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_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:| | 1.642 | 4.16 | 100 | 1.5891 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.2135 | 1.0 | 0.3519 | 19 | 0.0 | 0.0 | 0.0 | 22 | 0.2784 | 0.3034 | 0.3145 | 0.1905 | 97 | 0.3614 | 0.2784 | 0.2000 | 97 | 0.9780 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]] | | 1.4791 | 8.33 | 200 | 1.3227 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.2135 | 1.0 | 0.3519 | 19 | 0.0 | 0.0 | 0.0 | 22 | 0.2784 | 0.3034 | 0.3145 | 0.1905 | 97 | 0.3614 | 0.2784 | 0.2000 | 97 | 0.9780 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]] | | 1.2376 | 12.49 | 300 | 1.0446 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.2135 | 1.0 | 0.3519 | 19 | 0.0 | 0.0 | 0.0 | 22 | 0.2784 | 0.3034 | 0.3145 | 0.1905 | 97 | 0.3614 | 0.2784 | 0.2000 | 97 | 0.9780 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]] | | 0.9622 | 16.65 | 400 | 0.8811 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.2135 | 1.0 | 0.3519 | 19 | 0.0 | 0.0 | 0.0 | 22 | 0.2784 | 0.3034 | 0.3145 | 0.1905 | 97 | 0.3614 | 0.2784 | 0.2000 | 97 | 0.9780 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]] | | 0.8614 | 20.82 | 500 | 0.8174 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.2135 | 1.0 | 0.3519 | 19 | 0.0 | 0.0 | 0.0 | 22 | 0.2784 | 0.3034 | 0.3145 | 0.1905 | 97 | 0.3614 | 0.2784 | 0.2000 | 97 | 0.9780 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]] | | 0.8344 | 24.98 | 600 | 0.7498 | 1.0 | 1.0 | 1.0 | 31 | 1.0 | 1.0 | 1.0 | 25 | 1.0 | 1.0 | 1.0 | 19 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 1.0 | 1.0 | 1.0 | 97 | 1.0 | 1.0 | 1.0 | 97 | 1.0 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 19, 0], [3, 0, 0, 0, 22]] | | 0.8105 | 29.16 | 700 | 0.7907 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 0.96 | 0.9796 | 25 | 0.95 | 1.0 | 0.9744 | 19 | 1.0 | 0.9545 | 0.9767 | 22 | 0.9794 | 0.9797 | 0.9786 | 0.9787 | 97 | 0.9802 | 0.9794 | 0.9794 | 97 | 1.0 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 1, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]] | | 0.6168 | 33.33 | 800 | 0.5496 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 0.96 | 0.9796 | 25 | 0.95 | 1.0 | 0.9744 | 19 | 1.0 | 0.9545 | 0.9767 | 22 | 0.9794 | 0.9797 | 0.9786 | 0.9787 | 97 | 0.9802 | 0.9794 | 0.9794 | 97 | 0.5840 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 1, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]] | | 0.2701 | 37.49 | 900 | 0.2587 | 1.0 | 1.0 | 1.0 | 31 | 1.0 | 0.96 | 0.9796 | 25 | 0.9474 | 0.9474 | 0.9474 | 19 | 0.9565 | 1.0 | 0.9778 | 22 | 0.9794 | 0.9760 | 0.9768 | 0.9762 | 97 | 0.9798 | 0.9794 | 0.9794 | 97 | 0.2375 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 1, 0], [2, 0, 0, 18, 1], [3, 0, 0, 0, 22]] | | 0.1745 | 41.65 | 1000 | 0.2219 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 1.0 | 1.0 | 25 | 1.0 | 0.9474 | 0.9730 | 19 | 0.9545 | 0.9545 | 0.9545 | 22 | 0.9794 | 0.9808 | 0.9755 | 0.9779 | 97 | 0.9797 | 0.9794 | 0.9793 | 97 | 0.2445 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]] | | 0.1494 | 45.82 | 1100 | 0.2548 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 0.96 | 0.9796 | 25 | 1.0 | 0.9474 | 0.9730 | 19 | 0.9130 | 0.9545 | 0.9333 | 22 | 0.9691 | 0.9704 | 0.9655 | 0.9675 | 97 | 0.9703 | 0.9691 | 0.9693 | 97 | 0.2352 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 0, 1], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]] | | 0.1213 | 49.98 | 1200 | 0.1756 | 0.9688 | 1.0 | 0.9841 | 31 | 0.9615 | 1.0 | 0.9804 | 25 | 1.0 | 0.9474 | 0.9730 | 19 | 1.0 | 0.9545 | 0.9767 | 22 | 0.9794 | 0.9826 | 0.9755 | 0.9786 | 97 | 0.9801 | 0.9794 | 0.9793 | 97 | 0.2260 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 18, 0], [3, 1, 0, 0, 21]] | | 0.0964 | 54.16 | 1300 | 0.1884 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 1.0 | 1.0 | 25 | 1.0 | 0.9474 | 0.9730 | 19 | 0.9545 | 0.9545 | 0.9545 | 22 | 0.9794 | 0.9808 | 0.9755 | 0.9779 | 97 | 0.9797 | 0.9794 | 0.9793 | 97 | 0.2260 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]] | | 0.0859 | 58.33 | 1400 | 0.1212 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 1.0 | 1.0 | 25 | 1.0 | 1.0 | 1.0 | 19 | 1.0 | 0.9545 | 0.9767 | 22 | 0.9897 | 0.9922 | 0.9886 | 0.9902 | 97 | 0.9900 | 0.9897 | 0.9897 | 97 | 0.2202 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]] | | 0.0845 | 62.49 | 1500 | 0.1254 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 1.0 | 1.0 | 25 | 1.0 | 1.0 | 1.0 | 19 | 1.0 | 0.9545 | 0.9767 | 22 | 0.9897 | 0.9922 | 0.9886 | 0.9902 | 97 | 0.9900 | 0.9897 | 0.9897 | 97 | 0.2178 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]] | | 0.0831 | 66.65 | 1600 | 0.1590 | 0.9688 | 1.0 | 0.9841 | 31 | 1.0 | 1.0 | 1.0 | 25 | 1.0 | 0.9474 | 0.9730 | 19 | 0.9545 | 0.9545 | 0.9545 | 22 | 0.9794 | 0.9808 | 0.9755 | 0.9779 | 97 | 0.9797 | 0.9794 | 0.9793 | 97 | 0.2202 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]] | | 1c5529b1b019ee3613b451a4df7316b4 |
mit | ['BERT', 'token-classification', 'sequence-tagger-model'] | false | Arabic NER Model - [Github repo](https://github.com/edchengg/GigaBERT) - NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English). - ACE2005 Training data: English + Arabic - [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) including: PER, VEH, GPE, WEA, ORG, LOC, FAC | bc0e8a87d87eb9cb4f50af49ba9d2968 |
