license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['translation'] | false | opus-mt-en-ceb * source languages: en * target languages: ceb * OPUS readme: [en-ceb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ceb/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-ceb/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ceb/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ceb/opus-2020-01-08.eval.txt) | acf1b66e0b643366b2457a786a9bbde7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-swedisch-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.6439 - Wer: 0.9678 | a9f771bc714f6fa57569bae7dbb356b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8953 | 1.83 | 400 | 1.6439 | 0.9678 | | 765c44a1efe48131060d4e56259cc576 |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-finetuned-parsed-longer50 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-parsed20](https://huggingface.co/muhtasham/bert-small-finetuned-parsed20) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9278 | 6376d1b57636e6476759cfccc90181c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 153f4cab131a0126322d4f2ff6df6ede |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 2.9807 | | No log | 2.0 | 8 | 2.7267 | | No log | 3.0 | 12 | 3.3484 | | No log | 4.0 | 16 | 2.7573 | | No log | 5.0 | 20 | 2.7063 | | No log | 6.0 | 24 | 2.7353 | | No log | 7.0 | 28 | 3.1290 | | No log | 8.0 | 32 | 2.9371 | | No log | 9.0 | 36 | 3.4265 | | No log | 10.0 | 40 | 3.0537 | | No log | 11.0 | 44 | 3.1382 | | No log | 12.0 | 48 | 3.1454 | | No log | 13.0 | 52 | 2.8379 | | No log | 14.0 | 56 | 3.2760 | | No log | 15.0 | 60 | 3.0504 | | No log | 16.0 | 64 | 2.9001 | | No log | 17.0 | 68 | 2.8892 | | No log | 18.0 | 72 | 3.1837 | | No log | 19.0 | 76 | 2.6404 | | No log | 20.0 | 80 | 3.0600 | | No log | 21.0 | 84 | 3.1432 | | No log | 22.0 | 88 | 2.9608 | | No log | 23.0 | 92 | 3.0513 | | No log | 24.0 | 96 | 3.1038 | | No log | 25.0 | 100 | 3.0975 | | No log | 26.0 | 104 | 2.8977 | | No log | 27.0 | 108 | 2.9416 | | No log | 28.0 | 112 | 2.9015 | | No log | 29.0 | 116 | 2.7947 | | No log | 30.0 | 120 | 2.9278 | | 609a25d84614be7b51d5f62083184bf9 |
apache-2.0 | ['generated_from_trainer'] | false | door_inner_with_SA-bert-base-uncased 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: 2.1513 | 89d15bbdc6571ea0429e74ee28319ad1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 | 962a78ee5ab1374cbf3f81ecbef4870b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5492 | 1.0 | 96 | 2.3831 | | 2.4031 | 2.0 | 192 | 2.2963 | | 2.3391 | 3.0 | 288 | 2.2000 | | 2.2951 | 4.0 | 384 | 2.2505 | | 2.2151 | 5.0 | 480 | 2.1691 | | 2.2237 | 6.0 | 576 | 2.1855 | | 2.1984 | 7.0 | 672 | 2.2558 | | 2.1749 | 8.0 | 768 | 2.2019 | | 2.1475 | 9.0 | 864 | 2.1310 | | 2.1446 | 10.0 | 960 | 2.1334 | | 2.1374 | 11.0 | 1056 | 2.1909 | | 2.1117 | 12.0 | 1152 | 2.2028 | | b495c309694cd89e10610379d5b1e5c7 |
mit | [] | false | Arona [Arona / アロナ / 아로나 / 阿罗娜](https://huggingface.co/khanon/lora-training/blob/main/arona/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/arona/README.md) | 35c860ae83b74bde5e1584112d1f4065 |
mit | [] | false | Koharu [Shimoe Koharu / 下江コハル / 시모에 코하루 / 下江小春](https://huggingface.co/khanon/lora-training/blob/main/koharu/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/koharu/README.md) | 16afbaff98349606ab0bfae4fc590113 |
mit | [] | false | Kokona [Sunohara Kokona / 春原ココナ / 스노하라 코코나 / 春原心奈](https://huggingface.co/khanon/lora-training/blob/main/kokona/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/kokona/README.md) | fe0d593954cf1e163d7eb1beaba6472c |
mit | [] | false | Mari [Iochi Mari / 伊落マリー / 이오치 마리 / 伊落玛丽](https://huggingface.co/khanon/lora-training/blob/main/mari/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/mari/README.md) | 4af74896f387d57cb8a7cb5b48dfb1da |
mit | [] | false | Reisa [Uzawa Reisa / 宇沢レイサ / 우자와 레이사](https://huggingface.co/khanon/lora-training/blob/main/reisa/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/reisa/README.md) | 2ec0e2bddcb0219942f645cf47d0f0db |
mit | [] | false | Seia [Yurizono Seia / 百合園セイア / 유리조노 세이아 / 百合園圣娅](https://huggingface.co/khanon/lora-training/blob/main/seia/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/seia/README.md) | d569d12cdfa62f16b97d49a45a5c6c4e |
mit | [] | false | Shizuko [Kawawa Shizuko / 河和シズコ / 카와와 시즈코 / 河和静子](https://huggingface.co/khanon/lora-training/blob/main/shizuko/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/shizuko/README.md) | 9b56a75333b218afe4aec978c8161c46 |
