license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.4767 | 1.0 | 1346 | 0.6853 | 0.6323 | 0.6501 | 0.5789 | 0.7413 | 0.3677 | 290 | 248 | 119 | 341 | | 0.3783 | 2.0 | 2692 | 0.7041 | 0.6653 | 0.6528 | 0.6255 | 0.6826 | 0.3347 | 350 | 188 | 146 | 314 | | 0.2803 | 3.0 | 4038 | 0.8767 | 0.7094 | 0.7184 | 0.6491 | 0.8043 | 0.2906 | 338 | 200 | 90 | 370 | | 0a394ea85f262abcde2081ebd9d4a70b |
apache-2.0 | ['generated_from_trainer'] | false | patent-summarization-google-bigbird-pegasus-large-arxiv-2022-09-20 This model is a fine-tuned version of [google/bigbird-pegasus-large-arxiv](https://huggingface.co/google/bigbird-pegasus-large-arxiv) on the farleyknight/big_patent_5_percent dataset. It achieves the following results on the evaluation set: - Loss: 2.2617 - Rouge1: 37.3764 - Rouge2: 13.2442 - Rougel: 26.011 - Rougelsum: 31.0145 - Gen Len: 113.8789 | eb7bb2bdccee8fe0d2a99e2946ad3a03 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 | 6de90adadf8aec1475c78c6bd36cd854 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.6121 | 0.08 | 5000 | 2.5652 | 35.0673 | 12.0073 | 24.5471 | 28.9315 | 119.9866 | | 2.5182 | 0.17 | 10000 | 2.4797 | 34.6909 | 11.6432 | 24.87 | 28.1543 | 119.2043 | | 2.5102 | 0.25 | 15000 | 2.4238 | 35.8574 | 12.2402 | 25.0712 | 29.5607 | 115.2890 | | 2.4292 | 0.33 | 20000 | 2.3869 | 36.0133 | 12.2453 | 25.4039 | 29.483 | 112.5920 | | 2.3678 | 0.41 | 25000 | 2.3594 | 35.238 | 11.6833 | 25.0449 | 28.3313 | 119.1739 | | 2.3511 | 0.5 | 30000 | 2.3326 | 36.7755 | 12.8394 | 25.7218 | 30.2594 | 110.5819 | | 2.3334 | 0.58 | 35000 | 2.3125 | 36.6317 | 12.7493 | 25.5388 | 30.094 | 115.5998 | | 2.3833 | 0.66 | 40000 | 2.2943 | 37.1219 | 13.1564 | 25.7571 | 30.8666 | 113.8222 | | 2.341 | 0.75 | 45000 | 2.2813 | 36.4962 | 12.6225 | 25.6904 | 29.9741 | 115.9845 | | 2.3179 | 0.83 | 50000 | 2.2725 | 37.3535 | 13.1596 | 25.7385 | 31.056 | 117.7754 | | 2.3164 | 0.91 | 55000 | 2.2654 | 36.9191 | 12.9316 | 25.7586 | 30.4691 | 116.1670 | | 2.3046 | 0.99 | 60000 | 2.2618 | 37.3992 | 13.2731 | 26.0327 | 31.0338 | 114.5195 | | 65b9ec1ebb50598cbe64259ee4ac1fa3 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-2-16-5 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: 2.3560 - Accuracy: 0.2692 | 03a8773e42d22bb9fa5afeeac529a663 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qqp This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5151 | 57945e08ac82343fea33cfbb3c3a17c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8517 | 0.4 | 500 | 2.7156 | | 2.8184 | 0.8 | 1000 | 2.6309 | | 2.7461 | 1.2 | 1500 | 2.5335 | | 2.5785 | 1.6 | 2000 | 2.5472 | | 2.5753 | 2.0 | 2500 | 2.5667 | | 2.4744 | 2.4 | 3000 | 2.4824 | | 2.4448 | 2.8 | 3500 | 2.5490 | | 2.476 | 3.2 | 4000 | 2.4906 | | 2.3352 | 3.6 | 4500 | 2.5151 | | e8a46679f056ca5a1d5110176733250f |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-8_belgium-2_s365 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (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. | 34a665c2dbc6f1c26a9e882d9e2a94fb |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'mr', 'robust-speech-event'] | 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 - MR dataset. It achieves the following results on the mozilla-foundation/common_voice_8_0 mr test set: - Without LM + WER: 48.53 + CER: 10.63 - With LM + WER: 38.27 + CER: 8.91 | 8e8303f946185f1cc4d14c7acd4a4701 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'mr', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 400.0 - mixed_precision_training: Native AMP | c4e6e0afedebae63430724dcc4a59fc4 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'mr', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.2706 | 22.73 | 500 | 4.0174 | 1.0 | | 3.2492 | 45.45 | 1000 | 3.2309 | 0.9908 | | 1.9709 | 68.18 | 1500 | 1.0651 | 0.8440 | | 1.4088 | 90.91 | 2000 | 0.5765 | 0.6550 | | 1.1326 | 113.64 | 2500 | 0.4842 | 0.5760 | | 0.9709 | 136.36 | 3000 | 0.4785 | 0.6013 | | 0.8433 | 159.09 | 3500 | 0.5048 | 0.5419 | | 0.7404 | 181.82 | 4000 | 0.5052 | 0.5339 | | 0.6589 | 204.55 | 4500 | 0.5237 | 0.5897 | | 0.5831 | 227.27 | 5000 | 0.5166 | 0.5447 | | 0.5375 | 250.0 | 5500 | 0.5292 | 0.5487 | | 0.4784 | 272.73 | 6000 | 0.5480 | 0.5596 | | 0.4421 | 295.45 | 6500 | 0.5682 | 0.5467 | | 0.4047 | 318.18 | 7000 | 0.5681 | 0.5447 | | 0.3779 | 340.91 | 7500 | 0.5783 | 0.5347 | | 0.3525 | 363.64 | 8000 | 0.5856 | 0.5367 | | 0.3393 | 386.36 | 8500 | 0.5960 | 0.5359 | | 639de2dd1716fd7292098b552f5a6233 |
