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 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 | be5cb9d712c668db6a023fb85f8ada51 |
cc-by-sa-4.0 | ['belarusian', 'bulgarian', 'macedonian', 'russian', 'serbian', 'ukrainian', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained with Slavic-Cyrillic ([UD_Belarusian](https://universaldependencies.org/be/) [UD_Bulgarian](https://universaldependencies.org/bg/) [UD_Russian](https://universaldependencies.org/ru/) [UD_Serbian](https://universaldependencies.org/treebanks/sr_set/) [UD_Ukrainian](https://universaldependencies.org/treebanks/uk_iu/)) for POS-tagging and dependency-parsing, derived from [ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | a3cdd455c70041ff5c257086c0fe61a7 |
cc-by-sa-4.0 | ['belarusian', 'bulgarian', 'macedonian', 'russian', 'serbian', 'ukrainian', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-slavic-cyrillic-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-slavic-cyrillic-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-base-slavic-cyrillic-upos") ``` | 9793aa9f7ace2adf1f1cac34489856c2 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 | 6e4904548c9b968bdddb9c0fd376f106 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'et', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - EU dataset. It achieves the following results on the evaluation set: - Loss: 0.2278 - Wer: 0.1787 | 636814af433e27233018932fd57a2aa1 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'et', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2548 | 4.24 | 500 | 0.2470 | 0.3663 | | 0.1435 | 8.47 | 1000 | 0.2000 | 0.2791 | | 0.1158 | 12.71 | 1500 | 0.2030 | 0.2652 | | 0.1094 | 16.95 | 2000 | 0.2096 | 0.2605 | | 0.1004 | 21.19 | 2500 | 0.2150 | 0.2477 | | 0.0945 | 25.42 | 3000 | 0.2072 | 0.2369 | | 0.0844 | 29.66 | 3500 | 0.1981 | 0.2328 | | 0.0877 | 33.89 | 4000 | 0.2041 | 0.2425 | | 0.0741 | 38.14 | 4500 | 0.2353 | 0.2421 | | 0.0676 | 42.37 | 5000 | 0.2092 | 0.2213 | | 0.0623 | 46.61 | 5500 | 0.2217 | 0.2250 | | 0.0574 | 50.84 | 6000 | 0.2152 | 0.2179 | | 0.0583 | 55.08 | 6500 | 0.2207 | 0.2186 | | 0.0488 | 59.32 | 7000 | 0.2225 | 0.2159 | | 0.0456 | 63.56 | 7500 | 0.2293 | 0.2031 | | 0.041 | 67.79 | 8000 | 0.2277 | 0.2013 | | 0.0379 | 72.03 | 8500 | 0.2287 | 0.1991 | | 0.0381 | 76.27 | 9000 | 0.2233 | 0.1954 | | 0.0308 | 80.51 | 9500 | 0.2195 | 0.1835 | | 0.0291 | 84.74 | 10000 | 0.2266 | 0.1825 | | 0.0266 | 88.98 | 10500 | 0.2285 | 0.1801 | | 0.0266 | 93.22 | 11000 | 0.2292 | 0.1801 | | 0.0262 | 97.46 | 11500 | 0.2278 | 0.1788 | | c00b2ff2ae7b6f9dce54cb39078a3863 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-issues-128 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7582 | cb4d47a729aac8d2a084273b425bfaf5 |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 | f87ed17f3b5d848eccf7de09233bd4da |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4041 | 1.0 | 8 | 1.8568 | | 2.1982 | 2.0 | 16 | 2.0790 | | 1.7184 | 3.0 | 24 | 1.9246 | | 1.7248 | 4.0 | 32 | 1.8485 | | 1.5016 | 5.0 | 40 | 1.8484 | | 1.4943 | 6.0 | 48 | 1.8691 | | 1.526 | 7.0 | 56 | 1.7582 | | 64e9ceb936844eea3920ab1a441a0d5b |
apache-2.0 | ['translation'] | false | opus-mt-de-da * source languages: de * target languages: da * OPUS readme: [de-da](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-da/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-29.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-da/opus-2020-01-29.zip) * test set translations: [opus-2020-01-29.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-da/opus-2020-01-29.test.txt) * test set scores: [opus-2020-01-29.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-da/opus-2020-01-29.eval.txt) | b91cd94c300349a50bef557deb44b6bd |
apache-2.0 | ['generated_from_trainer'] | false | eval_masked_v4_cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6890 - Matthews Correlation: 0.5551 | 875abf014c201e679c4690a4a1fa32e8 |
mit | ['generated_from_trainer'] | false | BERiT_2000_custom_architecture_2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.9854 | 214ea873373bbe17a6af71324449d94e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 3314d7728b32dfcb8a68ccb81606ae24 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 16.4316 | 0.19 | 500 | 9.0685 | | 8.2958 | 0.39 | 1000 | 7.6483 | | 7.4324 | 0.58 | 1500 | 7.1707 | | 7.0054 | 0.77 | 2000 | 6.8592 | | 6.8522 | 0.97 | 2500 | 6.7710 | | 6.7538 | 1.16 | 3000 | 6.5845 | | 6.634 | 1.36 | 3500 | 6.4525 | | 6.5784 | 1.55 | 4000 | 6.3129 | | 6.5135 | 1.74 | 4500 | 6.3312 | | 6.4552 | 1.94 | 5000 | 6.2546 | | 6.4685 | 2.13 | 5500 | 6.2857 | | 6.4356 | 2.32 | 6000 | 6.2285 | | 6.3566 | 2.52 | 6500 | 6.2295 | | 6.394 | 2.71 | 7000 | 6.1790 | | 6.3412 | 2.9 | 7500 | 6.1880 | | 6.3115 | 3.1 | 8000 | 6.2130 | | 6.3163 | 3.29 | 8500 | 6.1831 | | 6.2978 | 3.49 | 9000 | 6.1945 | | 6.3082 | 3.68 | 9500 | 6.1485 | | 6.2729 | 3.87 | 10000 | 6.1752 | | 6.307 | 4.07 | 10500 | 6.1331 | | 6.2494 | 4.26 | 11000 | 6.1082 | | 6.2523 | 4.45 | 11500 | 6.2110 | | 6.2455 | 4.65 | 12000 | 6.1326 | | 6.2399 | 4.84 | 12500 | 6.1779 | | 6.2297 | 5.03 | 13000 | 6.1587 | | 6.2374 | 5.23 | 13500 | 6.1458 | | 6.2265 | 5.42 | 14000 | 6.1370 | | 6.2222 | 5.62 | 14500 | 6.1511 | | 6.2209 | 5.81 | 15000 | 6.1320 | | 6.2146 | 6.0 | 15500 | 6.1124 | | 6.214 | 6.2 | 16000 | 6.1439 | | 6.1907 | 6.39 | 16500 | 6.0981 | | 6.2119 | 6.58 | 17000 | 6.1465 | | 6.1858 | 6.78 | 17500 | 6.1594 | | 6.1552 | 6.97 | 18000 | 6.0742 | | 6.1926 | 7.16 | 18500 | 6.1176 | | 6.1813 | 7.36 | 19000 | 6.0107 | | 6.1812 | 7.55 | 19500 | 6.0852 | | 6.1852 | 7.75 | 20000 | 6.0845 | | 6.1945 | 7.94 | 20500 | 6.1260 | | 6.1542 | 8.13 | 21000 | 6.1032 | | 6.1685 | 8.33 | 21500 | 6.0650 | | 6.1619 | 8.52 | 22000 | 6.1028 | | 6.1279 | 8.71 | 22500 | 6.1269 | | 6.1575 | 8.91 | 23000 | 6.0793 | | 6.1401 | 9.1 | 23500 | 6.1479 | | 6.159 | 9.3 | 24000 | 6.0319 | | 6.1227 | 9.49 | 24500 | 6.0677 | | 6.1201 | 9.68 | 25000 | 6.0527 | | 6.1473 | 9.88 | 25500 | 6.1305 | | 6.1539 | 10.07 | 26000 | 6.1079 | | 6.091 | 10.26 | 26500 | 6.1219 | | 6.1015 | 10.46 | 27000 | 6.1317 | | 6.1048 | 10.65 | 27500 | 6.1149 | | 6.0955 | 10.84 | 28000 | 6.1216 | | 6.129 | 11.04 | 28500 | 6.0427 | | 6.1007 | 11.23 | 29000 | 6.1289 | | 6.1266 | 11.43 | 29500 | 6.0564 | | 6.1203 | 11.62 | 30000 | 6.1143 | | 6.1038 | 11.81 | 30500 | 6.0957 | | 6.0989 | 12.01 | 31000 | 6.0707 | | 6.0571 | 12.2 | 31500 | 6.0013 | | 6.1017 | 12.39 | 32000 | 6.1356 | | 6.0649 | 12.59 | 32500 | 6.0981 | | 6.0704 | 12.78 | 33000 | 6.0588 | | 6.088 | 12.97 | 33500 | 6.0796 | | 6.1112 | 13.17 | 34000 | 6.0809 | | 6.0888 | 13.36 | 34500 | 6.0776 | | 6.0482 | 13.56 | 35000 | 6.0710 | | 6.0588 | 13.75 | 35500 | 6.0877 | | 6.0517 | 13.94 | 36000 | 6.0650 | | 6.0832 | 14.14 | 36500 | 5.9890 | | 6.0655 | 14.33 | 37000 | 6.0445 | | 6.0705 | 14.52 | 37500 | 6.0037 | | 6.0789 | 14.72 | 38000 | 6.0777 | | 6.0645 | 14.91 | 38500 | 6.0475 | | 6.0347 | 15.1 | 39000 | 6.1148 | | 6.0478 | 15.3 | 39500 | 6.0639 | | 6.0638 | 15.49 | 40000 | 6.0373 | | 6.0377 | 15.69 | 40500 | 6.0116 | | 6.0402 | 15.88 | 41000 | 6.0483 | | 6.0382 | 16.07 | 41500 | 6.1025 | | 6.039 | 16.27 | 42000 | 6.0488 | | 6.0232 | 16.46 | 42500 | 6.0219 | | 5.9946 | 16.65 | 43000 | 6.0541 | | 6.063 | 16.85 | 43500 | 6.0436 | | 6.0141 | 17.04 | 44000 | 6.0609 | | 6.0196 | 17.23 | 44500 | 6.0551 | | 6.0331 | 17.43 | 45000 | 6.0576 | | 6.0174 | 17.62 | 45500 | 6.0498 | | 6.0366 | 17.82 | 46000 | 6.0782 | | 6.0299 | 18.01 | 46500 | 6.0196 | | 6.0009 | 18.2 | 47000 | 6.0262 | | 5.9758 | 18.4 | 47500 | 6.0824 | | 6.0285 | 18.59 | 48000 | 6.0799 | | 6.025 | 18.78 | 48500 | 5.9511 | | 5.9806 | 18.98 | 49000 | 6.0086 | | 5.9915 | 19.17 | 49500 | 6.0089 | | 5.9957 | 19.36 | 50000 | 6.0330 | | 6.0311 | 19.56 | 50500 | 6.0083 | | 5.995 | 19.75 | 51000 | 6.0394 | | 6.0034 | 19.95 | 51500 | 5.9854 | | 5881edbb8e015ef3819740d025e59754 |
apache-2.0 | ['translation'] | false | tgl-eng * source group: Tagalog * target group: English * OPUS readme: [tgl-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-eng/README.md) * model: transformer-align * source language(s): tgl_Latn * target language(s): eng * 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-eng/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.eval.txt) | 213e355bdea4bef9c2c264046c0e9bed |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tgl-eng - source_languages: tgl - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tl', 'en'] - src_constituents: {'tgl_Latn'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-eng/opus-2020-06-17.test.txt - src_alpha3: tgl - tgt_alpha3: eng - short_pair: tl-en - chrF2_score: 0.542 - bleu: 35.0 - brevity_penalty: 0.975 - ref_len: 18168.0 - src_name: Tagalog - tgt_name: English - train_date: 2020-06-17 - src_alpha2: tl - tgt_alpha2: en - prefer_old: False - long_pair: tgl-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | d84701e38c561290a187b9e9a1537e97 |
creativeml-openrail-m | [] | false | Basic explanation Token and Class words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the words, but it is recommended to include at least one. Adding token word/phrase class word/phrase at the start of the prompt in that order produces results most similar to the trained concept, but they can be included elsewhere as well. Some models produce better results when not including all token/class words. 3k models are are more flexible, while 5k models produce images closer to the trained concept. I recommend 2k/3k models for normal use, and 5k/6k models for model merging and use without token/class words. However it can be also very prompt specific. I highly recommend self-experimentation. | 81bc85100665910aef879726caff8517 |