mit | [] | false | Sora [Sora / ソラ / 소라 / 空](https://huggingface.co/khanon/lora-training/blob/main/sora/README.md) [](https://huggingface.co/khanon/lora-training/blob/main/sora/README.md) | 5dabca8ab9fa6a39fda9415a8b95208c |
mit | [] | false | Negative embedding I frequently use these negative embeddings in my prompts to improve the output quality. I recommend lowering the attention to ~0.75. - `bad-artist`, `bad-artist-anime` - https://huggingface.co/nick-x-hacker/bad-artist - `badpromptv2` - https://huggingface.co/datasets/Nerfgun3/bad_prompt - `bad-image-v2` (not sure of the original author) - [bad-image-v2.pt](https://huggingface.co/khanon/lora-training/blob/main/bad-image-v2.pt) | 6336ca5d7599de1a9e7db28a37cf4038 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.05-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2776 - Rmse: 0.5269 - Mse: 0.2776 - Mae: 0.4290 | 903d0fce8c7e752d5e6bc0e90c55d6cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2768 | 1.0 | 1245 | 0.2737 | 0.5232 | 0.2737 | 0.4163 | | 0.274 | 2.0 | 2490 | 0.2779 | 0.5271 | 0.2779 | 0.4234 | | 0.2721 | 3.0 | 3735 | 0.2776 | 0.5269 | 0.2776 | 0.4290 | | d9cc21c53c18f2ebf0e21070b02a31aa |
mit | ['generated_from_trainer'] | false | mdeberta-hate-final This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6223 - Accuracy: 0.7424 - Precision: 0.7410 - Recall: 0.7424 - F1: 0.7363 | d9ba71cf54373c8cd3d54bf9eb2a8ec1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5309 | 0.7519 | 0.7685 | 0.7519 | 0.7357 | | 0.5358 | 2.0 | 592 | 0.5228 | 0.7510 | 0.7663 | 0.7510 | 0.7351 | | 0.5358 | 3.0 | 888 | 0.5565 | 0.7510 | 0.7513 | 0.7510 | 0.7438 | | 0.4295 | 4.0 | 1184 | 0.5639 | 0.7481 | 0.7488 | 0.7481 | 0.7403 | | 0.4295 | 5.0 | 1480 | 0.5941 | 0.7510 | 0.7531 | 0.7510 | 0.7423 | | 0.3701 | 6.0 | 1776 | 0.6223 | 0.7424 | 0.7410 | 0.7424 | 0.7363 | | e7148e3f2962a6d0e6b8960109c1cc7d |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-mri This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0608 - Accuracy: 0.9807 | 2fd10b0899558553df39e340d5d009fe |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP | 952ed6da5b7afcca0105a5b19d36f272 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0592 | 1.0 | 447 | 0.0823 | 0.9695 | | 0.0196 | 2.0 | 894 | 0.0761 | 0.9739 | | 0.0058 | 3.0 | 1341 | 0.0608 | 0.9807 | | f75b707e762284fd8a56419e7f9fcdbb |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/distilbert-base-uncased-padded_left_allsides_news_e20 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9438 - Validation Loss: 1.8632 - Epoch: 19 | cc540a1ac9d4ec6b50473197e803e4f4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9974 | 1.9203 | 0 | | 1.9860 | 1.9246 | 1 | | 1.9588 | 1.8601 | 2 | | 1.9598 | 1.8668 | 3 | | 1.9330 | 1.8913 | 4 | | 1.9250 | 1.8517 | 5 | | 1.9200 | 1.8525 | 6 | | 1.9447 | 1.8755 | 7 | | 1.9331 | 1.8627 | 8 | | 1.9318 | 1.9064 | 9 | | 1.9304 | 1.8507 | 10 | | 1.9325 | 1.8616 | 11 | | 1.9397 | 1.8491 | 12 | | 1.9535 | 1.8660 | 13 | | 1.9327 | 1.8341 | 14 | | 1.9403 | 1.8686 | 15 | | 1.9488 | 1.8585 | 16 | | 1.9378 | 1.8515 | 17 | | 1.9293 | 1.8645 | 18 | | 1.9438 | 1.8632 | 19 | | cf23600435622d8b7a6d56b75df9f4bf |
apache-2.0 | ['translation'] | false | spa-glg * source group: Spanish * target group: Galician * OPUS readme: [spa-glg](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-glg/README.md) * model: transformer-align * source language(s): spa * target language(s): glg * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-glg/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-glg/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-glg/opus-2020-06-16.eval.txt) | d65942b911993f019f110738bcf611ab |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-glg - source_languages: spa - target_languages: glg - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-glg/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'gl'] - src_constituents: {'spa'} - tgt_constituents: {'glg'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-glg/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-glg/opus-2020-06-16.test.txt - src_alpha3: spa - tgt_alpha3: glg - short_pair: es-gl - chrF2_score: 0.8079999999999999 - bleu: 67.6 - brevity_penalty: 0.993 - ref_len: 16581.0 - src_name: Spanish - tgt_name: Galician - train_date: 2020-06-16 - src_alpha2: es - tgt_alpha2: gl - prefer_old: False - long_pair: spa-glg - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 82656367263fea473e2bb80c0a24e91c |
