license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.6013 | 4.2024 | 0 | | 5.8556 | 3.7335 | 1 | | 5.0930 | 3.5494 | 2 | | 4.6610 | 3.4502 | 3 | | 4.3874 | 3.4030 | 4 | | 4.2103 | 3.3568 | 5 | | 4.0930 | 3.3311 | 6 | | 4.0061 | 3.3257 | 7 | | b84657b6e8e4959901c97ebc22bf0a98 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4659 - Wer: 0.3080 | 20801c327db9adc3cdc1747f21b8ca73 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5787 | 0.87 | 500 | 1.7648 | 1.0305 | | 0.8692 | 1.73 | 1000 | 0.5136 | 0.5103 | | 0.4346 | 2.6 | 1500 | 0.4364 | 0.4515 | | 0.31 | 3.46 | 2000 | 0.3889 | 0.4070 | | 0.234 | 4.33 | 2500 | 0.4161 | 0.3863 | | 0.2054 | 5.19 | 3000 | 0.3845 | 0.3722 | | 0.165 | 6.06 | 3500 | 0.4035 | 0.3643 | | 0.1436 | 6.92 | 4000 | 0.4090 | 0.3623 | | 0.1381 | 7.79 | 4500 | 0.4007 | 0.3673 | | 0.1175 | 8.65 | 5000 | 0.4588 | 0.3632 | | 0.1052 | 9.52 | 5500 | 0.4441 | 0.3588 | | 0.0988 | 10.38 | 6000 | 0.4133 | 0.3489 | | 0.0877 | 11.25 | 6500 | 0.4758 | 0.3510 | | 0.0856 | 12.11 | 7000 | 0.4454 | 0.3425 | | 0.0731 | 12.98 | 7500 | 0.4252 | 0.3351 | | 0.0712 | 13.84 | 8000 | 0.4163 | 0.3370 | | 0.0711 | 14.71 | 8500 | 0.4166 | 0.3367 | | 0.06 | 15.57 | 9000 | 0.4195 | 0.3347 | | 0.0588 | 16.44 | 9500 | 0.4697 | 0.3367 | | 0.0497 | 17.3 | 10000 | 0.4255 | 0.3314 | | 0.0523 | 18.17 | 10500 | 0.4676 | 0.3307 | | 0.0444 | 19.03 | 11000 | 0.4570 | 0.3244 | | 0.0435 | 19.9 | 11500 | 0.4307 | 0.3243 | | 0.0348 | 20.76 | 12000 | 0.4763 | 0.3245 | | 0.036 | 21.63 | 12500 | 0.4635 | 0.3238 | | 0.0347 | 22.49 | 13000 | 0.4602 | 0.3212 | | 0.0333 | 23.36 | 13500 | 0.4472 | 0.3195 | | 0.0311 | 24.22 | 14000 | 0.4449 | 0.3183 | | 0.0294 | 25.09 | 14500 | 0.4631 | 0.3175 | | 0.025 | 25.95 | 15000 | 0.4466 | 0.3164 | | 0.023 | 26.82 | 15500 | 0.4581 | 0.3138 | | 0.0216 | 27.68 | 16000 | 0.4665 | 0.3114 | | 0.0198 | 28.55 | 16500 | 0.4590 | 0.3092 | | 0.0181 | 29.41 | 17000 | 0.4659 | 0.3080 | | 2067decaffd4e441ba4236df13d263f3 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_sst2_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4543 - Accuracy: 0.7982 | 56c1e7132163182d003c396121f97e3c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6677 | 1.0 | 527 | 0.6771 | 0.5757 | | 0.5966 | 2.0 | 1054 | 0.7135 | 0.5424 | | 0.5714 | 3.0 | 1581 | 0.7271 | 0.5550 | | 0.5573 | 4.0 | 2108 | 0.6892 | 0.5619 | | 0.501 | 5.0 | 2635 | 0.4546 | 0.7798 | | 0.2856 | 6.0 | 3162 | 0.4613 | 0.8050 | | 0.2288 | 7.0 | 3689 | 0.4543 | 0.7982 | | 0.2027 | 8.0 | 4216 | 0.4662 | 0.7993 | | 0.1883 | 9.0 | 4743 | 0.5168 | 0.8039 | | 0.1779 | 10.0 | 5270 | 0.5748 | 0.7856 | | 0.1691 | 11.0 | 5797 | 0.5196 | 0.8028 | | 0.1596 | 12.0 | 6324 | 0.5943 | 0.7947 | | 7993b8a7da6a6305638553e7aae60f92 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__subj__train-8-7 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: 0.2766 - Accuracy: 0.8845 | c7ef5497f5c218e7e4321b2bbb6e15f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7044 | 1.0 | 3 | 0.6909 | 0.5 | | 0.6678 | 2.0 | 6 | 0.6901 | 0.5 | | 0.6336 | 3.0 | 9 | 0.6807 | 0.5 | | 0.5926 | 4.0 | 12 | 0.6726 | 0.5 | | 0.5221 | 5.0 | 15 | 0.6648 | 0.5 | | 0.4573 | 6.0 | 18 | 0.6470 | 0.5 | | 0.4177 | 7.0 | 21 | 0.6251 | 0.5 | | 0.3252 | 8.0 | 24 | 0.5994 | 0.5 | | 0.2831 | 9.0 | 27 | 0.5529 | 0.5 | | 0.213 | 10.0 | 30 | 0.5078 | 0.75 | | 0.1808 | 11.0 | 33 | 0.4521 | 1.0 | | 0.1355 | 12.0 | 36 | 0.3996 | 1.0 | | 0.1027 | 13.0 | 39 | 0.3557 | 1.0 | | 0.0862 | 14.0 | 42 | 0.3121 | 1.0 | | 0.0682 | 15.0 | 45 | 0.2828 | 1.0 | | 0.0517 | 16.0 | 48 | 0.2603 | 1.0 | | 0.0466 | 17.0 | 51 | 0.2412 | 1.0 | | 0.038 | 18.0 | 54 | 0.2241 | 1.0 | | 0.0276 | 19.0 | 57 | 0.2096 | 1.0 | | 0.0246 | 20.0 | 60 | 0.1969 | 1.0 | | 0.0249 | 21.0 | 63 | 0.1859 | 1.0 | | 0.0201 | 22.0 | 66 | 0.1770 | 1.0 | | 0.018 | 23.0 | 69 | 0.1703 | 1.0 | | 0.0164 | 24.0 | 72 | 0.1670 | 1.0 | | 0.0172 | 25.0 | 75 | 0.1639 | 1.0 | | 0.0135 | 26.0 | 78 | 0.1604 | 1.0 | | 0.014 | 27.0 | 81 | 0.1585 | 1.0 | | 0.0108 | 28.0 | 84 | 0.1569 | 1.0 | | 0.0116 | 29.0 | 87 | 0.1549 | 1.0 | | 0.0111 | 30.0 | 90 | 0.1532 | 1.0 | | 0.0113 | 31.0 | 93 | 0.1513 | 1.0 | | 0.0104 | 32.0 | 96 | 0.1503 | 1.0 | | 0.01 | 33.0 | 99 | 0.1490 | 1.0 | | 0.0079 | 34.0 | 102 | 0.1479 | 1.0 | | 0.0097 | 35.0 | 105 | 0.1466 | 1.0 | | 0.0112 | 36.0 | 108 | 0.1458 | 1.0 | | 0.0091 | 37.0 | 111 | 0.1457 | 1.0 | | 0.0098 | 38.0 | 114 | 0.1454 | 1.0 | | 0.0076 | 39.0 | 117 | 0.1451 | 1.0 | | 0.0085 | 40.0 | 120 | 0.1448 | 1.0 | | 0.0079 | 41.0 | 123 | 0.1445 | 1.0 | | 0.0096 | 42.0 | 126 | 0.1440 | 1.0 | | 0.0081 | 43.0 | 129 | 0.1430 | 1.0 | | 0.0083 | 44.0 | 132 | 0.1424 | 1.0 | | 0.0088 | 45.0 | 135 | 0.1418 | 1.0 | | 0.0077 | 46.0 | 138 | 0.1414 | 1.0 | | 