mit | ['BERT', 'token-classification', 'sequence-tagger-model'] | false | How to use ```python >>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer >>> ner_model = AutoModelForTokenClassification.from_pretrained("ychenNLP/arabic-ner-ace") >>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace") >>> ner_pip = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True) >>> output = ner_pip('Protests break out across the US after Supreme Court overturns.') >>> print(output) [{'entity_group': 'GPE', 'score': 0.9979881, 'word': 'us', 'start': 30, 'end': 32}, {'entity_group': 'ORG', 'score': 0.99898684, 'word': 'supreme court', 'start': 39, 'end': 52}] >>> output = ner_pip('قال وزير العدل التركي بكير بوزداغ إن أنقرة تريد 12 مشتبهاً بهم من فنلندا و 21 من السويد') >>> print(output) [{'entity_group': 'PER', 'score': 0.9996214, 'word': 'وزير', 'start': 4, 'end': 8}, {'entity_group': 'ORG', 'score': 0.9952383, 'word': 'العدل', 'start': 9, 'end': 14}, {'entity_group': 'GPE', 'score': 0.9996675, 'word': 'التركي', 'start': 15, 'end': 21}, {'entity_group': 'PER', 'score': 0.9978992, 'word': 'بكير بوزداغ', 'start': 22, 'end': 33}, {'entity_group': 'GPE', 'score': 0.9997154, 'word': 'انقرة', 'start': 37, 'end': 42}, {'entity_group': 'PER', 'score': 0.9946885, 'word': 'مشتبها بهم', 'start': 51, 'end': 62}, {'entity_group': 'GPE', 'score': 0.99967396, 'word': 'فنلندا', 'start': 66, 'end': 72}, {'entity_group': 'PER', 'score': 0.99694425, 'word': '21', 'start': 75, 'end': 77}, {'entity_group': 'GPE', 'score': 0.99963355, 'word': 'السويد', 'start': 81, 'end': 87}] ``` | 48709a68598bbd611a52f484a2cab5eb |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v5_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v5_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1914 - Precision: 0.6408 - Recall: 0.7218 - F1: 0.6789 - Accuracy: 0.9356 | e56787c5e153482a93b33a956da657fe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 56 | 0.2937 | 0.4307 | 0.5257 | 0.4735 | 0.9010 | | No log | 2.0 | 112 | 0.2037 | 0.6089 | 0.695 | 0.6491 | 0.9305 | | No log | 3.0 | 168 | 0.1914 | 0.6408 | 0.7218 | 0.6789 | 0.9356 | | 2da8d4bde7ddb5fd36b04eb88a6d7906 |
mit | ['generated_from_trainer'] | false | roberta-large-mnli-misogyny-sexism-4tweets-2e-05-0.05 This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6222 - Accuracy: 0.7064 - F1: 0.7158 - Precision: 0.6462 - Recall: 0.8022 - Mae: 0.2936 - Tn: 336 - Fp: 202 - Fn: 91 - Tp: 369 | e5247d17e1945ba1073a64d3b07c1731 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:--:|:---:| | 0.5053 | 1.0 | 1346 | 0.6657 | 0.6663 | 0.7013 | 0.5969 | 0.85 | 0.3337 | 274 | 264 | 69 | 391 | | 0.4093 | 2.0 | 2692 | 0.6222 | 0.7064 | 0.7158 | 0.6462 | 0.8022 | 0.2936 | 336 | 202 | 91 | 369 | | 6a47e51c513f4dd91a588fbddc807e14 |
apache-2.0 | ['generated_from_keras_callback'] | false | Electra-base-squad-adversarialqa-epoch-3 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5566 - Epoch: 2 | 488b252ebbea3251d4a777e6a99eb0e8 |
apache-2.0 | ['generated_from_trainer'] | false | 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 | 313b533e1b3530b590270f2c9da55e95 |
apache-2.0 | ['generated_from_trainer'] | false | 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 | | 27209d5b2d1880323090aeac6c25e0c1 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_gender_male-2_female-8_s303 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](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. | b472cc4f120303b2a521cc2020f7b566 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-indomain-mix-bal-0 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5814 - Accuracy: 0.772 - F1: 0.7343 - Precision: 0.8799 - Recall: 0.63 - Mae: 0.228 - Tn: 457 - Fp: 43 - Fn: 185 - Tp: 315 | 631ada8a0e86a78ba280083f0142ab6e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:--:|:---:|:---:| | 0.4018 | 1.0 | 1356 | 0.5260 | 0.744 | 0.68 | 0.9067 | 0.544 | 0.256 | 472 | 28 | 228 | 272 | | 0.2932 | 2.0 | 2712 | 0.5655 | 0.757 | 0.7047 | 0.8978 | 0.58 | 0.243 | 467 | 33 | 210 | 290 | | 0.236 | 3.0 | 4068 | 0.5814 | 0.772 | 0.7343 | 0.8799 | 0.63 | 0.228 | 457 | 43 | 185 | 315 | | 4956289f5db585ae03e117d84d0027d0 |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | mt5-small-finetuned-multilingual-xlsum-new This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the 45 languages of the XL-Sum dataset. It achieves the following results on the evaluation set: - Loss: 2.7679 - Rouge1: 9.1993 - Rouge2: 2.3416 - Rougel: 7.6684 - Rougelsum: 7.7074 | 380efd0ebe2a3e99cd924b2da5b1e45d |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.9684 | 1.0 | 1687 | 2.8902 | 8.0531 | 1.8357 | 6.7234 | 6.7401 | | 3.62 | 2.0 | 3374 | 2.8486 | 8.4881 | 2.0178 | 7.0542 | 7.0854 | | 3.3765 | 3.0 | 5061 | 2.7986 | 8.7796 | 2.2342 | 7.3363 | 7.3645 | | 3.5043 | 4.0 | 6748 | 2.7677 | 9.0486 | 2.3099 | 7.5493 | 7.5685 | | 3.338 | 5.0 | 8435 | 2.7679 | 9.1993 | 2.3416 | 7.6684 | 7.7074 | | 41ab03c40c5b3444433c3b2efa43d44d |
mit | [] | false | model by chrisemoody This your the Stable Diffusion model fine-tuned the robeez baby girl water shoes concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks shoes** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:       | 96dfeac8e6d734b643e838b3e76ea8c9 |