creativeml-openrail-m | [] | false | Comparison Aeolian and aeolian_3000 are quite similar with slight differences. Epoch 5 and 6 versions were earlier in the waifu diffusion 1.3 training process, so it is easier to produce more varied, non anime, results. | 734133f32c58eb3133e0b6702b6f6264 |
creativeml-openrail-m | [] | false | License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | 429e70e73adce57d14bd3e162889a748 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | fastbooth-jsjessy-1200 Dreambooth model trained by eicu with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 92f047fb88ce375718c68cae2dc65c1c |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_unispeech-sat_s655 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (fr)](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. | 3981a3261bd1f67d0da66c18fdab0d8e |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_vp-es_s664 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](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. | 848f02a4dc31814b114d1b49214affd4 |
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: 0.0637 - Precision: 0.9335 - Recall: 0.9500 - F1: 0.9417 - Accuracy: 0.9862 | 26ba22cec7664bff426de7c7e6b18a14 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0888 | 1.0 | 1756 | 0.0636 | 0.9195 | 0.9366 | 0.9280 | 0.9830 | | 0.0331 | 2.0 | 3512 | 0.0667 | 0.9272 | 0.9490 | 0.9380 | 0.9855 | | 0.0167 | 3.0 | 5268 | 0.0637 | 0.9335 | 0.9500 | 0.9417 | 0.9862 | | 30c2ffbb12024b8927ebf9000d2c1d81 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-pytorch-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: - Loss: 2.1006 - Rouge1: 22.0585 - Rouge2: 9.4908 - Rougel: 18.3044 - Rougelsum: 20.9764 - Gen Len: 19.0 | b6053e2db851902aaf55996763ca90d2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 15 | 2.1859 | 21.551 | 8.7109 | 18.07 | 20.2469 | 19.0 | | No log | 2.0 | 30 | 2.1194 | 22.348 | 9.6498 | 18.7701 | 21.1714 | 19.0 | | No log | 3.0 | 45 | 2.1006 | 22.0585 | 9.4908 | 18.3044 | 20.9764 | 19.0 | | f7f2fe4b33c7a439d073c2b5acd7168a |
mit | ['generated_from_trainer'] | false | donut-base-mysterybox This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0075 | d78e22beb01b07f72068e7534f65fab4 |
apache-2.0 | ['generated_from_trainer'] | false | test-mlm This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6481 | 04d0672315e640b67dd0f4fc5ed5e875 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/EleutherAI). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were re-named in January 2023. For clarity, a <a href=" | 06169de4ef4c1817fbce24e39488ece8 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-160M for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-160M as a basis for your fine-tuned model, please conduct your own risk and bias assessment. | ac993afe2a3e3e0ba5f2b5dd006c19d9 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product, and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-160M has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-160M will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. | 58b0624d26b6ae60472c7f0cba2db298 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-160M to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-160M may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-160M. | 6a76c7a17e3f45ff99d3f77051220f49 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). The Pile was **not** deduplicated before being used to train Pythia-160M. | f21e3c0cbd0608593339f85a2badd107 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Naming convention and parameter count *Pythia* models were re-named in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure> | 77f2ecbd3fdd5942dac1ee353cfec5f7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-EU Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Basque using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | d82a70558501d91887666279cad49301 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | 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", "eu", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu") model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu") resampler = torchaudio.transforms.Resample(48_000, 16_000) | e1d1426e399bc01175ba148ecbef8c9d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): 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) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = 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["sentence"][:2]) ``` | 