apache-2.0 | ['generated_from_trainer'] | false | barthez-deft-chimie This model is a fine-tuned version of [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) on an unknown dataset. **Note**: this model is one of the preliminary experiments and it underperforms the models published in the paper (using [MBartHez](https://huggingface.co/moussaKam/mbarthez) and HAL/Wiki pre-training + copy mechanisms) It achieves the following results on the evaluation set: - Loss: 2.0710 - Rouge1: 31.8947 - Rouge2: 16.7563 - Rougel: 23.5428 - Rougelsum: 23.4918 - Gen Len: 38.5256 | 5a70c30594e5524f98f8897fc0e02457 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.8022 | 1.0 | 118 | 2.5491 | 16.8208 | 7.0027 | 13.957 | 14.0479 | 19.1538 | | 2.9286 | 2.0 | 236 | 2.3074 | 17.5356 | 7.8717 | 14.4874 | 14.5044 | 19.9487 | | 2.5422 | 3.0 | 354 | 2.2322 | 19.6491 | 9.4156 | 15.9467 | 15.9433 | 19.7051 | | 2.398 | 4.0 | 472 | 2.1500 | 18.7166 | 9.859 | 15.7535 | 15.8036 | 19.9231 | | 2.2044 | 5.0 | 590 | 2.1372 | 19.978 | 10.6235 | 16.1348 | 16.1274 | 19.6154 | | 1.9405 | 6.0 | 708 | 2.0992 | 20.226 | 10.551 | 16.6928 | 16.7211 | 19.9744 | | 1.8544 | 7.0 | 826 | 2.0841 | 19.8869 | 10.8456 | 16.1072 | 16.097 | 19.8846 | | 1.7536 | 8.0 | 944 | 2.0791 | 19.3017 | 9.4921 | 16.1541 | 16.2167 | 19.859 | | 1.6914 | 9.0 | 1062 | 2.0710 | 21.3848 | 10.4088 | 17.1963 | 17.2254 | 19.8846 | | 1.654 | 10.0 | 1180 | 2.1069 | 22.3811 | 10.7987 | 18.7595 | 18.761 | 19.9231 | | 1.5899 | 11.0 | 1298 | 2.0919 | 20.8546 | 10.6958 | 16.8637 | 16.9499 | 19.8077 | | 1.4661 | 12.0 | 1416 | 2.1065 | 22.3677 | 11.7472 | 18.262 | 18.3 | 19.9744 | | 1.4205 | 13.0 | 1534 | 2.1164 | 20.5845 | 10.7825 | 16.9972 | 17.0216 | 19.9359 | | 1.3797 | 14.0 | 1652 | 2.1240 | 22.2561 | 11.303 | 17.5064 | 17.5815 | 19.9744 | | 1.3724 | 15.0 | 1770 | 2.1187 | 23.2825 | 11.912 | 18.5208 | 18.5499 | 19.9359 | | 1.3404 | 16.0 | 1888 | 2.1394 | 22.1305 | 10.5258 | 17.772 | 17.8202 | 19.9744 | | 1.2846 | 17.0 | 2006 | 2.1502 | 21.567 | 11.0557 | 17.2562 | 17.2974 | 20.0 | | 1.2871 | 18.0 | 2124 | 2.1572 | 22.5871 | 11.702 | 18.2906 | 18.3826 | 19.9744 | | 1.2422 | 19.0 | 2242 | 2.1613 | 23.0935 | 11.6824 | 18.6087 | 18.6777 | 19.9744 | | 1.2336 | 20.0 | 2360 | 2.1581 | 22.6789 | 11.4363 | 18.1661 | 18.2346 | 19.9487 | | 067b102e3d0db39aacabe78c9a76aaf8 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_nosp_0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3829 - Train Accuracy: 0.0216 - Validation Loss: 0.8190 - Validation Accuracy: 0.0202 - Epoch: 14 | 94304584384d71e414c6988298b9d56b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 7.5559 | 0.0010 | 6.3853 | 0.0013 | 0 | | 6.3227 | 0.0021 | 5.7023 | 0.0038 | 1 | | 4.9825 | 0.0063 | 3.6302 | 0.0109 | 2 | | 2.9413 | 0.0126 | 2.1959 | 0.0154 | 3 | | 1.9349 | 0.0157 | 1.6630 | 0.0172 | 4 | | 1.4741 | 0.0171 | 1.3813 | 0.0181 | 5 | | 1.1975 | 0.0181 | 1.2161 | 0.0186 | 6 | | 1.0048 | 0.0188 | 1.0990 | 0.0191 | 7 | | 0.8598 | 0.0194 | 1.0165 | 0.0194 | 8 | | 0.7431 | 0.0199 | 0.9603 | 0.0196 | 9 | | 0.6489 | 0.0203 | 0.9106 | 0.0198 | 10 | | 0.5682 | 0.0207 | 0.8787 | 0.0199 | 11 | | 0.4985 | 0.0210 | 0.8548 | 0.0200 | 12 | | 0.4372 | 0.0213 | 0.8352 | 0.0201 | 13 | | 0.3829 | 0.0216 | 0.8190 | 0.0202 | 14 | | 7c7d974c5098c452372971ad5c2c7f48 |
apache-2.0 | ['generated_from_trainer'] | false | base-mlm-tweet-target-tweet This model is a fine-tuned version of [muhtasham/base-mlm-tweet](https://huggingface.co/muhtasham/base-mlm-tweet) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.9081 - Accuracy: 0.7674 - F1: 0.7679 | ab6e8bfd1c339bc11987a9025af974da |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3371 | 4.9 | 500 | 1.0062 | 0.7888 | 0.7891 | | 0.038 | 9.8 | 1000 | 1.4896 | 0.7754 | 0.7802 | | 0.0165 | 14.71 | 1500 | 1.6711 | 0.7834 | 0.7830 | | 0.018 | 19.61 | 2000 | 1.9081 | 0.7674 | 0.7679 | | ac127e8522d89d125d1757d9965d1de4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2421 - F1: 0.8248 | 0565cfe395465416f90a6e8c1063a3e6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.809 | 1.0 | 70 | 0.3380 | 0.7183 | | 0.2939 | 2.0 | 140 | 0.2582 | 0.7977 | | 0.1813 | 3.0 | 210 | 0.2421 | 0.8248 | | 67a7cfcf9aa959ec39ba68fe29e6e61a |