apache-2.0 | ['classification'] | false | camembert-fr-covid-tweet-sentiment-classification This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2. This model reaches an accuracy of 71% on the dev set. In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes: - 0 : negatif - 1 : neutre - 2 : positif | c38f5eed64d8d1e2bd18fe1bb6ee567c |
apache-2.0 | ['classification'] | false | Pipelining the Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification") nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer) nlp_topic_classif("tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les '' ont dit ''...") | 2dec0361663c680b760f6b4e2339b4a3 |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Base Cased Generator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the generator model used to sample synthetic text and pretrain the discriminator. Only use this model for retraining and mask-filling. For the actual model for downstream tasks, please refer to the discriminator models. | 03ee246e5ae5d2c479a0daa3be520ab0 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/stsb-bert-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. | 9684bef52f971a1e9448065ade0a62fb |
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/stsb-bert-base') embeddings = model.encode(sentences) print(embeddings) ``` | 1aa56071f41c25160a36834e0b7dfe39 |
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/stsb-bert-base) | e4d8ec22bb9957d4fdbc1d94f26b7dbc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8628 - Matthews Correlation: 0.5331 | 07897ae55e21a95bd58e7221f5989b1a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5253 | 1.0 | 535 | 0.5214 | 0.3943 | | 0.3459 | 2.0 | 1070 | 0.5551 | 0.4693 | | 0.2326 | 3.0 | 1605 | 0.6371 | 0.5059 | | 0.1718 | 4.0 | 2140 | 0.7851 | 0.5111 | | 0.1262 | 5.0 | 2675 | 0.8628 | 0.5331 | | 18d4e4036d48e6d5ef5dbead27ad3666 |
mit | ['generated_from_trainer'] | false | xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2864 | 81b1de3904f7f524c5a5504edad8b98e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6088 | 1.0 | 5533 | 1.4429 | | 1.3928 | 2.0 | 11066 | 1.3183 | | 1.3059 | 3.0 | 16599 | 1.2864 | | 1a27d9887e1a863a500b3b06b03bcfb6 |
mit | ['generated_from_keras_callback'] | false | deepiit98/Heresy-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1670 - Train End Logits Accuracy: 0.9688 - Train Start Logits Accuracy: 0.9444 - Validation Loss: 0.1247 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | 9ee051d3778fe133fbc6a6b0716daf80 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1670 | 0.9688 | 0.9444 | 0.1247 | 1.0 | 1.0 | 0 | | 192f8cb4af38223510ed996a4fd94745 |
apache-2.0 | ['generated_from_keras_callback'] | false | JustAdvanceTechonology/medical_research_dataset_marian-finetuned-kde4-fr-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6429 - Validation Loss: 0.8071 - Epoch: 2 | b79902db0216851c326b403f305b26df |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6423 | 0.8071 | 0 | | 0.6424 | 0.8071 | 1 | | 0.6429 | 0.8071 | 2 | | 0487e09cc6906c2ee8631e3230566b7b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5628 | 1.0 | 2249 | 6.4705 | | 6.1956 | 2.0 | 4498 | 6.2012 | | 6.021 | 3.0 | 6747 | 6.1128 | | e820f10d5a8f365b7c4eb837e7420dbf |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Breton This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 br dataset. It achieves the following results on the evaluation set: - Loss: 0.6425 - Wer: 35.1077 | 9b03d7700b62903a81b45a01501db1fe |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0065 | 5.03 | 3000 | 0.6425 | 35.1077 | | bccbe2a5b31bd101dc82d2396ad263a5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Wav2Vec2-Large-XLSR-53-English Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 51f7f32c597452762523f5bf16f769e6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. processor = Wav2Vec2Processor.from_pretrained("{model_id}") | 8a8c4e38951cd900778ea075e2bb37b7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \tbatch["speech"] = resampler(speech_array).squeeze().numpy() \treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` | 84c34c9b1355010024362ce1ca97e905 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace language with your {language}, *e.g.* French ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "{lang_id}", split="test") | a4d5903ba31a956e1c20b2d2fd901b2e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("{model_id}") | a1e8e392e492b7e02f9ca996e664e8b0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' | dbaceffe7df29a0e4be6a1805356d353 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | We need to read the aduio files as arrays def evaluate(batch): \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \twith torch.no_grad(): \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits \tpred_ids = torch.argmax(logits, dim=-1) \tbatch["pred_strings"] = processor.batch_decode(pred_ids) \treturn batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: XX.XX % | 091935f0f56247cddf071debc2d46976 |