5a72fb7970f030032c371322a822eb7b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Basque 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", "eu", split="test") wer = load_metric("wer") model_name = "pcuenq/wav2vec2-large-xlsr-53-eu" processor = Wav2Vec2Processor.from_pretrained(model_name) model = Wav2Vec2ForCTC.from_pretrained(model_name) model.to("cuda") | 07a287aa702cae6f0fb925933f7c9a5b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Text pre-processing chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]' chars_to_ignore_pattern = re.compile(chars_to_ignore_regex) def remove_special_characters(batch): batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " " return batch | dd227cfcbbba92d4ec1e2bb9720a510b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Audio pre-processing import librosa def speech_file_to_array_fn(batch): speech_array, sample_rate = torchaudio.load(batch["path"]) batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000) return batch | fa07f4432012f6041608bb918deb68f5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Number of CPUs or None num_proc = 16 test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc) 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=8) | 6d938609c0829f2709ede470c8789b7c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training. Training was performed for 22 + 20 epochs with the following parameters: - Batch size 16, 2 gradient accumulation steps. - Learning rate: 2.5e-4 - Activation dropout: 0.05 - Attention dropout: 0.1 - Hidden dropout: 0.05 - Feature proj. dropout: 0.05 - Mask time probability: 0.08 - Layer dropout: 0.05 | d22d753108754c009e9627035d04ef4b |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Elldreth's OG 4060 mix: Source(s): [CivitAI](https://civitai.com/models/1259/elldreths-og-4060-mix) This mixed model is a combination of my all-time favorites. A genuine simple mix of a very popular anime model and the powerful and Zeipher's fantastic f222. What's it good at? Realistic portraits Stylized characters Landscapes Fantasy Sci-Fi Anime Horror It's an all-around easy-to-prompt general purpose semi-realistic to realistic model that cranks out some really nice images. No trigger words required. All models were scanned prior to mixing and totally safe. | dd7a7e9cc211c942d30ada25fb00bc6d |
apache-2.0 | ['generated_from_trainer'] | false | language-detection-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: 0.2231 - Accuracy: 0.9512 | 7b21b482df78e301262acd97f60c989e |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mt5-small-itquad-qg-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 4e0d0ae6dc2f3215ff2b0c4b56eec478 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** it - **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default) - **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) | 946e7b58ee3241edac2b6cd3a1a46cb3 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qg-ae") | 3dd581b220d128e861f57c004a185a2a |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | question generation question = pipe("extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.") ``` | 8a0090c2c172955944da4fef1284f9a7 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 80.61 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_1 | 22.53 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_2 | 14.75 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_3 | 10.19 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_4 | 7.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | METEOR | 17.5 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | MoverScore | 56.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ROUGE_L | 21.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 81.81 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (MoverScore) | 56.02 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (BERTScore) | 81.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (MoverScore) | 55.76 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (BERTScore) | 82.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (MoverScore) | 56.32 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 57.85 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | AnswerF1Score | 72.09 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | BERTScore | 90.24 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_1 | 39.33 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_2 | 33.64 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_3 | 29.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_4 | 26.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | METEOR | 42.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | MoverScore | 81.