.9737 \| 0.5097 \| \| 0.3149 \| 21.0 \| 48489 \| 0.9992 \| 0.5095 \| \| 0.2313 \| 22.0 \| 50798 \| 1.0037 \| 0.5059 \| \| 0.2674 \| 23.0 \| 53107 \| 1.0091 \| 0.5040 \| \| 0.2056 \| 24.0 \| 55416 \| 1.0082 \| 0.5076 \| \| 0.2781 \| 25.0 \| 57725 \| 1.0160 \| 0.5015 \| \| 0.2005 \| 26.0 \| 60034 \| 1.0390 \| 0.5131 \| \| 0.2221 \| 27.0 \| 62343 \| 1.0401 \| 0.5074 \| \| 0.1857 \| 28.0 \| 64652 \| 1.0484 \| 0.5096 \| \| 0.1562 \| 29.0 \| 66961 \| 1.0516 \| 0.5064 \| \| 0.3027 \| 30.0 \| 69270 \| 1.0543 \| 0.5049 \| ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1
m-newhauser/distilbert-political-tweets	m-newhauser	2022-07-07T09:07:44Z	75	23	transformers	[ "transformers", "pytorch", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "en", "dataset:m-newhauser/senator-tweets", "license:lgpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en license: lgpl-3.0 library_name: transformers tags: - text-classification - transformers - pytorch - generated_from_keras_callback metrics: - accuracy - f1 datasets: - m-newhauser/senator-tweets widget: - text: "This pandemic has shown us clearly the vulgarity of our healthcare system. Highest costs in the world, yet not enough nurses or doctors. Many millions uninsured, while insurance company profits soar. The struggle continues. Healthcare is a human right. Medicare for all." example_title: "Bernie Sanders (D)" - text: "Team Biden would rather fund the Ayatollah's Death to America regime than allow Americans to produce energy for our own domestic consumption." example_title: "Ted Cruz (R)" --- # distilbert-political-tweets 🗣 🇺🇸 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [m-newhauser/senator-tweets](https://huggingface.co/datasets/m-newhauser/senator-tweets) dataset, which contains all tweets made by United States senators during the first year of the Biden Administration. It achieves the following results on the evaluation set: * Accuracy: 0.9076 * F1: 0.9117 ## Model description The goal of this model is to classify short pieces of text as having either Democratic or Republican sentiment. The model was fine-tuned on 99,693 tweets (51.6% Democrat, 48.4% Republican) made by US senators in 2021. Model accuracy may not hold up on pieces of text longer than a tweet. ### Training hyperparameters The following hyperparameters were used during training: - optimizer: Adam - training_precision: float32 - learning_rate = 5e-5 - num_epochs = 5 ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.6
osanseviero/ppo-LunarLander-v5	osanseviero	2022-07-07T08:59:26Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-07T08:47:49Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -479.21 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
osanseviero/ppo-LunarLander-v4	osanseviero	2022-07-07T08:47:35Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T19:12:02Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -247.76 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100	hsohn3	2022-07-07T08:33:59Z	3	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-06T16:29:49Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100 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. --> # hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9559 - Epoch: 99 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 4.1247 \| 0 \| \| 3.5129 \| 1 \| \| 3.4726 \| 2 \| \| 3.4483 \| 3 \| \| 3.4395 \| 4 \| \| 3.4301 \| 5 \| \| 3.4260 \| 6 \| \| 3.4131 \| 7 \| \| 3.3831 \| 8 \| \| 3.2925 \| 9 \| \| 3.2454 \| 10 \| \| 3.2092 \| 11 \| \| 3.1695 \| 12 \| \| 3.1346 \| 13 \| \| 3.0797 \| 14 \| \| 3.0154 \| 15 \| \| 2.9557 \| 16 \| \| 2.8814 \| 17 \| \| 2.7720 \| 18 \| \| 2.5472 \| 19 \| \| 2.3193 \| 20 \| \| 2.1005 \| 21 \| \| 1.9331 \| 22 \| \| 1.7971 \| 23 \| \| 1.6859 \| 24 \| \| 1.6062 \| 25 \| \| 1.5310 \| 26 \| \| 1.4706 \| 27 \| \| 1.4203 \| 28 \| \| 1.3681 \| 29 \| \| 1.3222 \| 30 \| \| 1.2939 \| 31 \| \| 1.2726 \| 32 \| \| 1.2494 \| 33 \| \| 1.2330 \| 34 \| \| 1.2161 \| 35 \| \| 1.1998 \| 36 \| \| 1.1874 \| 37 \| \| 1.1767 \| 38 \| \| 1.1641 \| 39 \| \| 1.1550 \| 40 \| \| 1.1407 \| 41 \| \| 1.1363 \| 42 \| \| 1.1272 \| 43 \| \| 1.1227 \| 44 \| \| 1.1163 \| 45 \| \| 1.1065 \| 46 \| \| 1.1008 \| 47 \| \| 1.0957 \| 48 \| \| 1.0837 \| 49 \| \| 1.0844 \| 50 \| \| 1.0778 \| 51 \| \| 1.0741 \| 52 \| \| 1.0693 \| 53 \| \| 1.0662 \| 54 \| \| 1.0608 \| 55 \| \| 1.0521 \| 56 \| \| 1.0526 \| 57 \| \| 1.0476 \| 58 \| \| 1.0454 \| 59 \| \| 1.0452 \| 60 \| \| 1.0348 \| 61 \| \| 1.0333 \| 62 \| \| 1.0342 \| 63 \| \| 1.0293 \| 64 \| \| 1.0249 \| 65 \| \| 1.0241 \| 66 \| \| 1.0194 \| 67 \| \| 1.0177 \| 68 \| \| 1.0102 \| 69 \| \| 1.0055 \| 70 \| \| 1.0052 \| 71 \| \| 1.0038 \| 72 \| \| 1.0005 \| 73 \| \| 0.9981 \| 74 \| \| 0.9991 \| 75 \| \| 0.9950 \| 76 \| \| 0.9928 \| 77 \| \| 0.9898 \| 78 \| \| 0.9906 \| 79 \| \| 0.9873 \| 80 \| \| 0.9849 \| 81 \| \| 0.9808 \| 82 \| \| 0.9804 \| 83 \| \| 0.9792 \| 84 \| \| 0.9789 \| 85 \| \| 0.9797 \| 86 \| \| 0.9741 \| 87 \| \| 0.9781 \| 88 \| \| 0.9678 \| 89 \| \| 0.9686 \| 90 \| \| 0.9651 \| 91 \| \| 0.9652 \| 92 \| \| 0.9613 \| 93 \| \| 0.9599 \| 94 \| \| 0.9566 \| 95 \| \| 0.9571 \| 96 \| \| 0.9577 \| 97 \| \| 0.9536 \| 98 \| \| 0.9559 \| 99 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
avichr/Legal-heBERT_ft	avichr	2022-07-07T07:31:58Z	28	3	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "arxiv:1911.03090", "arxiv:2010.02559", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-05-05T06:49:36Z	# Legal-HeBERT Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br> We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools. ## Training Data Our training datasets are: \| Name \| Hebrew Description \| Size (GB) \| Documents \| Sentences \| Words \| Notes \| \|----------------------------------------------------------------------------------------------------------------------------------- \|-------------------------------------------------------------------------- \|----------- \|----------- \|------------ \|------------- \|----------------------------------------- \| \| The Israeli Law Book \| ספר החוקים הישראלי \| 0.05 \| 2338 \| 293352 \| 4851063 \| \| \| Judgments of the Supreme Court \| מאגר פסקי הדין של בית המשפט העליון \| 0.7 \| 212348 \| 5790138 \| 79672415 \| \| \| custody courts \| החלטות בתי הדין למשמורת \| 2.46 \| 169,708 \| 8,555,893 \| 213,050,492 \| \| \| Law memoranda, drafts of secondary legislation and drafts of support tests that have been distributed to the public for comment \| תזכירי חוק, טיוטות חקיקת משנה וטיוטות מבחני תמיכה שהופצו להערות הציבור \| 0.4 \| 3,291 \| 294,752 \| 7,218,960 \| \| \| Supervisors of Land Registration judgments \| מאגר פסקי דין של המפקחים על רישום המקרקעין \| 0.02 \| 559 \| 67,639 \| 1,785,446 \| \| \| Decisions of the Labor Court - Corona \| מאגר החלטות בית הדין לעניין שירות התעסוקה – קורונה \| 0.001 \| 146 \| 3505 \| 60195 \| \| \| Decisions of the Israel Lands Council \| החלטות מועצת מקרקעי ישראל \| \| 118 \| 11283 \| 162692 \| aggregate file \| \| Judgments of the Disciplinary Tribunal and the Israel Police Appeals Tribunal \| פסקי דין של בית הדין למשמעת ובית הדין לערעורים של משטרת ישראל \| 0.02 \| 54 \| 83724 \| 1743419 \| aggregate files \| \| Disciplinary Appeals Committee in the Ministry of Health \| ועדת ערר לדין משמעתי במשרד הבריאות \| 0.004 \| 252 \| 21010 \| 429807 \| 465 files are scanned and didn't parser \| \| Attorney General's Positions \| מאגר התייצבויות היועץ המשפטי לממשלה \| 0.008 \| 281 \| 32724 \| 813877 \| \| \| Legal-Opinion of the Attorney General \| מאגר חוות דעת היועץ המשפטי לממשלה \| 0.002 \| 44 \| 7132 \| 188053 \| \| \| \| \| \| \| \| \| \| \| total \| \| 3.665 \| 389,139 \| 15,161,152 \| 309,976,419 \| \| We thank <b>Yair Gardin</b> for the referring to the governance data, <b>Elhanan Schwarts</b> for collecting and parsing The Israeli law book, and <b>Jonathan Schler</b> for collecting the judgments of the supreme court. ## Training process * Vocabulary size: 50,000 tokens * 4 epochs (1M steps±) * lr=5e-5 * mlm_probability=0.15 * batch size = 32 (for each gpu) * NVIDIA GeForce RTX 2080 TI + NVIDIA GeForce RTX 3090 (1 week training) ### Additional training settings: <b>Fine-tuned [HeBERT](https://github.com/avichaychriqui/HeBERT) model:</b> The first eight layers were freezed (like [Lee et al. (2019)](https://arxiv.org/abs/1911.03090) suggest)<br> <b>Legal-HeBERT trained from scratch:</b> The training process is similar to [HeBERT](https://github.com/avichaychriqui/HeBERT) and inspired by [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559) <br> ## How to use The models can be found in huggingface hub and can be fine-tunned to any down-stream task: ``` # !pip install transformers==4.14.1 from transformers import AutoTokenizer, AutoModel model_name = 'avichr/Legal-heBERT_ft' # for the fine-tuned HeBERT model model_name = 'avichr/Legal-heBERT' # for legal HeBERT model trained from scratch tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) from transformers import pipeline fill_mask = pipeline( "fill-mask", model=model_name, ) fill_mask("הקורונה לקחה את [MASK] ולנו לא נשאר דבר.") ``` ## Stay tuned! We are still working on our models and the datasets. We will edit this page as we progress. We are open for collaborations. ## If you used this model please cite us as : Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai, Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts (June 27, 2022). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4147127 ``` @article{chriqui2021hebert, title={Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts}, author={Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai}, journal={SSRN preprint:4147127}, year={2022} } ``` ## Contact us [Avichay Chriqui](mailto:avichayc@mail.tau.ac.il), The Coller AI Lab <br> [Inbal yahav](mailto:inbalyahav@tauex.tau.ac.il), The Coller AI Lab <br> [Ittai Bar-Siman-Tov](mailto:Ittai.Bar-Siman-Tov@biu.ac.il), the BIU Innovation Lab for Law, Data-Science and Digital Ethics <br> Thank you, תודה, شكرا <br>
ScarlettSun9/autotrain-ZuoZhuan-1100540141	ScarlettSun9	2022-07-07T07:08:04Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "token-classification", "autotrain", "unk", "dataset:ScarlettSun9/autotrain-data-ZuoZhuan", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-07T07:02:53Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ScarlettSun9/autotrain-data-ZuoZhuan co2_eq_emissions: 8.343592303925112 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1100540141 - CO2 Emissions (in grams): 8.343592303925112 ## Validation Metrics - Loss: 0.38094884157180786 - Accuracy: 0.8795777325860159 - Precision: 0.8171375141922127 - Recall: 0.8417033571821684 - F1: 0.8292385373953709 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ScarlettSun9/autotrain-ZuoZhuan-1100540141 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
go2k/q-Taxi-v3	go2k	2022-07-07T05:45:11Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-07T05:39:36Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="go2k/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
go2k/q-FrozenLake-v1-4x4-noSlippery	go2k	2022-07-07T05:26:00Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-07T05:25:54Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="go2k/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
Evelyn18/distilbert-base-uncased-becasv2-6	Evelyn18	2022-07-07T04:44:16Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-07-07T04:39:39Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-6 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-becasv2-6 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.8936 ## 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: 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 9 \| 4.0542 \| \| No log \| 2.0 \| 18 \| 3.0865 \| \| No log \| 3.0 \| 27 \| 2.8069 \| \| No log \| 4.0 \| 36 \| 3.3330 \| \| No log \| 5.0 \| 45 \| 3.4108 \| \| No log \| 6.0 \| 54 \| 3.5562 \| \| No log \| 7.0 \| 63 \| 3.8846 \| \| No log \| 8.0 \| 72 \| 3.8936 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Evelyn18/distilbert-base-uncased-becasv2-3	Evelyn18	2022-07-07T04:00:45Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-07-07T03:55:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-3 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-becasv2-3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.1218 ## 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: 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 9 \| 4.6377 \| \| No log \| 2.0 \| 18 \| 3.8511 \| \| No log \| 3.0 \| 27 \| 3.3758 \| \| No log \| 4.0 \| 36 \| 3.1910 \| \| No log \| 5.0 \| 45 \| 3.1187 \| \| No log \| 6.0 \| 54 \| 3.1009 \| \| No log \| 7.0 \| 63 \| 3.1131 \| \| No log \| 8.0 \| 72 \| 3.1218 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Evelyn18/distilbert-base-uncased-becasv2-2	Evelyn18	2022-07-07T03:47:53Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-07-07T03:43:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-becasv2-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.9170 ## 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 9 \| 4.8334 \| \| No log \| 2.0 \| 18 \| 3.9395 \| \| No log \| 3.0 \| 27 \| 3.4886 \| \| No log \| 4.0 \| 36 \| 3.2190 \| \| No log \| 5.0 \| 45 \| 3.0781 \| \| No log \| 6.0 \| 54 \| 2.9878 \| \| No log \| 7.0 \| 63 \| 2.9336 \| \| No log \| 8.0 \| 72 \| 2.9170 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Evelyn18/distilbert-base-uncased-becasv2-1	Evelyn18	2022-07-07T03:38:53Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-07-07T03:34:17Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-1 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-becasv2-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.9472 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 9 \| 4.6722 \| \| No log \| 2.0 \| 18 \| 3.9450 \| \| No log \| 3.0 \| 27 \| 3.4890 \| \| No log \| 4.0 \| 36 \| 3.2251 \| \| No log \| 5.0 \| 45 \| 2.9906 \| \| No log \| 6.0 \| 54 \| 3.0790 \| \| No log \| 7.0 \| 63 \| 2.8791 \| \| No log \| 8.0 \| 72 \| 2.9654 \| \| No log \| 9.0 \| 81 \| 2.9460 \| \| No log \| 10.0 \| 90 \| 2.9472 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ChauNguyen23/distilbert-base-uncased-finetuned-imdb	ChauNguyen23	2022-07-07T02:54:46Z	3	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	2022-07-07T02:48:22Z	--- 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 - mixed_precision_training: Native AMP ### 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 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
taln-ls2n/POET	taln-ls2n	2022-07-06T23:49:35Z	4	2	transformers	[ "transformers", "pytorch", "camembert", "token-classification", "Transformers", "sequence-tagger-model", "fr", "dataset:qanastek/ANTILLES", "arxiv:1911.03894", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-05-11T09:33:05Z	--- tags: - Transformers - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894) - Number of Epochs: 115 People Involved * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) Affiliations 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in HuggingFace Transformers Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` ```python from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline tokenizer = CamembertTokenizer.from_pretrained('taln-ls2n/POET') model = CamembertForTokenClassification.from_pretrained('taln-ls2n/POET') pos = TokenClassificationPipeline(model=model, tokenizer=tokenizer) def make_prediction(sentence): labels = [l['entity'] for l in pos(sentence)] return list(zip(sentence.split(" "), labels)) res = make_prediction("George Washington est allé à Washington") ``` Output: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. ## Original Tags ```plain PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ ``` ## New additional POS tags \| Abbreviation \| Description \| Examples \| \|:--------:\|:--------:\|:--------:\| \| PREP \| Preposition \| de \| \| AUX \| Auxiliary Verb \| est \| \| ADV \| Adverb \| toujours \| \| COSUB \| Subordinating conjunction \| que \| \| COCO \| Coordinating Conjunction \| et \| \| PART \| Demonstrative particle \| -t \| \| PRON \| Pronoun \| qui ce quoi \| \| PDEMMS \| Demonstrative Pronoun - Singular Masculine \| ce \| \| PDEMMP \| Demonstrative Pronoun - Plural Masculine \| ceux \| \| PDEMFS \| Demonstrative Pronoun - Singular Feminine \| cette \| \| PDEMFP \| Demonstrative Pronoun - Plural Feminine \| celles \| \| PINDMS \| Indefinite Pronoun - Singular Masculine \| tout \| \| PINDMP \| Indefinite Pronoun - Plural Masculine \| autres \| \| PINDFS \| Indefinite Pronoun - Singular Feminine \| chacune \| \| PINDFP \| Indefinite Pronoun - Plural Feminine \| certaines \| \| PROPN \| Proper noun \| Houston \| \| XFAMIL \| Last name \| Levy \| \| NUM \| Numerical Adjective \| trentaine vingtaine \| \| DINTMS \| Masculine Numerical Adjective \| un \| \| DINTFS \| Feminine Numerical Adjective \| une \| \| PPOBJMS \| Pronoun complements of objects - Singular Masculine \| le lui \| \| PPOBJMP \| Pronoun complements of objects - Plural Masculine \| eux y \| \| PPOBJFS \| Pronoun complements of objects - Singular Feminine \| moi la \| \| PPOBJFP \| Pronoun complements of objects - Plural Feminine \| en y \| \| PPER1S \| Personal Pronoun First-Person - Singular \| je \| \| PPER2S \| Personal Pronoun Second-Person - Singular \| tu \| \| PPER3MS \| Personal Pronoun Third-Person - Singular Masculine \| il \| \| PPER3MP \| Personal Pronoun Third-Person - Plural Masculine \| ils \| \| PPER3FS \| Personal Pronoun Third-Person - Singular Feminine \| elle \| \| PPER3FP \| Personal Pronoun Third-Person - Plural Feminine \| elles \| \| PREFS \| Reflexive Pronoun First-Person - Singular \| me m' \| \| PREF \| Reflexive Pronoun Third-Person - Singular \| se s' \| \| PREFP \| Reflexive Pronoun First / Second-Person - Plural \| nous vous \| \| VERB \| Verb \| obtient \| \| VPPMS \| Past Participle - Singular Masculine \| formulé \| \| VPPMP \| Past Participle - Plural Masculine \| classés \| \| VPPFS \| Past Participle - Singular Feminine \| appelée \| \| VPPFP \| Past Participle - Plural Feminine \| sanctionnées \| \| DET \| Determinant \| les l' \| \| DETMS \| Determinant - Singular Masculine \| les \| \| DETFS \| Determinant - Singular Feminine \| la \| \| ADJ \| Adjective \| capable sérieux \| \| ADJMS \| Adjective - Singular Masculine \| grand important \| \| ADJMP \| Adjective - Plural Masculine \| grands petits \| \| ADJFS \| Adjective - Singular Feminine \| française petite \| \| ADJFP \| Adjective - Plural Feminine \| légères petites \| \| NOUN \| Noun \| temps \| \| NMS \| Noun - Singular Masculine \| drapeau \| \| NMP \| Noun - Plural Masculine \| journalistes \| \| NFS \| Noun - Singular Feminine \| tête \| \| NFP \| Noun - Plural Feminine \| ondes \| \| PREL \| Relative Pronoun \| qui dont \| \| PRELMS \| Relative Pronoun - Singular Masculine \| lequel \| \| PRELMP \| Relative Pronoun - Plural Masculine \| lesquels \| \| PRELFS \| Relative Pronoun - Singular Feminine \| laquelle \| \| PRELFP \| Relative Pronoun - Plural Feminine \| lesquelles \| \| INTJ \| Interjection \| merci bref \| \| CHIF \| Numbers \| 1979 10 \| \| SYM \| Symbol \| € % \| \| YPFOR \| Endpoint \| . \| \| PUNCT \| Ponctuation \| : , \| \| MOTINC \| Unknown words \| Technology Lady \| \| X \| Typos & others \| sfeir 3D statu \| ## Evaluation results The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```plain precision recall f1-score support ADJ 0.9040 0.8828 0.8933 128 ADJFP 0.9811 0.9585 0.9697 434 ADJFS 0.9606 0.9826 0.9715 918 ADJMP 0.9613 0.9357 0.9483 451 ADJMS 0.9561 0.9611 0.9586 952 ADV 0.9870 0.9948 0.9908 1524 AUX 0.9956 0.9964 0.9960 1124 CHIF 0.9798 0.9774 0.9786 1239 COCO 1.0000 0.9989 0.9994 884 COSUB 0.9939 0.9939 0.9939 328 DET 0.9972 0.9972 0.9972 2897 DETFS 0.9990 1.0000 0.9995 1007 DETMS 1.0000 0.9993 0.9996 1426 DINTFS 0.9967 0.9902 0.9934 306 DINTMS 0.9923 0.9948 0.9935 387 INTJ 0.8000 0.8000 0.8000 5 MOTINC 0.5049 0.5827 0.5410 266 NFP 0.9807 0.9675 0.9740 892 NFS 0.9778 0.9699 0.9738 2588 NMP 0.9687 0.9495 0.9590 1367 NMS 0.9759 0.9560 0.9659 3181 NOUN 0.6164 0.8673 0.7206 113 NUM 0.6250 0.8333 0.7143 6 PART 1.0000 0.9375 0.9677 16 PDEMFP 1.0000 1.0000 1.0000 3 PDEMFS 1.0000 1.0000 1.0000 89 PDEMMP 1.0000 1.0000 1.0000 20 PDEMMS 1.0000 1.0000 1.0000 222 PINDFP 1.0000 1.0000 1.0000 3 PINDFS 0.8571 1.0000 0.9231 12 PINDMP 0.9000 1.0000 0.9474 9 PINDMS 0.9286 0.9701 0.9489 67 PINTFS 0.0000 0.0000 0.0000 2 PPER1S 1.0000 1.0000 1.0000 62 PPER2S 0.7500 1.0000 0.8571 3 PPER3FP 1.0000 1.0000 1.0000 9 PPER3FS 1.0000 1.0000 1.0000 96 PPER3MP 1.0000 1.0000 1.0000 31 PPER3MS 1.0000 1.0000 1.0000 377 PPOBJFP 1.0000 0.7500 0.8571 4 PPOBJFS 0.9167 0.8919 0.9041 37 PPOBJMP 0.7500 0.7500 0.7500 12 PPOBJMS 0.9371 0.9640 0.9504 139 PREF 1.0000 1.0000 1.0000 332 PREFP 1.0000 1.0000 1.0000 64 PREFS 1.0000 1.0000 1.0000 13 PREL 0.9964 0.9964 0.9964 277 PRELFP 1.0000 1.0000 1.0000 5 PRELFS 0.8000 1.0000 0.8889 4 PRELMP 1.0000 1.0000 1.0000 3 PRELMS 1.0000 1.0000 1.0000 11 PREP 0.9971 0.9977 0.9974 6161 PRON 0.9836 0.9836 0.9836 61 PROPN 0.9468 0.9503 0.9486 4310 PUNCT 1.0000 1.0000 1.0000 4019 SYM 0.9394 0.8158 0.8732 76 VERB 0.9956 0.9921 0.9938 2273 VPPFP 0.9145 0.9469 0.9304 113 VPPFS 0.9562 0.9597 0.9580 273 VPPMP 0.8827 0.9728 0.9256 147 VPPMS 0.9778 0.9794 0.9786 630 VPPRE 0.0000 0.0000 0.0000 1 X 0.9604 0.9935 0.9766 1073 XFAMIL 0.9386 0.9113 0.9248 1342 YPFOR 1.0000 1.0000 1.0000 2750 accuracy 0.9778 47574 macro avg 0.9151 0.9285 0.9202 47574 weighted avg 0.9785 0.9778 0.9780 47574 ``` ## BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ``` Flair Embeddings: ```latex @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` ## Acknowledgment This work was financially supported by [Zenidoc](https://zenidoc.fr/) and the [ANR project DIETS](https://anr-diets.univ-avignon.fr) under the contract [ANR-20-CE23-0005](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-fd7e69d902/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=cb6d54d24c9e21e0d50fabf46bd56646).
qanastek/pos-french-camembert-flair	qanastek	2022-07-06T23:49:12Z	52	3	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "dataset:qanastek/ANTILLES", "arxiv:1911.03894", "arxiv:1011.4088", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - flair - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings: [Flair](https://aclanthology.org/C18-1139.pdf) & [CamemBERT](https://arxiv.org/abs/1911.03894) - Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088) - Number of Epochs: 50 People Involved * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) Affiliations 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in Flair Requires [Flair](https://pypi.org/project/flair/): ```pip install flair``` ```python from flair.data import Sentence from flair.models import SequenceTagger # Load the model model = SequenceTagger.load("qanastek/pos-french") sentence = Sentence("George Washington est allé à Washington") # predict tags model.predict(sentence) # print predicted pos tags print(sentence.to_tagged_string()) ``` Output: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. ## Original Tags ```plain PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ ``` ## New additional POS tags \| Abbreviation \| Description \| Examples \| \|:--------:\|:--------:\|:--------:\| \| PREP \| Preposition \| de \| \| AUX \| Auxiliary Verb \| est \| \| ADV \| Adverb \| toujours \| \| COSUB \| Subordinating conjunction \| que \| \| COCO \| Coordinating Conjunction \| et \| \| PART \| Demonstrative particle \| -t \| \| PRON \| Pronoun \| qui ce quoi \| \| PDEMMS \| Demonstrative Pronoun - Singular Masculine \| ce \| \| PDEMMP \| Demonstrative Pronoun - Plural Masculine \| ceux \| \| PDEMFS \| Demonstrative Pronoun - Singular Feminine \| cette \| \| PDEMFP \| Demonstrative Pronoun - Plural Feminine \| celles \| \| PINDMS \| Indefinite Pronoun - Singular Masculine \| tout \| \| PINDMP \| Indefinite Pronoun - Plural Masculine \| autres \| \| PINDFS \| Indefinite Pronoun - Singular Feminine \| chacune \| \| PINDFP \| Indefinite Pronoun - Plural Feminine \| certaines \| \| PROPN \| Proper noun \| Houston \| \| XFAMIL \| Last name \| Levy \| \| NUM \| Numerical Adjective \| trentaine vingtaine \| \| DINTMS \| Masculine Numerical Adjective \| un \| \| DINTFS \| Feminine Numerical Adjective \| une \| \| PPOBJMS \| Pronoun complements of objects - Singular Masculine \| le lui \| \| PPOBJMP \| Pronoun complements of objects - Plural Masculine \| eux y \| \| PPOBJFS \| Pronoun complements of objects - Singular Feminine \| moi la \| \| PPOBJFP \| Pronoun complements of objects - Plural Feminine \| en y \| \| PPER1S \| Personal Pronoun First-Person - Singular \| je \| \| PPER2S \| Personal Pronoun Second-Person - Singular \| tu \| \| PPER3MS \| Personal Pronoun Third-Person - Singular Masculine \| il \| \| PPER3MP \| Personal Pronoun Third-Person - Plural Masculine \| ils \| \| PPER3FS \| Personal Pronoun Third-Person - Singular Feminine \| elle \| \| PPER3FP \| Personal Pronoun Third-Person - Plural Feminine \| elles \| \| PREFS \| Reflexive Pronoun First-Person - Singular \| me m' \| \| PREF \| Reflexive Pronoun Third-Person - Singular \| se s' \| \| PREFP \| Reflexive Pronoun First / Second-Person - Plural \| nous vous \| \| VERB \| Verb \| obtient \| \| VPPMS \| Past Participle - Singular Masculine \| formulé \| \| VPPMP \| Past Participle - Plural Masculine \| classés \| \| VPPFS \| Past Participle - Singular Feminine \| appelée \| \| VPPFP \| Past Participle - Plural Feminine \| sanctionnées \| \| DET \| Determinant \| les l' \| \| DETMS \| Determinant - Singular Masculine \| les \| \| DETFS \| Determinant - Singular Feminine \| la \| \| ADJ \| Adjective \| capable sérieux \| \| ADJMS \| Adjective - Singular Masculine \| grand important \| \| ADJMP \| Adjective - Plural Masculine \| grands petits \| \| ADJFS \| Adjective - Singular Feminine \| française petite \| \| ADJFP \| Adjective - Plural Feminine \| légères petites \| \| NOUN \| Noun \| temps \| \| NMS \| Noun - Singular Masculine \| drapeau \| \| NMP \| Noun - Plural Masculine \| journalistes \| \| NFS \| Noun - Singular Feminine \| tête \| \| NFP \| Noun - Plural Feminine \| ondes \| \| PREL \| Relative Pronoun \| qui dont \| \| PRELMS \| Relative Pronoun - Singular Masculine \| lequel \| \| PRELMP \| Relative Pronoun - Plural Masculine \| lesquels \| \| PRELFS \| Relative Pronoun - Singular Feminine \| laquelle \| \| PRELFP \| Relative Pronoun - Plural Feminine \| lesquelles \| \| INTJ \| Interjection \| merci bref \| \| CHIF \| Numbers \| 1979 10 \| \| SYM \| Symbol \| € % \| \| YPFOR \| Endpoint \| . \| \| PUNCT \| Ponctuation \| : , \| \| MOTINC \| Unknown words \| Technology Lady \| \| X \| Typos & others \| sfeir 3D statu \| ## Evaluation results The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```plain Results: - F-score (micro) 0.9797 - F-score (macro) 0.9178 - Accuracy 0.9797 By class: precision recall f1-score support PREP 0.9966 0.9987 0.9976 1483 PUNCT 1.0000 1.0000 1.0000 833 NMS 0.9634 0.9801 0.9717 753 DET 0.9923 0.9984 0.9954 645 VERB 0.9913 0.9811 0.9862 583 NFS 0.9667 0.9839 0.9752 560 ADV 0.9940 0.9821 0.9880 504 PROPN 0.9541 0.8937 0.9229 395 DETMS 1.0000 1.0000 1.0000 362 AUX 0.9860 0.9915 0.9888 355 YPFOR 1.0000 1.0000 1.0000 353 NMP 0.9666 0.9475 0.9570 305 COCO 0.9959 1.0000 0.9980 245 ADJMS 0.9463 0.9385 0.9424 244 DETFS 1.0000 1.0000 1.0000 240 CHIF 0.9648 0.9865 0.9755 222 NFP 0.9515 0.9849 0.9679 199 ADJFS 0.9657 0.9286 0.9468 182 VPPMS 0.9387 0.9745 0.9563 157 COSUB 1.0000 0.9844 0.9921 128 DINTMS 0.9918 0.9918 0.9918 122 XFAMIL 0.9298 0.9217 0.9258 115 PPER3MS 1.0000 1.0000 1.0000 87 ADJMP 0.9294 0.9634 0.9461 82 PDEMMS 1.0000 1.0000 1.0000 75 ADJFP 0.9861 0.9342 0.9595 76 PREL 0.9859 1.0000 0.9929 70 DINTFS 0.9839 1.0000 0.9919 61 PREF 1.0000 1.0000 1.0000 52 PPOBJMS 0.9565 0.9362 0.9462 47 PREFP 0.9778 1.0000 0.9888 44 PINDMS 1.0000 0.9773 0.9885 44 VPPFS 0.8298 0.9750 0.8966 40 PPER1S 1.0000 1.0000 1.0000 42 SYM 1.0000 0.9474 0.9730 38 NOUN 0.8824 0.7692 0.8219 39 PRON 1.0000 0.9677 0.9836 31 PDEMFS 1.0000 1.0000 1.0000 29 VPPMP 0.9286 1.0000 0.9630 26 ADJ 0.9524 0.9091 0.9302 22 PPER3MP 1.0000 1.0000 1.0000 20 VPPFP 1.0000 1.0000 1.0000 19 PPER3FS 1.0000 1.0000 1.0000 18 MOTINC 0.3333 0.4000 0.3636 15 PREFS 1.0000 1.0000 1.0000 10 PPOBJMP 1.0000 0.8000 0.8889 10 PPOBJFS 0.6250 0.8333 0.7143 6 INTJ 0.5000 0.6667 0.5714 6 PART 1.0000 1.0000 1.0000 4 PDEMMP 1.0000 1.0000 1.0000 3 PDEMFP 1.0000 1.0000 1.0000 3 PPER3FP 1.0000 1.0000 1.0000 2 NUM 1.0000 0.3333 0.5000 3 PPER2S 1.0000 1.0000 1.0000 2 PPOBJFP 0.5000 0.5000 0.5000 2 PRELMS 1.0000 1.0000 1.0000 2 PINDFS 0.5000 1.0000 0.6667 1 PINDMP 1.0000 1.0000 1.0000 1 X 0.0000 0.0000 0.0000 1 PINDFP 1.0000 1.0000 1.0000 1 micro avg 0.9797 0.9797 0.9797 10019 macro avg 0.9228 0.9230 0.9178 10019 weighted avg 0.9802 0.9797 0.9798 10019 samples avg 0.9797 0.9797 0.9797 10019 ``` ## BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ``` Flair Embeddings: ```latex @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` ## Acknowledgment This work was financially supported by [Zenidoc](https://zenidoc.fr/)
ricardo-filho/bert_base_tcm_teste	ricardo-filho	2022-07-06T23:23:13Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-06T18:05:49Z	--- license: mit tags: - generated_from_trainer model-index: - name: bert_base_tcm_teste results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_base_tcm_teste This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Criterio Julgamento Precision: 0.7209 - Criterio Julgamento Recall: 0.8942 - Criterio Julgamento F1: 0.7983 - Criterio Julgamento Number: 104 - Data Sessao Precision: 0.6351 - Data Sessao Recall: 0.8545 - Data Sessao F1: 0.7287 - Data Sessao Number: 55 - Modalidade Licitacao Precision: 0.9224 - Modalidade Licitacao Recall: 0.9596 - Modalidade Licitacao F1: 0.9406 - Modalidade Licitacao Number: 421 - Numero Exercicio Precision: 0.8872 - Numero Exercicio Recall: 0.9351 - Numero Exercicio F1: 0.9105 - Numero Exercicio Number: 185 - Objeto Licitacao Precision: 0.2348 - Objeto Licitacao Recall: 0.4576 - Objeto Licitacao F1: 0.3103 - Objeto Licitacao Number: 59 - Valor Objeto Precision: 0.5424 - Valor Objeto Recall: 0.7805 - Valor Objeto F1: 0.64 - Valor Objeto Number: 41 - Overall Precision: 0.7683 - Overall Recall: 0.8971 - Overall F1: 0.8277 - Overall Accuracy: 0.9948 ## 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: 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: 50.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Criterio Julgamento Precision \| Criterio Julgamento Recall \| Criterio Julgamento F1 \| Criterio Julgamento Number \| Data Sessao Precision \| Data Sessao Recall \| Data Sessao F1 \| Data Sessao Number \| Modalidade Licitacao Precision \| Modalidade Licitacao Recall \| Modalidade Licitacao F1 \| Modalidade Licitacao Number \| Numero Exercicio Precision \| Numero Exercicio Recall \| Numero Exercicio F1 \| Numero Exercicio Number \| Objeto Licitacao Precision \| Objeto Licitacao Recall \| Objeto Licitacao F1 \| Objeto Licitacao Number \| Valor Objeto Precision \| Valor Objeto Recall \| Valor Objeto F1 \| Valor Objeto Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------------------:\|:--------------------------:\|:----------------------:\|:--------------------------:\|:---------------------:\|:------------------:\|:--------------:\|:------------------:\|:------------------------------:\|:---------------------------:\|:-----------------------:\|:---------------------------:\|:--------------------------:\|:-----------------------:\|:-------------------:\|:-----------------------:\|:--------------------------:\|:-----------------------:\|:-------------------:\|:-----------------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.0346 \| 0.96 \| 2750 \| 0.0329 \| 0.6154 \| 0.8462 \| 0.7126 \| 104 \| 0.5495 \| 0.9091 \| 0.6849 \| 55 \| 0.8482 \| 0.9287 \| 0.8866 \| 421 \| 0.7438 \| 0.9730 \| 0.8431 \| 185 \| 0.0525 \| 0.3220 \| 0.0903 \| 59 \| 0.4762 \| 0.7317 \| 0.5769 \| 41 \| 0.5565 \| 0.8763 \| 0.6807 \| 0.9880 \| \| 0.0309 \| 1.92 \| 5500 \| 0.0322 \| 0.6694 \| 0.7788 \| 0.72 \| 104 \| 0.5976 \| 0.8909 \| 0.7153 \| 55 \| 0.9178 \| 0.9549 \| 0.9360 \| 421 \| 0.8211 \| 0.8432 \| 0.8320 \| 185 \| 0.15 \| 0.2034 \| 0.1727 \| 59 \| 0.2203 \| 0.3171 \| 0.26 \| 41 \| 0.7351 \| 0.8243 \| 0.7771 \| 0.9934 \| \| 0.0179 \| 2.88 \| 8250 \| 0.0192 \| 0.7209 \| 0.8942 \| 0.7983 \| 104 \| 0.6351 \| 0.8545 \| 0.7287 \| 55 \| 0.9224 \| 0.9596 \| 0.9406 \| 421 \| 0.8872 \| 0.9351 \| 0.9105 \| 185 \| 0.2348 \| 0.4576 \| 0.3103 \| 59 \| 0.5424 \| 0.7805 \| 0.64 \| 41 \| 0.7683 \| 0.8971 \| 0.8277 \| 0.9948 \| \| 0.0174 \| 3.84 \| 11000 \| 0.0320 \| 0.7522 \| 0.8173 \| 0.7834 \| 104 \| 0.5741 \| 0.5636 \| 0.5688 \| 55 \| 0.8881 \| 0.9430 \| 0.9147 \| 421 \| 0.8490 \| 0.8811 \| 0.8647 \| 185 \| 0.2436 \| 0.3220 \| 0.2774 \| 59 \| 0.5370 \| 0.7073 \| 0.6105 \| 41 \| 0.7719 \| 0.8370 \| 0.8031 \| 0.9946 \| \| 0.0192 \| 4.8 \| 13750 \| 0.0261 \| 0.6744 \| 0.8365 \| 0.7468 \| 104 \| 0.6190 \| 0.7091 \| 0.6610 \| 55 \| 0.9169 \| 0.9430 \| 0.9297 \| 421 \| 0.8404 \| 0.8541 \| 0.8472 \| 185 \| 0.2059 \| 0.3559 \| 0.2609 \| 59 \| 0.5088 \| 0.7073 \| 0.5918 \| 41 \| 0.7521 \| 0.8451 \| 0.7959 \| 0.9949 \| \| 0.0158 \| 5.76 \| 16500 \| 0.0250 \| 0.6641 \| 0.8173 \| 0.7328 \| 104 \| 0.5610 \| 0.8364 \| 0.6715 \| 55 \| 0.9199 \| 0.9549 \| 0.9371 \| 421 \| 0.9167 \| 0.9514 \| 0.9337 \| 185 \| 0.1912 \| 0.4407 \| 0.2667 \| 59 \| 0.4828 \| 0.6829 \| 0.5657 \| 41 \| 0.7386 \| 0.8821 \| 0.8040 \| 0.9948 \| \| 0.0126 \| 6.72 \| 19250 \| 0.0267 \| 0.6694 \| 0.7981 \| 0.7281 \| 104 \| 0.6386 \| 0.9636 \| 0.7681 \| 55 \| 0.8723 \| 0.9572 \| 0.9128 \| 421 \| 0.8812 \| 0.9622 \| 0.9199 \| 185 \| 0.2180 \| 0.4915 \| 0.3021 \| 59 \| 0.5323 \| 0.8049 \| 0.6408 \| 41 \| 0.7308 \| 0.9006 \| 0.8068 \| 0.9945 \| \| 0.0162 \| 7.68 \| 22000 \| 0.0328 \| 0.675 \| 0.7788 \| 0.7232 \| 104 \| 0.6604 \| 0.6364 \| 0.6481 \| 55 \| 0.9263 \| 0.9549 \| 0.9404 \| 421 \| 0.8535 \| 0.9135 \| 0.8825 \| 185 \| 0.2471 \| 0.3559 \| 0.2917 \| 59 \| 0.5091 \| 0.6829 \| 0.5833 \| 41 \| 0.7788 \| 0.8509 \| 0.8133 \| 0.9948 \| ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