apache-2.0 | ['translation'] | false | eng-mkh * source group: English * target group: Mon-Khmer languages * OPUS readme: [eng-mkh](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-mkh/README.md) * model: transformer * source language(s): eng * target language(s): kha khm khm_Latn mnw vie vie_Hani * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.zip) * test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.test.txt) * test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.eval.txt) | bf6cb25b9405ab8b0b664d1a15f42ccb |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-kha.eng.kha | 0.1 | 0.015 | | Tatoeba-test.eng-khm.eng.khm | 0.2 | 0.226 | | Tatoeba-test.eng-mnw.eng.mnw | 0.7 | 0.003 | | Tatoeba-test.eng.multi | 16.5 | 0.330 | | Tatoeba-test.eng-vie.eng.vie | 33.7 | 0.513 | | 78ea76bda9cbef98814dc9069553258c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-mkh - source_languages: eng - target_languages: mkh - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-mkh/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'vi', 'km', 'mkh'] - src_constituents: {'eng'} - tgt_constituents: {'vie_Hani', 'mnw', 'vie', 'kha', 'khm_Latn', 'khm'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-mkh/opus-2020-07-27.test.txt - src_alpha3: eng - tgt_alpha3: mkh - short_pair: en-mkh - chrF2_score: 0.33 - bleu: 16.5 - brevity_penalty: 1.0 - ref_len: 34734.0 - src_name: English - tgt_name: Mon-Khmer languages - train_date: 2020-07-27 - src_alpha2: en - tgt_alpha2: mkh - prefer_old: False - long_pair: eng-mkh - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 6940c60a37f5e88fca0fd54e8ca12be0 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.observational.sa.5-class.seed_42 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.9425 - Accuracy: 0.6091 - Macro-f1: 0.5206 - Weighted-macro-f1: 0.5595 | bf481afad6e6e57c7d525a4e037e243c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | eb068a8f3a5be7ea9d6d15bda602623e |
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: 0.2388 - F1: 0.8233 | 956b8323a75ddefd948dedbd362c95db |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8099 | 1.0 | 70 | 0.3035 | 0.7333 | | 0.2766 | 2.0 | 140 | 0.2661 | 0.7948 | | 0.1792 | 3.0 | 210 | 0.2388 | 0.8233 | | 7f705979d8f54aa18a8ef1a652e8caf3 |
cc-by-sa-4.0 | [] | false | How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp") sentence = '早稲田大学で自然言語処理を[MASK]する。' encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can fine-tune this model on downstream tasks. | b9fd9da9a2eb4e891394b2a790e2c19b |
cc-by-sa-4.0 | [] | false | Tokenization `BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-base-japanese](https://huggingface.co/nlp-waseda/roberta-base-japanese). Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). | be013839c29816fd61d8cd7de75c5d07 |
cc-by-sa-4.0 | [] | false | Vocabulary The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). | d78111d5f218e09292dc274ea1970481 |
cc-by-sa-4.0 | [] | false | Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took a week using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 1e-4 - per_device_train_batch_size: 256 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 4096 - max_seq_length: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 700000 - warmup_steps: 10000 - mixed_precision_training: Native AMP | 0beacadb1e7569bd697c8a482081d3c9 |
cc-by-sa-4.0 | [] | false | Electra Base Japanese Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [transformers-ud-japanese-electra-ginza](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator/tree/main), and later finetuned on a dataset containing ironic and sarcastic tweets. | 36bde219c40baa90855d2832dd971437 |
cc-by-sa-4.0 | [] | false | Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022electra-base-irony, title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Megagon Labs ver.)}, author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-basic-char-v2-irony" } ``` | 2ea90d331bd8b5c98fe5f5fa0755e7df |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4442 | b5c49d96d90dfd8ed2e40dc665f36b03 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6985 | 1.0 | 157 | 2.5612 | | 2.562 | 2.0 | 314 | 2.4226 | | 2.5316 | 3.0 | 471 | 2.4218 | | 856e833e272ce9a026dc6ee1bc8dcfd0 |
mit | [] | false | model by hans120791 This your the Stable Diffusion model fine-tuned the metahuman rkr concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks rkr** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:            | 67765a08cc2aa74ac06ee6dd7426979d |