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ROUGE_L | 45.15 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ced374fefcafad5e75808c3a0291f5ff |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_itquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 13 - batch: 16 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/trainer_config.json). | 872dcfc7fa74ae2295e90a1fa6a65d23 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-finetuned-cifar100 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 cifar100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4223 - Accuracy: 0.8735 | f6eb10126e030cddf0b50eb6549d47bf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 (with early stopping) | e431018421b4dcdd710f2f53780568bf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.6439 | 1.0 | 781 | 0.8138 | 0.6126 | | 0.6222 | 2.0 | 1562 | 0.8393 | 0.5094 | | 0.2912 | 3.0 | 2343 | 0.861 | 0.4452 | | 0.2234 | 4.0 | 3124 | 0.8679 | 0.4330 | | 0.121 | 5.0 | 3905 | 0.8735 | 0.4223 | | 0.2589 | 6.0 | 4686 | 0.8622 | 0.4775 | | 0.1419 | 7.0 | 5467 | 0.8642 | 0.4900 | | 0.1513 | 8.0 | 6248 | 0.8667 | 0.4956 | | 9e125bc0f517854540f1b8af5784c6ed |
apache-2.0 | [] | false | Model description **CAMeLBERT-Mix POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model. For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset . Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). | fc4da3d183dc750b61a272eacd9f3f91 |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf') >>> text = 'شلونك ؟ شخبارك ؟' >>> pos(text) [{'entity': 'pron_interrog', 'score': 0.82657206, 'index': 1, 'word': 'شلون', 'start': 0, 'end': 4}, {'entity': 'prep', 'score': 0.9771731, 'index': 2, 'word': ' | 716ac63363e92753e10719d8f3391667 |
apache-2.0 | [] | false | ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.9999568, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9977217, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.99993783, 'index': 5, 'word': ' | c7ed0a954244a7352c2cd6dc5e463bf1 |
apache-2.0 | [] | false | ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999575, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. | 79f893226bdf657297090a903c8e5476 |
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: 0.0625 - Precision: 0.9267 - Recall: 0.9359 - F1: 0.9313 - Accuracy: 0.9836 | bfa096e79d58e8becdf5d7d5cf6a5a8f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2395 | 1.0 | 878 | 0.0709 | 0.9148 | 0.9186 | 0.9167 | 0.9809 | | 0.0538 | 2.0 | 1756 | 0.0628 | 0.9228 | 0.9332 | 0.9280 | 0.9828 | | 0.03 | 3.0 | 2634 | 0.0625 | 0.9267 | 0.9359 | 0.9313 | 0.9836 | | 4a3451f83018488adfe3de9d22656a7f |
creativeml-openrail-m | [] | false | A Dreambooth model created with the sole purpose of generating the rarest and dankest pepes. StableDiffusion 1.5 was used as a base for this model. 22 instance images, 400 class images, 2.2k steps at a 1.3e-6 learning rate. Use the phrase 'pepestyle person' <img src="https://huggingface.co/SpiteAnon/Pepestyle/resolve/main/pepestylev2.png" alt="pepestylev2" width="400"/> <img src="https://huggingface.co/SpiteAnon/Pepestyle/resolve/main/pepestylev2-drawing.png" alt="pepestylev2-drawing" width="400"/> <img src="https://huggingface.co/SpiteAnon/Pepestyle/resolve/main/pepestylev2-suit-hat.png" alt="pepestylev2-suit" width="400"/> | 6c9594e950f27c0f106a141739eeb86d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-sdg This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OSDG dataset. It achieves the following results on the evaluation set: - Loss: 0.3094 - Acc: 0.9195 | f21a645d02e0a88817305efd2fda6129 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 61515a716c6dec5407db0af8dfd4f444 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Acc | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3768 | 1.0 | 269 | 0.3758 | 0.8933 | | 0.2261 | 2.0 | 538 | 0.3088 | 0.9095 | | 0.1038 | 3.0 | 807 | 0.3094 | 0.9195 | | 79f5755e5bd670f59f1c7c2a7226af81 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | **EpicSpaceMachine** This is the fine-tuned Stable Diffusion model trained on epic pictures of space ships and space stations. Use the tokens **_EpicSpaceMachine_** in your prompts for the effect. It generates OK, spaceships and space stations, including via img2img, but produces awesome images when given prompts that generate complex mechanical shapes such as the internals of car engines. **Examples rendered with the model:** Prompt: Photo of a futuristic space liner, 4K, award winning in EpicSpaceMachine style  Prompt: Photo of a GPU , 4K, close up in EpicSpaceMachine style  Propmt: Engine of a F1 race car, close up, 8K, in EpicSpaceMachine style  Prompt: A pile of paper clips, close up, 8K, in EpicSpaceMachine style  Prompt: A photo of the insides of a mechanical watch, close up, 8K, in EpicSpaceMachine style  Prompt: Photo of a mother board, close up, 4K in EpicSpaceMachine style  Prompt: Photo of a large excavator engine in EpicSpaceMachine style  Prompt: Photo of A10 Warthog, 4K, award winning in EpicSpaceMachine style  Prompt: A photo of a tangle of wires, close up, 8K, in EpicSpaceMachine style  | 65c52d213837f5bc1b016372fd3f87c0 |