0.0073 | 47.0 | 141 | 0.1413 | 1.0 | | 0.0084 | 48.0 | 144 | 0.1412 | 1.0 | | 0.0072 | 49.0 | 147 | 0.1411 | 1.0 | | 0.0077 | 50.0 | 150 | 0.1411 | 1.0 | | a0aa3d5f0354a55d39f465a057e2a8a2 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Greek This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 90ea8f90060e801ed106eea1a0535c9e |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") ``` | f2a919e3f6ee372b6983edd22e7c2e48 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-br-d2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set: - Loss: 1.1257 - Wer: 0.4631 | a7ed5d4710e245091b9bed890bdce717 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Breton language isn't available in speech-recognition-community-v2/dev_data | ec4aa26c838ad0c8ae41cb9cb4bccf4c |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00034 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 750 - num_epochs: 50 - mixed_precision_training: Native AMP | d187ae370ad795e0119b03468fd2e9b9 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 14.0379 | 0.68 | 100 | 5.6808 | 1.0 | | 3.9145 | 1.35 | 200 | 3.1970 | 1.0 | | 3.0293 | 2.03 | 300 | 2.9513 | 1.0 | | 2.0927 | 2.7 | 400 | 1.4545 | 0.8887 | | 1.1556 | 3.38 | 500 | 1.0966 | 0.7564 | | 0.9628 | 4.05 | 600 | 0.9808 | 0.7364 | | 0.7869 | 4.73 | 700 | 1.0488 | 0.7355 | | 0.703 | 5.41 | 800 | 0.9500 | 0.6881 | | 0.6657 | 6.08 | 900 | 0.9309 | 0.6259 | | 0.5663 | 6.76 | 1000 | 0.9133 | 0.6357 | | 0.496 | 7.43 | 1100 | 0.9890 | 0.6028 | | 0.4748 | 8.11 | 1200 | 0.9469 | 0.5894 | | 0.4135 | 8.78 | 1300 | 0.9270 | 0.6045 | | 0.3579 | 9.46 | 1400 | 0.8818 | 0.5708 | | 0.353 | 10.14 | 1500 | 0.9244 | 0.5781 | | 0.334 | 10.81 | 1600 | 0.9009 | 0.5638 | | 0.2917 | 11.49 | 1700 | 1.0132 | 0.5828 | | 0.29 | 12.16 | 1800 | 0.9696 | 0.5668 | | 0.2691 | 12.84 | 1900 | 0.9811 | 0.5455 | | 0.25 | 13.51 | 2000 | 0.9951 | 0.5624 | | 0.2467 | 14.19 | 2100 | 0.9653 | 0.5573 | | 0.2242 | 14.86 | 2200 | 0.9714 | 0.5378 | | 0.2066 | 15.54 | 2300 | 0.9829 | 0.5394 | | 0.2075 | 16.22 | 2400 | 1.0547 | 0.5520 | | 0.1923 | 16.89 | 2500 | 1.0014 | 0.5397 | | 0.1919 | 17.57 | 2600 | 0.9978 | 0.5477 | | 0.1908 | 18.24 | 2700 | 1.1064 | 0.5397 | | 0.157 | 18.92 | 2800 | 1.0629 | 0.5238 | | 0.159 | 19.59 | 2900 | 1.0642 | 0.5321 | | 0.1652 | 20.27 | 3000 | 1.0207 | 0.5328 | | 0.141 | 20.95 | 3100 | 0.9948 | 0.5312 | | 0.1417 | 21.62 | 3200 | 1.0338 | 0.5328 | | 0.1514 | 22.3 | 3300 | 1.0513 | 0.5313 | | 0.1365 | 22.97 | 3400 | 1.0357 | 0.5291 | | 0.1319 | 23.65 | 3500 | 1.0587 | 0.5167 | | 0.1298 | 24.32 | 3600 | 1.0636 | 0.5236 | | 0.1245 | 25.0 | 3700 | 1.1367 | 0.5280 | | 0.1114 | 25.68 | 3800 | 1.0633 | 0.5200 | | 0.1088 | 26.35 | 3900 | 1.0495 | 0.5210 | | 0.1175 | 27.03 | 4000 | 1.0897 | 0.5095 | | 0.1043 | 27.7 | 4100 | 1.0580 | 0.5309 | | 0.0951 | 28.38 | 4200 | 1.0448 | 0.5067 | | 0.1011 | 29.05 | 4300 | 1.0665 | 0.5137 | | 0.0889 | 29.73 | 4400 | 1.0579 | 0.5026 | | 0.0833 | 30.41 | 4500 | 1.0740 | 0.5037 | | 0.0889 | 31.08 | 4600 | 1.0933 | 0.5083 | | 0.0784 | 31.76 | 4700 | 1.0715 | 0.5089 | | 0.0767 | 32.43 | 4800 | 1.0658 | 0.5049 | | 0.0769 | 33.11 | 4900 | 1.1118 | 0.4979 | | 0.0722 | 33.78 | 5000 | 1.1413 | 0.4986 | | 0.0709 | 34.46 | 5100 | 1.0706 | 0.4885 | | 0.0664 | 35.14 | 5200 | 1.1217 | 0.4884 | | 0.0648 | 35.81 | 5300 | 1.1298 | 0.4941 | | 0.0657 | 36.49 | 5400 | 1.1330 | 0.4920 | | 0.0582 | 37.16 | 5500 | 1.0598 | 0.4835 | | 0.0602 | 37.84 | 5600 | 1.1097 | 0.4943 | | 0.0598 | 38.51 | 5700 | 1.0976 | 0.4876 | | 0.0547 | 39.19 | 5800 | 1.0734 | 0.4825 | | 0.0561 | 39.86 | 5900 | 1.0926 | 0.4850 | | 0.0516 | 40.54 | 6000 | 1.1579 | 0.4751 | | 0.0478 | 41.22 | 6100 | 1.1384 | 0.4706 | | 0.0396 | 41.89 | 6200 | 1.1462 | 0.4739 | | 0.0472 | 42.57 | 6300 | 1.1277 | 0.4732 | | 0.0447 | 43.24 | 6400 | 1.1517 | 0.4752 | | 0.0423 | 43.92 | 6500 | 1.1219 | 0.4784 | | 0.0426 | 44.59 | 6600 | 1.1311 | 0.4724 | | 0.0391 | 45.27 | 6700 | 1.1135 | 0.4692 | | 0.0362 | 45.95 | 6800 | 1.0878 | 0.4645 | | 0.0329 | 46.62 | 6900 | 1.1137 | 0.4668 | | 0.0356 | 47.3 | 7000 | 1.1233 | 0.4687 | | 0.0328 | 47.97 | 7100 | 1.1238 | 0.4653 | | 0.0323 | 48.65 | 7200 | 1.1307 | 0.4646 | | 0.0325 | 49.32 | 7300 | 1.1242 | 0.4645 | | 0.03 | 50.0 | 7400 | 1.1257 | 0.4631 | | aa29ab3938d5791dac227a14128d45b6 |
gpl-3.0 | [] | false | Pre-trained word embeddings using the text of published biomedical manuscripts. These embeddings use 100 dimensions and were trained using the GloVe algorithm on all published manuscripts found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/ Citation: ``` @article{flamholz2022word, title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information}, author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E}, journal={Journal of Biomedical Informatics}, volume={125}, pages={103971}, year={2022}, publisher={Elsevier} } ``` | be620bd2c8770dd6b23465cd9c85080f |