apache-2.0 | ['translation'] | false | opus-mt-sv-umb * source languages: sv * target languages: umb * OPUS readme: [sv-umb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-umb/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-umb/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-umb/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-umb/opus-2020-01-16.eval.txt) | c8e26708e2f80a37ff4114fa84f4b777 |
mit | ['generated_from_trainer'] | false | 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: 0.3847 - F1: 0.8178 | 345967def04cc90dd0cc75c74b49cd51 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.5654 | 1.0 | 17160 | 0.3847 | 0.8178 | | 7f439598edbce07e58da9ab65001d2f3 |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。 | 4333d3c5f32795aa69343e0cfdc0cb0c |
apache-2.0 | ['generated_from_keras_callback'] | false | MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0589 - Validation Loss: 5.3227 - Epoch: 0 | bf3b172a6d455ca2bd12069a0ef2d044 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6645 - Accuracy: 0.7917 - F1: 0.8590 | 272240755314f61678010b67d9a75710 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 63 | 0.5387 | 0.7402 | 0.8349 | | No log | 2.0 | 126 | 0.5770 | 0.7696 | 0.8513 | | No log | 3.0 | 189 | 0.5357 | 0.7574 | 0.8223 | | No log | 4.0 | 252 | 0.6645 | 0.7917 | 0.8590 | | No log | 5.0 | 315 | 0.6977 | 0.7721 | 0.8426 | | b862830429b7082c0fa889cac75b4068 |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and 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 the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the first version of the xxlarge model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 12 repeating layers - 128 embedding dimension - 4096 hidden dimension - 64 attention heads - 223M parameters | 863fad3912778b1bdc5e58930fefc0d3 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v1') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "score":0.05816134437918663, "token":12807, "token_str":"â–modeling" }, { "sequence":"[CLS] hello i'm a modelling model.[SEP]", "score":0.03748830780386925, "token":23089, "token_str":"â–modelling" }, { "sequence":"[CLS] hello i'm a model model.[SEP]", "score":0.033725276589393616, "token":1061, "token_str":"â–model" }, { "sequence":"[CLS] hello i'm a runway model.[SEP]", "score":0.017313428223133087, "token":8014, "token_str":"â–runway" }, { "sequence":"[CLS] hello i'm a lingerie model.[SEP]", "score":0.014405295252799988, "token":29104, "token_str":"â–lingerie" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v1') model = AlbertModel.from_pretrained("albert-xxlarge-v1") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v1') model = TFAlbertModel.from_pretrained("albert-xxlarge-v1") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | c1d5ab9e79b8c0045d5a85c6f4930a8e |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v1') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a chauffeur.[SEP]", "score":0.029577180743217468, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the man worked as a janitor.[SEP]", "score":0.028865724802017212, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the man worked as a shoemaker.[SEP]", "score":0.02581118606030941, "token":29024, "token_str":"â–shoemaker" }, { "sequence":"[CLS] the man worked as a blacksmith.[SEP]", "score":0.01849772222340107, "token":21238, "token_str":"â–blacksmith" }, { "sequence":"[CLS] the man worked as a lawyer.[SEP]", "score":0.01820771023631096, "token":3672, "token_str":"â–lawyer" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a receptionist.[SEP]", "score":0.04604868218302727, "token":25331, "token_str":"â–receptionist" }, { "sequence":"[CLS] the woman worked as a janitor.[SEP]", "score":0.028220869600772858, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the woman worked as a paramedic.[SEP]", "score":0.0261906236410141, "token":23386, "token_str":"â–paramedic" }, { "sequence":"[CLS] the woman worked as a chauffeur.[SEP]", "score":0.024797942489385605, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the woman worked as a waitress.[SEP]", "score":0.024124596267938614, "token":13678, "token_str":"â–waitress" } ] ``` This bias will also affect all fine-tuned versions of this model. | c7d5c1d82e7631f59fd2f54906353c6f |
mit | ['generated_from_trainer'] | false | inspiring_mirzakhani This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | 35081c8f2ca2aff226d823aacaa84bf2 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'name': 'Unlikelihood', 'score_threshold': 0.00078}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'inspiring_mirzakhani', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | c202f06200fcdf52b8f334c4db6d4105 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3455 - Accuracy: 0.8609 - F1: 0.9156 | ef991960c4d0e2ce781c0d740b1edd0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 81 | 0.4468 | 0.8235 | 0.8929 | | No log | 2.0 | 162 | 0.4497 | 0.8382 | 0.9 | | No log | 3.0 | 243 | 0.4861 | 0.8309 | 0.8940 | | No log | 4.0 | 324 | 0.5087 | 0.8235 | 0.8879 | | No log | 5.0 | 405 | 0.5228 | 0.8199 | 0.8858 | | c756991e61a9d3f847c99042ffb6fe3a |