gpl-3.0 | ['bicleaner-ai'] | false | Bicleaner AI full model for en-fr Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored with 0. Find out at our repository for further instructions on how to use it: https://github.com/bitextor/bicleaner-ai | 60f3312e6e42632a14e7d17686a9b4e5 |
gpl-3.0 | ['object-detection', 'computer-vision', 'vision', 'yolo', 'yolov5'] | false | save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --img 640 --batch 16 --weights kadirnar/deprem_model_v1 --epochs 10 --device cuda:0 ``` | c02ed1efc1ae94d71baeec97ad8e11ff |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-cnndm2-wikihow2 This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm2-wikihow1](https://huggingface.co/Chikashi/t5-small-finetuned-cnndm2-wikihow1) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.3311 - Rouge1: 27.0962 - Rouge2: 10.3575 - Rougel: 23.1099 - Rougelsum: 26.4664 - Gen Len: 18.5197 | 2e7757b5b6c4c942c85efd9e5d0d8f07 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.517 | 1.0 | 39313 | 2.3311 | 27.0962 | 10.3575 | 23.1099 | 26.4664 | 18.5197 | | 51063e988132fd48ffd34f2084cfbf3b |
['cc0-1.0'] | ['computer-vision', 'image-classification'] | false | Image Classification using MobileViT This repo contains the model and the notebook [to this Keras example on MobileViT](https://keras.io/examples/vision/mobilevit/). Full credits to: [Sayak Paul](https://twitter.com/RisingSayak) | 9291e622ea293c6bedbb1cec5b082b81 |
['cc0-1.0'] | ['computer-vision', 'image-classification'] | false | Background Information MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can capture long-range dependencies that result in global representations. With convolutions, we can capture spatial relationships that model locality. Besides combining the properties of Transformers and convolutions, the authors introduce MobileViT as a general-purpose mobile-friendly backbone for different image recognition tasks. Their findings suggest that, performance-wise, MobileViT is better than other models with the same or higher complexity (MobileNetV3, for example), while being efficient on mobile devices. | 8c89e1fb3215eeaca17a8fb9430621ec |
mit | [] | false | Model description It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between indonesia and Indonesia. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers) | b6c35fd9fb6c7ac063e4badc5744794b |
mit | [] | 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='cahya/roberta-base-indonesian-522M') >>> unmasker("Ibu ku sedang bekerja <mask> supermarket") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel model_name='cahya/roberta-base-indonesian-522M' tokenizer = RobertaTokenizer.from_pretrained(model_name) model = RobertaModel.from_pretrained(model_name) text = "Silakan diganti dengan text apa saja." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in Tensorflow: ```python from transformers import RobertaTokenizer, TFRobertaModel model_name='cahya/roberta-base-indonesian-522M' tokenizer = RobertaTokenizer.from_pretrained(model_name) model = TFRobertaModel.from_pretrained(model_name) text = "Silakan diganti dengan text apa saja." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | f7187eaa87ea753c6ec87c2a48feadf0 |
mit | [] | false | Training data This model was pre-trained with 522MB of indonesian Wikipedia. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ```<s> Sentence A </s> Sentence B </s>``` | 22e62564e882f39437c4f58f02398322 |
mit | ['bio', 'infrastructure', 'funding', 'natural language processing', 'BERT'] | false | Biodata Resource Inventory This repository holds the fine-tuned models used in the biodata resource inventory conducted in 2022 by the [Global Biodata Coalition](https://globalbiodata.org/) in collaboration with [Chan Zuckerberg Initiative](https://chanzuckerberg.com/). | dbbdfbab6875f6df2c6a45cf01f7fc8d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_stsb_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2586 - Pearson: -0.0814 - Spearmanr: -0.0816 - Combined Score: -0.0815 | 60c0f8661b78f56553c1f9ed8658b5e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 6.966 | 1.0 | 23 | 4.0539 | -0.0244 | -0.0244 | -0.0244 | | 4.4237 | 2.0 | 46 | 3.1176 | -0.0508 | -0.0503 | -0.0505 | | 3.3768 | 3.0 | 69 | 2.5232 | -0.1303 | -0.1323 | -0.1313 | | 2.6486 | 4.0 | 92 | 2.2586 | -0.0814 | -0.0816 | -0.0815 | | 2.2539 | 5.0 | 115 | 2.3547 | 0.0512 | 0.0505 | 