gpl-3.0 | [] | false | Quick start Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format. First download the files from this archive. Then load the embeddings into Python. ```python from gensim.models import FastText, Word2Vec, KeyedVectors | 653d6a4e0cfe50a1e9afa3f189ad2021 |
gpl-3.0 | [] | false | Try out cosine similarity model.similarity('copd', 'chronic_obstructive_pulmonary_disease') model.similarity('myocardial_infarction', 'heart_attack') model.similarity('lymphangioleiomyomatosis', 'lam') ``` | f3af6bfc63c651b087f9305e16ca5e75 |
apache-2.0 | ['generated_from_trainer'] | false | mbert-finetuned-azerbaijani-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1385 - Precision: 0.8899 - Recall: 0.9154 - F1: 0.9025 - Accuracy: 0.9669 | 94ed43b1b511a394b9bde45f679863ea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2928 | 1.0 | 625 | 0.1415 | 0.8584 | 0.8918 | 0.8748 | 0.9595 | | 0.1254 | 2.0 | 1250 | 0.1335 | 0.8875 | 0.9119 | 0.8996 | 0.9637 | | 0.077 | 3.0 | 1875 | 0.1385 | 0.8899 | 0.9154 | 0.9025 | 0.9669 | | d4c9f6eba58063f325c1194980af1669 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5499 - Wer: 0.3435 | 236fdd285948e0b7d9b95e208ecd601c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.599 | 1.0 | 500 | 2.1267 | 0.9976 | | 1.016 | 2.01 | 1000 | 0.6193 | 0.5443 | | 0.5299 | 3.01 | 1500 | 0.5324 | 0.4889 | | 0.3626 | 4.02 | 2000 | 0.4525 | 0.4402 | | 0.2854 | 5.02 | 2500 | 0.4266 | 0.4233 | | 0.2373 | 6.02 | 3000 | 0.4713 | 0.4082 | | 0.1979 | 7.03 | 3500 | 0.4778 | 0.4018 | | 0.1761 | 8.03 | 4000 | 0.4585 | 0.3947 | | 0.1537 | 9.04 | 4500 | 0.5297 | 0.3946 | | 0.1379 | 10.04 | 5000 | 0.4988 | 0.3856 | | 0.124 | 11.04 | 5500 | 0.5262 | 0.3852 | | 0.11 | 12.05 | 6000 | 0.5545 | 0.3854 | | 0.106 | 13.05 | 6500 | 0.5196 | 0.3805 | | 0.0918 | 14.06 | 7000 | 0.4515 | 0.3655 | | 0.0829 | 15.06 | 7500 | 0.5087 | 0.3722 | | 0.0775 | 16.06 | 8000 | 0.4980 | 0.3781 | | 0.0685 | 17.07 | 8500 | 0.5564 | 0.3650 | | 0.0655 | 18.07 | 9000 | 0.5323 | 0.3672 | | 0.0578 | 19.08 | 9500 | 0.5675 | 0.3637 | | 0.052 | 20.08 | 10000 | 0.5604 | 0.3664 | | 0.0512 | 21.08 | 10500 | 0.5922 | 0.3804 | | 0.0431 | 22.09 | 11000 | 0.6379 | 0.3754 | | 0.0428 | 23.09 | 11500 | 0.5905 | 0.3764 | | 0.0393 | 24.1 | 12000 | 0.5667 | 0.3542 | | 0.0326 | 25.1 | 12500 | 0.5612 | 0.3537 | | 0.0289 | 26.1 | 13000 | 0.5618 | 0.3475 | | 0.0298 | 27.11 | 13500 | 0.5578 | 0.3439 | | 0.0264 | 28.11 | 14000 | 0.5547 | 0.3433 | | 0.026 | 29.12 | 14500 | 0.5499 | 0.3435 | | 2e0d178a5a709f29e4ff9d82f12650ce |
apache-2.0 | ['generated_from_trainer'] | false | muril-base-cased-finetuned-combined-DS This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5291 - Accuracy: 0.6657 - Precision: 0.6355 - Recall: 0.6275 - F1: 0.6294 | 31a3e8ec2c600bcfd72eb41d80763683 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 | 2c3b59cee106d8d42ad6a08c83bd3739 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9961 | 2.0 | 711 | 0.9148 | 0.5625 | 0.5495 | 0.5636 | 0.5265 | | 0.8211 | 3.99 | 1422 | 0.8542 | 0.6096 | 0.6023 | 0.6071 | 0.5928 | | 0.6667 | 5.99 | 2133 | 0.8459 | 0.6601 | 0.6366 | 0.6379 | 0.6361 | | 0.5272 | 7.99 | 2844 | 0.9667 | 0.6517 | 0.6190 | 0.6223 | 0.6201 | | 0.4327 | 9.99 | 3555 | 1.0185 | 0.6503 | 0.6351 | 0.6222 | 0.6229 | | 0.3608 | 11.98 | 4266 | 1.1409 | 0.6313 | 0.6053 | 0.6100 | 0.6049 | | 0.3038 | 13.98 | 4977 | 1.2336 | 0.6601 | 0.6287 | 0.6269 | 0.6273 | | 0.2631 | 15.98 | 5688 | 1.3151 | 0.6503 | 0.6199 | 0.6167 | 0.6177 | | 0.2368 | 17.97 | 6399 | 1.4230 | 0.6594 | 0.6315 | 0.6233 | 0.6251 | | 0.2093 | 19.97 | 7110 | 1.4881 | 0.6629 | 0.6332 | 0.6220 | 0.6239 | | 0.1968 | 21.97 | 7821 | 1.5003 | 0.6559 | 0.6279 | 0.6230 | 0.6242 | | 0.1824 | 23.97 | 8532 | 1.5291 | 0.6657 | 0.6355 | 0.6275 | 0.6294 | | 934f12c76c655e74247f1349b828864b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Uzbek This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 uz dataset. It achieves the following results on the evaluation set: - Loss: 0.4357 - Wer: 25.7857 | f89547f81404ac8605fb4a222f5a1178 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 8000 - mixed_precision_training: Native AMP | b8847d624488bbb19a7904560527e684 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3621 | 1.03 | 1000 | 0.4819 | 32.3209 | | 0.2378 | 2.07 | 2000 | 0.4413 | 29.0077 | | 0.2342 | 4.01 | 3000 | 0.4224 | 27.3939 | | 0.1286 | 5.04 | 4000 | 0.4357 | 25.7857 | | 0.1192 | 6.08 | 5000 | 0.4727 | 27.2752 | | 0.0147 | 8.02 | 6000 | 0.5230 | 26.7267 | | 0.0425 | 9.05 | 7000 | 0.5336 | 26.3628 | | 0.0059 | 10.08 | 8000 | 0.5658 | 26.8476 | | 3949412066bb225e644406031ffa1e2a |