apache-2.0 | ['translation'] | false | opus-mt-fr-st * source languages: fr * target languages: st * OPUS readme: [fr-st](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-st/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/fr-st/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-st/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-st/opus-2020-01-16.eval.txt) | ddc7023a1996ad003a35b0a44ced717e |
apache-2.0 | ['generated_from_trainer'] | false | test-mlm This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2729 - Accuracy: 0.7100 | e19d590775792a9145e8b7d4c36be707 |
unknown | [] | false | A Big thank you to everyone in the community who did the work on Training the custom Models I used in this. My Mixes can be found a Civitai as well https://civitai.com/models/1348/ultimatemix https://civitai.com/models/1544/cartoon-mix I lost my Original Reciepe for Ultimate Mix 1&2, So I wont be able to upload them in checkpoint format. However I have Created a New Mix Umix3 that is probabaly better in all respects  >**Prompt** >- beautiful goddess of love, blonde long curly hair, elf eared, gorgeous detailed face, perfect body, pink off shoulder dress, jewelry, cinematic lighting, fantasy garden, hyper detailed illustration, gorgeous, elegant, intricate, alluring, stunning, award winning, realistic, sharp focus, 8k high definition, > >**Neg Prompt** >- monochrome, censored, bad anatomy, lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, simple background, anatomical nonsense > >**Steps: 20, CFG scale: 12, Seed: 3275590074, Size: 512x512** -------------------  >**Prompt** >- (Seductive nude witcher man) underwear, fierce looking face, long white wavy hair, white beard, ominous dark forest, dark fantasy, character portrait, sexy man, 8K, hdr >**No Negitive words** > >**Steps: 20, CFG scale: 12, Seed: 172159747, Size: 512x512** -------------------  >**Prompt** >- a beautiful empress crystal quartz portrait, with a brilliant, impossible striking big shiny crystal headpiece, quartz, clothes entirely made out of crystal quartz, black hair, crystal background, symmetrical, dramatic studio lighting, rococo, baroque, hyperrealism, closeup, fantasy, intricate, elegant, highly detailed, asian, digital painting > >**Negitive words** >- lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, artist name, (((ugly))), (((duplicate))), ((morbid)), ((mutilated)), out of frame, extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((blurry)), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), bad proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (too many fingers) > >**Steps: 20, CFG scale: 12, Seed: 3275590074, Size: 512x512** | ca3459cc4a233fa1010891dd635cb92f |
cc-by-sa-4.0 | [] | false | How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese-wikipedia') >>> set_seed(42) >>> generator("早稲田 大学 で 自然 言語 処理 を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5) [{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 1969 年 に は 同 大学院 を 修了 。 東京 芝浦 電気 株式 会社 に 就職 後 、 情報 処理'}, {'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 帰国 後 は 立教 大学 理学部 助手 を 務めた 。 1978 年 に 神奈川 県立 湘南 高等 学校 校長 に 就任'}, {'generated_text': '早稲田 大学 で 自然 言語 処理 を 研究 。 1972 年 に 早稲田 大学 文学部 ドイツ 文学 専攻 を 卒業 し 、 同 年 から 1979 年 まで 上智 大学'}, {'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 する 。 1979 年 東京 農工 大学 農学 部 卒業 。 1980 年 同 大学院 農学 研究 科 修士 課程 修了 。'}, {'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 し ながら 、 日本 で 活動 する 自然 言語 研究 家 。 大学 時代 は 東京 大学 理学部 の 助手 を 務め'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import ReformerTokenizer, GPT2Model tokenizer = ReformerTokenizer.from_pretrained('nlp-waseda/gpt2-small-japanese-wikipedia') model = GPT2Model.from_pretrained('nlp-waseda/gpt2-small-japanese-wikipedia') text = "早稲田 大学 で 自然 言語 処理 を" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 920950a7977af3738175029703bf51eb |
cc-by-sa-4.0 | [] | false | Preprocessing The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining. The model was trained on 8 NVIDIA A100 GPUs. | ede86d1d51e160c0ee2cdb9de6253b1d |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-travel-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1384 - Accuracy: 0.4289 | a4f89aa0780a5471b777d20183a7e14b |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-12 - train_batch_size: 256 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_inv_gamma: 1.0 - ema_inv_gamma: 0.75 - ema_inv_gamma: 0.9999 - mixed_precision: fp16 | be883ed36829284c3f9687e9dd3fa314 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-5_nortepeninsular-5_s463 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 (es)](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. | 5ebe7d1d527e1fee9cb24e81cf5fa486 |