apache-2.0 | ['translation'] | false | opus-mt-en-sv * source languages: en * target languages: sv * OPUS readme: [en-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-sv/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sv/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sv/opus-2020-02-26.eval.txt) | dce8373387c4c8726d94904a0887d89e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3978 - Accuracy: 0.5883 | 21fc3d482a8d9df66db99b744c557929 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4154 | 1.0 | 410 | 0.3986 | 0.5779 | | 0.3986 | 2.0 | 820 | 0.3978 | 0.5883 | | 0.3909 | 3.0 | 1230 | 0.3990 | 0.5887 | | 0.384 | 4.0 | 1640 | 0.3988 | 0.5913 | | 0.3761 | 5.0 | 2050 | 0.4001 | 0.5900 | | 0.3634 | 6.0 | 2460 | 0.4026 | 0.6121 | | 0.3413 | 7.0 | 2870 | 0.4068 | 0.6174 | | 09bdb46ab0917c2d552355897de075a8 |
apache-2.0 | ['translation'] | false | tgl-por * source group: Tagalog * target group: Portuguese * OPUS readme: [tgl-por](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-por/README.md) * model: transformer-align * source language(s): tgl_Latn * target language(s): por * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-por/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-por/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-por/opus-2020-06-17.eval.txt) | 5ff818b6d65a6b0b2c81218e0048e1d7 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tgl-por - source_languages: tgl - target_languages: por - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-por/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tl', 'pt'] - src_constituents: {'tgl_Latn'} - tgt_constituents: {'por'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-por/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-por/opus-2020-06-17.test.txt - src_alpha3: tgl - tgt_alpha3: por - short_pair: tl-pt - chrF2_score: 0.522 - bleu: 28.8 - brevity_penalty: 0.981 - ref_len: 12826.0 - src_name: Tagalog - tgt_name: Portuguese - train_date: 2020-06-17 - src_alpha2: tl - tgt_alpha2: pt - prefer_old: False - long_pair: tgl-por - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | d3607900a39ff321b7d7905db90bddec |
mit | ['translation'] | false | Usage ```bash pip3 install ctranslate2 pyonmttok ``` Simple translation using Python: ```python import ctranslate2 from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="softcatala/opennmt-eng-cat", revision="main") translator = ctranslate2.Translator(model_dir) print(translator.translate_batch([["▁Hello", "▁world", "!"]])) [[{'tokens': ['▁Hola', '▁món', '!']}]] ``` Simple tokenization & translation using Python: ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="softcatala/opennmt-eng-cat", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/sp_m.model") tokenized=tokenizer.tokenize("Hello world!") import ctranslate2 translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) Hola món! ``` | 3e76737d545b1568a16f83ddc9a8d35b |
apache-2.0 | ['text-classification', 'neural-compressor', 'int8'] | false | Model Details **Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized and pruned using a magnitude pruning strategy to obtain a sparsity of 10% with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). - **Model Type:** Text Classification - **Language(s):** English - **License:** Apache-2.0 - **Parent Model:** For more details on the original model, we encourage users to check out [this](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model card. | 06a2da2bb07a1b94c230afd1cdeeacf0 |
apache-2.0 | ['text-classification', 'neural-compressor', 'int8'] | false | How to Get Started With the Model To load the quantized model and run inference using the Transformers [pipelines](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines), you can do as follows: ```python from transformers import AutoTokenizer, pipeline from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification model_id = "echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1" model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) text = "He's a dreadful magician." outputs = cls_pipe(text) ``` | b6cf4fd444451ffbc45810d8346bc9b5 |
['apache-2.0'] | ['causal-lm', 'summarization'] | false | How to use Colab: [link](https://colab.research.google.com/drive/1eR-ev0Y5ISWIwGnzYYoHyGMaSIUz8GTN) ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "IlyaGusev/rugpt3medium_sum_gazeta" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) article_text = "..." text_tokens = tokenizer( article_text, max_length=600, add_special_tokens=False, padding=False, truncation=True )["input_ids"] input_ids = text_tokens + [tokenizer.sep_token_id] input_ids = torch.LongTensor([input_ids]) output_ids = model.generate( input_ids=input_ids, no_repeat_ngram_size=4 ) summary = tokenizer.decode(output_ids[0], skip_special_tokens=False) summary = summary.split(tokenizer.sep_token)[1] summary = summary.split(tokenizer.eos_token)[0] print(summary) ``` | c3b5903c38d21c857983b6319f4abd14 |
['apache-2.0'] | ['causal-lm', 'summarization'] | false | Training procedure - Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py) - Config: [gpt_training_config.json](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/configs/gpt_training_config.json) | f79f31bec2707d33c03b8457dc187c26 |