domenicrosati/deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing	domenicrosati	2022-07-06T21:12:29Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-06T20:34:04Z	--- license: mit tags: - text-classification - generated_from_trainer model-index: - name: deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing 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. --> # deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing This model is a fine-tuned version of [domenicrosati/deberta-v3-xsmall-finetuned-review_classifier](https://huggingface.co/domenicrosati/deberta-v3-xsmall-finetuned-review_classifier) 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: 4.5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu116 - Datasets 2.3.2 - Tokenizers 0.12.1
BigTimeCoderSean/q-Taxi-v3	BigTimeCoderSean	2022-07-06T18:13:18Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-06T18:13:12Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.54 +/- 2.70 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="BigTimeCoderSean/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
BigTimeCoderSean/q-FrozenLake-v1-4x4-noSlippery	BigTimeCoderSean	2022-07-06T17:57:12Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-06T17:57:05Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 0.74 +/- 0.44 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="BigTimeCoderSean/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
bigscience/tr11-176B-logs	bigscience	2022-07-06T17:01:14Z	0	250	null	[ "tensorboard", "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu", "region:us" ]	null	2022-03-03T04:38:09Z	--- language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu --- # BigScience Large Language Model Training Training a multilingual 176 billion parameters model in the open ![BigScience Logo](https://assets.website-files.com/6139f3cdcbbff3a68486761d/613cd8997b270da063e230c5_Tekengebied%201-p-500.png) [BigScience](https://bigscience.huggingface.co) is a open and collaborative workshop around the study and creation of very large language models gathering more than 1000 researchers around the worlds. You can find more information on the main website at https://bigscience.huggingface.co. The training of BigScience’s main model started on March 11, 2022 11:42am PST and will continue for 3-4 months on 384 A100 80GB GPUs of the Jean Zay public supercomputer You can follow the training at [https://twitter.com/BigScienceLLM](https://twitter.com/BigScienceLLM) or on [the Tensorboards tab above](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss). ## More information on the model, dataset, hardware, environmental consideration: ### The model - 176B parameters decoder-only architecture (GPT-like) - 70 layers - 112 attention heads per layers - hidden dimensionality of 14336 - 2048 tokens sequence length - ALiBi positional embeddings - GeLU activation function - More information: - Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: [https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours](https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours) - More details on the architecture/optimizer: [https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml) ### The dataset - Multilingual: 46 languages: Full list is here: [https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling](https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling) - 341.6 billion tokens (1.5 TB of text data) - Tokenizer vocabulary: 250,680 tokens - More information: - Blog post detailing the design choices during the dataset creation: [https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling](https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling) ### The engineering side - number of GPU used for the training: 384 A100 GPU with 80 GB of memory each - one copy of the model takes 48 GPUs (using 60 GB of memory on each GPU) - checkpoint size: the bf16 weights are 329GB, the full checkpoint with optimizer states is 2.3TB - training throughput: ~150 TFLOPs - estimated training time: 3-4 months depending on throughput and unexpected events - More information: - Blog post on the hardware/engineering side: [https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model](https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model) - Details on the distributed setup used for the training: [https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml) - Tensorboard updated during the training: [https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss) - Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): [https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md](https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md) ### Environmental considerations - [Jean Zay](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html), the supercomputer we are using for model training, is mostly powered by nuclear energy, which is a low carbon energy source. - Significant efforts were made to make sure that the computing infrastructure is as efficient as possible — the heat generated by the hardware even gets used for heating buildings on campus! - More information: - We are currently working on making a precise estimate of the carbon emitted during all of the steps of model training, including intermediate experiments as well as inference. - More soon!
hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10	hsohn3	2022-07-06T15:57:53Z	4	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-06T14:22:52Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 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. --> # hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2895 - Epoch: 9 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 4.1298 \| 0 \| \| 3.5157 \| 1 \| \| 3.4732 \| 2 \| \| 3.4565 \| 3 \| \| 3.4444 \| 4 \| \| 3.4349 \| 5 \| \| 3.4197 \| 6 \| \| 3.4109 \| 7 \| \| 3.3493 \| 8 \| \| 3.2895 \| 9 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
saekomdalkom/t5-small-finetuned-xsum	saekomdalkom	2022-07-06T15:25:39Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-06T13:04:22Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 28.3577 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4783 - Rouge1: 28.3577 - Rouge2: 7.759 - Rougel: 22.274 - Rougelsum: 22.2869 - Gen Len: 18.8298 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:------:\|:---------:\|:-------:\| \| 2.7158 \| 1.0 \| 12753 \| 2.4783 \| 28.3577 \| 7.759 \| 22.274 \| 22.2869 \| 18.8298 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.3.2 - Tokenizers 0.12.1
cacauvicosa/heart1ohr2x9e-target-classification	cacauvicosa	2022-07-06T15:11:05Z	0	0	sklearn	[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]	tabular-classification	2022-07-06T15:11:03Z	--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on heart1ohr2x9e to apply classification on target Metrics of the best model: accuracy 0.885854 average_precision 0.949471 roc_auc 0.050633 recall_macro 0.885324 f1_macro 0.885610 Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64 See model plot below: <style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-8" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False Fa...... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False Fa...... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False False False ... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div> Disclaimer:* This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). Logs of training including the models tried in the process can be found in logs.txt
huggingtweets/frnsw-nswrfs-nswses	huggingtweets	2022-07-06T14:32:52Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-07-06T14:32:45Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1150678663265832960/ujqrCyuu_400x400.png')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/895892720194957313/RVLTWlDI_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500778204294180868/3B6rKocs_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">NSW RFS & NSW SES & Fire and Rescue NSW</div> <div style="text-align: center; font-size: 14px;">@frnsw-nswrfs-nswses</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from NSW RFS & NSW SES & Fire and Rescue NSW. \| Data \| NSW RFS \| NSW SES \| Fire and Rescue NSW \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3250 \| 3248 \| 3249 \| \| Retweets \| 275 \| 2093 \| 875 \| \| Short tweets \| 12 \| 12 \| 48 \| \| Tweets kept \| 2963 \| 1143 \| 2326 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1cxt6027/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @frnsw-nswrfs-nswses's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tjbhow2z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tjbhow2z/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/frnsw-nswrfs-nswses') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
TestZee/t5-small-finetuned-custom-wion-test	TestZee	2022-07-06T13:28:44Z	4	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-06T13:23:31Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TestZee/t5-small-finetuned-custom-wion-test 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. --> # TestZee/t5-small-finetuned-custom-wion-test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9773 - Validation Loss: 0.8028 - Epoch: 9 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.2933 \| 0.9052 \| 0 \| \| 2.3077 \| 0.8923 \| 1 \| \| 2.1972 \| 0.8797 \| 2 \| \| 2.1740 \| 0.8677 \| 3 \| \| 2.1535 \| 0.8564 \| 4 \| \| 2.1772 \| 0.8452 \| 5 \| \| 2.1227 \| 0.8342 \| 6 \| \| 2.0875 \| 0.8234 \| 7 \| \| 2.0279 \| 0.8129 \| 8 \| \| 1.9773 \| 0.8028 \| 9 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Tokenizers 0.12.1
luizapzbn/titanicht_mp88q-Survived-classification	luizapzbn	2022-07-06T13:25:48Z	0	0	sklearn	[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]	tabular-classification	2022-07-06T13:25:46Z	--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on titanicht_mp88q to apply classification on Survived Metrics of the best model: accuracy 0.803597 average_precision 0.801332 roc_auc 0.848079 recall_macro 0.795883 f1_macro 0.793746 Name: DecisionTreeClassifier(class_weight='balanced', max_depth=5), dtype: float64 See model plot below: <style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-7" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Pclass False False False ... False False False Name False False False ... False True False Sex False False False ... False False False Age True False False ... False False False SibSp False False False ... False False False Parch False False False ... False False False Ticket False False False ... False True False Fare True False False ... False False False Cabin False False False ... False True False Embarked False False False ... False False False[10 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Pclass False False False ... False False False Name False False False ... False True False Sex False False False ... False False False Age True False False ... False False False SibSp False False False ... False False False Parch False False False ... False False False Ticket False False False ... False True False Fare True False False ... False False False Cabin False False False ... False True False Embarked False False False ... False False False[10 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=5))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-22" type="checkbox" ><label for="sk-estimator-id-22" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Pclass False False False ... False False False Name False False False ... False True False Sex False False False ... False False False Age True False False ... False False False SibSp False False False ... False False False Parch False False False ... False False False Ticket False False False ... False True False Fare True False False ... False False False Cabin False False False ... False True False Embarked False False False ... False False False[10 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight='balanced', max_depth=5)</pre></div></div></div></div></div></div></div> Disclaimer:* This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). Logs of training including the models tried in the process can be found in logs.txt
sumitrsch/muril_base_multiconer22_bn	sumitrsch	2022-07-06T12:33:20Z	4	3	transformers	[ "transformers", "pytorch", "bert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-30T07:24:11Z	--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track. https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO
srg/outhimar_64-Close-regression	srg	2022-07-06T12:33:04Z	0	4	sklearn	[ "sklearn", "tabular-regression", "baseline-trainer", "license:apache-2.0", "region:us" ]	tabular-regression	2022-07-06T12:33:02Z	--- license: apache-2.0 library_name: sklearn tags: - tabular-regression - baseline-trainer --- ## Baseline Model trained on outhimar_64 to apply regression on Close Metrics of the best model: r2 0.999858 neg_mean_squared_error -1.067685 Name: Ridge(alpha=10), dtype: float64 See model plot below: <style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-18" type="checkbox" ><label for="sk-estimator-id-18" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-19" type="checkbox" ><label for="sk-estimator-id-19" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label sk-toggleable__label-arrow">Ridge</label><div class="sk-toggleable__content"><pre>Ridge(alpha=10)</pre></div></div></div></div></div></div></div> Disclaimer:* This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). Logs of training including the models tried in the process can be found in logs.txt
sumitrsch/Indic-bert_multiconer22_bn	sumitrsch	2022-07-06T12:32:40Z	3	2	transformers	[ "transformers", "pytorch", "albert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-06T10:07:47Z	--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track. https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO
sumitrsch/xlm_R_large_multiconer22_bn	sumitrsch	2022-07-06T12:32:05Z	3	2	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-06T10:33:33Z	--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track. https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO
sumitrsch/muril_base_multiconer22_hi	sumitrsch	2022-07-06T12:27:42Z	3	3	transformers	[ "transformers", "pytorch", "bert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-28T07:57:21Z	--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP
dandelin/vilt-b32-mlm	dandelin	2022-07-06T12:18:37Z	66,336	11	transformers	[ "transformers", "pytorch", "vilt", "fill-mask", "arxiv:2102.03334", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 --- # Vision-and-Language Transformer (ViLT), pre-trained only Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Note: this model only includes the language modeling head. Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the raw model for masked language modeling given an image and a piece of text with [MASK] tokens. ### How to use Here is how to use this model in PyTorch: ``` from transformers import ViltProcessor, ViltForMaskedLM import requests from PIL import Image import re url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "a bunch of [MASK] laying on a [MASK]." processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm") model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) tl = len(re.findall("\[MASK\]", text)) inferred_token = [text] # gradually fill in the MASK tokens, one by one with torch.no_grad(): for i in range(tl): encoded = processor.tokenizer(inferred_token) input_ids = torch.tensor(encoded.input_ids).to(device) encoded = encoded["input_ids"][0][1:-1] outputs = model(input_ids=input_ids, pixel_values=pixel_values) mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size) # only take into account text features (minus CLS and SEP token) mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :] mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1) # only take into account text mlm_values[torch.tensor(encoded) != 103] = 0 select = mlm_values.argmax().item() encoded[select] = mlm_ids[select].item() inferred_token = [processor.decode(encoded)] selected_token = "" encoded = processor.tokenizer(inferred_token) processor.decode(encoded.input_ids[0], skip_special_tokens=True) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```
Lakshya/q-Taxi-v3	Lakshya	2022-07-06T12:06:33Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-06T12:06:26Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.46 +/- 2.73 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Lakshya/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
SiddharthaM/beit-base-patch16-224-pt22k-ft22k-rim_one-new	SiddharthaM	2022-07-06T11:17:32Z	55	0	transformers	[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-06T10:31:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-base-patch16-224-pt22k-ft22k-rim_one-new results: - task: type: image-classification name: Image Classification dataset: type: rimonedl name: RIM ONE DL split: test metrics: - type: f1 value: 0.9197860962566845 name: F1 - task: type: image-classification name: Image Classification dataset: type: rim one name: RIMONEDL split: test metrics: - type: precision value: 0.9247311827956989 name: precision - type: recall value: 0.9148936170212766 name: Recall - type: accuracy value: 0.8972602739726028 name: Accuracy - type: roc_auc value: 0.8901391162029461 name: AUC --- <!-- 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-base-patch16-224-pt22k-ft22k-rim_one-new This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4550 - Accuracy: 0.8767 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.73 \| 2 \| 0.2411 \| 0.9178 \| \| No log \| 1.73 \| 4 \| 0.2182 \| 0.8973 \| \| No log \| 2.73 \| 6 \| 0.3085 \| 0.8973 \| \| No log \| 3.73 \| 8 \| 0.2794 \| 0.8973 \| \| 0.1392 \| 4.73 \| 10 \| 0.2398 \| 0.9110 \| \| 0.1392 \| 5.73 \| 12 \| 0.2925 \| 0.8973 \| \| 0.1392 \| 6.73 \| 14 \| 0.2798 \| 0.9110 \| \| 0.1392 \| 7.73 \| 16 \| 0.2184 \| 0.9178 \| \| 0.1392 \| 8.73 \| 18 \| 0.3007 \| 0.9110 \| \| 0.0416 \| 9.73 \| 20 \| 0.3344 \| 0.9041 \| \| 0.0416 \| 10.73 \| 22 \| 0.3626 \| 0.9110 \| \| 0.0416 \| 11.73 \| 24 \| 0.4842 \| 0.8904 \| \| 0.0416 \| 12.73 \| 26 \| 0.3664 \| 0.8973 \| \| 0.0416 \| 13.73 \| 28 \| 0.3458 \| 0.9110 \| \| 0.0263 \| 14.73 \| 30 \| 0.2810 \| 0.9110 \| \| 0.0263 \| 15.73 \| 32 \| 0.4695 \| 0.8699 \| \| 0.0263 \| 16.73 \| 34 \| 0.3723 \| 0.9041 \| \| 0.0263 \| 17.73 \| 36 \| 0.3447 \| 0.9041 \| \| 0.0263 \| 18.73 \| 38 \| 0.3708 \| 0.8904 \| \| 0.0264 \| 19.73 \| 40 \| 0.4052 \| 0.9110 \| \| 0.0264 \| 20.73 \| 42 \| 0.4492 \| 0.9041 \| \| 0.0264 \| 21.73 \| 44 \| 0.4649 \| 0.8904 \| \| 0.0264 \| 22.73 \| 46 \| 0.4061 \| 0.9178 \| \| 0.0264 \| 23.73 \| 48 \| 0.4136 \| 0.9110 \| \| 0.0139 \| 24.73 \| 50 \| 0.4183 \| 0.8973 \| \| 0.0139 \| 25.73 \| 52 \| 0.4504 \| 0.8904 \| \| 0.0139 \| 26.73 \| 54 \| 0.4368 \| 0.8973 \| \| 0.0139 \| 27.73 \| 56 \| 0.4711 \| 0.9110 \| \| 0.0139 \| 28.73 \| 58 \| 0.3928 \| 0.9110 \| \| 0.005 \| 29.73 \| 60 \| 0.4550 \| 0.8767 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
kws/q-Taxi-v3	kws	2022-07-06T10:24:02Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-06T10:23:57Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="kws/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
sumitrsch/Indic-bert_multiconer22_hi	sumitrsch	2022-07-06T10:00:34Z	4	2	transformers	[ "transformers", "pytorch", "albert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-06T09:43:28Z	--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP
dnouri/brats_mri_segmentation	dnouri	2022-07-06T09:54:53Z	0	1	null	[ "monai", "arxiv:1810.11654", "region:us" ]	null	2022-07-06T09:13:12Z	--- tags: - monai --- # Model Overview A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation). ## Workflow The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR). - The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c. - The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor. - The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR. ## Data The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018/data.html). - Target: 3 tumor subregions - Task: Segmentation - Modality: MRI - Size: 285 3D volumes (4 channels each) The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets. Please run `scripts/prepare_datalist.py` to produce the data list. The command is like: ``` python scripts/prepare_datalist.py --path your-brats18-dataset-path ``` ## Training configuration This model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following: - GPU: At least 16GB of GPU memory. - Actual Model Input: 224 x 224 x 144 - AMP: True - Optimizer: Adam - Learning Rate: 1e-4 - Loss: DiceLoss ## Input Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm) 1. Normalizing to unit std with zero mean 2. Randomly cropping to (224, 224, 144) 3. Randomly spatial flipping 4. Randomly scaling and shifting intensity of the volume ## Output Output: 3 channels - Label 0: TC tumor subregion - Label 1: WT tumor subregion - Label 2: ET tumor subregion ## Model Performance The achieved Dice scores on the validation data are: - Tumor core (TC): 0.8559 - Whole tumor (WT): 0.9026 - Enhancing tumor (ET): 0.7905 - Average: 0.8518 ## commands example Execute training: ``` python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf ``` Override the `train` config to execute multi-GPU training: ``` torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf ``` Override the `train` config to execute evaluation with the trained model: ``` python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf ``` Execute inference: ``` python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf ``` # Disclaimer This is an example, not to be used for diagnostic purposes. # References [1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.
vinayak361/token_fine_tunned_flipkart	vinayak361	2022-07-06T09:32:50Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-06T07:42:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: token_fine_tunned_flipkart 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. --> # token_fine_tunned_flipkart 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: 0.0992 - Precision: 0.9526 - Recall: 0.9669 - F1: 0.9597 - Accuracy: 0.9730 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 135 \| 0.5967 \| 0.7227 \| 0.7830 \| 0.7516 \| 0.7932 \| \| No log \| 2.0 \| 270 \| 0.3673 \| 0.8105 \| 0.8623 \| 0.8356 \| 0.8747 \| \| No log \| 3.0 \| 405 \| 0.2679 \| 0.8676 \| 0.8854 \| 0.8764 \| 0.9094 \| \| 0.6219 \| 4.0 \| 540 \| 0.1972 \| 0.8955 \| 0.9217 \| 0.9084 \| 0.9355 \| \| 0.6219 \| 5.0 \| 675 \| 0.1500 \| 0.9229 \| 0.9374 \| 0.9301 \| 0.9525 \| \| 0.6219 \| 6.0 \| 810 \| 0.1240 \| 0.9341 \| 0.9509 \| 0.9424 \| 0.9609 \| \| 0.6219 \| 7.0 \| 945 \| 0.1041 \| 0.9516 \| 0.9650 \| 0.9582 \| 0.9720 \| \| 0.2085 \| 8.0 \| 1080 \| 0.0992 \| 0.9526 \| 0.9669 \| 0.9597 \| 0.9730 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
laurian/pouet	laurian	2022-07-06T08:44:36Z	0	0	null	[ "region:us" ]	null	2022-07-06T08:42:05Z	valkiry robot desert technology
messham/ppo-LunarLander-v2_1pt5m	messham	2022-07-06T08:33:46Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-06T08:33:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 275.55 +/- 24.60 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
ArneD/xlm-roberta-base-finetuned-panx-de	ArneD	2022-07-06T07:23:24Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-06T06:47:26Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2575 \| 1.0 \| 525 \| 0.1621 \| 0.8292 \| \| 0.1287 \| 2.0 \| 1050 \| 0.1378 \| 0.8526 \| \| 0.0831 \| 3.0 \| 1575 \| 0.1372 \| 0.8621 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
espnet/aishell2_transducer	espnet	2022-07-06T07:11:48Z	2	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "zh", "dataset:aishell2", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-07-06T06:55:04Z	--- tags: - espnet - audio - automatic-speech-recognition language: zh datasets: - aishell2 license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/aishell2_transducer` This model was trained by jctian98 using aishell2 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 40c5f6919244c2ec8eac14b9011854dd02511a04 pip install -e . cd egs2/aishell2/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/aishell2_transducer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Tue Jul 5 22:02:55 CST 2022` - python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]` - espnet version: `espnet 202205` - pytorch version: `pytorch 1.7.1` - Git hash: `40c5f6919244c2ec8eac14b9011854dd02511a04` - Commit date: `Fri Jun 17 11:07:26 2022 +0800` ## asr_train_conformer-rnn_transducer_raw_zh_char_sp ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_model_valid.cer_transducer.ave/test_android\|5000\|5002\|63.2\|36.8\|0.0\|0.0\|36.8\|36.8\| \|decode_asr_model_valid.cer_transducer.ave/test_ios\|5000\|5002\|66.2\|33.7\|0.0\|0.0\|33.8\|33.8\| \|decode_asr_model_valid.cer_transducer.ave/test_mic\|5000\|5002\|63.9\|36.1\|0.0\|0.0\|36.1\|36.1\| \|decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_android\|5000\|5002\|64.4\|35.5\|0.0\|0.0\|35.6\|35.6\| \|decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_ios\|5000\|5002\|67.4\|32.5\|0.0\|0.0\|32.6\|32.6\| \|decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_mic\|5000\|5002\|65.3\|34.6\|0.0\|0.0\|34.7\|34.7\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_model_valid.cer_transducer.ave/test_android\|5000\|49534\|94.0\|5.7\|0.2\|0.1\|6.1\|36.8\| \|decode_asr_model_valid.cer_transducer.ave/test_ios\|5000\|49534\|94.8\|5.0\|0.2\|0.1\|5.4\|33.8\| \|decode_asr_model_valid.cer_transducer.ave/test_mic\|5000\|49534\|94.1\|5.7\|0.2\|0.1\|6.0\|36.1\| \|decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_android\|5000\|49534\|94.2\|5.5\|0.3\|0.1\|5.9\|35.6\| \|decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_ios\|5000\|49534\|94.9\|4.9\|0.2\|0.1\|5.2\|32.6\| \|decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_mic\|5000\|49534\|94.3\|5.4\|0.2\|0.1\|5.8\|34.7\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/transducer/train_conformer-rnn_transducer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer-rnn_transducer_raw_zh_char_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 51051 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - cer_transducer - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true 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: 20000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_zh_char_sp/train/speech_shape - exp/asr_stats_raw_zh_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_zh_char_sp/valid/speech_shape - exp/asr_stats_raw_zh_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 51200 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_noeng_sp/wav.scp - speech - sound - - dump/raw/train_noeng_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev_ios/wav.scp - speech - sound - - dump/raw/dev_ios/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 scheduler: warmuplr scheduler_conf: warmup_steps: 30000 token_list: - <blank> - <unk> - 的 - 一 - 十 - 二 - 三 - 有 - 我 - 在 - 度 - 五 - 是 - 四 - 人 - 六 - 七 - 八 - 九 - 中 - 百 - 不 - 了 - 零 - 大 - 到 - 为 - 开 - 上 - 国 - 调 - 市 - 点 - 业 - 歌 - 么 - 来 - 个 - 这 - 年 - 要 - 公 - 什 - 会 - 出 - 地 - 发 - 行 - 能 - 温 - 电 - 空 - 万 - 千 - 成 - 和 - 分 - 时 - 下 - 你 - 场 - 新 - 家 - 打 - 产 - 机 - 对 - 以 - 房 - 生 - 把 - 小 - 首 - 放 - 之 - 现 - 日 - 动 - 高 - 子 - 后 - 多 - 们 - 者 - 方 - 前 - 也 - 他 - 视 - 资 - 将 - 关 - 金 - 天 - 于 - 进 - 过 - 经 - 听 - 月 - 可 - 用 - 自 - 最 - 司 - 幺 - 车 - 比 - 体 - 手 - 目 - 化 - 道 - 作 - 部 - 被 - 给 - 报 - 加 - 就 - 第 - 全 - 乐 - 定 - 得 - 还 - 事 - 城 - 本 - 想 - 女 - 赛 - 面 - 工 - 设 - 都 - 音 - 力 - 品 - 理 - 保 - 记 - 心 - 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稔 - 弋 - 篝 - 蹋 - 窠 - 谟 - 浠 - 悱 - 蜍 - 孬 - 芥 - 馏 - 屐 - 栎 - 玷 - 萸 - 扞 - 阡 - 荀 - 曳 - 邕 - 诛 - 阉 - 堀 - 骠 - 琤 - 盂 - 妲 - 虻 - 醐 - 谀 - 舀 - 鳟 - 绀 - 呲 - 娩 - 牾 - 僮 - 笳 - 渥 - 仡 - 镊 - 嶝 - 泱 - 汴 - 咣 - 嘭 - 锟 - 咂 - 宓 - 侗 - 洹 - 妫 - Ａ - 峁 - 蜊 - 攫 - 膑 - 毂 - 秣 - 泠 - 尅 - 冼 - 嶂 - 浈 - 陬 - 啖 - 兖 - 褴 - 褛 - 妯 - 娌 - 恣 - 恸 - 掬 - 篦 - 蹚 - 逡 - 鲷 - 叵 - 驷 - 飧 - 釉 - 粼 - 踯 - 躅 - 讷 - 吮 - 琮 - 啾 - 粲 - 佻 - 疸 - 臊 - 蓦 - 椋 - 眬 - 憷 - 绋 - 珙 - 揶 - 谏 - 䶮 - 帛 - 衮 - 晷 - 裨 - 鸾 - 槎 - 讣 - 嫚 - 遨 - 瘙 - 疱 - 呻 - 鞅 - 痉 - 挛 - 骰 - 瘳 - 棂 - 偃 - 鸢 - 钲 - 尕 - 呸 - 埇 - 浃 - 濯 - 坩 - 埚 - 嗝 - 炀 - 隗 - 扈 - 谆 - 丕 - 魉 - 噙 - 圹 - 埕 - 恪 - 孱 - 凛 - 曜 - 拚 - 浔 - 吖 - 轱 - 搽 - 芪 - 箕 - 箔 - 戊 - 蛆 - 蜱 - 嗔 - 榛 - 蹒 - 跚 - 镣 - 鲫 - 镂 - 摈 - 愫 - 纂 - 麝 - 趺 - 碜 - 馁 - 唷 - 悻 - 伫 - 樾 - 剜 - 咝 - 銮 - 撺 - 掇 - 哂 - 咻 - 酐 - 訾 - 鳕 - 稷 - 嘣 - 碣 - 扦 - 柩 - 蟠 - 芩 - 鬓 - 裱 - 嗄 - 枋 - 钇 - 怼 - 喁 - 龊 - 疴 - 蛹 - 偓 - 蓼 - 汩 - 疖 - 蛎 - 诘 - 焓 - 荠 - 闩 - 噌 - 苷 - 藓 - 蚱 - 亘 - 缎 - 鼬 - 籼 - 疣 - 轸 - 玹 - 潺 - 妪 - 馀 - 啶 - 耄 - 耋 - 鬃 - 滹 - 莅 - 倜 - 傥 - 蓁 - 岬 - 貉 - 獾 - 敝 - 瘁 - 蒯 - 碓 - 殚 - 漭 - 嵛 - 榉 - 诓 - 泖 - 艋 - 凇 - 靑 - 沏 - 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吠 - 歙 - 谖 - 俟 - 刽 - 槭 - 硖 - 髯 - 饯 - 藐 - 娈 - 勐 - 颧 - 荻 - 焗 - 鳃 - 昴 - 黟 - 羧 - 趵 - 澶 - 骞 - 鸩 - 婢 - 圄 - 佝 - 偻 - 嗫 - 囯 - 跬 - 朕 - 袅 - 锲 - 杵 - 豢 - 骺 - 诹 - 椹 - 谮 - 㶧 - <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: 512 use_preprocessor: true token_type: char bpemodel: null 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 frontend: default frontend_conf: 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 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.0 preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish macaron_style: true use_cnn_module: true cnn_module_kernel: 15 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 512 dropout: 0.1 dropout_embed: 0.2 required: - output_dir - token_list version: '202205' distributed: true ``` </details> ### 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} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, 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}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