apache-2.0 | ['irish', 'electra'] | false | gaELECTRA [gaELECTRA](https://arxiv.org/abs/2107.12930) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model. | 0bb6104f28cb58accf9c0c46f900dd67 |
apache-2.0 | ['irish', 'electra'] | false | Limitations and bias Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. | 54168863f82301268316009cae11c251 |
apache-2.0 | ['irish', 'electra'] | false | BibTeX entry and citation info If you use this model in your research, please consider citing our paper: ``` @article{DBLP:journals/corr/abs-2107-12930, author = {James Barry and Joachim Wagner and Lauren Cassidy and Alan Cowap and Teresa Lynn and Abigail Walsh and M{\'{\i}}che{\'{a}}l J. {\'{O}} Meachair and Jennifer Foster}, title = {gaBERT - an Irish Language Model}, journal = {CoRR}, volume = {abs/2107.12930}, year = {2021}, url = {https://arxiv.org/abs/2107.12930}, archivePrefix = {arXiv}, eprint = {2107.12930}, timestamp = {Fri, 30 Jul 2021 13:03:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2107-12930.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | 607e3d93bc2ef96ca22381ab43947000 |
mit | ['generated_from_keras_callback'] | false | Deep98/Web_browser-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1604 - Train End Logits Accuracy: 0.9826 - Train Start Logits Accuracy: 0.9375 - Validation Loss: 0.0757 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | f549ea963a900fe656e52ea5a6b941b0 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1604 | 0.9826 | 0.9375 | 0.0757 | 1.0 | 1.0 | 0 | | cb2d4a38af8c440cc849c9ade57ebf2b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | p-AI-nter -- v0.2 Core model is SD-1.5, trained on artworks of different painters (Rob Hefferan, Anna Marinova, Omar Ortiz, Thomas Saliot, Serge Marshennikov). Use the token 'oil painting' in your prompts for better effect. > Trained with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook. Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb). Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). | 195d40540be7980cadbc6e823b40f234 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Prompt and settings for samples ``` (portrait photo)++ of (young)+ woman on river bank, dressed in silk shirt, golden and white and bronze color scheme, (oil painting)+, (epic composition)+, intricate, Highly Detailed, Sharp focus, dramatic light, (high bun black hair)++, (bokeh)+, (deep eyes)+, (sunset)++, (model pose)+, (ideal hands)++, (ray tracing)++, (cleavage)+, (ideal breast)+ ``` __negative:__ ``` Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, extra limb, ugly, poorly drawn hands, missing limb, blurry, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, mutated hands and fingers, fat, overweight, multiple heads, group of people, three or more legs, cross-eye, nude, naked, naked, (extra fingers)+, (fused fingers)+ ``` * Steps: 50 * Scale: 9 * Sampler: Euler_A - - - | 0ecfca210d992bf9345624e468baa6a2 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-fr_s957 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 0bcdb32e482a3e319979b83f304d6731 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4601 - Precision: 0.1802 - Recall: 0.0830 - F1: 0.1137 - Accuracy: 0.8143 | 5ce21c7682bbc88881cfb2885576d6dd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 33 | 0.5687 | 0.0882 | 0.0015 | 0.0030 | 0.7791 | | No log | 2.0 | 66 | 0.5410 | 0.1319 | 0.0270 | 0.0448 | 0.7946 | | No log | 3.0 | 99 | 0.4601 | 0.1802 | 0.0830 | 0.1137 | 0.8143 | | 9623bb9b7762de0e24f044286e454ef2 |
other | ['vision', 'image-segmentation'] | false | MaskFormer MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py | 5484ff243f1c40a01cf212a3250665ae |
other | ['vision', 'image-segmentation'] | false | Model description MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation.  | da1151ba5570964761e40d501d4cd8e9 |
other | ['vision', 'image-segmentation'] | false | Intended uses & limitations You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other fine-tuned versions on a task that interests you. | bc815cf8c1cb42302c4840d0b895a5eb |
other | ['vision', 'image-segmentation'] | false | load MaskFormer fine-tuned on COCO panoptic segmentation feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-coco") model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-coco") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) | e0c1fac3e4a6bcbe462d43a028155b7c |
other | ['vision', 'image-segmentation'] | false | we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer). | 7a2b3b38e617bdebf0c1fba674cd0266 |