0.0508 | | 2.1692 | 6.0 | 138 | 2.3367 | 0.0642 | 0.0568 | 0.0605 | | 2.1268 | 7.0 | 161 | 2.4285 | 0.0444 | 0.0649 | 0.0546 | | 1.9924 | 8.0 | 184 | 2.6031 | 0.0781 | 0.0846 | 0.0814 | | 1.8254 | 9.0 | 207 | 2.6306 | 0.1155 | 0.1187 | 0.1171 | | ec2e276643230cc071cb5b8015be0966 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1709 - Accuracy: 0.9305 - F1: 0.9306 | 0d5d461700d4960f7fa5a365f42a2b96 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1755 | 1.0 | 250 | 0.1831 | 0.925 | 0.9249 | | 0.1118 | 2.0 | 500 | 0.1709 | 0.9305 | 0.9306 | | 3c17b89f3078a596a2c583e51e22c5c9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-wandb-week-3-complaints-classifier-256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set: - Loss: 0.5453 - Accuracy: 0.8235 - F1: 0.8176 - Recall: 0.8235 - Precision: 0.8171 | bf686c42a6ebf24cdca20af424d3db02 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.097565552226687e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 256 - num_epochs: 2 - mixed_precision_training: Native AMP | d395964f4a89cbd5d0b10b3c340a395c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.6691 | 0.61 | 1500 | 0.6475 | 0.7962 | 0.7818 | 0.7962 | 0.7875 | | 0.5361 | 1.22 | 3000 | 0.5794 | 0.8161 | 0.8080 | 0.8161 | 0.8112 | | 0.4659 | 1.83 | 4500 | 0.5453 | 0.8235 | 0.8176 | 0.8235 | 0.8171 | | 5b6bfa1ed79170b62a47cd800d8141db |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-emotions-augmented This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6063 - Accuracy: 0.7789 - F1: 0.7770 | b4b9f53974b0de9924e47b93944ccfb3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | b2832ce494646efc1f5a49717fe6e8e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.855 | 1.0 | 819 | 0.6448 | 0.7646 | 0.7606 | | 0.5919 | 2.0 | 1638 | 0.6067 | 0.7745 | 0.7730 | | 0.5077 | 3.0 | 2457 | 0.6063 | 0.7789 | 0.7770 | | 0.4364 | 4.0 | 3276 | 0.6342 | 0.7725 | 0.7687 | | 0.3698 | 5.0 | 4095 | 0.6832 | 0.7693 | 0.7686 | | 0.3153 | 6.0 | 4914 | 0.7364 | 0.7636 | 0.7596 | | 0.2723 | 7.0 | 5733 | 0.7578 | 0.7661 | 0.7648 | | 0.2429 | 8.0 | 6552 | 0.7816 | 0.7623 | 0.7599 | | bb008d9ee1d056ed26937eb6303c7631 |
openrail | ['nsfw', 'stable diffusion'] | false | PoV Skin Textures - Dreamlike r34 [pov-skin-texture-dreamlike-r34](https://civitai.com/models/4481/pov-skin-texture-dreamlike-r34) This version has vae-ft-mse-840000-ema-pruned.ckpt baked in. Due to using Dreamlike Diffusion 1.0, this model has the following license: License This model is licensed under a modified CreativeML OpenRAIL-M license. - You can't host or use the model or its derivatives on websites/apps/etc., from which you earn, will earn, or plan to earn revenue or donations. If you want to, please email us at contact@dreamlike.art - You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Diffusion 1.0) and include a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0) - You are free to host the model or its derivatives on completely non-commercial websites/apps/etc (Meaning you are not getting ANY revenue or donations). Please state the full model name (Dreamlike Diffusion 1.0) and include a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0) - You are free to use the outputs of the model or the outputs of the model's derivatives for commercial purposes in teams of 10 or less - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the modified CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/blob/main/LICENSE.md | c1574146cc4f329791c681f3acde70a7 |
apache-2.0 | [] | false | Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Spanish-Catalan datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, technology, biomedical, and news). | fddee50ed051ef54b070c0992807f0ea |
apache-2.0 | [] | false | Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="PlanTL-GOB-ES/mt-plantl-es-ca", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Bienvenido al Proyecto PlanTL!") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) ``` | 11eec6c9c9550aae3510c6949358ffee |
apache-2.0 | [] | false | Training data The model was trained on a combination of the following datasets: | Dataset | Sentences | Tokens | |-------------------|----------------|-------------------| | DOCG v2 | 8.472.786 | 188.929.206 | | El Periodico | 6.483.106 | 145.591.906 | | EuroParl | 1.876.669 | 49.212.670 | | WikiMatrix | 1.421.077 | 34.902.039 | | Wikimedia | 335.955 | 8.682.025 | | QED | 71.867 | 1.079.705 | | TED2020 v1 | 52.177 | 836.882 | | CCMatrix v1 | 56.103.820 | 1.064.182.320 | | MultiCCAligned v1 | 2.433.418 | 48.294.144 | | ParaCrawl | 15.327.808 | 334.199.408 | | **Total** | **92.578.683** | **1.875.910.305** | | 94fa640f9a3e42b9df0c63dbe7721b46 |