miyoung/newProject	miyoung	2022-07-06T01:16:16Z	0	0	null	[ "region:us" ]	null	2022-06-17T04:39:53Z	### What's Hugging Face?!!! https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!.
domenicrosati/deberta-v3-xsmall-finetuned-review_classifier	domenicrosati	2022-07-06T01:09:25Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-05T20:16:35Z	--- license: mit tags: - text-classification - generated_from_trainer metrics: - accuracy - f1 model-index: - name: deberta-v3-xsmall-finetuned-review_classifier 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. --> # deberta-v3-xsmall-finetuned-review_classifier This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - Accuracy: 0.9513 - F1: 0.7458 ## 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: 4.5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.1518 \| 1.0 \| 6667 \| 0.1575 \| 0.9510 \| 0.7155 \| \| 0.1247 \| 2.0 \| 13334 \| 0.1441 \| 0.9513 \| 0.7458 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
rbawden/CCASS-semi-auto-titrages-base	rbawden	2022-07-05T21:42:57Z	16	1	transformers	[ "transformers", "pytorch", "fsmt", "fr", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2022-06-16T09:32:27Z	--- language: fr license: cc-by-4.0 --- # Cour de Cassation semi-automatic titrage prediction model Model for the semi-automatic prediction of titrages (keyword sequence) from sommaires (synthesis of legal cases). The models are similar to the automatic models described in [this paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf) and to the model available [here](https://huggingface.co/rbawden/CCASS-pred-titrages-base). If you use this semi-automatic model, please cite our research paper (see [below](#cite)). ## Model description The model is a transformer-base model trained on parallel data (sommaires-titrages) provided by the Cour de Cassation. The model was intially trained using the Fairseq toolkit, converted to HuggingFace and then fine-tuned on the original training data to smooth out minor differences that arose during the conversion process. Tokenisation is performed using a SentencePiece model, the BPE strategy and a vocab size of 8000. ### Intended uses & limitations This model is to be used to help in the production of titrages for those sommaires that do not have them or to complement existing (manually) created titrages. ### How to use Contrary to the [automatic titrage prediction model](https://huggingface.co/rbawden/CCASS-pred-titrages-base) (designed to predict the entire sequence), this model is designed to help in the manual production of titrages, by proposing the next titre (keyword) in the sequence given a sommaire and the beginning of the titrage. Model input is the matière (matter) concatenated to the titres already decided on (separated by <t>), concatenated to the text from the sommaire separated by the token `<t>`. Each example should be on a single line. E.g. `bail <t> résiliation <t> causes <t> La recommendation du tribunal selon l'article...` (fictive example for illustrative purposes, where the matter=bail, the beginning of the titrage=résiliation <t> causes. The maximum input length of the model is 1024 input tokens (after tokenisation). ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokeniser = AutoTokenizer.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") model = AutoModelForSeq2SeqLM.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") matiere_and_titrage_prefix = "matter <t> titre" sommaire = "full text from the sommaire on a single line" inputs = tokeniser([matiere_and_titrage_prefix + " <t> " + sommaire], return_tensors='pt') outputs = model.generate(inputs['input_ids']) tokeniser.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenisation_spaces=True) ``` ### Limitations and bias The models' predictions should not be taken as ground-truth titrages and the final decision should be the expert's. The model is not constrained to predict titres that have previously been seen, so this should be taken into account in the deployment of this model as a titrage tool in order to avoid the multiplication of different titres. ## Training data Training data is provided by the Cour de Cassation (the original source being Jurinet data, but with pseudo-anonymisation applied). For training, we use a total of 159,836 parallel examples (each example is a sommaire-titrage pair). Our development data consists of 1,833 held-out examples. ## Training procedure ### Preprocessing We use SentencePiece, the BPE strategy and a joint vocabulary of 8000 tokens. This model was converted into the HuggingFace format and integrates a number of normalisation processes (e.g. removing double doubles, apostrophes and quotes, normalisation of different accent formats, lowercasing). ### Training The model was initialised trained using Fairseq until convergence on the development set (according to our customised weighted accuracy measure - please see [the paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf) for more details). The model was then converted to HuggingFace and training continued to smooth out incoherences introduced during the conversion procedure (incompatibilities in the way the SentencePiece and NMT vocabularies are defined, linked to HuggingFace vocabularies being necessarily the same as the tokeniser vocabulary, a constraint that is not imposed in Fairseq). ### Evaluation results Full results for the initial (automatic) Fairseq models can be found in [the paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf). Results on this semi-automatic model coming soon! ## BibTex entry and citation info <a name="cite"></a> If you use this work, please cite the following article: Thibault Charmet, Inès Cherichi, Matthieu Allain, Urszula Czerwinska, Amaury Fouret, Benoît Sagot and Rachel Bawden, 2022. [Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.509.pdf). In Proceedings of the 13th Language Resources and Evaluation Conference, Marseille, France.] ``` @inproceedings{charmet-et-al-2022-complex, tite = {Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings}, author = {Charmet, Thibault and Cherichi, Inès and Allain, Matthieu and Czerwinska, Urszula and Fouret, Amaury, and Sagot, Benoît and Bawden, Rachel}, booktitle = {Proceedings of the 13th Language Resources and Evaluation Conference}, year = {2022}, address = {Marseille, France}, pages = {4754--4766}, url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.509.pdf} ```
pm390/Reinforce-pong-01	pm390	2022-07-05T19:49:27Z	0	0	null	[ "Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T19:49:16Z	--- tags: - Pong-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pong-01 results: - metrics: - type: mean_reward value: -16.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pong-PLE-v0 type: Pong-PLE-v0 --- # Reinforce Agent playing Pong-PLE-v0 This is a trained model of a Reinforce agent playing Pong-PLE-v0 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
Tinchoroman/distilbert-base-uncased-finetuned-imdb	Tinchoroman	2022-07-05T19:25:58Z	3	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-05T13:43:17Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Tinchoroman/distilbert-base-uncased-finetuned-imdb 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. --> # Tinchoroman/distilbert-base-uncased-finetuned-imdb 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: 2.8509 - Validation Loss: 2.5629 - 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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 2.8509 \| 2.5629 \| 0 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
osanseviero/ppo-LunarLander-v2	osanseviero	2022-07-05T19:07:18Z	4	1	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-03-02T23:29:05Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -580.22 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
coledie/reinforce-Pixelcopter-PLE-v0	coledie	2022-07-05T18:37:39Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T18:04:20Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-Pixelcopter-PLE-v0 results: - metrics: - type: mean_reward value: 17.20 +/- 18.85 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 --- # 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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
osanseviero/tipsuhtxfu-sex-classification	osanseviero	2022-07-05T17:18:06Z	0	0	sklearn	[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]	tabular-classification	2022-07-05T17:18:04Z	--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on tipsuhtxfu to apply classification on sex Metrics of the best model: accuracy 0.647364 average_precision 0.507660 roc_auc 0.625546 recall_macro 0.589832 f1_macro 0.585292 Name: MultinomialNB(), dtype: float64 See model plot below: <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div> Disclaimer:* This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). Logs of training including the models tried in the process can be found in logs.txt
abhishek/autotrain-adult-census-xgboost	abhishek	2022-07-05T17:14:07Z	28	3	transformers	[ "transformers", "joblib", "xgboost", "autotrain", "tabular", "classification", "tabular-classification", "dataset:abhishek/autotrain-data-adult-train", "dataset:scikit-learn/adult-census-income", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	tabular-classification	2022-07-05T12:06:35Z	--- tags: - autotrain - tabular - classification - tabular-classification datasets: - abhishek/autotrain-data-adult-train - scikit-learn/adult-census-income co2_eq_emissions: 0.12693590577861977 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 9725286 - CO2 Emissions (in grams): 0.12693590577861977 ## Validation Metrics - Loss: 0.26716182056213406 - Accuracy: 0.8750191923844618 - Precision: 0.7840481565086531 - Recall: 0.6641172721478649 - AUC: 0.9345322809861784 - F1: 0.7191166321601105 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```
Eleven/xlm-roberta-base-finetuned-panx-it	Eleven	2022-07-05T16:53:50Z	4	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-05T16:37:09Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8247845711940912 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2421 - F1: 0.8248 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.809 \| 1.0 \| 70 \| 0.3380 \| 0.7183 \| \| 0.2939 \| 2.0 \| 140 \| 0.2582 \| 0.7977 \| \| 0.1813 \| 3.0 \| 210 \| 0.2421 \| 0.8248 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
a-doering/MLAgents-Pyramids	a-doering	2022-07-05T16:49:09Z	18	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-07-05T16:49:02Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: a-doering/MLAgents-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Eleven/xlm-roberta-base-finetuned-panx-fr	Eleven	2022-07-05T16:36:53Z	4	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-05T16:20:12Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.835464333781965 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2867 - F1: 0.8355 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5817 \| 1.0 \| 191 \| 0.3395 \| 0.7854 \| \| 0.2617 \| 2.0 \| 382 \| 0.2856 \| 0.8278 \| \| 0.1708 \| 3.0 \| 573 \| 0.2867 \| 0.8355 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Eleven/xlm-roberta-base-finetuned-panx-de-fr	Eleven	2022-07-05T15:59:42Z	6	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-05T15:37:17Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1644 - F1: 0.8617 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2891 \| 1.0 \| 715 \| 0.1780 \| 0.8288 \| \| 0.1471 \| 2.0 \| 1430 \| 0.1627 \| 0.8509 \| \| 0.0947 \| 3.0 \| 2145 \| 0.1644 \| 0.8617 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
enoriega/rule_learning_margin_1mm_spanpred_nospec	enoriega	2022-07-05T13:56:15Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "generated_from_trainer", "dataset:enoriega/odinsynth_dataset", "endpoints_compatible", "region:us" ]	null	2022-07-05T03:00:49Z	--- tags: - generated_from_trainer datasets: - enoriega/odinsynth_dataset model-index: - name: rule_learning_margin_1mm_spanpred_nospec 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. --> # rule_learning_margin_1mm_spanpred_nospec This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.3972 - Margin Accuracy: 0.8136 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2000 - total_train_batch_size: 8000 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Margin Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------------:\| \| 0.5864 \| 0.16 \| 20 \| 0.5454 \| 0.7564 \| \| 0.4995 \| 0.32 \| 40 \| 0.4761 \| 0.7867 \| \| 0.4866 \| 0.48 \| 60 \| 0.4353 \| 0.8057 \| \| 0.4568 \| 0.64 \| 80 \| 0.4229 \| 0.8098 \| \| 0.4409 \| 0.8 \| 100 \| 0.4136 \| 0.8140 \| \| 0.4369 \| 0.96 \| 120 \| 0.4124 \| 0.8118 \| \| 0.4172 \| 1.12 \| 140 \| 0.4043 \| 0.8118 \| \| 0.4208 \| 1.28 \| 160 \| 0.4072 \| 0.8119 \| \| 0.4256 \| 1.44 \| 180 \| 0.4041 \| 0.8124 \| \| 0.4201 \| 1.6 \| 200 \| 0.4041 \| 0.8127 \| \| 0.4159 \| 1.76 \| 220 \| 0.4006 \| 0.8125 \| \| 0.4103 \| 1.92 \| 240 \| 0.4004 \| 0.8131 \| \| 0.4282 \| 2.08 \| 260 \| 0.3999 \| 0.8138 \| \| 0.4169 \| 2.24 \| 280 \| 0.4006 \| 0.8136 \| \| 0.4263 \| 2.4 \| 300 \| 0.3962 \| 0.8133 \| \| 0.4252 \| 2.56 \| 320 \| 0.3994 \| 0.8137 \| \| 0.4202 \| 2.72 \| 340 \| 0.3965 \| 0.8137 \| \| 0.4146 \| 2.88 \| 360 \| 0.3967 \| 0.8139 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.1 - Tokenizers 0.12.1
ramonzaca/dqn-SpaceInvadersNoFrameskip-v4	ramonzaca	2022-07-05T13:32:19Z	5	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T13:31:59Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 480.00 +/- 135.11 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ramonzaca -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ramonzaca ``` ## 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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
amyeroberts/resnet-18-finetuned-eurosat	amyeroberts	2022-07-05T12:36:20Z	51	0	transformers	[ "transformers", "tf", "tensorboard", "resnet", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-05T12:25:12Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: amyeroberts/resnet-18-finetuned-eurosat 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. --> # amyeroberts/resnet-18-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5582 - Validation Loss: 2.1533 - Validation Accuracy: 0.2059 - 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': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:---------------:\|:-------------------:\|:-----:\| \| 3.0662 \| 2.7376 \| 0.1374 \| 0 \| \| 1.3977 \| 2.3876 \| 0.1685 \| 1 \| \| 0.5582 \| 2.1533 \| 0.2059 \| 2 \| ### Framework versions - Transformers 4.21.0.dev0 - TensorFlow 2.9.1 - Datasets 2.3.3.dev0 - Tokenizers 0.11.0
datien228/distilbart-ftn-wiki_lingua	datien228	2022-07-05T12:12:07Z	14	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "en", "dataset:wiki_lingua", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-07-03T16:21:47Z	--- language: - en tags: - summarization license: mit datasets: - wiki_lingua metrics: - rouge --- #### Pre-trained BART Model fine-tune on WikiLingua dataset The repository for the fine-tuned BART model (by sshleifer) using the wiki_lingua dataset (English) Purpose: Examine the performance of a fine-tuned model research purposes Observation: - Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary - Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short - In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence - Some data pre-processing and models' hyperparameter are also need to be tuned more properly.
arashba/xlm-roberta-base-finetuned-panx-de	arashba	2022-07-05T12:05:52Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-05T11:41:42Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2575 \| 1.0 \| 525 \| 0.1621 \| 0.8292 \| \| 0.1287 \| 2.0 \| 1050 \| 0.1378 \| 0.8526 \| \| 0.0831 \| 3.0 \| 1575 \| 0.1372 \| 0.8621 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
abhishek/autotrain-iris-knn	abhishek	2022-07-05T11:59:16Z	9	0	transformers	[ "transformers", "joblib", "knn", "autotrain", "tabular", "classification", "tabular-classification", "dataset:abhishek/autotrain-data-iris-train", "dataset:scikit-learn/iris", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	tabular-classification	2022-07-05T11:37:31Z	--- tags: - autotrain - tabular - classification - tabular-classification datasets: - abhishek/autotrain-data-iris-train - scikit-learn/iris co2_eq_emissions: 0.15028701199056024 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 9705277 - CO2 Emissions (in grams): 0.15028701199056024 ## Validation Metrics - Loss: 0.15622713916762193 - Accuracy: 0.9 - Macro F1: 0.899749373433584 - Micro F1: 0.9 - Weighted F1: 0.8997493734335841 - Macro Precision: 0.9023569023569024 - Micro Precision: 0.9 - Weighted Precision: 0.9023569023569024 - Macro Recall: 0.9 - Micro Recall: 0.9 - Weighted Recall: 0.9 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```
abhishek/autotrain-iris-logistic-regression	abhishek	2022-07-05T11:58:57Z	13	0	transformers	[ "transformers", "joblib", "logistic_regression", "autotrain", "tabular", "classification", "tabular-classification", "dataset:abhishek/autotrain-data-iris-train", "dataset:scikit-learn/iris", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	tabular-classification	2022-07-05T11:36:06Z	--- tags: - autotrain - tabular - classification - tabular-classification datasets: - abhishek/autotrain-data-iris-train - scikit-learn/iris co2_eq_emissions: 0.0006300767567816624 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 9705273 - CO2 Emissions (in grams): 0.0006300767567816624 ## Validation Metrics - Loss: 0.15987505325856152 - Accuracy: 0.9 - Macro F1: 0.899749373433584 - Micro F1: 0.9 - Weighted F1: 0.8997493734335841 - Macro Precision: 0.9023569023569024 - Micro Precision: 0.9 - Weighted Precision: 0.9023569023569025 - Macro Recall: 0.9 - Micro Recall: 0.9 - Weighted Recall: 0.9 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```
HekmatTaherinejad/swin-tiny-patch4-window7-224-finetuned-eurosat	HekmatTaherinejad	2022-07-05T09:17:32Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-05T08:15:28Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.98 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0653 - Accuracy: 0.98 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.203 \| 1.0 \| 190 \| 0.1294 \| 0.9574 \| \| 0.2017 \| 2.0 \| 380 \| 0.0773 \| 0.9763 \| \| 0.1563 \| 3.0 \| 570 \| 0.0653 \| 0.98 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
kws/ppo-LunarLander-v2	kws	2022-07-05T07:50:05Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T06:55:50Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 252.49 +/- 42.04 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
shubhamitra/TinyBERT_General_4L_312D-finetuned-toxic-classification	shubhamitra	2022-07-05T07:29:35Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-03T13:23:24Z	--- tags: - generated_from_trainer model-index: - name: TinyBERT_General_4L_312D-finetuned-toxic-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. --> # TinyBERT_General_4L_312D-finetuned-toxic-classification This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) 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: 64 - eval_batch_size: 64 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| No log \| 1.0 \| 498 \| 0.0483 \| 0.7486 \| 0.8563 \| 0.9171 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
mmeet611/finetuning-sentiment-model-3000-samples	mmeet611	2022-07-05T07:16:18Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-15T07:33:52Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8633333333333333 - name: F1 type: f1 value: 0.8628762541806019 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3052 - Accuracy: 0.8633 - F1: 0.8629 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
hsohn3/cchs-bert-event-uncased-wordlevel-block512-batch8-ep10	hsohn3	2022-07-05T06:37:28Z	4	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-05T05:33:36Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/cchs-bert-event-uncased-wordlevel-block512-batch8-ep10 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. --> # hsohn3/cchs-bert-event-uncased-wordlevel-block512-batch8-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9667 - Epoch: 9 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 4.3518 \| 0 \| \| 3.1030 \| 1 \| \| 3.0459 \| 2 \| \| 3.0120 \| 3 \| \| 2.9969 \| 4 \| \| 2.9879 \| 5 \| \| 2.9823 \| 6 \| \| 2.9811 \| 7 \| \| 2.9722 \| 8 \| \| 2.9667 \| 9 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
Samlit/rare-puppers2	Samlit	2022-07-05T06:14:13Z	54	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-05T05:49:48Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers2 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.6222222447395325 --- # rare-puppers2 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### La Goulue Toulouse-Lautrec ![La Goulue Toulouse-Lautrec](images/La_Goulue_Toulouse-Lautrec.jpg) #### Marcelle Lender Bolero ![Marcelle Lender Bolero](images/Marcelle_Lender_Bolero.jpg) #### aristide bruant Lautrec ![aristide bruant Lautrec](images/aristide_bruant_Lautrec.jpg) #### la goulue Toulouse-Lautrec ![la goulue Toulouse-Lautrec](images/la_goulue_Toulouse-Lautrec.jpg)
steven123/Check_GoodBad_Teeth	steven123	2022-07-05T03:52:40Z	126	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-05T03:52:30Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Check_GoodBad_Teeth results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # Check_GoodBad_Teeth Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Bad Teeth ![Bad Teeth](images/Bad_Teeth.jpg) #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg)
liuxuefei01/q-Taxi-v3	liuxuefei01	2022-07-05T02:35:13Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T02:35:07Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.50 +/- 2.72 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="liuxuefei01/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
liuxuefei01/q-FrozenLake-v1-4x4-noSlippery	liuxuefei01	2022-07-05T02:20:02Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T02:19:56Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="liuxuefei01/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
coledie/reinforce-CartPole-v1	coledie	2022-07-05T01:27:42Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-07-05T00:39:33Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-CartPole-v1 results: - metrics: - type: mean_reward value: 273.60 +/- 40.64 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # 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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
NAOKITY/bert-squad	NAOKITY	2022-07-05T01:05:50Z	15	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-07-04T23:36:55Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: NAOKITY/bert-squad 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. --> # NAOKITY/bert-squad This model is a fine-tuned version of [pierreguillou/bert-base-cased-squad-v1.1-portuguese](https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9778 - Validation Loss: 0.0 - Epoch: 1 ## 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': 987, '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: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.5286 \| 0.0 \| 0 \| \| 0.9778 \| 0.0 \| 1 \| ### Framework versions - Transformers 4.20.0 - TensorFlow 2.9.1 - Datasets 2.3.2 - Tokenizers 0.12.1
teven/all_bs160_allneg	teven	2022-07-05T00:14:56Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-07-05T00:14:48Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # teven/all_bs160_allneg 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('teven/all_bs160_allneg') embeddings = model.encode(sentences) print(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=teven/all_bs160_allneg) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 780828 with parameters: ``` {'batch_size': 20, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` DataLoader: `torch.utils.data.dataloader.DataLoader` of length 315504 with parameters: ``` {'batch_size': 20, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` DataLoader: `torch.utils.data.dataloader.DataLoader` of length 300017 with parameters: ``` {'batch_size': 20, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 2000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
hsohn3/mayo-bert-uncased-wordlevel-block512-ep10	hsohn3	2022-07-04T22:52:37Z	3	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-04T01:17:58Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/mayo-bert-uncased-wordlevel-block512-ep10 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. --> # hsohn3/mayo-bert-uncased-wordlevel-block512-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3171 - Epoch: 9 ## Model description - base_model: bert-base-uncased - block_size: 512 - tokenizer: ehr-bert-wordlevel-uncased ## Intended uses & limitations More information needed ## Training and evaluation data - MAYO visit-level texts ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 - mlm_probability: 0.15 - batch_size: 8 - epochs: 10 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 3.0885 \| 0 \| \| 2.8340 \| 1 \| \| 2.7975 \| 2 \| \| 2.6720 \| 3 \| \| 2.4868 \| 4 \| \| 2.1750 \| 5 \| \| 1.8143 \| 6 \| \| 1.0948 \| 7 \| \| 0.4915 \| 8 \| \| 0.3171 \| 9 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
Danitg95/autotrain-kaggle-effective-arguments-1086739296	Danitg95	2022-07-04T21:53:10Z	8	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "en", "dataset:Danitg95/autotrain-data-kaggle-effective-arguments", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-04T21:49:45Z	--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - Danitg95/autotrain-data-kaggle-effective-arguments co2_eq_emissions: 5.2497206864306065 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1086739296 - CO2 Emissions (in grams): 5.2497206864306065 ## Validation Metrics - Loss: 0.744236171245575 - Accuracy: 0.6719238613188308 - Macro F1: 0.5450301061253738 - Micro F1: 0.6719238613188308 - Weighted F1: 0.6349879540623229 - Macro Precision: 0.6691326843926052 - Micro Precision: 0.6719238613188308 - Weighted Precision: 0.6706209016443158 - Macro Recall: 0.5426627824078865 - Micro Recall: 0.6719238613188308 - Weighted Recall: 0.6719238613188308 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Danitg95/autotrain-kaggle-effective-arguments-1086739296 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Danitg95/autotrain-kaggle-effective-arguments-1086739296", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Danitg95/autotrain-kaggle-effective-arguments-1086739296", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Farshid/distilbert-base-uncased_allagree3	Farshid	2022-07-04T21:04:03Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:financial_phrasebank", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-04T17:35:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased_allagree3 results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank args: sentences_allagree metrics: - name: Accuracy type: accuracy value: 0.9778761061946902 - name: F1 type: f1 value: 0.9780006392634297 --- <!-- 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_allagree3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.0937 - Accuracy: 0.9779 - F1: 0.9780 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.6418 \| 1.0 \| 57 \| 0.3340 \| 0.8805 \| 0.8768 \| \| 0.1821 \| 2.0 \| 114 \| 0.1088 \| 0.9690 \| 0.9691 \| \| 0.0795 \| 3.0 \| 171 \| 0.0822 \| 0.9823 \| 0.9823 \| \| 0.0385 \| 4.0 \| 228 \| 0.0939 \| 0.9646 \| 0.9646 \| \| 0.0218 \| 5.0 \| 285 \| 0.1151 \| 0.9735 \| 0.9737 \| \| 0.0149 \| 6.0 \| 342 \| 0.1126 \| 0.9690 \| 0.9694 \| \| 0.006 \| 7.0 \| 399 \| 0.0989 \| 0.9779 \| 0.9780 \| \| 0.0093 \| 8.0 \| 456 \| 0.1009 \| 0.9779 \| 0.9780 \| \| 0.0063 \| 9.0 \| 513 \| 0.0899 \| 0.9779 \| 0.9780 \| \| 0.0039 \| 10.0 \| 570 \| 0.0937 \| 0.9779 \| 0.9780 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.3.2 - Tokenizers 0.12.1
samuelrince/bert-base-cased-finetuned-panx-en	samuelrince	2022-07-04T20:08:03Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-04T19:46:35Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xtreme model-index: - name: bert-base-cased-finetuned-panx-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-panx-en This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2478 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2941 \| 1.0 \| 1250 \| 0.2432 \| \| 0.186 \| 2.0 \| 2500 \| 0.2214 \| \| 0.1387 \| 3.0 \| 3750 \| 0.2478 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ramonzaca/q-FrozenLake-v1-4x4-noSlippery	ramonzaca	2022-07-04T19:53:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-07-04T19:53:03Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ramonzaca/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
reso/DialoGPT-medium-v3ga	reso	2022-07-04T19:39:14Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-07-04T18:49:13Z	--- thumbnail: https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png tags: - conversational license: mit --- # DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script). I built a Discord AI chatbot based on this model. [Check out my GitHub repo.](https://github.com/RuolinZheng08/twewy-discord-chatbot) Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```
pcuenq/lpips-jax	pcuenq	2022-07-04T18:47:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2022-07-04T18:24:46Z	--- license: apache-2.0 --- ## Weights for JAX/Flax version of VGG - VGG16 weights, taken from [the `flaxmodels` repo](https://github.com/matthias-wright/flaxmodels/blob/main/flaxmodels/vgg/vgg.py). - Additional weights to use VGG16 as a feature extractor for LPIPS. They were downloaded in PyTorch format from [the URL referenced in the Taming Transformers repo](https://github.com/CompVis/taming-transformers/blob/master/taming/modules/losses/lpips.py), and converted to hdf5 format. ## License Apache 2, for this compilation. Please, refer to the original licenses of the source repos. - [Taming Transformers License](https://github.com/CompVis/taming-transformers/blob/master/License.txt). Weights for additional layers. - [Perceptual Similarity License](https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE). Weights for additional layers. - [Flaxmodels / VGG License](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/vgg#license), for the VGG model and (I presume) VGG weights.
YKXBCi/vit-base-patch16-224-in21k-aidSat	YKXBCi	2022-07-04T18:46:44Z	29	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-04T13:39:01Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: YKXBCi/vit-base-patch16-224-in21k-aidSat 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. --> # YKXBCi/vit-base-patch16-224-in21k-aidSat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4026 - Train Accuracy: 0.9981 - Train Top-3-accuracy: 0.9998 - Validation Loss: 0.4715 - Validation Accuracy: 0.9796 - Validation Top-3-accuracy: 0.9980 - Epoch: 4 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1325, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 2.3544 \| 0.7383 \| 0.8687 \| 1.5415 \| 0.9266 \| 0.9857 \| 0 \| \| 1.1313 \| 0.9522 \| 0.9942 \| 0.8788 \| 0.9613 \| 0.9966 \| 1 \| \| 0.6741 \| 0.9841 \| 0.9985 \| 0.6268 \| 0.9640 \| 0.9986 \| 2 \| \| 0.4785 \| 0.9953 \| 0.9995 \| 0.5058 \| 0.9755 \| 0.9980 \| 3 \| \| 0.4026 \| 0.9981 \| 0.9998 \| 0.4715 \| 0.9796 \| 0.9980 \| 4 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.0 - Datasets 2.1.0 - Tokenizers 0.12.1