creativeml-openrail-m | ['text-to-image'] | false | CoalForest Dreambooth model trained by DrEsker with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You 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). Don't forget to use the concept prompts! Sample pictures of: CoalForestSIM (use that on your prompt)  | f46631017df1357a2cfb6e0b1c35aef6 |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/repvgg_a1").eval() img = Image.open(path_to_an_image).convert("RGB") | 2214a2d253364146ac912d12f714b703 |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-small-finetuned-xlsum-india-test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9172 - Validation Loss: 2.5929 - Epoch: 0 | 57f1a21f96365ce4b7f1f0ae82b15a76 |
mit | ['generated_from_trainer'] | false | MiniLM-L12-H384-uncased-finetuned-imdb This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 3.9328 | 4cc6670f8c65cb4059ea49d48329f634 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.2464 | 1.0 | 391 | 4.2951 | | 4.2302 | 2.0 | 782 | 4.0023 | | 4.0726 | 3.0 | 1173 | 3.9328 | | 81cef8b5156b1b25c8308188c81281b3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | anything-berry-30.ckpt [Re-uploaded from](https://huggingface.co/misobarisic/anything-berrymix) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.30 | Anything V3 | Berry Mix | n/a | **anything-berry-30** | 0e0078aaec4f800ef00ab2dfacd9f978 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | anything-f222-15.ckpt [Recipe Source](https://www.reddit.com/r/WaifuDiffusion/comments/zdbs3r/comment/iz0nr48/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.15 | Anything V3 | Zeipher F222 | n/a | **anything-f222-15** | 1f6382543613b133187a851c30f6164e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | anything-f222-15-elysiumv2-10.ckpt [Recipe Source](https://www.reddit.com/r/WaifuDiffusion/comments/zg1d8x/comment/izei93c/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.10 | anything-f222-15 | Elysium Anime v2 | n/a | **anything-f222-15-elysiumv2-10** | ebdfd478d23ae761758fe821adeb3640 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | berrymix-v3-535d98a3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.05 | AnythingV3.0 | Stable Diffusion 1.5 | n/a | Anything Fix 2 | Add Difference @ 1 | Anything fix | Zeipher F222 | Stable Diffusion 1.5 | berrymix3 lite 3 | Weighted Sum @ 0.25 | berrymix3 lite |r34_e4 | n/a | **berrymix V3** | ded03a249a8aa607f4efd77a7b9f3e19 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | blossom-extract.safetensors [Recipe Source](https://www.reddit.com/r/StableDiffusion/comments/zk8y50/comment/izyhn8w/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Add Difference @ 1 | Anything V3 | Zeipher F222 | Stable Diffusion 1.4 | **blossom-extract** | d2b0209091992561c29586b226eee91e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | hentai-elysium-50.safetensors [Recipe Source](https://www.reddit.com/r/WaifuDiffusion/comments/zn6wdb/comment/j0fabe6/?utm_source=reddit&utm_medium=web2x&context=3) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.5 | Hentai Diffusion 17 | Elysium Anime v2 | n/a | **hentai-elysium-50** | fcf0977200dc2ead25fff1a2df58c58c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | nutmegmix-aa3e502b) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.05 | NovelAI | Stable Diffusion 1.5 | n/a | nutmegmix-part1 2 | Weighted Sum @ 0.05 | nutmegmix-part1 | Zeipher F222 | n/a | nutmegmix-part2 3 | Weighted Sum @ 0.05 | nutmegmix-part2 | r34_e4 | n/a | nutmegmix-part3 4 | Weighted Sum @ 0.05 | nutmegmix-part3 | SmirkingFace | n/a | nutmegmix-part4 5 | Weighted Sum @ 0.3 | AnythingV3.0 | nutmegmix-part4 | n/a | **nutmeg-mix** | 1b152555351385bd228a70b4559a2615 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | raspberry-mix-4d202242) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.25 | AnythingV3.0 | Stable Diffusion 1.5 | n/a | AnyV3-SD1.5 2 | Add Difference @ 1 | AnyV3-SD1.5 | Zeipher F222 | Stable Diffusion 1.4 | raspberry-lite 3 | Weighted Sum @ 0.15 | raspberry-lite | r34_e4 | n/a | **raspberry mix** | c4990ba3f33115ec2c83fbaba047864b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | strawberry-mix-e043dfc5) Step | Interpolation Method | Primary Model | Secondary model | Tertiary Model | Merge Name --- | --- | --- | --- | --- | --- 1 | Weighted Sum @ 0.25 | AnythingV3.0 | Stable Diffusion 1.4 | n/a | AnyV3-SD1.4 2 | Add Difference @ 1 | AnyV3-SD1.4 | Zeipher F111 | Stable Diffusion 1.4 | strawberry-lite 3 | Weighted Sum @ 0.15 | strawberry-lite | r34_e4 | n/a | **strawberry mix** | 99ddcd5da058548c7115f6c1dcebddda |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2-shake-wiki 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: 0.0096 - Accuracy: 0.9994 | ee6a2ffee71d34346ae626520d08b032 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.001 | 1.0 | 5029 | 0.0120 | 0.9988 | | 0.0017 | 2.0 | 10058 | 0.0028 | 0.9996 | | 0.0 | 3.0 | 15087 | 0.0094 | 0.9992 | | 0.0 | 4.0 | 20116 | 0.0091 | 0.9994 | | 0.0 | 5.0 | 25145 | 0.0096 | 0.9994 | | b916eb12be1dd768ad1cd0aaba0ca5e0 |
apache-2.0 | ['t5', 'contrastive learning', 'ranking', 'decoding', 'metric learning', 'pytorch', 'text generation', 'retrieval'] | false | Method-2: Loading the model with HuggingFace APIs ``` from transformers import T5Tokenizer, AutoModel tokenizer = T5Tokenizer.from_pretrained(f"google/t5-v1_1-base") model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True) ``` | 3b2ef37c693c8101e798f0f6ed8fc525 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | distilroberta-base-mrpc-glue-tadeous This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.6243 - Accuracy: 0.8211 - F1: 0.8726 | be4206d88c0d7af2cb9adb23d31e9159 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3219 | 1.09 | 500 | 0.6243 | 0.8211 | 0.8726 | | 0.3173 | 2.18 | 1000 | 0.6243 | 0.8211 | 0.8726 | | 2630041ebcb31001fa2f0df711ad9d4c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-stsb 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: 0.5644 - Pearson: 0.8666 - Spearmanr: 0.8636 | 924da22ad9fc19fa5e4fd60808682f98 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6366 | 0.8537 | 0.8516 | | 1.0464 | 2.0 | 720 | 0.6171 | 0.8632 | 0.8626 | | 0.4002 | 3.0 | 1080 | 0.6082 | 0.8663 | 0.8643 | | 0.4002 | 4.0 | 1440 | 0.5644 | 0.8666 | 0.8636 | | 0.2479 | 5.0 | 1800 | 0.5780 | 0.8654 | 0.8624 | | fd11e31b6c165259de5d4fe1b33ec7a1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | supastazzz Dreambooth model trained by supastazz 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) Sample pictures of this concept: | 8d4e4a3595c4861aea81e5f912c7f546 |
mit | [] | false | hebrew-gpt_neo-tiny Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. | 1ebdbd00147218d34ba3f1bafafd9324 |
mit | [] | false | 4INvMes-56m_WUi7jQMbJQ) 2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he) The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. | 4ec314cc5393cb741433c0bc8fabe95d |
mit | [] | false | Simple usage sample code ```python !pip install tokenizers==0.10.2 transformers==4.6.0 from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-tiny") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-tiny", pad_token_id=tokenizer.eos_token_id) prompt_text = "אני אוהב שוקולד ועוגות" max_len = 512 sample_output_num = 3 seed = 1000 import numpy as np import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count() print(f"device: {device}, n_gpu: {n_gpu}") np.random.seed(seed) torch.manual_seed(seed) if n_gpu > 0: torch.cuda.manual_seed_all(seed) model.to(device) encoded_prompt = tokenizer.encode( prompt_text, add_special_tokens=False, return_tensors="pt") encoded_prompt = encoded_prompt.to(device) if encoded_prompt.size()[-1] == 0: input_ids = None else: input_ids = encoded_prompt print("input_ids = " + str(input_ids)) if input_ids != None: max_len += len(encoded_prompt[0]) if max_len > 1024: max_len = 1024 print("Updated max_len = " + str(max_len)) stop_token = "<|endoftext|>" new_lines = "\n\n\n" sample_outputs = model.generate( input_ids, do_sample=True, max_length=max_len, top_k=50, top_p=0.95, num_return_sequences=sample_output_num ) print(100 * '-' + "\n\t\tOutput\n" + 100 * '-') for i, sample_output in enumerate(sample_outputs): text = tokenizer.decode(sample_output, skip_special_tokens=True) | 7e92afce87a62f5482514a8e33666c32 |