['apache-2.0'] | ['causal-lm', 'summarization'] | false | Eval results * Train dataset: **Gazeta v1 train** * Test dataset: **Gazeta v1 test** * Source max_length: **600** * Target max_length: **200** * no_repeat_ngram_size: **4** * num_beams: **5** | Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU | Avg char length | |:--------------------------|:------|:------|:------|:-------|:-------|:-----|:-----| | [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) | **32.4** | 14.3 | 28.0 | 39.7 | **26.4** | 12.1 | 371 | | [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) | 32.2 | **14.4** | **28.1** | **39.8** | 25.7 | **12.3** | 330 | | [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) | 26.2 | 7.7 | 21.7 | 33.8 | 18.2 | 4.3 | 244 | * Train dataset: **Gazeta v1 train** * Test dataset: **Gazeta v2 test** * Source max_length: **600** * Target max_length: **200** * no_repeat_ngram_size: **4** * num_beams: **5** | Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU | Avg char length | |:--------------------------|:------|:------|:------|:-------|:-------|:-----|:-----| | [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) | **28.7** | **11.1** | 24.4 | **37.3** | **22.7** | **9.4** | 373 | | [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) | 28.6 | **11.1** | **24.5** | 37.2 | 22.0 | **9.4** | 331 | | [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) | 24.1 | 6.5 | 19.8 | 32.1 | 16.3 | 3.6 | 242 | Evaluation script: [evaluate.py](https://github.com/IlyaGusev/summarus/blob/master/evaluate.py) Flags: --language ru --tokenize-after --lower | d13b7a3068262e91553065ab89781014 |
mit | [] | false | reksio dog on Stable Diffusion This is the `<reksio-dog>` 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`:       | e219e12da9b7a1ad66d63ea9a014edd4 |
mit | ['generated_from_trainer'] | false | finetuned-pflegeinterventionen-evidenzbasiert-und-patientenorientiert-umsetzen This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4393 - Accuracy: 0.8187 - F1: 0.8137 | c3bbe91781c50be01e61f9faf92ccfc0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.446 | 1.0 | 1365 | 0.4393 | 0.8115 | 0.8059 | | 0.3457 | 2.0 | 2730 | 0.4393 | 0.8187 | 0.8137 | | f02f12b4a15ea572db35f77c8acdc2d9 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-100k_s449 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](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. | 21f0b0662c20eba6c46c900f0d711df6 |
mit | ['generated_from_trainer'] | false | roberta_base_fine_tuned_mind 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: 0.4252 - Accuracy: 0.8881 | e91f0f12affa2247c014bae172b5b048 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7414 | 1.0 | 3054 | 0.6344 | 0.7878 | | 0.5612 | 2.0 | 6108 | 0.4568 | 0.8563 | | 0.3903 | 3.0 | 9162 | 0.4252 | 0.8881 | | db0ce63f20956eb68d4314f8f062f4dd |
apache-2.0 | ['generated_from_trainer'] | false | correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1801 - Precision: 0.6153 - Recall: 0.7301 - F1: 0.6678 - Accuracy: 0.9346 | 7044e7838eff6d380cfad933c8fb4fea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.2746 | 0.4586 | 0.5922 | 0.5169 | 0.9031 | | No log | 2.0 | 22 | 0.2223 | 0.5233 | 0.6181 | 0.5668 | 0.9148 | | No log | 3.0 | 33 | 0.2162 | 0.5335 | 0.6699 | 0.5940 | 0.9274 | | No log | 4.0 | 44 | 0.2053 | 0.5989 | 0.7055 | 0.6478 | 0.9237 | | No log | 5.0 | 55 | 0.2123 | 0.5671 | 0.7249 | 0.6364 | 0.9267 | | 01ef133daf60c5125e4507ec245ba7e3 |
apache-2.0 | ['Early Modern French', 'Historical', 'POS', 'flair'] | false | D'AlemBERT-POS model This model is fine-tuned version of a [D'AlemBERT](https://huggingface.co/pjox/dalembert) on the [FreEMLPM corpus](https://doi.org/10.5281/zenodo.6481300) for Early Modern French. It was introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). | e18b553b0d70149ff2bcfaa92713911e |
mit | ['generated_from_trainer'] | false | bart-cnn-science-v3-e6 This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8057 - Rouge1: 53.7462 - Rouge2: 34.9622 - Rougel: 37.5676 - Rougelsum: 51.0619 - Gen Len: 142.0 | 9fd9c3c24bb55d1f7b277a823e0dccf3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9961 | 52.632 | 32.8104 | 35.0789 | 50.3747 | 142.0 | | 1.174 | 2.0 | 796 | 0.8565 | 52.8308 | 32.7064 | 34.6605 | 50.3348 | 142.0 | | 0.7073 | 3.0 | 1194 | 0.8322 | 52.2418 | 32.8677 | 36.1806 | 49.6297 | 141.5556 | | 0.4867 | 4.0 | 1592 | 0.8137 | 53.5537 | 34.5404 | 36.7194 | 50.8394 | 142.0 | | 0.4867 | 5.0 | 1990 | 0.7996 | 53.4959 | 35.1017 | 37.5143 | 50.9972 | 141.8704 | | 0.3529 | 6.0 | 2388 | 0.8057 | 53.7462 | 34.9622 | 37.5676 | 51.0619 | 142.0 | | a7499faa4d63e4d50ec445c37218de86 |
apache-2.0 | ['generated_from_trainer'] | false | wac2vec-lllfantomlll 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: 0.5560 - Wer: 0.3417 | 36e477dc880914858dcea9576c72e6ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5768 | 1.0 | 500 | 2.0283 | 1.0238 | | 0.9219 | 2.01 | 1000 | 0.5103 | 0.5022 | | 0.4497 | 3.01 | 1500 | 0.4746 | 0.4669 | | 0.3163 | 4.02 | 2000 | 0.4144 | 0.4229 | | 0.2374 | 5.02 | 2500 | 0.4186 | 0.4161 | | 0.2033 | 6.02 | 3000 | 0.4115 | 0.3975 | | 0.1603 | 7.03 | 3500 | 0.4424 | 0.3817 | | 0.1455 | 8.03 | 4000 | 0.4151 | 0.3918 | | 0.1276 | 9.04 | 4500 | 0.4940 | 0.3798 | | 0.108 | 10.04 | 5000 | 0.4580 | 0.3688 | | 0.1053 | 11.04 | 5500 | 0.4243 | 0.3700 | | 0.0929 | 12.05 | 6000 | 0.4999 | 0.3727 | | 0.0896 | 13.05 | 6500 | 0.4991 | 0.3624 | | 0.0748 | 14.06 | 7000 | 0.4924 | 0.3602 | | 0.0681 | 15.06 | 7500 | 0.4908 | 0.3544 | | 0.0619 | 16.06 | 8000 | 0.5021 | 0.3559 | | 0.0569 | 17.07 | 8500 | 0.5448 | 0.3518 | | 0.0549 | 18.07 | 9000 | 0.4919 | 0.3508 | | 0.0478 | 19.08 | 9500 | 0.4704 | 0.3513 | | 0.0437 | 20.08 | 10000 | 0.5058 | 0.3555 | | 0.0421 | 21.08 | 10500 | 0.5127 | 0.3489 | | 0.0362 | 22.09 | 11000 | 0.5439 | 0.3527 | | 0.0322 | 23.09 | 11500 | 0.5418 | 0.3469 | | 0.0327 | 24.1 | 12000 | 0.5298 | 0.3422 | | 0.0292 | 25.1 | 12500 | 0.5511 | 0.3426 | | 0.0246 | 26.1 | 13000 | 0.5349 | 0.3472 | | 0.0251 | 27.11 | 13500 | 0.5646 | 0.3391 | | 0.0214 | 28.11 | 14000 | 0.5821 | 0.3424 | | 0.0217 | 29.12 | 14500 | 0.5560 | 0.3417 | | 760536681c0586192dd5a561a56acdc0 |