pmch/fgflex

pmch

2022-07-07T14:15:07Z

0

null

[ "region:us" ]

null

2022-07-07T10:53:17Z

# FgFlex: A flexible multitasking sequence-labeler for fine-grained sentiment analysis

cherrypaca/puppies_classify

cherrypaca

2022-07-07T13:25:43Z

81

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-07-07T13:25:31Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: puppies_classify results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9701492786407471 --- # puppies_classify Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### husky ![husky](images/husky.jpg) #### pomeranian ![pomeranian](images/pomeranian.jpg)

dminiotas05/distilbert-base-uncased-finetuned-ft500_6class600

dminiotas05

2022-07-07T13:23:59Z

3

0

transformers

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

text-classification

2022-07-07T12:40:35Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-ft500_6class600 results: [] ---  # distilbert-base-uncased-finetuned-ft500_6class600 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: 1.6317 - Accuracy: 0.35 - F1: 0.3327 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.5717 | 1.0 | 188 | 1.5375 | 0.3067 | 0.2820 | | 1.4338 | 2.0 | 376 | 1.5354 | 0.3207 | 0.2824 | | 1.3516 | 3.0 | 564 | 1.4852 | 0.3573 | 0.3287 | | 1.2722 | 4.0 | 752 | 1.4997 | 0.366 | 0.3534 | | 1.1923 | 5.0 | 940 | 1.5474 | 0.362 | 0.3454 | | 1.1156 | 6.0 | 1128 | 1.5998 | 0.3547 | 0.3387 | | 1.0522 | 7.0 | 1316 | 1.6154 | 0.3473 | 0.3316 | | 1.0148 | 8.0 | 1504 | 1.6317 | 0.35 | 0.3327 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

kabelomalapane/En-Nso

kabelomalapane

2022-07-07T13:11:05Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-07-07T11:32:38Z

--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: En-Nso results: [] ---  # En-Nso This model is a fine-tuned version of [kabelomalapane/en_nso_ukuxhumana_model](https://huggingface.co/kabelomalapane/en_nso_ukuxhumana_model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9067 - Bleu: 23.5436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - 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 | Bleu | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.0 | 14 | 3.7614 | 8.0360 | | No log | 2.0 | 28 | 3.3181 | 20.7201 | | No log | 3.0 | 42 | 3.1627 | 21.5932 | | No log | 4.0 | 56 | 3.0935 | 22.0268 | | No log | 5.0 | 70 | 3.0227 | 21.0859 | | No log | 6.0 | 84 | 2.9740 | 21.6963 | | No log | 7.0 | 98 | 2.9419 | 23.2214 | | No log | 8.0 | 112 | 2.9227 | 24.4649 | | No log | 9.0 | 126 | 2.9102 | 23.5293 | | No log | 10.0 | 140 | 2.9067 | 23.5516 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07

Zengwei

2022-07-07T13:03:44Z

0

null

[ "tensorboard", "region:us" ]

null

2022-07-07T07:51:32Z

Introduction See https://github.com/k2-fsa/icefall/pull/330 and https://github.com/k2-fsa/icefall/pull/452 It has random combiner inside. Note: There is something wrong in the log file, which has been fixed in https://github.com/k2-fsa/icefall/pull/468.

Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-B-2022-07-07

Zengwei

2022-07-07T12:34:11Z

0

null

[ "tensorboard", "region:us" ]

null

2022-07-07T09:00:28Z

Introduction See https://github.com/k2-fsa/icefall/pull/330 and https://github.com/k2-fsa/icefall/pull/452 It has random combiner inside.

Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-07-07

Zengwei

2022-07-07T12:30:37Z

0

null

[ "tensorboard", "region:us" ]

null

2022-07-07T10:17:44Z

Introduction See https://github.com/k2-fsa/icefall/pull/330 and https://github.com/k2-fsa/icefall/pull/452 It has random combiner inside.

paola-md/recipe-roberta-is

paola-md

2022-07-07T11:53:27Z

3

0

transformers

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

fill-mask

2022-07-07T08:40:25Z

--- license: mit tags: - generated_from_trainer model-index: - name: recipe-roberta-is results: [] ---  # recipe-roberta-is This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8382 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.334 | 1.0 | 961 | 1.1217 | | 1.1638 | 2.0 | 1922 | 1.0369 | | 1.0936 | 3.0 | 2883 | 0.9922 | | 1.0503 | 4.0 | 3844 | 0.9606 | | 1.0188 | 5.0 | 4805 | 0.9314 | | 0.9953 | 6.0 | 5766 | 0.9256 | | 0.9769 | 7.0 | 6727 | 0.9109 | | 0.9599 | 8.0 | 7688 | 0.8978 | | 0.9461 | 9.0 | 8649 | 0.8813 | | 0.9377 | 10.0 | 9610 | 0.8777 | | 0.9253 | 11.0 | 10571 | 0.8755 | | 0.918 | 12.0 | 11532 | 0.8601 | | 0.9112 | 13.0 | 12493 | 0.8541 | | 0.9043 | 14.0 | 13454 | 0.8548 | | 0.8984 | 15.0 | 14415 | 0.8470 | | 0.8958 | 16.0 | 15376 | 0.8412 | | 0.8914 | 17.0 | 16337 | 0.8345 | | 0.8882 | 18.0 | 17298 | 0.8353 | | 0.8871 | 19.0 | 18259 | 0.8344 | | 0.8839 | 20.0 | 19220 | 0.8382 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

dminiotas05/distilbert-base-uncased-finetuned-ft500_6class

dminiotas05

2022-07-07T11:11:18Z

6

0

transformers

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

text-classification

2022-07-07T10:45:38Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-ft500_6class results: [] ---  # distilbert-base-uncased-finetuned-ft500_6class 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: 1.5162 - Accuracy: 0.356 - F1: 0.3347 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.579 | 1.0 | 188 | 1.5575 | 0.2933 | 0.2521 | | 1.4527 | 2.0 | 376 | 1.5043 | 0.3227 | 0.2821 | | 1.3767 | 3.0 | 564 | 1.4982 | 0.34 | 0.2938 | | 1.3122 | 4.0 | 752 | 1.4784 | 0.368 | 0.3454 | | 1.2678 | 5.0 | 940 | 1.5162 | 0.356 | 0.3347 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178

zhifei

2022-07-07T10:49:19Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "unk", "dataset:zhifei/autotrain-data-autotrain-chinese-title-summarization-9", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-07-07T10:48:04Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - zhifei/autotrain-data-autotrain-chinese-title-summarization-9 co2_eq_emissions: 1.565396518204961 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1101340178 - CO2 Emissions (in grams): 1.565396518204961 ## Validation Metrics - Loss: 0.00012778821110259742 - Rouge1: 29.2308 - Rouge2: 0.0 - RougeL: 29.2308 - RougeLsum: 29.2308 - Gen Len: 18.4462 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178 ```

Fulccrum/trainii_ac94u-label-classification

Fulccrum

2022-07-07T10:48:17Z

0

sklearn

[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]

tabular-classification

2022-07-07T10:48:16Z

--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on trainii_ac94u to apply classification on label **Metrics of the best model:** accuracy 0.361046 recall_macro 0.353192 precision_macro 0.240667 f1_macro 0.278231 Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64 **See model plot below:** <style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-9" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False text False False False ... False True False[2 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

TestZee/t5-small-finetuned-custom-wion-test-BIG

TestZee

2022-07-07T10:31:54Z

5

0

transformers

[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-07-07T10:30:30Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TestZee/t5-small-finetuned-custom-wion-test-BIG results: [] ---  # TestZee/t5-small-finetuned-custom-wion-test-BIG This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1165 - Validation Loss: 0.4609 - Epoch: 29 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9622 | 0.8875 | 0 | | 1.9276 | 0.8601 | 1 | | 1.8301 | 0.8342 | 2 | | 1.7776 | 0.8104 | 3 | | 1.7345 | 0.7878 | 4 | | 1.7733 | 0.7660 | 5 | | 1.5626 | 0.7448 | 6 | | 1.6111 | 0.7245 | 7 | | 1.6754 | 0.7050 | 8 | | 1.5030 | 0.6867 | 9 | | 1.5101 | 0.6696 | 10 | | 1.4328 | 0.6536 | 11 | | 1.4311 | 0.6383 | 12 | | 1.3917 | 0.6232 | 13 | | 1.4102 | 0.6071 | 14 | | 1.3732 | 0.5948 | 15 | | 1.3468 | 0.5828 | 16 | | 1.2817 | 0.5712 | 17 | | 1.2920 | 0.5600 | 18 | | 1.2696 | 0.5491 | 19 | | 1.2552 | 0.5385 | 20 | | 1.1859 | 0.5285 | 21 | | 1.1995 | 0.5188 | 22 | | 1.1690 | 0.5094 | 23 | | 1.1678 | 0.5003 | 24 | | 1.1420 | 0.4916 | 25 | | 1.0959 | 0.4830 | 26 | | 1.0848 | 0.4750 | 27 | | 1.1248 | 0.4677 | 28 | | 1.1165 | 0.4609 | 29 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

hugginglearners/malayalam-blurr-xlm-roberta-base

hugginglearners

2022-07-07T10:17:28Z

0

2

fastai

[ "fastai", "text-generation", "ml", "dataset:rajeshradhakrishnan/malayalam_wiki", "region:us" ]

text-generation

2022-07-06T11:10:26Z

--- tags: - fastai - text-generation language: ml widget: - text: "ഓഹരി വിപണി തകരുമ്പോള്‍ നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം" example_title: "Malayalam Casual Language Model" datasets: - rajeshradhakrishnan/malayalam_wiki --- # Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress) [![മലയാളം: notebook](https://img.shields.io/badge/മലയാളം%20-notebook-green.svg)](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/main/ml/malayalam_blurr_xlm_roberta_base.ipynb) --- # malayalam-blurr-xlm-roberta-base (base-sized model) malayalam-blurr-xlm-roberta-base model is pre-trained on [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) using the library [blurr](https://ohmeow.github.io/blurr/) Language Model using fastai x huggingface frameworks. Ref: [Causal Language Modeling](https://ohmeow.github.io/blurr/text-modeling-language-modeling.html#Causal-language-modeling). ## Usage ``` !pip install -Uqq huggingface_hub["fastai"] ohmeow-blurr from huggingface_hub import from_pretrained_fastai learner = from_pretrained_fastai(repo_id) learner.blurr_generate("ബ്‌ളൂർ പഠിക്കാൻ വളെരെ എളുപ്പമാണ് എന്തുകൊണ്ട് എന്നാൽ", max_length=50, do_sample=True, top_k=25) ``` ## Intended uses & limitations It's not fine tuned to the state of the art accuracy ## Training and evaluation data [Wiki 2020 Malayalam Dataset ](https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_wiki)

osanseviero/ppo-LunarLander-v10

osanseviero

2022-07-07T09:42:36Z

0

stable-baselines3

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

reinforcement-learning

2022-07-07T09:38:00Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -574.85 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

huggingtweets/marsajal

huggingtweets

2022-07-07T09:42:16Z

4

0

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/marsajal/1657186931820/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1463196823728771079/wZc0m7cd_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ajeng🦦</div> <div style="text-align: center; font-size: 14px;">@marsajal</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ajeng🦦. | Data | ajeng🦦 | | --- | --- | | Tweets downloaded | 214 | | Retweets | 37 | | Short tweets | 41 | | Tweets kept | 136 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kdiymty/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @marsajal's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lfk0v9ey) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lfk0v9ey/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/marsajal') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

osanseviero/ppo-LunarLander-v9

osanseviero

2022-07-07T09:37:00Z

0

stable-baselines3

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

reinforcement-learning

2022-07-07T09:36:34Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -30.40 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

osanseviero/ppo-LunarLander-v6

osanseviero

2022-07-07T09:29:20Z

0

stable-baselines3

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

reinforcement-learning

2022-07-07T09:07:08Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -443.18 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53

gary109

2022-07-07T09:10:42Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-07-01T03:42:00Z

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 results: [] ---  # ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset. It achieves the following results on the evaluation set: - Loss: 0.8797 - Wer: 0.5513 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.9613 | 1.0 | 2309 | 1.0171 | 0.7271 | | 0.8254 | 2.0 | 4618 | 0.9771 | 0.6650 | | 0.7406 | 3.0 | 6927 | 0.9174 | 0.6420 | | 0.74 | 4.0 | 9236 | 0.9551 | 0.6371 | | 0.5855 | 5.0 | 11545 | 0.9262 | 0.6453 | | 0.5536 | 6.0 | 13854 | 0.9056 | 0.5894 | | 0.505 | 7.0 | 16163 | 0.9166 | 0.6029 | | 0.449 | 8.0 | 18472 | 0.8816 | 0.5873 | | 0.4219 | 9.0 | 20781 | 0.8970 | 0.5589 | | 0.5764 | 10.0 | 23090 | 0.9189 | 0.5649 | | 0.5075 | 11.0 | 25399 | 0.8797 | 0.5513 | | 0.4366 | 12.0 | 27708 | 0.9011 | 0.5567 | | 0.4915 | 13.0 | 30017 | 0.9248 | 0.5455 | | 0.3554 | 14.0 | 32326 | 0.9309 | 0.5374 | | 0.3975 | 15.0 | 34635 | 0.9103 | 0.5259 | | 0.4119 | 16.0 | 36944 | 0.9402 | 0.5290 | | 0.267 | 17.0 | 39253 | 0.9479 | 0.5115 | | 0.3107 | 18.0 | 41562 | 0.9428 | 0.5099 | | 0.2684 | 19.0 | 43871 | 0.9508 | 0.5133 | | 0.2125 | 20.0 | 46180 | 0.9737 | 0.5097 | | 0.3149 | 21.0 | 48489 | 0.9992 | 0.5095 | | 0.2313 | 22.0 | 50798 | 1.0037 | 0.5059 | | 0.2674 | 23.0 | 53107 | 1.0091 | 0.5040 | | 0.2056 | 24.0 | 55416 | 1.0082 | 0.5076 | | 0.2781 | 25.0 | 57725 | 1.0160 | 0.5015 | | 0.2005 | 26.0 | 60034 | 1.0390 | 0.5131 | | 0.2221 | 27.0 | 62343 | 1.0401 | 0.5074 | | 0.1857 | 28.0 | 64652 | 1.0484 | 0.5096 | | 0.1562 | 29.0 | 66961 | 1.0516 | 0.5064 | | 0.3027 | 30.0 | 69270 | 1.0543 | 0.5049 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

m-newhauser/distilbert-political-tweets

m-newhauser

2022-07-07T09:07:44Z

75

23

transformers

[ "transformers", "pytorch", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "en", "dataset:m-newhauser/senator-tweets", "license:lgpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- language: - en license: lgpl-3.0 library_name: transformers tags: - text-classification - transformers - pytorch - generated_from_keras_callback metrics: - accuracy - f1 datasets: - m-newhauser/senator-tweets widget: - text: "This pandemic has shown us clearly the vulgarity of our healthcare system. Highest costs in the world, yet not enough nurses or doctors. Many millions uninsured, while insurance company profits soar. The struggle continues. Healthcare is a human right. Medicare for all." example_title: "Bernie Sanders (D)" - text: "Team Biden would rather fund the Ayatollah's Death to America regime than allow Americans to produce energy for our own domestic consumption." example_title: "Ted Cruz (R)" --- # distilbert-political-tweets 🗣 🇺🇸 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [m-newhauser/senator-tweets](https://huggingface.co/datasets/m-newhauser/senator-tweets) dataset, which contains all tweets made by United States senators during the first year of the Biden Administration. It achieves the following results on the evaluation set: * Accuracy: 0.9076 * F1: 0.9117 ## Model description The goal of this model is to classify short pieces of text as having either Democratic or Republican sentiment. The model was fine-tuned on 99,693 tweets (51.6% Democrat, 48.4% Republican) made by US senators in 2021. Model accuracy may not hold up on pieces of text longer than a tweet. ### Training hyperparameters The following hyperparameters were used during training: - optimizer: Adam - training_precision: float32 - learning_rate = 5e-5 - num_epochs = 5 ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.6

osanseviero/ppo-LunarLander-v5

osanseviero

2022-07-07T08:59:26Z

0

stable-baselines3

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

reinforcement-learning

2022-07-07T08:47:49Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -479.21 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

osanseviero/ppo-LunarLander-v4

osanseviero

2022-07-07T08:47:35Z

0

stable-baselines3

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

reinforcement-learning

2022-07-05T19:12:02Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -247.76 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100

hsohn3

2022-07-07T08:33:59Z

3

0

transformers

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

fill-mask

2022-07-06T16:29:49Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100 results: [] ---  # hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep100 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9559 - Epoch: 99 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.1247 | 0 | | 3.5129 | 1 | | 3.4726 | 2 | | 3.4483 | 3 | | 3.4395 | 4 | | 3.4301 | 5 | | 3.4260 | 6 | | 3.4131 | 7 | | 3.3831 | 8 | | 3.2925 | 9 | | 3.2454 | 10 | | 3.2092 | 11 | | 3.1695 | 12 | | 3.1346 | 13 | | 3.0797 | 14 | | 3.0154 | 15 | | 2.9557 | 16 | | 2.8814 | 17 | | 2.7720 | 18 | | 2.5472 | 19 | | 2.3193 | 20 | | 2.1005 | 21 | | 1.9331 | 22 | | 1.7971 | 23 | | 1.6859 | 24 | | 1.6062 | 25 | | 1.5310 | 26 | | 1.4706 | 27 | | 1.4203 | 28 | | 1.3681 | 29 | | 1.3222 | 30 | | 1.2939 | 31 | | 1.2726 | 32 | | 1.2494 | 33 | | 1.2330 | 34 | | 1.2161 | 35 | | 1.1998 | 36 | | 1.1874 | 37 | | 1.1767 | 38 | | 1.1641 | 39 | | 1.1550 | 40 | | 1.1407 | 41 | | 1.1363 | 42 | | 1.1272 | 43 | | 1.1227 | 44 | | 1.1163 | 45 | | 1.1065 | 46 | | 1.1008 | 47 | | 1.0957 | 48 | | 1.0837 | 49 | | 1.0844 | 50 | | 1.0778 | 51 | | 1.0741 | 52 | | 1.0693 | 53 | | 1.0662 | 54 | | 1.0608 | 55 | | 1.0521 | 56 | | 1.0526 | 57 | | 1.0476 | 58 | | 1.0454 | 59 | | 1.0452 | 60 | | 1.0348 | 61 | | 1.0333 | 62 | | 1.0342 | 63 | | 1.0293 | 64 | | 1.0249 | 65 | | 1.0241 | 66 | | 1.0194 | 67 | | 1.0177 | 68 | | 1.0102 | 69 | | 1.0055 | 70 | | 1.0052 | 71 | | 1.0038 | 72 | | 1.0005 | 73 | | 0.9981 | 74 | | 0.9991 | 75 | | 0.9950 | 76 | | 0.9928 | 77 | | 0.9898 | 78 | | 0.9906 | 79 | | 0.9873 | 80 | | 0.9849 | 81 | | 0.9808 | 82 | | 0.9804 | 83 | | 0.9792 | 84 | | 0.9789 | 85 | | 0.9797 | 86 | | 0.9741 | 87 | | 0.9781 | 88 | | 0.9678 | 89 | | 0.9686 | 90 | | 0.9651 | 91 | | 0.9652 | 92 | | 0.9613 | 93 | | 0.9599 | 94 | | 0.9566 | 95 | | 0.9571 | 96 | | 0.9577 | 97 | | 0.9536 | 98 | | 0.9559 | 99 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

avichr/Legal-heBERT_ft

avichr

2022-07-07T07:31:58Z

28

3

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "arxiv:1911.03090", "arxiv:2010.02559", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-05-05T06:49:36Z

# Legal-HeBERT Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br> We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools. ## Training Data Our training datasets are: | Name | Hebrew Description | Size (GB) | Documents | Sentences | Words | Notes | |----------------------------------------------------------------------------------------------------------------------------------- |-------------------------------------------------------------------------- |----------- |----------- |------------ |------------- |----------------------------------------- | | The Israeli Law Book | ספר החוקים הישראלי | 0.05 | 2338 | 293352 | 4851063 | | | Judgments of the Supreme Court | מאגר פסקי הדין של בית המשפט העליון | 0.7 | 212348 | 5790138 | 79672415 | | | custody courts | החלטות בתי הדין למשמורת | 2.46 | 169,708 | 8,555,893 | 213,050,492 | | | Law memoranda, drafts of secondary legislation and drafts of support tests that have been distributed to the public for comment | תזכירי חוק, טיוטות חקיקת משנה וטיוטות מבחני תמיכה שהופצו להערות הציבור | 0.4 | 3,291 | 294,752 | 7,218,960 | | | Supervisors of Land Registration judgments | מאגר פסקי דין של המפקחים על רישום המקרקעין | 0.02 | 559 | 67,639 | 1,785,446 | | | Decisions of the Labor Court - Corona | מאגר החלטות בית הדין לעניין שירות התעסוקה – קורונה | 0.001 | 146 | 3505 | 60195 | | | Decisions of the Israel Lands Council | החלטות מועצת מקרקעי ישראל | | 118 | 11283 | 162692 | aggregate file | | Judgments of the Disciplinary Tribunal and the Israel Police Appeals Tribunal | פסקי דין של בית הדין למשמעת ובית הדין לערעורים של משטרת ישראל | 0.02 | 54 | 83724 | 1743419 | aggregate files | | Disciplinary Appeals Committee in the Ministry of Health | ועדת ערר לדין משמעתי במשרד הבריאות | 0.004 | 252 | 21010 | 429807 | 465 files are scanned and didn't parser | | Attorney General's Positions | מאגר התייצבויות היועץ המשפטי לממשלה | 0.008 | 281 | 32724 | 813877 | | | Legal-Opinion of the Attorney General | מאגר חוות דעת היועץ המשפטי לממשלה | 0.002 | 44 | 7132 | 188053 | | | | | | | | | | | total | | 3.665 | 389,139 | 15,161,152 | 309,976,419 | | We thank <b>Yair Gardin</b> for the referring to the governance data, <b>Elhanan Schwarts</b> for collecting and parsing The Israeli law book, and <b>Jonathan Schler</b> for collecting the judgments of the supreme court. ## Training process * Vocabulary size: 50,000 tokens * 4 epochs (1M steps±) * lr=5e-5 * mlm_probability=0.15 * batch size = 32 (for each gpu) * NVIDIA GeForce RTX 2080 TI + NVIDIA GeForce RTX 3090 (1 week training) ### Additional training settings: <b>Fine-tuned [HeBERT](https://github.com/avichaychriqui/HeBERT) model:</b> The first eight layers were freezed (like [Lee et al. (2019)](https://arxiv.org/abs/1911.03090) suggest)<br> <b>Legal-HeBERT trained from scratch:</b> The training process is similar to [HeBERT](https://github.com/avichaychriqui/HeBERT) and inspired by [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559) <br> ## How to use The models can be found in huggingface hub and can be fine-tunned to any down-stream task: ``` # !pip install transformers==4.14.1 from transformers import AutoTokenizer, AutoModel model_name = 'avichr/Legal-heBERT_ft' # for the fine-tuned HeBERT model model_name = 'avichr/Legal-heBERT' # for legal HeBERT model trained from scratch tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) from transformers import pipeline fill_mask = pipeline( "fill-mask", model=model_name, ) fill_mask("הקורונה לקחה את [MASK] ולנו לא נשאר דבר.") ``` ## Stay tuned! We are still working on our models and the datasets. We will edit this page as we progress. We are open for collaborations. ## If you used this model please cite us as : Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai, Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts (June 27, 2022). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4147127 ``` @article{chriqui2021hebert, title={Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts}, author={Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai}, journal={SSRN preprint:4147127}, year={2022} } ``` ## Contact us [Avichay Chriqui](mailto:avichayc@mail.tau.ac.il), The Coller AI Lab <br> [Inbal yahav](mailto:inbalyahav@tauex.tau.ac.il), The Coller AI Lab <br> [Ittai Bar-Siman-Tov](mailto:Ittai.Bar-Siman-Tov@biu.ac.il), the BIU Innovation Lab for Law, Data-Science and Digital Ethics <br> Thank you, תודה, شكرا <br>

ScarlettSun9/autotrain-ZuoZhuan-1100540141

ScarlettSun9

2022-07-07T07:08:04Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "token-classification", "autotrain", "unk", "dataset:ScarlettSun9/autotrain-data-ZuoZhuan", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-07T07:02:53Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ScarlettSun9/autotrain-data-ZuoZhuan co2_eq_emissions: 8.343592303925112 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1100540141 - CO2 Emissions (in grams): 8.343592303925112 ## Validation Metrics - Loss: 0.38094884157180786 - Accuracy: 0.8795777325860159 - Precision: 0.8171375141922127 - Recall: 0.8417033571821684 - F1: 0.8292385373953709 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ScarlettSun9/autotrain-ZuoZhuan-1100540141 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("ScarlettSun9/autotrain-ZuoZhuan-1100540141", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

go2k/q-Taxi-v3

go2k

2022-07-07T05:45:11Z

0

null

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

reinforcement-learning

2022-07-07T05:39:36Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="go2k/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

go2k/q-FrozenLake-v1-4x4-noSlippery

go2k

2022-07-07T05:26:00Z

0

null

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

reinforcement-learning

2022-07-07T05:25:54Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="go2k/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

Evelyn18/distilbert-base-uncased-becasv2-6

Evelyn18

2022-07-07T04:44:16Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-07-07T04:39:39Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-6 results: [] ---  # distilbert-base-uncased-becasv2-6 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.8936 ## 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: 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.0542 | | No log | 2.0 | 18 | 3.0865 | | No log | 3.0 | 27 | 2.8069 | | No log | 4.0 | 36 | 3.3330 | | No log | 5.0 | 45 | 3.4108 | | No log | 6.0 | 54 | 3.5562 | | No log | 7.0 | 63 | 3.8846 | | No log | 8.0 | 72 | 3.8936 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Evelyn18/distilbert-base-uncased-becasv2-3

Evelyn18

2022-07-07T04:00:45Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-07-07T03:55:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-3 results: [] ---  # distilbert-base-uncased-becasv2-3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.1218 ## 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: 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.6377 | | No log | 2.0 | 18 | 3.8511 | | No log | 3.0 | 27 | 3.3758 | | No log | 4.0 | 36 | 3.1910 | | No log | 5.0 | 45 | 3.1187 | | No log | 6.0 | 54 | 3.1009 | | No log | 7.0 | 63 | 3.1131 | | No log | 8.0 | 72 | 3.1218 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Evelyn18/distilbert-base-uncased-becasv2-2

Evelyn18

2022-07-07T03:47:53Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-07-07T03:43:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-2 results: [] ---  # distilbert-base-uncased-becasv2-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.9170 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.8334 | | No log | 2.0 | 18 | 3.9395 | | No log | 3.0 | 27 | 3.4886 | | No log | 4.0 | 36 | 3.2190 | | No log | 5.0 | 45 | 3.0781 | | No log | 6.0 | 54 | 2.9878 | | No log | 7.0 | 63 | 2.9336 | | No log | 8.0 | 72 | 2.9170 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Evelyn18/distilbert-base-uncased-becasv2-1

Evelyn18

2022-07-07T03:38:53Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:becasv2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-07-07T03:34:17Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-becasv2-1 results: [] ---  # distilbert-base-uncased-becasv2-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.9472 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.6722 | | No log | 2.0 | 18 | 3.9450 | | No log | 3.0 | 27 | 3.4890 | | No log | 4.0 | 36 | 3.2251 | | No log | 5.0 | 45 | 2.9906 | | No log | 6.0 | 54 | 3.0790 | | No log | 7.0 | 63 | 2.8791 | | No log | 8.0 | 72 | 2.9654 | | No log | 9.0 | 81 | 2.9460 | | No log | 10.0 | 90 | 2.9472 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ChauNguyen23/distilbert-base-uncased-finetuned-imdb

ChauNguyen23

2022-07-07T02:54:46Z

3

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

2022-07-07T02:48:22Z

--- 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 - mixed_precision_training: Native AMP ### 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 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

taln-ls2n/POET

taln-ls2n

2022-07-06T23:49:35Z

4

2

transformers

[ "transformers", "pytorch", "camembert", "token-classification", "Transformers", "sequence-tagger-model", "fr", "dataset:qanastek/ANTILLES", "arxiv:1911.03894", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-05-11T09:33:05Z