apache-2.0 | [] | false | Data preparation All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata) and cleaned using the clean-corpus-n.pl script from [moses](https://github.com/moses-smt/mosesdecoder), allowing sentences between 5 and 150 words. Before training, the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py) | 5eea0ad4a9ca0316348cee930fda1610 |
apache-2.0 | [] | false | Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|----------------------------------| | Architecture | transformer_vaswani_wmt_en_de_bi | | Embedding size | 1024 | | Feedforward size | 4096 | | Number of heads | 16 | | Encoder layers | 24 | | Decoder layers | 6 | | Normalize before attention | True | | --share-decoder-input-output-embed | True | | --share-all-embeddings | True | | Effective batch size | 96.000 | | Optimizer | adam | | Adam betas | (0.9, 0.980) | | Clip norm | 0.0 | | Learning rate | 1e-3 | | Lr. schedurer | inverse sqrt | | Warmup updates | 4000 | | Dropout | 0.1 | | Label smoothing | 0.1 | The model was trained using shards of 10 million sentences, for a total of 8.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints. | 905957339f16bb3bb32f8b9b7cccc9dc |
apache-2.0 | [] | false | Evaluation results Below are the evaluation results on the machine translation from Spanish to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate |mt-plantl-es-ca| |----------------------|------------|------------------|---------------| | Spanish Constitution | **63,6** | 61,7 | 63,0 | | United Nations | 73,8 | 74,8 | **74,9** | | Flores 101 dev | 22 | **23,1** | 22,5 | | Flores 101 devtest | 22,7 | **23,6** | 23,1 | | Cybersecurity | 61,4 | **69,5** | 67,3 | | wmt 19 biomedical | 60,2 | 59,7 | **60,6** | | wmt 13 news | 21,3 | **22,4** | 22,0 | | Average | 46,4 | **47,8** | 47,6 | | 42c361296545a6f2b383c26fa2df7862 |
mit | ['generated_from_trainer'] | false | bertimbau-base-lener-br-finetuned-lener-br This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: nan - Precision: 0.8943 - Recall: 0.8970 - F1: 0.8956 - Accuracy: 0.9696 | 46f3c02c122276c27216d916fbfb6c30 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | 51de41106e8bb8e97441d2da4fa503c8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0678 | 1.0 | 1957 | nan | 0.8148 | 0.8882 | 0.8499 | 0.9689 | | 0.0371 | 2.0 | 3914 | nan | 0.8347 | 0.9022 | 0.8671 | 0.9671 | | 0.0242 | 3.0 | 5871 | nan | 0.8491 | 0.8905 | 0.8693 | 0.9716 | | 0.0197 | 4.0 | 7828 | nan | 0.9014 | 0.8772 | 0.8892 | 0.9780 | | 0.0135 | 5.0 | 9785 | nan | 0.8651 | 0.9060 | 0.8851 | 0.9765 | | 0.013 | 6.0 | 11742 | nan | 0.8882 | 0.9054 | 0.8967 | 0.9767 | | 0.0084 | 7.0 | 13699 | nan | 0.8559 | 0.9097 | 0.8820 | 0.9751 | | 0.0069 | 8.0 | 15656 | nan | 0.8916 | 0.8828 | 0.8872 | 0.9696 | | 0.0047 | 9.0 | 17613 | nan | 0.8964 | 0.8931 | 0.8948 | 0.9716 | | 0.0028 | 10.0 | 19570 | nan | 0.8864 | 0.9047 | 0.8955 | 0.9691 | | 0.0023 | 11.0 | 21527 | nan | 0.8860 | 0.9011 | 0.8935 | 0.9693 | | 0.0009 | 12.0 | 23484 | nan | 0.8952 | 0.8987 | 0.8970 | 0.9686 | | 0.0014 | 13.0 | 25441 | nan | 0.8929 | 0.8985 | 0.8957 | 0.9699 | | 0.0025 | 14.0 | 27398 | nan | 0.8914 | 0.8981 | 0.8947 | 0.9700 | | 0.001 | 15.0 | 29355 | nan | 0.8943 | 0.8970 | 0.8956 | 0.9696 | | 02695619ee1f91563bc0829486e7fea9 |
mit | ['generated_from_trainer'] | false | deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa-1epoch](https://huggingface.co/stevemobs/deberta-base-combined-squad1-aqa-1epoch) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6807 | 39f86d2697588d1662d6c4ef3e87556d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 27f181431c55058b95aa07809df41f53 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'ASR', 'Kinyarwanda', 'Swahili', 'Luganda', 'Multilingual', 'audio', 'CTC', 'Conformer', 'Transformer', 'NeMo', 'pytorch'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="yonas/stt_rw_sw_lg_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 4028daead998335d3b94fe276b0a506c |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_unispeech-ml_s246 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) 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. | 813014c8ca971ff34bb1b52cc04c5df7 |
apache-2.0 | ['translation'] | false | opus-mt-de-ht * source languages: de * target languages: ht * OPUS readme: [de-ht](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ht/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-ht/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-ht/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-ht/opus-2020-01-20.eval.txt) | 839d0f27f988b12beeb52c48d8eb4172 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-tiny-ar This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8394 - Wer: 86.0500 | e69f669a99c0193598c38cb951c22358 |
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