apache-2.0 | ['irish'] | false | BERTreach ([beirtreach](https://www.teanglann.ie/en/fgb/beirtreach) means 'oyster bed') **Model size:** 84M **Training data:** * [PARSEME 1.2](https://gitlab.com/parseme/parseme_corpus_ga/-/blob/master/README.md) * Newscrawl 300k portion of the [Leipzig Corpora](https://wortschatz.uni-leipzig.de/en/download/irish) * Private news corpus crawled with [Corpus Crawler](https://github.com/google/corpuscrawler) (2125804 sentences, 47419062 tokens, as reckoned by wc) ``` from transformers import pipeline fill_mask = pipeline("fill-mask", model="jimregan/BERTreach", tokenizer="jimregan/BERTreach") ``` | 9c2ac4cc92c4121e659c20c0dd7647e8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_pretrain_sst2 This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.7266 - Accuracy: 0.8085 | ebfec7f46e574456467a482bf68e1553 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9277 | 1.0 | 264 | 0.7266 | 0.8085 | | 0.4581 | 2.0 | 528 | 0.9527 | 0.7844 | | 0.3282 | 3.0 | 792 | 0.8676 | 0.8142 | | 0.2532 | 4.0 | 1056 | 0.7918 | 0.8039 | | 0.1926 | 5.0 | 1320 | 0.8852 | 0.7982 | | 0.1573 | 6.0 | 1584 | 1.0020 | 0.7947 | | 4abd0bc347edbaa246aa33f300359894 |
apache-2.0 | [] | false | Question Answering model for Hindi and Tamil This model is part of the ensemble that ranked 4/943 in the [Hindi and Tamil Question Answering](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition held by Google Research India at Kaggle. ``` from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yuchen/muril-large-cased-hita-qa") model = AutoModelForQuestionAnswering.from_pretrained("Yuchen/muril-large-cased-hita-qa") ``` | 292abb5a29157ec10958821a722cb4a7 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_250v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3389 - Precision: 0.5352 - Recall: 0.4795 - F1: 0.5058 - Accuracy: 0.8947 | e2854803a0e62bda2f7e5b9b0221c0f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 95 | 0.4305 | 0.3497 | 0.1814 | 0.2389 | 0.8488 | | No log | 2.0 | 190 | 0.3469 | 0.4995 | 0.4281 | 0.4611 | 0.8875 | | No log | 3.0 | 285 | 0.3389 | 0.5352 | 0.4795 | 0.5058 | 0.8947 | | adfc76c232b572ce1f943488cff1dd80 |
mit | ['generated_from_keras_callback'] | false | javilonso/classificationPolEsp1 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3728 - Validation Loss: 0.6217 - Epoch: 2 | 4ac2b83a8f42677b9fbe3305f23ef2ec |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6282 | 0.6017 | 0 | | 0.5129 | 0.6177 | 1 | | 0.3728 | 0.6217 | 2 | | 9e469785c089f692c932e7d4a81f42fc |
mit | ['russian', 'fill-mask', 'pretraining', 'embeddings', 'masked-lm', 'tiny', 'feature-extraction', 'sentence-similarity'] | false | This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. This [post in Russian](https://habr.com/ru/post/669674/) gives more details. The differences from the previous version include: - a larger vocabulary: 83828 tokens instead of 29564; - larger supported sequences: 2048 instead of 512; - sentence embeddings approximate LaBSE closer than before; - meaningful segment embeddings (tuned on the NLI task) - the model is focused only on Russian. The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task. Sentence embeddings can be produced as follows: ```python | b61cee9c523542c3cbcfa0fd318f5e6e |
mit | ['russian', 'fill-mask', 'pretraining', 'embeddings', 'masked-lm', 'tiny', 'feature-extraction', 'sentence-similarity'] | false | pip install transformers sentencepiece import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2") model = AutoModel.from_pretrained("cointegrated/rubert-tiny2") | 92ea2953163607ddd161d012d61476e8 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_hubert_s507 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (et)](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. | 5666fe1a5ca5745f35b19727e5763663 |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP). This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. | 9e127671c90f64a17b82f83fcd0947e9 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0661 - Precision: 0.9318 - Recall: 0.9495 - F1: 0.9406 - Accuracy: 0.9854 | 3c1fd26f3b7f84ba938cdf5ddcbc7d0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0885 | 1.0 | 1756 | 0.0664 | 0.9189 | 0.9327 | 0.9257 | 0.9820 | | 0.0346 | 2.0 | 3512 | 0.0650 | 0.9260 | 0.9456 | 0.9357 | 0.9856 | | 0.017 | 3.0 | 5268 | 0.0661 | 0.9318 | 0.9495 | 0.9406 | 0.9854 | | 5b3b3cf3012681d33df9063a393de059 |
apache-2.0 | ['translation'] | false | opus-mt-fr-fj * source languages: fr * target languages: fj * OPUS readme: [fr-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-fj/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-fj/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-fj/opus-2020-01-09.eval.txt) | 57d932a49c5b0b1d5239f6a8abcb065b |