--- tags: - Transformers - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894) - Number of Epochs: 115 **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in HuggingFace Transformers Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` ```python from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline tokenizer = CamembertTokenizer.from_pretrained('taln-ls2n/POET') model = CamembertForTokenClassification.from_pretrained('taln-ls2n/POET') pos = TokenClassificationPipeline(model=model, tokenizer=tokenizer) def make_prediction(sentence): labels = [l['entity'] for l in pos(sentence)] return list(zip(sentence.split(" "), labels)) res = make_prediction("George Washington est allé à Washington") ``` Output: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. ## Original Tags ```plain PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ ``` ## New additional POS tags | Abbreviation | Description | Examples | |:--------:|:--------:|:--------:| | PREP | Preposition | de | | AUX | Auxiliary Verb | est | | ADV | Adverb | toujours | | COSUB | Subordinating conjunction | que | | COCO | Coordinating Conjunction | et | | PART | Demonstrative particle | -t | | PRON | Pronoun | qui ce quoi | | PDEMMS | Demonstrative Pronoun - Singular Masculine | ce | | PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux | | PDEMFS | Demonstrative Pronoun - Singular Feminine | cette | | PDEMFP | Demonstrative Pronoun - Plural Feminine | celles | | PINDMS | Indefinite Pronoun - Singular Masculine | tout | | PINDMP | Indefinite Pronoun - Plural Masculine | autres | | PINDFS | Indefinite Pronoun - Singular Feminine | chacune | | PINDFP | Indefinite Pronoun - Plural Feminine | certaines | | PROPN | Proper noun | Houston | | XFAMIL | Last name | Levy | | NUM | Numerical Adjective | trentaine vingtaine | | DINTMS | Masculine Numerical Adjective | un | | DINTFS | Feminine Numerical Adjective | une | | PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui | | PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y | | PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la | | PPOBJFP | Pronoun complements of objects - Plural Feminine | en y | | PPER1S | Personal Pronoun First-Person - Singular | je | | PPER2S | Personal Pronoun Second-Person - Singular | tu | | PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il | | PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils | | PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle | | PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles | | PREFS | Reflexive Pronoun First-Person - Singular | me m' | | PREF | Reflexive Pronoun Third-Person - Singular | se s' | | PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous | | VERB | Verb | obtient | | VPPMS | Past Participle - Singular Masculine | formulé | | VPPMP | Past Participle - Plural Masculine | classés | | VPPFS | Past Participle - Singular Feminine | appelée | | VPPFP | Past Participle - Plural Feminine | sanctionnées | | DET | Determinant | les l' | | DETMS | Determinant - Singular Masculine | les | | DETFS | Determinant - Singular Feminine | la | | ADJ | Adjective | capable sérieux | | ADJMS | Adjective - Singular Masculine | grand important | | ADJMP | Adjective - Plural Masculine | grands petits | | ADJFS | Adjective - Singular Feminine | française petite | | ADJFP | Adjective - Plural Feminine | légères petites | | NOUN | Noun | temps | | NMS | Noun - Singular Masculine | drapeau | | NMP | Noun - Plural Masculine | journalistes | | NFS | Noun - Singular Feminine | tête | | NFP | Noun - Plural Feminine | ondes | | PREL | Relative Pronoun | qui dont | | PRELMS | Relative Pronoun - Singular Masculine | lequel | | PRELMP | Relative Pronoun - Plural Masculine | lesquels | | PRELFS | Relative Pronoun - Singular Feminine | laquelle | | PRELFP | Relative Pronoun - Plural Feminine | lesquelles | | INTJ | Interjection | merci bref | | CHIF | Numbers | 1979 10 | | SYM | Symbol | € % | | YPFOR | Endpoint | . | | PUNCT | Ponctuation | : , | | MOTINC | Unknown words | Technology Lady | | X | Typos & others | sfeir 3D statu | ## Evaluation results The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```plain precision recall f1-score support ADJ 0.9040 0.8828 0.8933 128 ADJFP 0.9811 0.9585 0.9697 434 ADJFS 0.9606 0.9826 0.9715 918 ADJMP 0.9613 0.9357 0.9483 451 ADJMS 0.9561 0.9611 0.9586 952 ADV 0.9870 0.9948 0.9908 1524 AUX 0.9956 0.9964 0.9960 1124 CHIF 0.9798 0.9774 0.9786 1239 COCO 1.0000 0.9989 0.9994 884 COSUB 0.9939 0.9939 0.9939 328 DET 0.9972 0.9972 0.9972 2897 DETFS 0.9990 1.0000 0.9995 1007 DETMS 1.0000 0.9993 0.9996 1426 DINTFS 0.9967 0.9902 0.9934 306 DINTMS 0.9923 0.9948 0.9935 387 INTJ 0.8000 0.8000 0.8000 5 MOTINC 0.5049 0.5827 0.5410 266 NFP 0.9807 0.9675 0.9740 892 NFS 0.9778 0.9699 0.9738 2588 NMP 0.9687 0.9495 0.9590 1367 NMS 0.9759 0.9560 0.9659 3181 NOUN 0.6164 0.8673 0.7206 113 NUM 0.6250 0.8333 0.7143 6 PART 1.0000 0.9375 0.9677 16 PDEMFP 1.0000 1.0000 1.0000 3 PDEMFS 1.0000 1.0000 1.0000 89 PDEMMP 1.0000 1.0000 1.0000 20 PDEMMS 1.0000 1.0000 1.0000 222 PINDFP 1.0000 1.0000 1.0000 3 PINDFS 0.8571 1.0000 0.9231 12 PINDMP 0.9000 1.0000 0.9474 9 PINDMS 0.9286 0.9701 0.9489 67 PINTFS 0.0000 0.0000 0.0000 2 PPER1S 1.0000 1.0000 1.0000 62 PPER2S 0.7500 1.0000 0.8571 3 PPER3FP 1.0000 1.0000 1.0000 9 PPER3FS 1.0000 1.0000 1.0000 96 PPER3MP 1.0000 1.0000 1.0000 31 PPER3MS 1.0000 1.0000 1.0000 377 PPOBJFP 1.0000 0.7500 0.8571 4 PPOBJFS 0.9167 0.8919 0.9041 37 PPOBJMP 0.7500 0.7500 0.7500 12 PPOBJMS 0.9371 0.9640 0.9504 139 PREF 1.0000 1.0000 1.0000 332 PREFP 1.0000 1.0000 1.0000 64 PREFS 1.0000 1.0000 1.0000 13 PREL 0.9964 0.9964 0.9964 277 PRELFP 1.0000 1.0000 1.0000 5 PRELFS 0.8000 1.0000 0.8889 4 PRELMP 1.0000 1.0000 1.0000 3 PRELMS 1.0000 1.0000 1.0000 11 PREP 0.9971 0.9977 0.9974 6161 PRON 0.9836 0.9836 0.9836 61 PROPN 0.9468 0.9503 0.9486 4310 PUNCT 1.0000 1.0000 1.0000 4019 SYM 0.9394 0.8158 0.8732 76 VERB 0.9956 0.9921 0.9938 2273 VPPFP 0.9145 0.9469 0.9304 113 VPPFS 0.9562 0.9597 0.9580 273 VPPMP 0.8827 0.9728 0.9256 147 VPPMS 0.9778 0.9794 0.9786 630 VPPRE 0.0000 0.0000 0.0000 1 X 0.9604 0.9935 0.9766 1073 XFAMIL 0.9386 0.9113 0.9248 1342 YPFOR 1.0000 1.0000 1.0000 2750 accuracy 0.9778 47574 macro avg 0.9151 0.9285 0.9202 47574 weighted avg 0.9785 0.9778 0.9780 47574 ``` ## BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ``` Flair Embeddings: ```latex @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` ## Acknowledgment This work was financially supported by [Zenidoc](https://zenidoc.fr/) and the [ANR project DIETS](https://anr-diets.univ-avignon.fr) under the contract [ANR-20-CE23-0005](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-fd7e69d902/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=cb6d54d24c9e21e0d50fabf46bd56646).

qanastek/pos-french-camembert-flair

qanastek

2022-07-06T23:49:12Z

52

3

flair

[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "fr", "dataset:qanastek/ANTILLES", "arxiv:1911.03894", "arxiv:1011.4088", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- tags: - flair - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings: [Flair](https://aclanthology.org/C18-1139.pdf) & [CamemBERT](https://arxiv.org/abs/1911.03894) - Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088) - Number of Epochs: 50 **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in Flair Requires [Flair](https://pypi.org/project/flair/): ```pip install flair``` ```python from flair.data import Sentence from flair.models import SequenceTagger # Load the model model = SequenceTagger.load("qanastek/pos-french") sentence = Sentence("George Washington est allé à Washington") # predict tags model.predict(sentence) # print predicted pos tags print(sentence.to_tagged_string()) ``` Output: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. ## Original Tags ```plain PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ ``` ## New additional POS tags | Abbreviation | Description | Examples | |:--------:|:--------:|:--------:| | PREP | Preposition | de | | AUX | Auxiliary Verb | est | | ADV | Adverb | toujours | | COSUB | Subordinating conjunction | que | | COCO | Coordinating Conjunction | et | | PART | Demonstrative particle | -t | | PRON | Pronoun | qui ce quoi | | PDEMMS | Demonstrative Pronoun - Singular Masculine | ce | | PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux | | PDEMFS | Demonstrative Pronoun - Singular Feminine | cette | | PDEMFP | Demonstrative Pronoun - Plural Feminine | celles | | PINDMS | Indefinite Pronoun - Singular Masculine | tout | | PINDMP | Indefinite Pronoun - Plural Masculine | autres | | PINDFS | Indefinite Pronoun - Singular Feminine | chacune | | PINDFP | Indefinite Pronoun - Plural Feminine | certaines | | PROPN | Proper noun | Houston | | XFAMIL | Last name | Levy | | NUM | Numerical Adjective | trentaine vingtaine | | DINTMS | Masculine Numerical Adjective | un | | DINTFS | Feminine Numerical Adjective | une | | PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui | | PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y | | PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la | | PPOBJFP | Pronoun complements of objects - Plural Feminine | en y | | PPER1S | Personal Pronoun First-Person - Singular | je | | PPER2S | Personal Pronoun Second-Person - Singular | tu | | PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il | | PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils | | PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle | | PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles | | PREFS | Reflexive Pronoun First-Person - Singular | me m' | | PREF | Reflexive Pronoun Third-Person - Singular | se s' | | PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous | | VERB | Verb | obtient | | VPPMS | Past Participle - Singular Masculine | formulé | | VPPMP | Past Participle - Plural Masculine | classés | | VPPFS | Past Participle - Singular Feminine | appelée | | VPPFP | Past Participle - Plural Feminine | sanctionnées | | DET | Determinant | les l' | | DETMS | Determinant - Singular Masculine | les | | DETFS | Determinant - Singular Feminine | la | | ADJ | Adjective | capable sérieux | | ADJMS | Adjective - Singular Masculine | grand important | | ADJMP | Adjective - Plural Masculine | grands petits | | ADJFS | Adjective - Singular Feminine | française petite | | ADJFP | Adjective - Plural Feminine | légères petites | | NOUN | Noun | temps | | NMS | Noun - Singular Masculine | drapeau | | NMP | Noun - Plural Masculine | journalistes | | NFS | Noun - Singular Feminine | tête | | NFP | Noun - Plural Feminine | ondes | | PREL | Relative Pronoun | qui dont | | PRELMS | Relative Pronoun - Singular Masculine | lequel | | PRELMP | Relative Pronoun - Plural Masculine | lesquels | | PRELFS | Relative Pronoun - Singular Feminine | laquelle | | PRELFP | Relative Pronoun - Plural Feminine | lesquelles | | INTJ | Interjection | merci bref | | CHIF | Numbers | 1979 10 | | SYM | Symbol | € % | | YPFOR | Endpoint | . | | PUNCT | Ponctuation | : , | | MOTINC | Unknown words | Technology Lady | | X | Typos & others | sfeir 3D statu | ## Evaluation results The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```plain Results: - F-score (micro) 0.9797 - F-score (macro) 0.9178 - Accuracy 0.9797 By class: precision recall f1-score support PREP 0.9966 0.9987 0.9976 1483 PUNCT 1.0000 1.0000 1.0000 833 NMS 0.9634 0.9801 0.9717 753 DET 0.9923 0.9984 0.9954 645 VERB 0.9913 0.9811 0.9862 583 NFS 0.9667 0.9839 0.9752 560 ADV 0.9940 0.9821 0.9880 504 PROPN 0.9541 0.8937 0.9229 395 DETMS 1.0000 1.0000 1.0000 362 AUX 0.9860 0.9915 0.9888 355 YPFOR 1.0000 1.0000 1.0000 353 NMP 0.9666 0.9475 0.9570 305 COCO 0.9959 1.0000 0.9980 245 ADJMS 0.9463 0.9385 0.9424 244 DETFS 1.0000 1.0000 1.0000 240 CHIF 0.9648 0.9865 0.9755 222 NFP 0.9515 0.9849 0.9679 199 ADJFS 0.9657 0.9286 0.9468 182 VPPMS 0.9387 0.9745 0.9563 157 COSUB 1.0000 0.9844 0.9921 128 DINTMS 0.9918 0.9918 0.9918 122 XFAMIL 0.9298 0.9217 0.9258 115 PPER3MS 1.0000 1.0000 1.0000 87 ADJMP 0.9294 0.9634 0.9461 82 PDEMMS 1.0000 1.0000 1.0000 75 ADJFP 0.9861 0.9342 0.9595 76 PREL 0.9859 1.0000 0.9929 70 DINTFS 0.9839 1.0000 0.9919 61 PREF 1.0000 1.0000 1.0000 52 PPOBJMS 0.9565 0.9362 0.9462 47 PREFP 0.9778 1.0000 0.9888 44 PINDMS 1.0000 0.9773 0.9885 44 VPPFS 0.8298 0.9750 0.8966 40 PPER1S 1.0000 1.0000 1.0000 42 SYM 1.0000 0.9474 0.9730 38 NOUN 0.8824 0.7692 0.8219 39 PRON 1.0000 0.9677 0.9836 31 PDEMFS 1.0000 1.0000 1.0000 29 VPPMP 0.9286 1.0000 0.9630 26 ADJ 0.9524 0.9091 0.9302 22 PPER3MP 1.0000 1.0000 1.0000 20 VPPFP 1.0000 1.0000 1.0000 19 PPER3FS 1.0000 1.0000 1.0000 18 MOTINC 0.3333 0.4000 0.3636 15 PREFS 1.0000 1.0000 1.0000 10 PPOBJMP 1.0000 0.8000 0.8889 10 PPOBJFS 0.6250 0.8333 0.7143 6 INTJ 0.5000 0.6667 0.5714 6 PART 1.0000 1.0000 1.0000 4 PDEMMP 1.0000 1.0000 1.0000 3 PDEMFP 1.0000 1.0000 1.0000 3 PPER3FP 1.0000 1.0000 1.0000 2 NUM 1.0000 0.3333 0.5000 3 PPER2S 1.0000 1.0000 1.0000 2 PPOBJFP 0.5000 0.5000 0.5000 2 PRELMS 1.0000 1.0000 1.0000 2 PINDFS 0.5000 1.0000 0.6667 1 PINDMP 1.0000 1.0000 1.0000 1 X 0.0000 0.0000 0.0000 1 PINDFP 1.0000 1.0000 1.0000 1 micro avg 0.9797 0.9797 0.9797 10019 macro avg 0.9228 0.9230 0.9178 10019 weighted avg 0.9802 0.9797 0.9798 10019 samples avg 0.9797 0.9797 0.9797 10019 ``` ## BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ``` Flair Embeddings: ```latex @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` ## Acknowledgment This work was financially supported by [Zenidoc](https://zenidoc.fr/)

ricardo-filho/bert_base_tcm_teste

ricardo-filho

2022-07-06T23:23:13Z

12

0

transformers

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

token-classification

2022-07-06T18:05:49Z

--- license: mit tags: - generated_from_trainer model-index: - name: bert_base_tcm_teste results: [] ---  # bert_base_tcm_teste This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Criterio Julgamento Precision: 0.7209 - Criterio Julgamento Recall: 0.8942 - Criterio Julgamento F1: 0.7983 - Criterio Julgamento Number: 104 - Data Sessao Precision: 0.6351 - Data Sessao Recall: 0.8545 - Data Sessao F1: 0.7287 - Data Sessao Number: 55 - Modalidade Licitacao Precision: 0.9224 - Modalidade Licitacao Recall: 0.9596 - Modalidade Licitacao F1: 0.9406 - Modalidade Licitacao Number: 421 - Numero Exercicio Precision: 0.8872 - Numero Exercicio Recall: 0.9351 - Numero Exercicio F1: 0.9105 - Numero Exercicio Number: 185 - Objeto Licitacao Precision: 0.2348 - Objeto Licitacao Recall: 0.4576 - Objeto Licitacao F1: 0.3103 - Objeto Licitacao Number: 59 - Valor Objeto Precision: 0.5424 - Valor Objeto Recall: 0.7805 - Valor Objeto F1: 0.64 - Valor Objeto Number: 41 - Overall Precision: 0.7683 - Overall Recall: 0.8971 - Overall F1: 0.8277 - Overall Accuracy: 0.9948 ## 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: 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: 50.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0346 | 0.96 | 2750 | 0.0329 | 0.6154 | 0.8462 | 0.7126 | 104 | 0.5495 | 0.9091 | 0.6849 | 55 | 0.8482 | 0.9287 | 0.8866 | 421 | 0.7438 | 0.9730 | 0.8431 | 185 | 0.0525 | 0.3220 | 0.0903 | 59 | 0.4762 | 0.7317 | 0.5769 | 41 | 0.5565 | 0.8763 | 0.6807 | 0.9880 | | 0.0309 | 1.92 | 5500 | 0.0322 | 0.6694 | 0.7788 | 0.72 | 104 | 0.5976 | 0.8909 | 0.7153 | 55 | 0.9178 | 0.9549 | 0.9360 | 421 | 0.8211 | 0.8432 | 0.8320 | 185 | 0.15 | 0.2034 | 0.1727 | 59 | 0.2203 | 0.3171 | 0.26 | 41 | 0.7351 | 0.8243 | 0.7771 | 0.9934 | | 0.0179 | 2.88 | 8250 | 0.0192 | 0.7209 | 0.8942 | 0.7983 | 104 | 0.6351 | 0.8545 | 0.7287 | 55 | 0.9224 | 0.9596 | 0.9406 | 421 | 0.8872 | 0.9351 | 0.9105 | 185 | 0.2348 | 0.4576 | 0.3103 | 59 | 0.5424 | 0.7805 | 0.64 | 41 | 0.7683 | 0.8971 | 0.8277 | 0.9948 | | 0.0174 | 3.84 | 11000 | 0.0320 | 0.7522 | 0.8173 | 0.7834 | 104 | 0.5741 | 0.5636 | 0.5688 | 55 | 0.8881 | 0.9430 | 0.9147 | 421 | 0.8490 | 0.8811 | 0.8647 | 185 | 0.2436 | 0.3220 | 0.2774 | 59 | 0.5370 | 0.7073 | 0.6105 | 41 | 0.7719 | 0.8370 | 0.8031 | 0.9946 | | 0.0192 | 4.8 | 13750 | 0.0261 | 0.6744 | 0.8365 | 0.7468 | 104 | 0.6190 | 0.7091 | 0.6610 | 55 | 0.9169 | 0.9430 | 0.9297 | 421 | 0.8404 | 0.8541 | 0.8472 | 185 | 0.2059 | 0.3559 | 0.2609 | 59 | 0.5088 | 0.7073 | 0.5918 | 41 | 0.7521 | 0.8451 | 0.7959 | 0.9949 | | 0.0158 | 5.76 | 16500 | 0.0250 | 0.6641 | 0.8173 | 0.7328 | 104 | 0.5610 | 0.8364 | 0.6715 | 55 | 0.9199 | 0.9549 | 0.9371 | 421 | 0.9167 | 0.9514 | 0.9337 | 185 | 0.1912 | 0.4407 | 0.2667 | 59 | 0.4828 | 0.6829 | 0.5657 | 41 | 0.7386 | 0.8821 | 0.8040 | 0.9948 | | 0.0126 | 6.72 | 19250 | 0.0267 | 0.6694 | 0.7981 | 0.7281 | 104 | 0.6386 | 0.9636 | 0.7681 | 55 | 0.8723 | 0.9572 | 0.9128 | 421 | 0.8812 | 0.9622 | 0.9199 | 185 | 0.2180 | 0.4915 | 0.3021 | 59 | 0.5323 | 0.8049 | 0.6408 | 41 | 0.7308 | 0.9006 | 0.8068 | 0.9945 | | 0.0162 | 7.68 | 22000 | 0.0328 | 0.675 | 0.7788 | 0.7232 | 104 | 0.6604 | 0.6364 | 0.6481 | 55 | 0.9263 | 0.9549 | 0.9404 | 421 | 0.8535 | 0.9135 | 0.8825 | 185 | 0.2471 | 0.3559 | 0.2917 | 59 | 0.5091 | 0.6829 | 0.5833 | 41 | 0.7788 | 0.8509 | 0.8133 | 0.9948 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

domenicrosati/deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing

domenicrosati

2022-07-06T21:12:29Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-07-06T20:34:04Z

--- license: mit tags: - text-classification - generated_from_trainer model-index: - name: deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing results: [] ---  # deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier_testing This model is a fine-tuned version of [domenicrosati/deberta-v3-xsmall-finetuned-review_classifier](https://huggingface.co/domenicrosati/deberta-v3-xsmall-finetuned-review_classifier) 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: 4.5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu116 - Datasets 2.3.2 - Tokenizers 0.12.1

BigTimeCoderSean/q-Taxi-v3

BigTimeCoderSean

2022-07-06T18:13:18Z

0

null

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

reinforcement-learning

2022-07-06T18:13:12Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.54 +/- 2.70 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="BigTimeCoderSean/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

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

BigTimeCoderSean

2022-07-06T17:57:12Z

0

null

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

reinforcement-learning

2022-07-06T17:57:05Z

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 0.74 +/- 0.44 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="BigTimeCoderSean/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

bigscience/tr11-176B-logs

bigscience

2022-07-06T17:01:14Z

0

250

null

[ "tensorboard", "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu", "region:us" ]

null

2022-03-03T04:38:09Z

--- language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu --- # BigScience Large Language Model Training Training a multilingual 176 billion parameters model in the open ![BigScience Logo](https://assets.website-files.com/6139f3cdcbbff3a68486761d/613cd8997b270da063e230c5_Tekengebied%201-p-500.png) [BigScience](https://bigscience.huggingface.co) is a open and collaborative workshop around the study and creation of very large language models gathering more than 1000 researchers around the worlds. You can find more information on the main website at https://bigscience.huggingface.co. The training of BigScience’s main model started on **March 11, 2022 11:42am PST** and will continue for 3-4 months on 384 A100 80GB GPUs of the Jean Zay public supercomputer You can follow the training at [https://twitter.com/BigScienceLLM](https://twitter.com/BigScienceLLM) or on [the Tensorboards tab above](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss). ## More information on the model, dataset, hardware, environmental consideration: ### **The model** - 176B parameters decoder-only architecture (GPT-like) - 70 layers - 112 attention heads per layers - hidden dimensionality of 14336 - 2048 tokens sequence length - ALiBi positional embeddings - GeLU activation function - **More information**: - Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: [https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours](https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours) - More details on the architecture/optimizer: [https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml) ### **The dataset** - Multilingual: 46 languages: Full list is here: [https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling](https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling) - 341.6 billion tokens (1.5 TB of text data) - Tokenizer vocabulary: 250,680 tokens - More information: - Blog post detailing the design choices during the dataset creation: [https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling](https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling) ### **The engineering side** - number of GPU used for the training: 384 A100 GPU with 80 GB of memory each - one copy of the model takes 48 GPUs (using 60 GB of memory on each GPU) - checkpoint size: the bf16 weights are 329GB, the full checkpoint with optimizer states is 2.3TB - training throughput: ~150 TFLOPs - estimated training time: 3-4 months depending on throughput and unexpected events - **More information**: - Blog post on the hardware/engineering side: [https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model](https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model) - Details on the distributed setup used for the training: [https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml) - Tensorboard updated during the training: [https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss) - Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): [https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md](https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md) ### **Environmental considerations** - [Jean Zay](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html), the supercomputer we are using for model training, is mostly powered by nuclear energy, which is a low carbon energy source. - Significant efforts were made to make sure that the computing infrastructure is as efficient as possible — the heat generated by the hardware even gets used for heating buildings on campus! - **More information**: - We are currently working on making a precise estimate of the carbon emitted during all of the steps of model training, including intermediate experiments as well as inference. - More soon!

hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10

hsohn3

2022-07-06T15:57:53Z

4

0

transformers

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

fill-mask

2022-07-06T14:22:52Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 results: [] ---  # hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2895 - Epoch: 9 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.1298 | 0 | | 3.5157 | 1 | | 3.4732 | 2 | | 3.4565 | 3 | | 3.4444 | 4 | | 3.4349 | 5 | | 3.4197 | 6 | | 3.4109 | 7 | | 3.3493 | 8 | | 3.2895 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

saekomdalkom/t5-small-finetuned-xsum

saekomdalkom

2022-07-06T15:25:39Z

4

0

transformers

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

text2text-generation

2022-07-06T13:04:22Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 28.3577 ---  # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4783 - Rouge1: 28.3577 - Rouge2: 7.759 - Rougel: 22.274 - Rougelsum: 22.2869 - Gen Len: 18.8298 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 2.7158 | 1.0 | 12753 | 2.4783 | 28.3577 | 7.759 | 22.274 | 22.2869 | 18.8298 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.3.2 - Tokenizers 0.12.1

cacauvicosa/heart1ohr2x9e-target-classification

cacauvicosa

2022-07-06T15:11:05Z

0

sklearn

[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]

tabular-classification

2022-07-06T15:11:03Z

--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on heart1ohr2x9e to apply classification on target **Metrics of the best model:** accuracy 0.885854 average_precision 0.949471 roc_auc 0.050633 recall_macro 0.885324 f1_macro 0.885610 Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64 **See model plot below:** <style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-8" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False Fa...... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False Fa...... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False False ... False False False trestbps True False False ... False False False chol True False False ... False False False fbs False False False ... False False False restecg False False False ... False False False thalach True False False ... False False False exang False False False ... False False False oldpeak True False False ... False False False slope False False False ... False False False ca False False False ... False False False thal False False False ... False False False[13 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

huggingtweets/frnsw-nswrfs-nswses

huggingtweets

2022-07-06T14:32:52Z

3

0

transformers

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

text-generation

2022-07-06T14:32:45Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1150678663265832960/ujqrCyuu_400x400.png')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/895892720194957313/RVLTWlDI_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500778204294180868/3B6rKocs_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">NSW RFS & NSW SES & Fire and Rescue NSW</div> <div style="text-align: center; font-size: 14px;">@frnsw-nswrfs-nswses</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from NSW RFS & NSW SES & Fire and Rescue NSW. | Data | NSW RFS | NSW SES | Fire and Rescue NSW | | --- | --- | --- | --- | | Tweets downloaded | 3250 | 3248 | 3249 | | Retweets | 275 | 2093 | 875 | | Short tweets | 12 | 12 | 48 | | Tweets kept | 2963 | 1143 | 2326 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1cxt6027/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @frnsw-nswrfs-nswses's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tjbhow2z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tjbhow2z/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/frnsw-nswrfs-nswses') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

TestZee/t5-small-finetuned-custom-wion-test

TestZee

2022-07-06T13:28:44Z

4

0

transformers

[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-07-06T13:23:31Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TestZee/t5-small-finetuned-custom-wion-test results: [] ---  # TestZee/t5-small-finetuned-custom-wion-test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9773 - Validation Loss: 0.8028 - Epoch: 9 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.2933 | 0.9052 | 0 | | 2.3077 | 0.8923 | 1 | | 2.1972 | 0.8797 | 2 | | 2.1740 | 0.8677 | 3 | | 2.1535 | 0.8564 | 4 | | 2.1772 | 0.8452 | 5 | | 2.1227 | 0.8342 | 6 | | 2.0875 | 0.8234 | 7 | | 2.0279 | 0.8129 | 8 | | 1.9773 | 0.8028 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Tokenizers 0.12.1

luizapzbn/titanicht_mp88q-Survived-classification

luizapzbn

2022-07-06T13:25:48Z

0

sklearn

[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]

tabular-classification

2022-07-06T13:25:46Z

--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on titanicht_mp88q to apply classification on Survived **Metrics of the best model:** accuracy 0.803597 average_precision 0.801332 roc_auc 0.848079 recall_macro 0.795883 f1_macro 0.793746 Name: DecisionTreeClassifier(class_weight='balanced', max_depth=5), dtype: float64 **See model plot below:** <style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-7" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Pclass False False False ... False False False Name False False False ... False True False Sex False False False ... False False False Age True False False ... False False False SibSp False False False ... False False False Parch False False False ... False False False Ticket False False False ... False True False Fare True False False ... False False False Cabin False False False ... False True False Embarked False False False ... False False False[10 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Pclass False False False ... False False False Name False False False ... False True False Sex False False False ... False False False Age True False False ... False False False SibSp False False False ... False False False Parch False False False ... False False False Ticket False False False ... False True False Fare True False False ... False False False Cabin False False False ... False True False Embarked False False False ... False False False[10 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=5))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-22" type="checkbox" ><label for="sk-estimator-id-22" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Pclass False False False ... False False False Name False False False ... False True False Sex False False False ... False False False Age True False False ... False False False SibSp False False False ... False False False Parch False False False ... False False False Ticket False False False ... False True False Fare True False False ... False False False Cabin False False False ... False True False Embarked False False False ... False False False[10 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight='balanced', max_depth=5)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

sumitrsch/muril_base_multiconer22_bn

sumitrsch

2022-07-06T12:33:20Z

4

3

transformers

[ "transformers", "pytorch", "bert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-06-30T07:24:11Z