apache-2.0 | [] | false | ALBERT XXLarge model HPU configuration This model only contains the `GaudiConfig` file for running the [albert-xxlarge-v1](https://huggingface.co/albert-xxlarge-v1) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP) - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html | 642c0634d870300e3aa069e10d9c564a |
apache-2.0 | [] | false | Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with ALBERT XXL with the following command: ```bash python run_qa.py \ --model_name_or_path albert-xxlarge-v1 \ --gaudi_config_name Habana/albert-xxlarge-v1 \ --dataset_name squad \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --per_device_eval_batch_size 2 \ --learning_rate 5e-6 \ --num_train_epochs 2 \ --max_seq_length 384 \ --output_dir /tmp/squad/ \ --use_habana \ --use_lazy_mode \ --throughput_warmup_steps 2 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. | 9642a212db4599088889a9cc1f626ad3 |
mit | [] | false | ResNet101 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, [add the Metalhead.jl package to your environment](https://pkgdocs.julialang.org/v1/managing-packages/ | 2dd4b489734d607e3a21bfce1e682670 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Zulu This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs zu_za dataset. It achieves the following results on the evaluation set: - Loss: 1.1143 - Wer: 56.7866 | 2d495a840fe25838c750e9581e364ba9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 200 - mixed_precision_training: Native AMP | aa05b0903ed858aa32b5731c0d5b5aa6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6219 | 9.01 | 100 | 1.0758 | 62.0201 | | 0.0318 | 18.01 | 200 | 1.1143 | 56.7866 | | adcf3ab518d72be669cb5495aecb7c89 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-hu-uk Neural machine translation model for translating from Hungarian (hu) to Ukrainian (uk). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` | 3c00dfc21e5c98427a070bbd439d3290 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-08 * source language(s): hun * target language(s): ukr * model: transformer-align * data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pbt_transformer-align_2022-03-08.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.zip) * more information released models: [OPUS-MT hun-ukr README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hun-ukr/README.md) | 20885920063c788e237bc0a3a64edfdb |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "1000 dollárral tartozom neked.", "Vizet iszom." ] model_name = "pytorch-models/opus-mt-tc-base-hu-uk" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | 197817f52a3c96dd18cac59fb88e78dc |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Я п'ю воду. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-hu-uk") print(pipe("1000 dollárral tartozom neked.")) | 6b2c547d6a89b942c8dc12c5bbf8de2d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt) * test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 122050e6af5ce249e422953ad9ad6e0a |
apache-2.0 | ['generated_from_trainer'] | false | medium-mlm-imdb-target-tweet This model is a fine-tuned version of [muhtasham/medium-mlm-imdb](https://huggingface.co/muhtasham/medium-mlm-imdb) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.6869 - Accuracy: 0.7620 - F1: 0.7599 | 65ea4f667ccf1781c0f52eb6f1888016 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.456 | 4.9 | 500 | 0.8890 | 0.7754 | 0.7720 | | 0.0578 | 9.8 | 1000 | 1.3492 | 0.7540 | 0.7509 | | 0.0173 | 14.71 | 1500 | 1.6143 | 0.7594 | 0.7584 | | 0.0124 | 19.61 | 2000 | 1.6869 | 0.7620 | 0.7599 | | 56b77c01a99f27b6774e87b0beb6d8e8 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-finetuned-cifar10-test This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. | 21fa78d0e16cb5addd4c80c7fdbe7ced |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 | ee3b7c981ab6c6a1802530640087b31e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-checkpoint-8 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-7.1](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-7.1) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9561 - Wer: 0.3271 | 296e1a04a9b876b3b681fe913a4c0255 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3117 | 1.59 | 1000 | 0.5514 | 0.3451 | | 0.2509 | 3.19 | 2000 | 0.5912 | 0.3328 | | 0.1918 | 4.78 | 3000 | 0.6103 | 0.3346 | | 0.1612 | 6.38 | 4000 | 0.6469 | 0.3377 | | 0.1388 | 7.97 | 5000 | 0.6597 | 0.3391 | | 0.121 | 9.57 | 6000 | 0.6911 | 0.3472 | | 0.1096 | 11.16 | 7000 | 0.7300 | 0.3457 | | 0.0959 | 12.76 | 8000 | 0.7660 | 0.3400 | | 0.0882 | 14.35 | 9000 | 0.8316 | 0.3394 | | 0.0816 | 15.95 | 10000 | 0.8042 | 0.3357 | | 0.0739 | 17.54 | 11000 | 0.8087 | 0.3346 | | 0.0717 | 19.14 | 12000 | 0.8590 | 0.3353 | | 0.066 | 20.73 | 13000 | 0.8750 | 0.3336 | | 0.0629 | 22.33 | 14000 | 0.8759 | 0.3333 | | 0.0568 | 23.92 | 15000 | 0.8963 | 0.3321 | | 0.0535 | 25.52 | 16000 | 0.9391 | 0.3323 | | 0.0509 | 27.11 | 17000 | 0.9279 | 0.3296 | | 0.0498 | 28.71 | 18000 | 0.9561 | 0.3271 | | e77fa532ea2a7cf0e24f7ffff6ffb762 |