apache-2.0 | ['translation'] | false | roa-eng * source group: Romance languages * target group: English * OPUS readme: [roa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/roa-eng/README.md) * model: transformer * source language(s): arg ast cat cos egl ext fra frm_Latn gcf_Latn glg hat ind ita lad lad_Latn lij lld_Latn lmo max_Latn mfe min mwl oci pap pms por roh ron scn spa tmw_Latn vec wln zlm_Latn zsm_Latn * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.eval.txt) | a022956fc0d1b32ffb17568b42ae9487 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-enro-roneng.ron.eng | 37.1 | 0.631 | | newsdiscussdev2015-enfr-fraeng.fra.eng | 31.6 | 0.564 | | newsdiscusstest2015-enfr-fraeng.fra.eng | 36.1 | 0.592 | | newssyscomb2009-fraeng.fra.eng | 29.3 | 0.563 | | newssyscomb2009-itaeng.ita.eng | 33.1 | 0.589 | | newssyscomb2009-spaeng.spa.eng | 29.2 | 0.562 | | news-test2008-fraeng.fra.eng | 25.2 | 0.533 | | news-test2008-spaeng.spa.eng | 26.6 | 0.542 | | newstest2009-fraeng.fra.eng | 28.6 | 0.557 | | newstest2009-itaeng.ita.eng | 32.0 | 0.580 | | newstest2009-spaeng.spa.eng | 28.9 | 0.559 | | newstest2010-fraeng.fra.eng | 29.9 | 0.573 | | newstest2010-spaeng.spa.eng | 33.3 | 0.596 | | newstest2011-fraeng.fra.eng | 31.2 | 0.585 | | newstest2011-spaeng.spa.eng | 32.3 | 0.584 | | newstest2012-fraeng.fra.eng | 31.3 | 0.580 | | newstest2012-spaeng.spa.eng | 35.3 | 0.606 | | newstest2013-fraeng.fra.eng | 31.9 | 0.575 | | newstest2013-spaeng.spa.eng | 32.8 | 0.592 | | newstest2014-fren-fraeng.fra.eng | 34.6 | 0.611 | | newstest2016-enro-roneng.ron.eng | 35.8 | 0.614 | | Tatoeba-test.arg-eng.arg.eng | 38.7 | 0.512 | | Tatoeba-test.ast-eng.ast.eng | 35.2 | 0.520 | | Tatoeba-test.cat-eng.cat.eng | 54.9 | 0.703 | | Tatoeba-test.cos-eng.cos.eng | 68.1 | 0.666 | | Tatoeba-test.egl-eng.egl.eng | 6.7 | 0.209 | | Tatoeba-test.ext-eng.ext.eng | 24.2 | 0.427 | | Tatoeba-test.fra-eng.fra.eng | 53.9 | 0.691 | | Tatoeba-test.frm-eng.frm.eng | 25.7 | 0.423 | | Tatoeba-test.gcf-eng.gcf.eng | 14.8 | 0.288 | | Tatoeba-test.glg-eng.glg.eng | 54.6 | 0.703 | | Tatoeba-test.hat-eng.hat.eng | 37.0 | 0.540 | | Tatoeba-test.ita-eng.ita.eng | 64.8 | 0.768 | | Tatoeba-test.lad-eng.lad.eng | 21.7 | 0.452 | | Tatoeba-test.lij-eng.lij.eng | 11.2 | 0.299 | | Tatoeba-test.lld-eng.lld.eng | 10.8 | 0.273 | | Tatoeba-test.lmo-eng.lmo.eng | 5.8 | 0.260 | | Tatoeba-test.mfe-eng.mfe.eng | 63.1 | 0.819 | | Tatoeba-test.msa-eng.msa.eng | 40.9 | 0.592 | | Tatoeba-test.multi.eng | 54.9 | 0.697 | | Tatoeba-test.mwl-eng.mwl.eng | 44.6 | 0.674 | | Tatoeba-test.oci-eng.oci.eng | 20.5 | 0.404 | | Tatoeba-test.pap-eng.pap.eng | 56.2 | 0.669 | | Tatoeba-test.pms-eng.pms.eng | 10.3 | 0.324 | | Tatoeba-test.por-eng.por.eng | 59.7 | 0.738 | | Tatoeba-test.roh-eng.roh.eng | 14.8 | 0.378 | | Tatoeba-test.ron-eng.ron.eng | 55.2 | 0.703 | | Tatoeba-test.scn-eng.scn.eng | 10.2 | 0.259 | | Tatoeba-test.spa-eng.spa.eng | 56.2 | 0.714 | | Tatoeba-test.vec-eng.vec.eng | 13.8 | 0.317 | | Tatoeba-test.wln-eng.wln.eng | 17.3 | 0.323 | | f6b0300877f6443a76a0f1aa729f07e5 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: roa-eng - source_languages: roa - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/roa-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'ca', 'rm', 'es', 'ro', 'gl', 'co', 'wa', 'pt', 'oc', 'an', 'id', 'fr', 'ht', 'roa', 'en'] - src_constituents: {'ita', 'cat', 'roh', 'spa', 'pap', 'lmo', 'mwl', 'lij', 'lad_Latn', 'ext', 'ron', 'ast', 'glg', 'pms', 'zsm_Latn', 'gcf_Latn', 'lld_Latn', 'min', 'tmw_Latn', 'cos', 'wln', 'zlm_Latn', 'por', 'egl', 'oci', 'vec', 'arg', 'ind', 'fra', 'hat', 'lad', 'max_Latn', 'frm_Latn', 'scn', 'mfe'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.test.txt - src_alpha3: roa - tgt_alpha3: eng - short_pair: roa-en - chrF2_score: 0.6970000000000001 - bleu: 54.9 - brevity_penalty: 0.9790000000000001 - ref_len: 74762.0 - src_name: Romance languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: roa - tgt_alpha2: en - prefer_old: False - long_pair: roa-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 21dc894766254de5e2cd7a59df320baf |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-mrpc This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1575 - Accuracy: 0.7083 - F1: 0.8027 | 4e4129ae190d01ed47fbb4db32306a69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.596 | 4.35 | 500 | 0.5737 | 0.7034 | 0.8045 | | 0.5008 | 8.7 | 1000 | 0.6054 | 0.7132 | 0.8104 | | 0.4191 | 13.04 | 1500 | 0.6542 | 0.7034 | 0.7939 | | 0.332 | 17.39 | 2000 | 0.7210 | 0.7157 | 0.7993 | | 0.2612 | 21.74 | 2500 | 0.8037 | 0.7206 | 0.81 | | 0.2045 | 26.09 | 3000 | 0.8845 | 0.7083 | 0.7993 | | 0.1645 | 30.43 | 3500 | 0.9976 | 0.7181 | 0.8080 | | 0.1301 | 34.78 | 4000 | 1.1575 | 0.7083 | 0.8027 | | c1c282872ba83fb3c45a2b510cfcfd0e |