--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track. https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO

srg/outhimar_64-Close-regression

srg

2022-07-06T12:33:04Z

0

4

sklearn

[ "sklearn", "tabular-regression", "baseline-trainer", "license:apache-2.0", "region:us" ]

tabular-regression

2022-07-06T12:33:02Z

--- license: apache-2.0 library_name: sklearn tags: - tabular-regression - baseline-trainer --- ## Baseline Model trained on outhimar_64 to apply regression on Close **Metrics of the best model:** r2 0.999858 neg_mean_squared_error -1.067685 Name: Ridge(alpha=10), dtype: float64 **See model plot below:** <style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-18" type="checkbox" ><label for="sk-estimator-id-18" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-19" type="checkbox" ><label for="sk-estimator-id-19" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless Date False False False ... True False False Open True False False ... False False False High True False False ... False False False Low True False False ... False False False Adj Close True False False ... False False False Volume True False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label sk-toggleable__label-arrow">Ridge</label><div class="sk-toggleable__content"><pre>Ridge(alpha=10)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

sumitrsch/Indic-bert_multiconer22_bn

sumitrsch

2022-07-06T12:32:40Z

3

2

transformers

[ "transformers", "pytorch", "albert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-06T10:07:47Z

--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track. https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO

sumitrsch/xlm_R_large_multiconer22_bn

sumitrsch

2022-07-06T12:32:05Z

3

2

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-06T10:33:33Z

--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track. https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO

sumitrsch/muril_base_multiconer22_hi

sumitrsch

2022-07-06T12:27:42Z

3

transformers

[ "transformers", "pytorch", "bert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-06-28T07:57:21Z

--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP

dandelin/vilt-b32-mlm

dandelin

2022-07-06T12:18:37Z

66,336

11

transformers

[ "transformers", "pytorch", "vilt", "fill-mask", "arxiv:2102.03334", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- license: apache-2.0 --- # Vision-and-Language Transformer (ViLT), pre-trained only Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Note: this model only includes the language modeling head. Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the raw model for masked language modeling given an image and a piece of text with [MASK] tokens. ### How to use Here is how to use this model in PyTorch: ``` from transformers import ViltProcessor, ViltForMaskedLM import requests from PIL import Image import re url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "a bunch of [MASK] laying on a [MASK]." processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm") model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) tl = len(re.findall("\[MASK\]", text)) inferred_token = [text] # gradually fill in the MASK tokens, one by one with torch.no_grad(): for i in range(tl): encoded = processor.tokenizer(inferred_token) input_ids = torch.tensor(encoded.input_ids).to(device) encoded = encoded["input_ids"][0][1:-1] outputs = model(input_ids=input_ids, pixel_values=pixel_values) mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size) # only take into account text features (minus CLS and SEP token) mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :] mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1) # only take into account text mlm_values[torch.tensor(encoded) != 103] = 0 select = mlm_values.argmax().item() encoded[select] = mlm_ids[select].item() inferred_token = [processor.decode(encoded)] selected_token = "" encoded = processor.tokenizer(inferred_token) processor.decode(encoded.input_ids[0], skip_special_tokens=True) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```

Lakshya/q-Taxi-v3

Lakshya

2022-07-06T12:06:33Z

0

null

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

reinforcement-learning

2022-07-06T12:06:26Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.46 +/- 2.73 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Lakshya/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

SiddharthaM/beit-base-patch16-224-pt22k-ft22k-rim_one-new

SiddharthaM

2022-07-06T11:17:32Z

55

0

transformers

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

image-classification

2022-07-06T10:31:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: beit-base-patch16-224-pt22k-ft22k-rim_one-new results: - task: type: image-classification name: Image Classification dataset: type: rimonedl name: RIM ONE DL split: test metrics: - type: f1 value: 0.9197860962566845 name: F1 - task: type: image-classification name: Image Classification dataset: type: rim one name: RIMONEDL split: test metrics: - type: precision value: 0.9247311827956989 name: precision - type: recall value: 0.9148936170212766 name: Recall - type: accuracy value: 0.8972602739726028 name: Accuracy - type: roc_auc value: 0.8901391162029461 name: AUC ---  # beit-base-patch16-224-pt22k-ft22k-rim_one-new This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4550 - Accuracy: 0.8767 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.73 | 2 | 0.2411 | 0.9178 | | No log | 1.73 | 4 | 0.2182 | 0.8973 | | No log | 2.73 | 6 | 0.3085 | 0.8973 | | No log | 3.73 | 8 | 0.2794 | 0.8973 | | 0.1392 | 4.73 | 10 | 0.2398 | 0.9110 | | 0.1392 | 5.73 | 12 | 0.2925 | 0.8973 | | 0.1392 | 6.73 | 14 | 0.2798 | 0.9110 | | 0.1392 | 7.73 | 16 | 0.2184 | 0.9178 | | 0.1392 | 8.73 | 18 | 0.3007 | 0.9110 | | 0.0416 | 9.73 | 20 | 0.3344 | 0.9041 | | 0.0416 | 10.73 | 22 | 0.3626 | 0.9110 | | 0.0416 | 11.73 | 24 | 0.4842 | 0.8904 | | 0.0416 | 12.73 | 26 | 0.3664 | 0.8973 | | 0.0416 | 13.73 | 28 | 0.3458 | 0.9110 | | 0.0263 | 14.73 | 30 | 0.2810 | 0.9110 | | 0.0263 | 15.73 | 32 | 0.4695 | 0.8699 | | 0.0263 | 16.73 | 34 | 0.3723 | 0.9041 | | 0.0263 | 17.73 | 36 | 0.3447 | 0.9041 | | 0.0263 | 18.73 | 38 | 0.3708 | 0.8904 | | 0.0264 | 19.73 | 40 | 0.4052 | 0.9110 | | 0.0264 | 20.73 | 42 | 0.4492 | 0.9041 | | 0.0264 | 21.73 | 44 | 0.4649 | 0.8904 | | 0.0264 | 22.73 | 46 | 0.4061 | 0.9178 | | 0.0264 | 23.73 | 48 | 0.4136 | 0.9110 | | 0.0139 | 24.73 | 50 | 0.4183 | 0.8973 | | 0.0139 | 25.73 | 52 | 0.4504 | 0.8904 | | 0.0139 | 26.73 | 54 | 0.4368 | 0.8973 | | 0.0139 | 27.73 | 56 | 0.4711 | 0.9110 | | 0.0139 | 28.73 | 58 | 0.3928 | 0.9110 | | 0.005 | 29.73 | 60 | 0.4550 | 0.8767 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

kws/q-Taxi-v3

kws

2022-07-06T10:24:02Z

0

null

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

reinforcement-learning

2022-07-06T10:23:57Z

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

sumitrsch/Indic-bert_multiconer22_hi

sumitrsch

2022-07-06T10:00:34Z

4

2

transformers

[ "transformers", "pytorch", "albert", "token-classification", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-06T09:43:28Z

--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP

dnouri/brats_mri_segmentation

dnouri

2022-07-06T09:54:53Z

0

1

null

[ "monai", "arxiv:1810.11654", "region:us" ]

null

2022-07-06T09:13:12Z

--- tags: - monai --- # Model Overview A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation). ## Workflow The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR). - The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c. - The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor. - The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR. ## Data The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018/data.html). - Target: 3 tumor subregions - Task: Segmentation - Modality: MRI - Size: 285 3D volumes (4 channels each) The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets. Please run `scripts/prepare_datalist.py` to produce the data list. The command is like: ``` python scripts/prepare_datalist.py --path your-brats18-dataset-path ``` ## Training configuration This model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following: - GPU: At least 16GB of GPU memory. - Actual Model Input: 224 x 224 x 144 - AMP: True - Optimizer: Adam - Learning Rate: 1e-4 - Loss: DiceLoss ## Input Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm) 1. Normalizing to unit std with zero mean 2. Randomly cropping to (224, 224, 144) 3. Randomly spatial flipping 4. Randomly scaling and shifting intensity of the volume ## Output Output: 3 channels - Label 0: TC tumor subregion - Label 1: WT tumor subregion - Label 2: ET tumor subregion ## Model Performance The achieved Dice scores on the validation data are: - Tumor core (TC): 0.8559 - Whole tumor (WT): 0.9026 - Enhancing tumor (ET): 0.7905 - Average: 0.8518 ## commands example Execute training: ``` python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf ``` Override the `train` config to execute multi-GPU training: ``` torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf ``` Override the `train` config to execute evaluation with the trained model: ``` python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf ``` Execute inference: ``` python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf ``` # Disclaimer This is an example, not to be used for diagnostic purposes. # References [1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.

vinayak361/token_fine_tunned_flipkart

vinayak361

2022-07-06T09:32:50Z

3

0

transformers

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

token-classification

2022-07-06T07:42:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: token_fine_tunned_flipkart results: [] ---  # token_fine_tunned_flipkart 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: 0.0992 - Precision: 0.9526 - Recall: 0.9669 - F1: 0.9597 - Accuracy: 0.9730 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 135 | 0.5967 | 0.7227 | 0.7830 | 0.7516 | 0.7932 | | No log | 2.0 | 270 | 0.3673 | 0.8105 | 0.8623 | 0.8356 | 0.8747 | | No log | 3.0 | 405 | 0.2679 | 0.8676 | 0.8854 | 0.8764 | 0.9094 | | 0.6219 | 4.0 | 540 | 0.1972 | 0.8955 | 0.9217 | 0.9084 | 0.9355 | | 0.6219 | 5.0 | 675 | 0.1500 | 0.9229 | 0.9374 | 0.9301 | 0.9525 | | 0.6219 | 6.0 | 810 | 0.1240 | 0.9341 | 0.9509 | 0.9424 | 0.9609 | | 0.6219 | 7.0 | 945 | 0.1041 | 0.9516 | 0.9650 | 0.9582 | 0.9720 | | 0.2085 | 8.0 | 1080 | 0.0992 | 0.9526 | 0.9669 | 0.9597 | 0.9730 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

laurian/pouet

laurian

2022-07-06T08:44:36Z

0

null

[ "region:us" ]

null

2022-07-06T08:42:05Z

valkiry robot desert technology

messham/ppo-LunarLander-v2_1pt5m

messham

2022-07-06T08:33:46Z

0

stable-baselines3

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

reinforcement-learning

2022-07-06T08:33:19Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 275.55 +/- 24.60 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

ArneD/xlm-roberta-base-finetuned-panx-de

ArneD

2022-07-06T07:23:24Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-06T06:47:26Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

espnet/aishell2_transducer

espnet

2022-07-06T07:11:48Z

2

0

espnet

[ "espnet", "audio", "automatic-speech-recognition", "zh", "dataset:aishell2", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]

automatic-speech-recognition

2022-07-06T06:55:04Z

--- tags: - espnet - audio - automatic-speech-recognition language: zh datasets: - aishell2 license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/aishell2_transducer` This model was trained by jctian98 using aishell2 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 40c5f6919244c2ec8eac14b9011854dd02511a04 pip install -e . cd egs2/aishell2/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/aishell2_transducer ```  # RESULTS ## Environments - date: `Tue Jul 5 22:02:55 CST 2022` - python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]` - espnet version: `espnet 202205` - pytorch version: `pytorch 1.7.1` - Git hash: `40c5f6919244c2ec8eac14b9011854dd02511a04` - Commit date: `Fri Jun 17 11:07:26 2022 +0800` ## asr_train_conformer-rnn_transducer_raw_zh_char_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.cer_transducer.ave/test_android|5000|5002|63.2|36.8|0.0|0.0|36.8|36.8| |decode_asr_model_valid.cer_transducer.ave/test_ios|5000|5002|66.2|33.7|0.0|0.0|33.8|33.8| |decode_asr_model_valid.cer_transducer.ave/test_mic|5000|5002|63.9|36.1|0.0|0.0|36.1|36.1| |decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_android|5000|5002|64.4|35.5|0.0|0.0|35.6|35.6| |decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_ios|5000|5002|67.4|32.5|0.0|0.0|32.6|32.6| |decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_mic|5000|5002|65.3|34.6|0.0|0.0|34.7|34.7| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.cer_transducer.ave/test_android|5000|49534|94.0|5.7|0.2|0.1|6.1|36.8| |decode_asr_model_valid.cer_transducer.ave/test_ios|5000|49534|94.8|5.0|0.2|0.1|5.4|33.8| |decode_asr_model_valid.cer_transducer.ave/test_mic|5000|49534|94.1|5.7|0.2|0.1|6.0|36.1| |decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_android|5000|49534|94.2|5.5|0.3|0.1|5.9|35.6| |decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_ios|5000|49534|94.9|4.9|0.2|0.1|5.2|32.6| |decode_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.cer_transducer.ave/test_mic|5000|49534|94.3|5.4|0.2|0.1|5.8|34.7| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/transducer/train_conformer-rnn_transducer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer-rnn_transducer_raw_zh_char_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 51051 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - cer_transducer - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true 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: 20000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_zh_char_sp/train/speech_shape - exp/asr_stats_raw_zh_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_zh_char_sp/valid/speech_shape - exp/asr_stats_raw_zh_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 51200 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_noeng_sp/wav.scp - speech - sound - - dump/raw/train_noeng_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev_ios/wav.scp - speech - sound - - dump/raw/dev_ios/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 scheduler: warmuplr scheduler_conf: warmup_steps: 30000 token_list: - <blank> - <unk> - 的 - 一 - 十 - 二 - 三 - 有 - 我 - 在 - 度 - 五 - 是 - 四 - 人 - 六 - 七 - 八 - 九 - 中 - 百 - 不 - 了 - 零 - 大 - 到 - 为 - 开 - 上 - 国 - 调 - 市 - 点 - 业 - 歌 - 么 - 来 - 个 - 这 - 年 - 要 - 公 - 什 - 会 - 出 - 地 - 发 - 行 - 能 - 温 - 电 - 空 - 万 - 千 - 成 - 和 - 分 - 时 - 下 - 你 - 场 - 新 - 家 - 打 - 产 - 机 - 对 - 以 - 房 - 生 - 把 - 小 - 首 - 放 - 之 - 现 - 日 - 动 - 高 - 子 - 后 - 多 - 们 - 者 - 方 - 前 - 也 - 他 - 视 - 资 - 将 - 关 - 金 - 天 - 于 - 进 - 过 - 经 - 听 - 月 - 可 - 用 - 自 - 最 - 司 - 幺 - 车 - 比 - 体 - 手 - 目 - 化 - 道 - 作 - 部 - 被 - 给 - 报 - 加 - 就 - 第 - 全 - 乐 - 定 - 得 - 还 - 事 - 城 - 本 - 想 - 女 - 赛 - 面 - 工 - 设 - 都 - 音 - 力 - 品 - 理 - 保 - 记 - 心 - 好 - 而 - 企 - 法 - 实 - 帮 - 价 - 长 - 看 - 合 - 已 - 海 - 但 - 与 - 名 - 北 - 同 - 入 - 元 - 商 - 通 - 量 - 区 - 学 - 情 - 京 - 网 - 所 - 务 - 主 - 说 - 两 - 政 - 播 - 利 - 重 - 制 - 员 - 平 - 其 - 交 - 内 - 风 - 提 - 器 - 间 - 没 - 请 - 去 - 相 - 台 - 美 - 期 - 增 - 明 - 信 - 式 - 次 - 爱 - 曲 - 建 - 安 - 当 - 管 - 表 - 东 - 店 - 里 - 起 - 并 - 从 - 果 - 回 - 民 - 影 - 展 - 据 - 着 - 示 - 更 - 等 - 应 - 很 - 无 - 门 - 外 - 数 - 运 - 因 - 投 - 正 - 今 - 收 - 路 - 些 - 需 - 儿 - 性 - 南 - 计 - 色 - 如 - 然 - 世 - 亿 - 物 - 光 - 项 - 特 - 联 - 智 - 持 - 随 - 向 - 搜 - 老 - 西 - 位 - 院 - 模 - 规 - 身 - 气 - 消 - 达 - 意 - 切 - 男 - 队 - 斯 - 米 - 低 - 格 - 水 - 张 - 此 - 布 - 灯 - 华 - 那 - 住 - 步 - 集 - 受 - 基 - 换 - 整 - 险 - 科 - 续 - 让 - 线 - 广 - 股 - 求 - 转 - 强 - 演 - 件 - 息 - 费 - 变 - 做 - 样 - 该 - 未 - 近 - 她 - 系 - 至 - 代 - 技 - 查 - 证 - 少 - 接 - 山 - 统 - 楼 - 节 - 标 - 只 - 战 - 及 - 文 - 总 - 王 - 局 - 己 - 再 - 问 - 监 - 处 - 传 - 服 - 州 - 显 - 销 - 快 - 由 - 频 - 改 - 便 - 卫 - 题 - 购 - 林 - 告 - 创 - 限 - 售 - 讯 - 常 - 界 - 营 - 原 - 单 - 超 - 认 - 种 - 流 - 亮 - 净 - 排 - 案 - 知 - 推 - 降 - 环 - 获 - 程 - 走 - 友 - 源 - 立 - 马 - 客 - 称 - 速 - 剧 - 周 - 决 - 尔 - 别 - 跑 - 取 - 完 - 片 - 警 - 头 - 球 - 选 - 士 - 级 - 拉 - 解 - 策 - 结 - 术 - 约 - 银 - 江 - 星 - 活 - 口 - 直 - 备 - 支 - 供 - 户 - 医 - 存 - 花 - 易 - 各 - 造 - 置 - 准 - 任 - 非 - 红 - 游 - 专 - 较 - 款 - 预 - 积 - 站 - 园 - 升 - 先 - 牌 - 社 - 办 - 每 - 李 - 村 - 型 - 使 - 难 - 势 - 真 - 带 - 指 - 停 - 构 - 导 - 深 - 唱 - 参 - 清 - 见 - 龙 - 研 - 团 - 照 - 确 - 阳 - 响 - 太 - 亚 - 克 - 闭 - 火 - 央 - 微 - 感 - 组 - 减 - 或 - 委 - 领 - 军 - 率 - 伤 - 始 - 类 - 书 - 融 - 具 - 济 - 土 - 施 - 望 - 教 - 奥 - 吗 - 际 - 育 - 权 - 涨 - 德 - 几 - 控 - 师 - 热 - 死 - 共 - 则 - 话 - 汽 - 许 - 份 - 府 - 居 - 态 - 连 - 黄 - 白 - 烦 - 引 - 英 - 声 - 狐 - 何 - 划 - 除 - 媒 - 季 - 继 - 孩 - 眼 - 财 - 岁 - 买 - 越 - 健 - 责 - 卡 - 助 - 索 - 宝 - 负 - 镇 - 争 - 松 - 况 - 半 - 条 - 税 - 注 - 校 - 终 - 仅 - 刘 - 某 - 号 - 福 - 才 - 额 - 博 - 包 - 优 - 众 - 质 - 究 - 反 - 农 - 苹 - 晚 - 紧 - 县 - 景 - 诉 - 酒 - 落 - 离 - 观 - 青 - 致 - 装 - 又 - 仍 - 套 - 亲 - 复 - 河 - 依 - 飞 - 故 - 极 - 娱 - 普 - 失 - 范 - 效 - 互 - 启 - 神 - 左 - 湖 - 击 - 值 - 绩 - 陈 - 语 - 段 - 兴 - 容 - 采 - 充 - 右 - 曾 - 往 - 票 - 均 - 举 - 域 - 形 - 维 - 找 - 像 - 纪 - 属 - 图 - 断 - 贷 - 省 - 康 - 试 - 杨 - 港 - 喜 - 街 - 益 - 拿 - 幅 - 功 - 苏 - 药 - 杰 - 足 - 考 - 疑 - 觉 - 配 - 香 - 宅 - 厂 - 根 - 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磴 - 氪 - 诲 - 忪 - 炷 - 杓 - 暾 - 藿 - Ｔ - Ｍ - 洺 - 擢 - 藠 - 晌 - 瞠 - 桁 - 遑 - 囗 - 谑 - 嗬 - 卲 - 硒 - 鼾 - 觥 - 茳 - 枇 - 杷 - 邡 - 桷 - 椁 - 鹳 - 饴 - 跶 - 绉 - 浐 - 迩 - 啲 - 颌 - 泺 - 睑 - 踮 - 荛 - 镔 - 祢 - 韫 - 笸 - 俎 - 羸 - 怿 - 昝 - 艿 - 薷 - 赅 - 怆 - 刍 - 獭 - 蚴 - 噶 - 噤 - 氤 - 氲 - 豺 - 倭 - 豉 - 葺 - 珥 - 痨 - 蹁 - 跹 - 蚬 - 唳 - 舐 - 竽 - 馑 - 徇 - 垌 - 魍 - 葚 - 涑 - 跛 - 荏 - 吋 - 髌 - 髂 - 骓 - 悌 - 戌 - 揄 - 矽 - 钒 - 𫖯 - 谶 - 捌 - 矍 - 铧 - 骈 - 枥 - 殁 - 鲢 - 腭 - 弭 - 镕 - 篑 - 馕 - 堃 - 锑 - 搧 - 闾 - 囫 - 囵 - 鞑 - 辊 - 魟 - 𫚉 - 鲼 - 郅 - 坭 - 栌 - 佗 - 驮 - 哕 - 颦 - 偌 - 颀 - 耜 - 仞 - 贲 - 烀 - 瘢 - 祚 - 悭 - 沢 - 瑠 - 钼 - 鹧 - 鸪 - 蛳 - 苞 - 柃 - 麂 - 暌 - 刎 - 溟 - 菘 - 钐 - 蹉 - 跎 - 篁 - 耆 - 纡 - 熵 - 簪 - 铋 - 幔 - 巳 - 陉 - 増 - 鹁 - 矬 - 锉 - 偈 - 篼 - 龃 - 龉 - 郇 - 孑 - 忒 - 龌 - 稞 - 囔 - 蝮 - 蠊 - 苫 - 菅 - 霪 - 藁 - 膈 - 敕 - 潸 - 槃 - 湎 - 椟 - 茼 - 戗 - 奁 - 芗 - 褔 - 稹 - 澧 - 嬴 - 铍 - 潆 - 橐 - 堺 - 佚 - 嫒 - 葳 - 氚 - 酚 - 椤 - 赉 - 砭 - 匏 - 戾 - 恁 - 腴 - 蛉 - 麸 - 玑 - 痍 - 啜 - 劾 - 忖 - 蛔 - 芾 - 餍 - 诤 - 逋 - 鸵 - 荸 - 夔 - 懑 - 嘏 - 檗 - 牠 - 痔 - 酞 - 猹 - 盅 - 旖 - 鸫 - 椴 - 戍 - 耪 - 豇 - 牍 - 铑 - 噻 - 龅 - 猁 - 蝽 - 欸 - 肱 - 桴 - 镏 - 缬 - 怫 - 唑 - 曈 - 缛 - 吠 - 歙 - 谖 - 俟 - 刽 - 槭 - 硖 - 髯 - 饯 - 藐 - 娈 - 勐 - 颧 - 荻 - 焗 - 鳃 - 昴 - 黟 - 羧 - 趵 - 澶 - 骞 - 鸩 - 婢 - 圄 - 佝 - 偻 - 嗫 - 囯 - 跬 - 朕 - 袅 - 锲 - 杵 - 豢 - 骺 - 诹 - 椹 - 谮 - 㶧 - <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: 512 use_preprocessor: true token_type: char bpemodel: null 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 frontend: default frontend_conf: 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 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.0 preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish macaron_style: true use_cnn_module: true cnn_module_kernel: 15 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 512 dropout: 0.1 dropout_embed: 0.2 required: - output_dir - token_list version: '202205' distributed: true ``` </details> ### 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} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, 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}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

miyoung/newProject

miyoung

2022-07-06T01:16:16Z

0

null

[ "region:us" ]

null

2022-06-17T04:39:53Z

### What's Hugging Face?!!! https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!.

domenicrosati/deberta-v3-xsmall-finetuned-review_classifier

domenicrosati

2022-07-06T01:09:25Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-07-05T20:16:35Z

--- license: mit tags: - text-classification - generated_from_trainer metrics: - accuracy - f1 model-index: - name: deberta-v3-xsmall-finetuned-review_classifier results: [] ---  # deberta-v3-xsmall-finetuned-review_classifier This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - Accuracy: 0.9513 - F1: 0.7458 ## 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: 4.5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.1518 | 1.0 | 6667 | 0.1575 | 0.9510 | 0.7155 | | 0.1247 | 2.0 | 13334 | 0.1441 | 0.9513 | 0.7458 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

rbawden/CCASS-semi-auto-titrages-base

rbawden

2022-07-05T21:42:57Z

16

1

transformers

[ "transformers", "pytorch", "fsmt", "fr", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2022-06-16T09:32:27Z