other | ['generated_from_trainer'] | false | 6.7b-dalio-book-handwritten-io-constant-1e-6-v2 This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 dataset. It achieves the following results on the evaluation set: - Loss: 2.4238 - Accuracy: 0.2793 | 09b1fb93898d3adf131471d12f2024c5 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 | a24d8803d8daf7e840170c2d4a991fc4 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.5852 | 0.08 | 6 | 2.5957 | 0.2697 | | 2.5956 | 0.16 | 12 | 2.5762 | 0.2706 | | 2.5961 | 0.24 | 18 | 2.5547 | 0.2711 | | 2.5731 | 0.32 | 24 | 2.5312 | 0.2722 | | 2.5415 | 0.4 | 30 | 2.5117 | 0.2734 | | 2.5168 | 0.48 | 36 | 2.4961 | 0.2746 | | 2.4972 | 0.56 | 42 | 2.4824 | 0.2756 | | 2.4354 | 0.64 | 48 | 2.4727 | 0.2761 | | 2.4055 | 0.72 | 54 | 2.4609 | 0.2768 | | 2.4681 | 0.8 | 60 | 2.4492 | 0.2778 | | 2.5866 | 0.88 | 66 | 2.4355 | 0.2784 | | 2.4221 | 0.96 | 72 | 2.4238 | 0.2793 | | 16228c7ef14e5e40ba46cb293367e709 |
mit | [] | false | SAS style on Stable Diffusion This is the `<smooth-aesthetic-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:     | 7b90cbfccec9ba9777e6b3cb83dd78a7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | shru Dreambooth model trained by Suniljl with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | a199be4e0b806848371a28132f19e431 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | RPG: Source(s): [Hugging Face](https://huggingface.co/Anashel/rpg) - [CivitAI](https://civitai.com/models/1116/rpg) **Latest Update: Feb 5th, 2023** - Version 4.0 is live **[available here](https://huggingface.co/Anashel/rpg/tree/main/RPG-V4-Model-Download)** - New Prompt User Guide for RPG v4 **[Download Now](https://huggingface.co/Anashel/rpg/resolve/main/RPG-V4-Model-Download/RPG-Guide-v4.pdf)** | 2ea10b32f609e697465d95049f81ba69 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Contribute If you wish to support the prompt research on this project. - Rate RPG V4 on **[CivitAI](https://civitai.com/models/1116/rpg)** - Donate (ETH Only): anashel.eth | 0xc4055f3c65D01a48Bc47bE87751794eA9f42E367 | 7c742602e3b5bbf73c959570c35e3999 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Future Updates I am in the process of writing a detailed guide with a list of word you can switch easily in the main prompt. Ex: Blood Elf Knight, Female Death Knight Mage, etc... In the meantime, fell free to share your creation on my *[Discord Server](https://discord.gg/7CGDRjDz7P)* --- | f83f31169b6fef91d2716db8f8faa65d |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | RPG v4 Render Sample       --- **How to reach me** - Reddit: [u/Anashel](https://www.reddit.com/user/anashel) - Discord: [RPG V3 Channel](https://discord.gg/rDrhtWZk8u) ---- | b009ca9fc40f4ae3d896a26f22dc187d |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | RPG v3 Render Sample      | 0f78836fc4bf7a7f90cb0e9d9b6f1911 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | RPG v2 Render Sample Genereated with RPG V2. [Available here](https://huggingface.co/Anashel/rpg/tree/main/All-Concept-Zip-Format)     ---- | ec7ede4e7d97b7c77295c3d43d8ad2a8 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | OTHER EXAMPLE        | e7541cef40b10e7aed6d85a9fa826d31 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-fr_s473 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 80adf6cb378b7e0ca110058c34346948 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-distilbert-base 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. | 0e621e0158df0fb5f98a76634bc7d467 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | 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('sentence-transformers/nli-distilbert-base') embeddings = model.encode(sentences) print(embeddings) ``` | 07392fcc848e98199d4a186f09aa4cdf |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/nli-distilbert-base) | e73ab560e1ebe59ef2db662285f8a316 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (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}) ) ``` | 653b731264175e7302889c412c94c952 |
apache-2.0 | ['tapas', 'TapasModel'] | false | TAPAS mini model This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is the one with absolute position embeddings: - `revision="no_reset"`, which corresponds to `tapas_inter_masklm_mini` Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. | 509fd3b0bfb988dbf4584e45e7f015ea |