mit | ['generated_from_keras_callback'] | false | ksabeh/xlnet-base-cased-attribute-correction This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0599 - Validation Loss: 0.0214 - Epoch: 0 | 906a367390a1468e2ef0f6da32838074 |
mit | ['generated_from_keras_callback'] | false | recklessrecursion/Wayback_Machine-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: 0.2349 - Train End Logits Accuracy: 0.9618 - Train Start Logits Accuracy: 0.9306 - Validation Loss: 2.6776 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 0.6667 - Epoch: 0 | 1200bd98170480bb60f6d2ea9b582463 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.2349 | 0.9618 | 0.9306 | 2.6776 | 0.6667 | 0.6667 | 0 | | 1075b288bf86e05be29df7fa5046144a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Wav2Vec2-Large-XLSR-53-Vietnamese Fine-tuned [dragonSwing/wav2vec2-base-pretrain-vietnamese](https://huggingface.co/dragonSwing/wav2vec2-base-pretrain-vietnamese) on Vietnamese Speech Recognition task using 100h labelled data from [VSLP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?usp=sharing). When using this model, make sure that your speech input is sampled at 16kHz. | 0d3b444de4f36fcd62029f9c58d2954f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "vi", split="test") processor = Wav2Vec2Processor.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") model = Wav2Vec2ForCTC.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") resampler = torchaudio.transforms.Resample(48_000, 16_000) | f00efe338435ed4dc7e92162ec68cc29 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Evaluation The model can be evaluated as follows on the Vietnamese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "vi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") model = Wav2Vec2ForCTC.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") model.to("cuda") chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | ae6693e9ba80e5f5c59b31210d38caa8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | 939c3d13cec7db17296b6edcf72d071f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=1) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 31.353591% | a2690cdc64cb61d4262c0566aa1bdac2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Base-960h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec | cb754bcbce29df8a4b290f2ae72437d6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-960h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 3.4 | 8.6 | | b219a048c844c9413a856b618f011b27 |
gpl-3.0 | ['generated_from_trainer'] | false | bert-base-chinese-ws-finetuned-ner_all This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/bert-base-chinese-ws) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0330 - Precision: 0.9723 - Recall: 0.9734 - F1: 0.9728 - Accuracy: 0.9879 | 1fc189e40ac32b89c45175f94ee7356a |
gpl-3.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 18 - eval_batch_size: 18 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 7c9e6401d90d95bb63dd0ab32613707b |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0648 | 0.29 | 500 | 0.0524 | 0.9586 | 0.9572 | 0.9579 | 0.9813 | | 0.0509 | 0.59 | 1000 | 0.0460 | 0.9615 | 0.9628 | 0.9622 | 0.9832 | | 0.0478 | 0.88 | 1500 | 0.0429 | 0.9624 | 0.9660 | 0.9642 | 0.9840 | | 0.0417 | 1.17 | 2000 | 0.0409 | 0.9650 | 0.9680 | 0.9665 | 0.9851 | | 0.0402 | 1.47 | 2500 | 0.0387 | 0.9662 | 0.9693 | 0.9677 | 0.9856 | | 0.0378 | 1.76 | 3000 | 0.0359 | 0.9699 | 0.9717 | 0.9708 | 0.9869 | | 0.0385 | 2.05 | 3500 | 0.0353 | 0.9703 | 0.9718 | 0.9710 | 0.9871 | | 0.0337 | 2.34 | 4000 | 0.0341 | 0.9709 | 0.9731 | 0.9720 | 0.9875 | | 0.0348 | 2.64 | 4500 | 0.0333 | 0.9721 | 0.9733 | 0.9727 | 0.9878 | | 0.0346 | 2.93 | 5000 | 0.0331 | 0.9722 | 0.9735 | 0.9729 | 0.9879 | | 7215ed0e53d6b1453ada59421adf7f84 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Portuguese 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 pt dataset. It achieves the following results on the evaluation set: - Loss: 0.2568 - Wer: 11.6487 - Cer: 4.4764 | 9f73135ca0b33507ede3b93b26b5f4d6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.2476 | 0.92 | 500 | 0.2900 | 13.2049 | 4.9765 | | 0.1886 | 1.84 | 1000 | 0.2611 | 12.2804 | 4.6173 | | 0.1066 | 2.76 | 1500 | 0.2568 | 11.6487 | 4.4764 | | 0.0698 | 3.68 | 2000 | 0.2701 | 11.8798 | 4.6145 | | 0.0403 | 4.6 | 2500 | 0.2831 | 11.8644 | 4.4405 | | 0.019 | 5.52 | 3000 | 0.3148 | 11.7565 | 4.4653 | | 8ce58b6280b1bdffbbc4e58d38fbce43 |
mit | ['generated_from_trainer'] | false | umit_txtclass2 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on a full dataset at Home PC i5 9600K RTX2060 6GB.It achieves the following results on the evaluation set: - Loss: 0.5844 - Accuracy: 0.9116 | d2a6f0d5524c31cb9be4487f4f1d1744 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2855 | 1.0 | 858 | 0.6071 | 0.8986 | | 0.2077 | 2.0 | 1716 | 0.5425 | 0.9109 | | 0.112 | 3.0 | 2574 | 0.5844 | 0.9116 | | 69dd3c2a29b4803df1933c15c086e905 |
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