--- language: fr license: cc-by-4.0 --- # Cour de Cassation semi-automatic *titrage* prediction model Model for the semi-automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are similar to the automatic models described in [this paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf) and to the model available [here](https://huggingface.co/rbawden/CCASS-pred-titrages-base). If you use this semi-automatic model, please cite our research paper (see [below](#cite)). ## Model description The model is a transformer-base model trained on parallel data (sommaires-titrages) provided by the Cour de Cassation. The model was intially trained using the Fairseq toolkit, converted to HuggingFace and then fine-tuned on the original training data to smooth out minor differences that arose during the conversion process. Tokenisation is performed using a SentencePiece model, the BPE strategy and a vocab size of 8000. ### Intended uses & limitations This model is to be used to help in the production of *titrages* for those *sommaires* that do not have them or to complement existing (manually) created *titrages*. ### How to use Contrary to the [automatic *titrage* prediction model](https://huggingface.co/rbawden/CCASS-pred-titrages-base) (designed to predict the entire sequence), this model is designed to help in the manual production of *titrages*, by proposing the next *titre* (keyword) in the sequence given a *sommaire* and the beginning of the *titrage*. Model input is the *matière* (matter) concatenated to the *titres* already decided on (separated by <t>), concatenated to the text from the sommaire separated by the token `<t>`. Each example should be on a single line. E.g. `bail <t> résiliation <t> causes <t> La recommendation du tribunal selon l'article...` (fictive example for illustrative purposes, where the matter=bail, the beginning of the *titrage*=résiliation <t> causes. The maximum input length of the model is 1024 input tokens (after tokenisation). ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokeniser = AutoTokenizer.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") model = AutoModelForSeq2SeqLM.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") matiere_and_titrage_prefix = "matter <t> titre" sommaire = "full text from the sommaire on a single line" inputs = tokeniser([matiere_and_titrage_prefix + " <t> " + sommaire], return_tensors='pt') outputs = model.generate(inputs['input_ids']) tokeniser.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenisation_spaces=True) ``` ### Limitations and bias The models' predictions should not be taken as ground-truth *titrages* and the final decision should be the expert's. The model is not constrained to predict *titres* that have previously been seen, so this should be taken into account in the deployment of this model as a *titrage* tool in order to avoid the multiplication of different *titres*. ## Training data Training data is provided by the Cour de Cassation (the original source being Jurinet data, but with pseudo-anonymisation applied). For training, we use a total of 159,836 parallel examples (each example is a sommaire-titrage pair). Our development data consists of 1,833 held-out examples. ## Training procedure ### Preprocessing We use SentencePiece, the BPE strategy and a joint vocabulary of 8000 tokens. This model was converted into the HuggingFace format and integrates a number of normalisation processes (e.g. removing double doubles, apostrophes and quotes, normalisation of different accent formats, lowercasing). ### Training The model was initialised trained using Fairseq until convergence on the development set (according to our customised weighted accuracy measure - please see [the paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf) for more details). The model was then converted to HuggingFace and training continued to smooth out incoherences introduced during the conversion procedure (incompatibilities in the way the SentencePiece and NMT vocabularies are defined, linked to HuggingFace vocabularies being necessarily the same as the tokeniser vocabulary, a constraint that is not imposed in Fairseq). ### Evaluation results Full results for the initial (automatic) Fairseq models can be found in [the paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf). Results on this semi-automatic model coming soon! ## BibTex entry and citation info <a name="cite"></a> If you use this work, please cite the following article: Thibault Charmet, Inès Cherichi, Matthieu Allain, Urszula Czerwinska, Amaury Fouret, Benoît Sagot and Rachel Bawden, 2022. [**Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings**](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.509.pdf). In Proceedings of the 13th Language Resources and Evaluation Conference, Marseille, France.] ``` @inproceedings{charmet-et-al-2022-complex, tite = {Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings}, author = {Charmet, Thibault and Cherichi, Inès and Allain, Matthieu and Czerwinska, Urszula and Fouret, Amaury, and Sagot, Benoît and Bawden, Rachel}, booktitle = {Proceedings of the 13th Language Resources and Evaluation Conference}, year = {2022}, address = {Marseille, France}, pages = {4754--4766}, url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.509.pdf} ```

pm390/Reinforce-pong-01

pm390

2022-07-05T19:49:27Z

0

null

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

reinforcement-learning

2022-07-05T19:49:16Z

--- tags: - Pong-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pong-01 results: - metrics: - type: mean_reward value: -16.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pong-PLE-v0 type: Pong-PLE-v0 --- # **Reinforce** Agent playing **Pong-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

Tinchoroman/distilbert-base-uncased-finetuned-imdb

Tinchoroman

2022-07-05T19:25:58Z

3

0

transformers

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

fill-mask

2022-07-05T13:43:17Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Tinchoroman/distilbert-base-uncased-finetuned-imdb results: [] ---  # Tinchoroman/distilbert-base-uncased-finetuned-imdb 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: 2.8509 - Validation Loss: 2.5629 - 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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 | |:----------:|:---------------:|:-----:| | 2.8509 | 2.5629 | 0 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

osanseviero/ppo-LunarLander-v2

osanseviero

2022-07-05T19:07:18Z

4

1

stable-baselines3

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

reinforcement-learning

2022-03-02T23:29:05Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: -580.22 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

coledie/reinforce-Pixelcopter-PLE-v0

coledie

2022-07-05T18:37:39Z

0

null

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

reinforcement-learning

2022-07-05T18:04:20Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-Pixelcopter-PLE-v0 results: - metrics: - type: mean_reward value: 17.20 +/- 18.85 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 --- # **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

osanseviero/tipsuhtxfu-sex-classification

osanseviero

2022-07-05T17:18:06Z

0

sklearn

[ "sklearn", "tabular-classification", "baseline-trainer", "license:apache-2.0", "region:us" ]

tabular-classification

2022-07-05T17:18:04Z

--- license: apache-2.0 library_name: sklearn tags: - tabular-classification - baseline-trainer --- ## Baseline Model trained on tipsuhtxfu to apply classification on sex **Metrics of the best model:** accuracy 0.647364 average_precision 0.507660 roc_auc 0.625546 recall_macro 0.589832 f1_macro 0.585292 Name: MultinomialNB(), dtype: float64 **See model plot below:** <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),('pipeline',Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('minmaxscaler', MinMaxScaler()),('multinomialnb', MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt

abhishek/autotrain-adult-census-xgboost

abhishek

2022-07-05T17:14:07Z

28

3

transformers

[ "transformers", "joblib", "xgboost", "autotrain", "tabular", "classification", "tabular-classification", "dataset:abhishek/autotrain-data-adult-train", "dataset:scikit-learn/adult-census-income", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

tabular-classification

2022-07-05T12:06:35Z

--- tags: - autotrain - tabular - classification - tabular-classification datasets: - abhishek/autotrain-data-adult-train - scikit-learn/adult-census-income co2_eq_emissions: 0.12693590577861977 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 9725286 - CO2 Emissions (in grams): 0.12693590577861977 ## Validation Metrics - Loss: 0.26716182056213406 - Accuracy: 0.8750191923844618 - Precision: 0.7840481565086531 - Recall: 0.6641172721478649 - AUC: 0.9345322809861784 - F1: 0.7191166321601105 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```

Eleven/xlm-roberta-base-finetuned-panx-it

Eleven

2022-07-05T16:53:50Z

4

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-05T16:37:09Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8247845711940912 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2421 - F1: 0.8248 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.809 | 1.0 | 70 | 0.3380 | 0.7183 | | 0.2939 | 2.0 | 140 | 0.2582 | 0.7977 | | 0.1813 | 3.0 | 210 | 0.2421 | 0.8248 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

a-doering/MLAgents-Pyramids

a-doering

2022-07-05T16:49:09Z

18

0

ml-agents

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

reinforcement-learning

2022-07-05T16:49:02Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: a-doering/MLAgents-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Eleven/xlm-roberta-base-finetuned-panx-fr

Eleven

2022-07-05T16:36:53Z

4

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-05T16:20:12Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.835464333781965 ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2867 - F1: 0.8355 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5817 | 1.0 | 191 | 0.3395 | 0.7854 | | 0.2617 | 2.0 | 382 | 0.2856 | 0.8278 | | 0.1708 | 3.0 | 573 | 0.2867 | 0.8355 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Eleven/xlm-roberta-base-finetuned-panx-de-fr

Eleven

2022-07-05T15:59:42Z

6

0

transformers

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

token-classification

2022-07-05T15:37:17Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1644 - F1: 0.8617 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 | | 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 | | 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

enoriega/rule_learning_margin_1mm_spanpred_nospec

enoriega

2022-07-05T13:56:15Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "generated_from_trainer", "dataset:enoriega/odinsynth_dataset", "endpoints_compatible", "region:us" ]

null

2022-07-05T03:00:49Z

--- tags: - generated_from_trainer datasets: - enoriega/odinsynth_dataset model-index: - name: rule_learning_margin_1mm_spanpred_nospec results: [] ---  # rule_learning_margin_1mm_spanpred_nospec This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.3972 - Margin Accuracy: 0.8136 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2000 - total_train_batch_size: 8000 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Margin Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------:| | 0.5864 | 0.16 | 20 | 0.5454 | 0.7564 | | 0.4995 | 0.32 | 40 | 0.4761 | 0.7867 | | 0.4866 | 0.48 | 60 | 0.4353 | 0.8057 | | 0.4568 | 0.64 | 80 | 0.4229 | 0.8098 | | 0.4409 | 0.8 | 100 | 0.4136 | 0.8140 | | 0.4369 | 0.96 | 120 | 0.4124 | 0.8118 | | 0.4172 | 1.12 | 140 | 0.4043 | 0.8118 | | 0.4208 | 1.28 | 160 | 0.4072 | 0.8119 | | 0.4256 | 1.44 | 180 | 0.4041 | 0.8124 | | 0.4201 | 1.6 | 200 | 0.4041 | 0.8127 | | 0.4159 | 1.76 | 220 | 0.4006 | 0.8125 | | 0.4103 | 1.92 | 240 | 0.4004 | 0.8131 | | 0.4282 | 2.08 | 260 | 0.3999 | 0.8138 | | 0.4169 | 2.24 | 280 | 0.4006 | 0.8136 | | 0.4263 | 2.4 | 300 | 0.3962 | 0.8133 | | 0.4252 | 2.56 | 320 | 0.3994 | 0.8137 | | 0.4202 | 2.72 | 340 | 0.3965 | 0.8137 | | 0.4146 | 2.88 | 360 | 0.3967 | 0.8139 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.1 - Tokenizers 0.12.1

ramonzaca/dqn-SpaceInvadersNoFrameskip-v4

ramonzaca

2022-07-05T13:32:19Z

5

0

stable-baselines3

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

reinforcement-learning

2022-07-05T13:31:59Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 480.00 +/- 135.11 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ramonzaca -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ramonzaca ``` ## 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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

amyeroberts/resnet-18-finetuned-eurosat

amyeroberts

2022-07-05T12:36:20Z

51

0

transformers

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

image-classification

2022-07-05T12:25:12Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: amyeroberts/resnet-18-finetuned-eurosat results: [] ---  # amyeroberts/resnet-18-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5582 - Validation Loss: 2.1533 - Validation Accuracy: 0.2059 - 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': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Validation Accuracy | Epoch | |:----------:|:---------------:|:-------------------:|:-----:| | 3.0662 | 2.7376 | 0.1374 | 0 | | 1.3977 | 2.3876 | 0.1685 | 1 | | 0.5582 | 2.1533 | 0.2059 | 2 | ### Framework versions - Transformers 4.21.0.dev0 - TensorFlow 2.9.1 - Datasets 2.3.3.dev0 - Tokenizers 0.11.0

datien228/distilbart-ftn-wiki_lingua

datien228

2022-07-05T12:12:07Z

14

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "en", "dataset:wiki_lingua", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-07-03T16:21:47Z

--- language: - en tags: - summarization license: mit datasets: - wiki_lingua metrics: - rouge --- #### Pre-trained BART Model fine-tune on WikiLingua dataset The repository for the fine-tuned BART model (by sshleifer) using the **wiki_lingua** dataset (English) **Purpose:** Examine the performance of a fine-tuned model research purposes **Observation:** - Pre-trained model was trained on the XSum dataset, which summarize a not-too-long documents into one-liner summary - Fine-tuning this model using WikiLingua is appropriate since the summaries for that dataset are also short - In the end, however, the model cannot capture much clearer key points, but instead it mostly extracts the opening sentence - Some data pre-processing and models' hyperparameter are also need to be tuned more properly.

arashba/xlm-roberta-base-finetuned-panx-de

arashba

2022-07-05T12:05:52Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-05T11:41:42Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

abhishek/autotrain-iris-knn

abhishek

2022-07-05T11:59:16Z

9

0

transformers

[ "transformers", "joblib", "knn", "autotrain", "tabular", "classification", "tabular-classification", "dataset:abhishek/autotrain-data-iris-train", "dataset:scikit-learn/iris", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

tabular-classification

2022-07-05T11:37:31Z

--- tags: - autotrain - tabular - classification - tabular-classification datasets: - abhishek/autotrain-data-iris-train - scikit-learn/iris co2_eq_emissions: 0.15028701199056024 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 9705277 - CO2 Emissions (in grams): 0.15028701199056024 ## Validation Metrics - Loss: 0.15622713916762193 - Accuracy: 0.9 - Macro F1: 0.899749373433584 - Micro F1: 0.9 - Weighted F1: 0.8997493734335841 - Macro Precision: 0.9023569023569024 - Micro Precision: 0.9 - Weighted Precision: 0.9023569023569024 - Macro Recall: 0.9 - Micro Recall: 0.9 - Weighted Recall: 0.9 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```

abhishek/autotrain-iris-logistic-regression

abhishek

2022-07-05T11:58:57Z

13

0

transformers

[ "transformers", "joblib", "logistic_regression", "autotrain", "tabular", "classification", "tabular-classification", "dataset:abhishek/autotrain-data-iris-train", "dataset:scikit-learn/iris", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

tabular-classification

2022-07-05T11:36:06Z

--- tags: - autotrain - tabular - classification - tabular-classification datasets: - abhishek/autotrain-data-iris-train - scikit-learn/iris co2_eq_emissions: 0.0006300767567816624 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 9705273 - CO2 Emissions (in grams): 0.0006300767567816624 ## Validation Metrics - Loss: 0.15987505325856152 - Accuracy: 0.9 - Macro F1: 0.899749373433584 - Micro F1: 0.9 - Weighted F1: 0.8997493734335841 - Macro Precision: 0.9023569023569024 - Micro Precision: 0.9 - Weighted Precision: 0.9023569023569025 - Macro Recall: 0.9 - Micro Recall: 0.9 - Weighted Recall: 0.9 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```

HekmatTaherinejad/swin-tiny-patch4-window7-224-finetuned-eurosat

HekmatTaherinejad

2022-07-05T09:17:32Z

75

0

transformers

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

image-classification

2022-07-05T08:15:28Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.98 ---  # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0653 - Accuracy: 0.98 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.203 | 1.0 | 190 | 0.1294 | 0.9574 | | 0.2017 | 2.0 | 380 | 0.0773 | 0.9763 | | 0.1563 | 3.0 | 570 | 0.0653 | 0.98 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

kws/ppo-LunarLander-v2

kws

2022-07-05T07:50:05Z

1

0

stable-baselines3

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

reinforcement-learning

2022-07-05T06:55:50Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 252.49 +/- 42.04 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

shubhamitra/TinyBERT_General_4L_312D-finetuned-toxic-classification

shubhamitra

2022-07-05T07:29:35Z

5

0

transformers

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

text-classification

2022-07-03T13:23:24Z

--- tags: - generated_from_trainer model-index: - name: TinyBERT_General_4L_312D-finetuned-toxic-classification results: [] ---  # TinyBERT_General_4L_312D-finetuned-toxic-classification This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) 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: 64 - eval_batch_size: 64 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 498 | 0.0483 | 0.7486 | 0.8563 | 0.9171 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

mmeet611/finetuning-sentiment-model-3000-samples

mmeet611

2022-07-05T07:16:18Z

6

0

transformers

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

text-classification

2022-06-15T07:33:52Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8633333333333333 - name: F1 type: f1 value: 0.8628762541806019 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3052 - Accuracy: 0.8633 - F1: 0.8629 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

hsohn3/cchs-bert-event-uncased-wordlevel-block512-batch8-ep10

hsohn3

2022-07-05T06:37:28Z

4

0

transformers

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

fill-mask

2022-07-05T05:33:36Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/cchs-bert-event-uncased-wordlevel-block512-batch8-ep10 results: [] ---  # hsohn3/cchs-bert-event-uncased-wordlevel-block512-batch8-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9667 - Epoch: 9 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.3518 | 0 | | 3.1030 | 1 | | 3.0459 | 2 | | 3.0120 | 3 | | 2.9969 | 4 | | 2.9879 | 5 | | 2.9823 | 6 | | 2.9811 | 7 | | 2.9722 | 8 | | 2.9667 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

Samlit/rare-puppers2

Samlit

2022-07-05T06:14:13Z

54

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-07-05T05:49:48Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers2 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.6222222447395325 --- # rare-puppers2 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### La Goulue Toulouse-Lautrec ![La Goulue Toulouse-Lautrec](images/La_Goulue_Toulouse-Lautrec.jpg) #### Marcelle Lender Bolero ![Marcelle Lender Bolero](images/Marcelle_Lender_Bolero.jpg) #### aristide bruant Lautrec ![aristide bruant Lautrec](images/aristide_bruant_Lautrec.jpg) #### la goulue Toulouse-Lautrec ![la goulue Toulouse-Lautrec](images/la_goulue_Toulouse-Lautrec.jpg)

steven123/Check_GoodBad_Teeth

steven123

2022-07-05T03:52:40Z

126

1

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-07-05T03:52:30Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Check_GoodBad_Teeth results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # Check_GoodBad_Teeth Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Bad Teeth ![Bad Teeth](images/Bad_Teeth.jpg) #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg)

liuxuefei01/q-Taxi-v3

liuxuefei01

2022-07-05T02:35:13Z

0

null

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

reinforcement-learning

2022-07-05T02:35:07Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.50 +/- 2.72 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="liuxuefei01/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

liuxuefei01/q-FrozenLake-v1-4x4-noSlippery

liuxuefei01

2022-07-05T02:20:02Z

0

null

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

reinforcement-learning

2022-07-05T02:19:56Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="liuxuefei01/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

coledie/reinforce-CartPole-v1

coledie

2022-07-05T01:27:42Z

0

null

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

reinforcement-learning

2022-07-05T00:39:33Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-CartPole-v1 results: - metrics: - type: mean_reward value: 273.60 +/- 40.64 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

NAOKITY/bert-squad

NAOKITY

2022-07-05T01:05:50Z

15

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2022-07-04T23:36:55Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: NAOKITY/bert-squad results: [] ---  # NAOKITY/bert-squad This model is a fine-tuned version of [pierreguillou/bert-base-cased-squad-v1.1-portuguese](https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9778 - Validation Loss: 0.0 - Epoch: 1 ## 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': 987, '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: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.5286 | 0.0 | 0 | | 0.9778 | 0.0 | 1 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.9.1 - Datasets 2.3.2 - Tokenizers 0.12.1

teven/all_bs160_allneg

teven

2022-07-05T00:14:56Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-07-05T00:14:48Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # teven/all_bs160_allneg 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('teven/all_bs160_allneg') embeddings = model.encode(sentences) print(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=teven/all_bs160_allneg) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 780828 with parameters: ``` {'batch_size': 20, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 315504 with parameters: ``` {'batch_size': 20, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 300017 with parameters: ``` {'batch_size': 20, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 2000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors

hsohn3/mayo-bert-uncased-wordlevel-block512-ep10

hsohn3

2022-07-04T22:52:37Z

3

0

transformers

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

fill-mask

2022-07-04T01:17:58Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hsohn3/mayo-bert-uncased-wordlevel-block512-ep10 results: [] ---  # hsohn3/mayo-bert-uncased-wordlevel-block512-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3171 - Epoch: 9 ## Model description - base_model: bert-base-uncased - block_size: 512 - tokenizer: ehr-bert-wordlevel-uncased ## Intended uses & limitations More information needed ## Training and evaluation data - MAYO visit-level texts ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 - mlm_probability: 0.15 - batch_size: 8 - epochs: 10 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.0885 | 0 | | 2.8340 | 1 | | 2.7975 | 2 | | 2.6720 | 3 | | 2.4868 | 4 | | 2.1750 | 5 | | 1.8143 | 6 | | 1.0948 | 7 | | 0.4915 | 8 | | 0.3171 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

Danitg95/autotrain-kaggle-effective-arguments-1086739296

Danitg95

2022-07-04T21:53:10Z

8

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "en", "dataset:Danitg95/autotrain-data-kaggle-effective-arguments", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-07-04T21:49:45Z

--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - Danitg95/autotrain-data-kaggle-effective-arguments co2_eq_emissions: 5.2497206864306065 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1086739296 - CO2 Emissions (in grams): 5.2497206864306065 ## Validation Metrics - Loss: 0.744236171245575 - Accuracy: 0.6719238613188308 - Macro F1: 0.5450301061253738 - Micro F1: 0.6719238613188308 - Weighted F1: 0.6349879540623229 - Macro Precision: 0.6691326843926052 - Micro Precision: 0.6719238613188308 - Weighted Precision: 0.6706209016443158 - Macro Recall: 0.5426627824078865 - Micro Recall: 0.6719238613188308 - Weighted Recall: 0.6719238613188308 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Danitg95/autotrain-kaggle-effective-arguments-1086739296 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Danitg95/autotrain-kaggle-effective-arguments-1086739296", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Danitg95/autotrain-kaggle-effective-arguments-1086739296", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Farshid/distilbert-base-uncased_allagree3

Farshid

2022-07-04T21:04:03Z

5

0

transformers

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

text-classification

2022-07-04T17:35:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased_allagree3 results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank args: sentences_allagree metrics: - name: Accuracy type: accuracy value: 0.9778761061946902 - name: F1 type: f1 value: 0.9780006392634297 ---  # distilbert-base-uncased_allagree3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.0937 - Accuracy: 0.9779 - F1: 0.9780 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6418 | 1.0 | 57 | 0.3340 | 0.8805 | 0.8768 | | 0.1821 | 2.0 | 114 | 0.1088 | 0.9690 | 0.9691 | | 0.0795 | 3.0 | 171 | 0.0822 | 0.9823 | 0.9823 | | 0.0385 | 4.0 | 228 | 0.0939 | 0.9646 | 0.9646 | | 0.0218 | 5.0 | 285 | 0.1151 | 0.9735 | 0.9737 | | 0.0149 | 6.0 | 342 | 0.1126 | 0.9690 | 0.9694 | | 0.006 | 7.0 | 399 | 0.0989 | 0.9779 | 0.9780 | | 0.0093 | 8.0 | 456 | 0.1009 | 0.9779 | 0.9780 | | 0.0063 | 9.0 | 513 | 0.0899 | 0.9779 | 0.9780 | | 0.0039 | 10.0 | 570 | 0.0937 | 0.9779 | 0.9780 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.3.2 - Tokenizers 0.12.1

samuelrince/bert-base-cased-finetuned-panx-en

samuelrince

2022-07-04T20:08:03Z

9

0

transformers

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

token-classification

2022-07-04T19:46:35Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xtreme model-index: - name: bert-base-cased-finetuned-panx-en results: [] ---  # bert-base-cased-finetuned-panx-en This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2478 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2941 | 1.0 | 1250 | 0.2432 | | 0.186 | 2.0 | 2500 | 0.2214 | | 0.1387 | 3.0 | 3750 | 0.2478 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

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

ramonzaca

2022-07-04T19:53:18Z

0

null

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

reinforcement-learning

2022-07-04T19:53:03Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="ramonzaca/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

reso/DialoGPT-medium-v3ga

reso

2022-07-04T19:39:14Z

3

0

transformers

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

text-generation

2022-07-04T18:49:13Z

--- thumbnail: https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png tags: - conversational license: mit --- # DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script). I built a Discord AI chatbot based on this model. [Check out my GitHub repo.](https://github.com/RuolinZheng08/twewy-discord-chatbot) Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

pcuenq/lpips-jax

pcuenq

2022-07-04T18:47:30Z

0

null

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

null

2022-07-04T18:24:46Z

--- license: apache-2.0 --- ## Weights for JAX/Flax version of VGG - VGG16 weights, taken from [the `flaxmodels` repo](https://github.com/matthias-wright/flaxmodels/blob/main/flaxmodels/vgg/vgg.py). - Additional weights to use VGG16 as a feature extractor for LPIPS. They were downloaded in PyTorch format from [the URL referenced in the Taming Transformers repo](https://github.com/CompVis/taming-transformers/blob/master/taming/modules/losses/lpips.py), and converted to hdf5 format. ## License Apache 2, for this compilation. Please, refer to the original licenses of the source repos. - [Taming Transformers License](https://github.com/CompVis/taming-transformers/blob/master/License.txt). Weights for additional layers. - [Perceptual Similarity License](https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE). Weights for additional layers. - [Flaxmodels / VGG License](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/vgg#license), for the VGG model and (I presume) VGG weights.

YKXBCi/vit-base-patch16-224-in21k-aidSat

YKXBCi

2022-07-04T18:46:44Z

29

0

transformers

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

image-classification

2022-07-04T13:39:01Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: YKXBCi/vit-base-patch16-224-in21k-aidSat results: [] ---  # YKXBCi/vit-base-patch16-224-in21k-aidSat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4026 - Train Accuracy: 0.9981 - Train Top-3-accuracy: 0.9998 - Validation Loss: 0.4715 - Validation Accuracy: 0.9796 - Validation Top-3-accuracy: 0.9980 - Epoch: 4 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1325, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 2.3544 | 0.7383 | 0.8687 | 1.5415 | 0.9266 | 0.9857 | 0 | | 1.1313 | 0.9522 | 0.9942 | 0.8788 | 0.9613 | 0.9966 | 1 | | 0.6741 | 0.9841 | 0.9985 | 0.6268 | 0.9640 | 0.9986 | 2 | | 0.4785 | 0.9953 | 0.9995 | 0.5058 | 0.9755 | 0.9980 | 3 | | 0.4026 | 0.9981 | 0.9998 | 0.4715 | 0.9796 | 0.9980 | 4 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.0 - Datasets 2.1.0 - Tokenizers 0.12.1