apache-2.0 | ['tapas', 'TapasModel'] | false | Model description TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of a table and associated text. - Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements. This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refuted by the contents of a table. Fine-tuning is done by adding one or more classification heads on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on a downstream task. | cdeb2219b998abe97bb06ae1c67a76ef |
apache-2.0 | ['tapas', 'TapasModel'] | false | Intended uses & limitations You can use the raw model for getting hidden representatons about table-question pairs, but it's mostly intended to be fine-tuned on a downstream task such as question answering or sequence classification. See the [model hub](https://huggingface.co/models?filter=tapas) to look for fine-tuned versions on a task that interests you. | 6adaa5d74277db23d48a7b24ef2835cd |
apache-2.0 | ['tapas', 'TapasModel'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence [SEP] Flattened table [SEP] ``` | cc6c4a1bb3888ee9000ded6b6b8bc19d |
apache-2.0 | ['tapas', 'TapasModel'] | false | Pre-training The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512. In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details. The optimizer used is Adam with a learning rate of 5e-5, and a warmup ratio of 0.01. | 023873ec760345864b65d129a90bad90 |
apache-2.0 | ['tapas', 'TapasModel'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ```bibtex @misc{eisenschlos2020understanding, title={Understanding tables with intermediate pre-training}, author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller}, year={2020}, eprint={2010.00571}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 39a1c1d382a04a6b2e7c4fa35343d32d |
apache-2.0 | ['translation'] | false | eng-iir * source group: English * target group: Indo-Iranian languages * OPUS readme: [eng-iir](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-iir/README.md) * model: transformer * source language(s): eng * target language(s): asm awa ben bho gom guj hif_Latn hin jdt_Cyrl kur_Arab kur_Latn mai mar npi ori oss pan_Guru pes pes_Latn pes_Thaa pnb pus rom san_Deva sin snd_Arab tgk_Cyrl tly_Latn urd zza * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.eval.txt) | 013c4f02c4f9bee330c7e24f29e3bf8c |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014-enghin.eng.hin | 6.7 | 0.326 | | newsdev2019-engu-engguj.eng.guj | 6.0 | 0.283 | | newstest2014-hien-enghin.eng.hin | 10.4 | 0.353 | | newstest2019-engu-engguj.eng.guj | 6.6 | 0.282 | | Tatoeba-test.eng-asm.eng.asm | 2.7 | 0.249 | | Tatoeba-test.eng-awa.eng.awa | 0.4 | 0.122 | | Tatoeba-test.eng-ben.eng.ben | 15.3 | 0.459 | | Tatoeba-test.eng-bho.eng.bho | 3.7 | 0.161 | | Tatoeba-test.eng-fas.eng.fas | 3.4 | 0.227 | | Tatoeba-test.eng-guj.eng.guj | 18.5 | 0.365 | | Tatoeba-test.eng-hif.eng.hif | 1.0 | 0.064 | | Tatoeba-test.eng-hin.eng.hin | 17.0 | 0.461 | | Tatoeba-test.eng-jdt.eng.jdt | 3.9 | 0.122 | | Tatoeba-test.eng-kok.eng.kok | 5.5 | 0.059 | | Tatoeba-test.eng-kur.eng.kur | 4.0 | 0.125 | | Tatoeba-test.eng-lah.eng.lah | 0.3 | 0.008 | | Tatoeba-test.eng-mai.eng.mai | 9.3 | 0.445 | | Tatoeba-test.eng-mar.eng.mar | 20.7 | 0.473 | | Tatoeba-test.eng.multi | 13.7 | 0.392 | | Tatoeba-test.eng-nep.eng.nep | 0.6 | 0.060 | | Tatoeba-test.eng-ori.eng.ori | 2.4 | 0.193 | | Tatoeba-test.eng-oss.eng.oss | 2.1 | 0.174 | | Tatoeba-test.eng-pan.eng.pan | 9.7 | 0.355 | | Tatoeba-test.eng-pus.eng.pus | 1.0 | 0.126 | | Tatoeba-test.eng-rom.eng.rom | 1.3 | 0.230 | | Tatoeba-test.eng-san.eng.san | 1.3 | 0.101 | | Tatoeba-test.eng-sin.eng.sin | 11.7 | 0.384 | | Tatoeba-test.eng-snd.eng.snd | 2.8 | 0.180 | | Tatoeba-test.eng-tgk.eng.tgk | 8.1 | 0.353 | | Tatoeba-test.eng-tly.eng.tly | 0.5 | 0.015 | | Tatoeba-test.eng-urd.eng.urd | 12.3 | 0.409 | | Tatoeba-test.eng-zza.eng.zza | 0.5 | 0.025 | | a3f63276318014d59c37019e588164e6 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-iir - source_languages: eng - target_languages: iir - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-iir/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'bn', 'or', 'gu', 'mr', 'ur', 'hi', 'ps', 'os', 'as', 'si', 'iir'] - src_constituents: {'eng'} - tgt_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'pes', 'bho', 'kur_Arab', 'tgk_Cyrl', 'hin', 'kur_Latn', 'pes_Thaa', 'pus', 'san_Deva', 'oss', 'tly_Latn', 'jdt_Cyrl', 'asm', 'zza', 'rom', 'mai', 'pes_Latn', 'awa', 'sin'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: iir - short_pair: en-iir - chrF2_score: 0.392 - bleu: 13.7 - brevity_penalty: 1.0 - ref_len: 63351.0 - src_name: English - tgt_name: Indo-Iranian languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: iir - prefer_old: False - long_pair: eng-iir - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | e7ac9442f643e01b7e62df35573aa2fe |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Coptic Scriptorium Corpora for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-coptic](https://huggingface.co/KoichiYasuoka/roberta-base-coptic). | dc3fd5fc4dfe13ee664a02056b1a6624 |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/roberta-base-coptic-ud-goeswith") print(nlp("ⲧⲉⲛⲟⲩⲇⲉⲛ̄ⲟⲩⲟⲉⲓⲛϩ︤ⲙ︥ⲡϫⲟⲉⲓⲥ·")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-coptic-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("ⲧⲉⲛⲟⲩⲇⲉⲛ̄ⲟⲩⲟⲉⲓⲛϩ︤ⲙ︥ⲡϫⲟⲉⲓⲥ·")) ``` | 3ba0cd2f942fd27eb6b205c4eb423039 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0612 - Precision: 0.9247 - Recall: 0.9385 - F1: 0.9315 - Accuracy: 0.9837 | 30ef77f28c888bfb47d38d5e87c5b24f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2421 | 1.0 | 878 | 0.0701 | 0.9083 | 0.9217 | 0.9149 | 0.9801 | | 0.0555 | 2.0 | 1756 | 0.0599 | 0.9204 | 0.9357 | 0.9280 | 0.9830 | | 0.0311 | 3.0 | 2634 | 0.0612 | 0.9247 | 0.9385 | 0.9315 | 0.9837 | | f4b8c180074b24e99c323e44e5b80ee1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 244 | 2.6029 | 29.4956 | 13.5156 | 25.8306 | 25.842 | 18.2896 | | d08ec19bb2ec4871486c775be2b82f82 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-es_s399 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | e6caa9fad84807b3ef1b10d401d8207b |
mit | [] | false | 🇹🇷 Turkish ELECTRA model <p align="center"> <img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png"> </p> [](https://zenodo.org/badge/latestdoi/237817454) We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉 Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk. Logo is provided by [Merve Noyan](https://twitter.com/mervenoyann). | 1f65a0c07628621a88ffaa40c07ba562 |
mit | [] | false | Stats We've also trained an ELECTRA (cased) model on the recently released Turkish part of the [multiligual C4 (mC4) corpus](https://github.com/allenai/allennlp/discussions/5265) from the AI2 team. After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting in 31,240,963,926 tokens. We used the original 32k vocab (instead of creating a new one). | 462f40fa41b39e0109968813143c324e |
mit | [] | false | mC4 ELECTRA In addition to the ELEC**TR**A base model, we also trained an ELECTRA model on the Turkish part of the mC4 corpus. We use a sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU. | 12f70195f7ac05610ed843d26884fd9f |
mit | [] | false | Model usage All trained models can be used from the [DBMDZ](https://github.com/dbmdz) Hugging Face [model hub page](https://huggingface.co/dbmdz) using their model name. Example usage with 🤗/Transformers: ```python tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-generator") model = AutoModel.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-generator") ``` | 540d121f9d91bd7b61a6af533acc8a7a |
mit | [] | false | Citation You can use the following BibTeX entry for citation: ```bibtex @software{stefan_schweter_2020_3770924, author = {Stefan Schweter}, title = {BERTurk - BERT models for Turkish}, month = apr, year = 2020, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.3770924}, url = {https://doi.org/10.5281/zenodo.3770924} } ``` | 53b2f31e660be4de2da3c905b88067bd |
mit | [] | false | Acknowledgments Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation. We would like to thank [Merve Noyan](https://twitter.com/mervenoyann) for the awesome logo! Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ | ec70cbc8e47af56f96a7ec76b9f303b2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.0332 - Accuracy: 0.9303 | 153670f8e579f7583b01d5bfaf148170 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4409 | 1.0 | 318 | 0.2288 | 0.6206 | | 0.1898 | 2.0 | 636 | 0.1106 | 0.8461 | | 0.116 | 3.0 | 954 | 0.0729 | 0.8994 | | 0.0861 | 4.0 | 1272 | 0.0548 | 0.9097 | | 0.0707 | 5.0 | 1590 | 0.0454 | 0.9184 | | 0.0613 | 6.0 | 1908 | 0.0399 | 0.9239 | | 0.0557 | 7.0 | 2226 | 0.0371 | 0.9294 | | 0.0522 | 8.0 | 2544 | 0.0348 | 0.93 | | 0.05 | 9.0 | 2862 | 0.0336 | 0.9297 | | 0.0487 | 10.0 | 3180 | 0.0332 | 0.9303 | | be4277e3571622a7c4a454870897f6e3 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-summarization-app This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6614 - Rouge1: 24.5589 - Rouge2: 11.8509 - Rougel: 20.3011 - Rougelsum: 23.1768 - Gen Len: 19.0 | f21dc429b575c603b0b5036e285fc8c6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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 - num_epochs: 2 - mixed_precision_training: Native AMP | dace0b27b7e0bfcdbcab480e34b84303 |
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