mit | [] | false | transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - [MIYAGINO](https://www.ntj.jac.go.jp/assets/images/member/pertopics/image/per100510_3.jpg) This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences. The input text is tokenized by [SudachiTra](https://github.com/WorksApplications/SudachiTra) with the WordPiece subword tokenizer. See `tokenizer_config.json` for the setting details. | ced3f526036ec0b0cb59b5c5be96c9f1 |
mit | [] | false | How to use ```python from transformers import ElectraModel from sudachitra import ElectraSudachipyTokenizer model = ElectraModel.from_pretrained("megagonlabs/transformers-ud-japanese-electra-base-discriminator") tokenizer = ElectraSudachipyTokenizer.from_pretrained("megagonlabs/transformers-ud-japanese-electra-base-discriminator") model(**tokenizer("まさにオールマイティーな商品だ。", return_tensors="pt")).last_hidden_state tensor([[[-0.0498, -0.0285, 0.1042, ..., 0.0062, -0.1253, 0.0338], [-0.0686, 0.0071, 0.0087, ..., -0.0210, -0.1042, -0.0320], [-0.0636, 0.1465, 0.0263, ..., 0.0309, -0.1841, 0.0182], ..., [-0.1500, -0.0368, -0.0816, ..., -0.0303, -0.1653, 0.0650], [-0.0457, 0.0770, -0.0183, ..., -0.0108, -0.1903, 0.0694], [-0.0981, -0.0387, 0.1009, ..., -0.0150, -0.0702, 0.0455]]], grad_fn=<NativeLayerNormBackward>) ``` | 51b489c1111c8dfd02de1aa25f17d31b |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-rte This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 1.5187 - Accuracy: 0.6643 | b64e8096c6df898bda334f1927a42f13 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6969 | 1.0 | 623 | 0.7039 | 0.5343 | | 0.5903 | 2.0 | 1246 | 0.6461 | 0.7184 | | 0.4557 | 3.0 | 1869 | 1.5187 | 0.6643 | | 2c877a5a0d841954078022e21d1b854e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8747 | 1.0 | 1063 | 3.7718 | | 3.7769 | 2.0 | 2126 | 3.7559 | | 3.7321 | 3.0 | 3189 | 3.7535 | | 4c509b8c505ab8dffe13182a3e5076f9 |
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: 0.0599 - Precision: 0.9274 - Recall: 0.9372 - F1: 0.9323 - Accuracy: 0.9840 | 55ee578a9053ecf498fb2b1f69de5aaa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2378 | 1.0 | 878 | 0.0719 | 0.9107 | 0.9200 | 0.9154 | 0.9801 | | 0.0509 | 2.0 | 1756 | 0.0620 | 0.9156 | 0.9311 | 0.9233 | 0.9821 | | 0.0307 | 3.0 | 2634 | 0.0599 | 0.9274 | 0.9372 | 0.9323 | 0.9840 | | e3e49a8079d39ccaa59e2e069d074bcb |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squadshifts-vanilla-nyt-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | b2e4aca995e9be9cdcc9f755c30be233 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | a1066a9b2a414e4bb4fc8edb41415de8 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-squadshifts-vanilla-nyt-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | cb2e6b2c406e45fb3b22f3d409311b07 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:---------------------------------------------------------------------------| | BERTScore | 92.67 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 24.7 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 16.38 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 11.53 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 8.43 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 24.58 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 64.38 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 24.57 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | 3a0b5060553849930be7b9612bcc390e |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-nyt-qg/raw/main/trainer_config.json). | d39e65f890165de1eef1498cc30daf81 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-uk-fi Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi). 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", } ``` | a8da2249dc2a36e9ebd64565e3802c6b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-17 * source language(s): ukr * target language(s): fin * model: transformer-align * data: opusTCv20210807+pft+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip) * more information released models: [OPUS-MT ukr-fin README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-fin/README.md) | 8fe00fc8b1960ed1cd3a75e4d6286974 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Африка є колискою людства.", "Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять." ] model_name = "pytorch-models/opus-mt-tc-base-uk-fi" 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) ) | 300bd7447c847c336da3dcee2b44989c |
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