mit | [] | false | ResNet18 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, [add the Metalhead.jl package to your environment](https://pkgdocs.julialang.org/v1/managing-packages/ | 446c94a325304485aab993d0a59d3c00 |
apache-2.0 | ['lexical normalization'] | false | Fine-tuned ByT5-small for MultiLexNorm (Turkish version)  This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). | be4a736c2a22698ebcca68d42e39cfe8 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-large-v2-japanese-5k-steps This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Japanese CommonVoice dataset (v11).. It achieves the following results on the evaluation set: - Loss: 0.4200 - Wer: 0.7449 | 7bb614aedc087cf849959999be0ccdea |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 50 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP | d44b3136292fe743e6d6da386941838e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0111 | 7.63 | 1000 | 0.3210 | 0.7888 | | 0.0007 | 15.27 | 2000 | 0.3585 | 0.7478 | | 0.0003 | 22.9 | 3000 | 0.3937 | 0.7432 | | 0.0002 | 30.53 | 4000 | 0.4123 | 0.7443 | | 0.0002 | 38.17 | 5000 | 0.4200 | 0.7449 | | a049710d6ca65fad039a617bef8b9922 |
apache-2.0 | ['generated_from_trainer'] | false | load the model processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps") model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps").to(device) forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe") | 9a2cb46eb961e28ffa02ae8bf6064356 |
apache-2.0 | ['generated_from_trainer'] | false | load the dataset commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="validation", streaming=True) commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000)) sample = next(iter(commonvoice_eval))["audio"] | ba39d555d5dd4bf5f80d007855eb34e4 |
apache-2.0 | ['generated_from_trainer'] | false | features and generate token ids input_features = processor(sample["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids) | f22d3cd02a087d84eebdab8549601b00 |
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: 0.2116 - Accuracy: 0.9295 - F1: 0.9293 | 1eb4f69e7dd3438d2715e9e2cb4df52f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8487 | 1.0 | 250 | 0.3135 | 0.909 | 0.9051 | | 0.2515 | 2.0 | 500 | 0.2116 | 0.9295 | 0.9293 | | 5af54f98c28b4f23335a2f7c8a9f1340 |
apache-2.0 | ['generate', 'gpt2'] | false | 模型信息 Model Information 类似于Wenzhong2.0-GPT2-3.5B-chinese，我们实现了一个small版本的12层的Wenzhong2.0-GPT2-110M-BertTokenizer-chinese，并在悟道（300G版本）上面进行预训练。本次开源别于之前开源的闻仲-GPT2系列，主要在于将BPE的分词换成了BertTokenzier的字级别分词。 Similar to Wenzhong2.0-GPT2-3.5B-chinese, we implement a small size Wenzhong2.0-GPT2-110M-BertTokenizer-chinese with 12 layers, which is pre-trained on Wudao Corpus (300G version).This open source version is different from the previous open source Wenzhong-GPT2 series, mainly because the word segmentation of BPE is replaced by the word level word segmentation of BertTokenzier. | 804ac62285d22a2171c5afca265b3e5c |
apache-2.0 | ['generate', 'gpt2'] | false | 加载模型 Loading Models ```python from transformers import BertTokenizer,GPT2LMHeadModel hf_model_path = 'IDEA-CCNL/Wenzhong-GPT2-110M' tokenizer = BertTokenizer.from_pretrained(hf_model_path) model = GPT2LMHeadModel.from_pretrained(hf_model_path) ``` | c9275e869a139767039f5c04e98e294b |
apache-2.0 | ['generate', 'gpt2'] | false | 使用示例 Usage Examples 这里需要提一点，GPT在训练的时候是没有添加special_tokens的，BertTokenizer会默认补充special_tokens，所以在tokenzier的时候需要将add_special_tokens设置为false，这样生产效果会更好。 ```python def generate_word_level(input_text,n_return=5,max_length=128,top_p=0.9): inputs = tokenizer(input_text,return_tensors='pt',add_special_tokens=False).to(model.device) gen = model.generate( inputs=inputs['input_ids'], max_length=max_length, do_sample=True, top_p=top_p, eos_token_id=21133, pad_token_id=0, num_return_sequences=n_return) sentences = tokenizer.batch_decode(gen) for idx,sentence in enumerate(sentences): print(f'sentence {idx}: {sentence}') print('*'*20) return gen outputs = generate_word_level('西湖的景色',n_return=5,max_length=128) ``` | 5c316bafa4b8511fc7bcccc5e39ff602 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | cb1719ed2ea0c0249aafc8dd318caa3a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-test2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3055 - Precision: 0.5278 - Recall: 0.3957 - F1: 0.4523 - Accuracy: 0.9462 | a9e4f3ec4e8ac85fcd4cee0e67967c34 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2889 | 0.5439 | 0.3503 | 0.4262 | 0.9453 | | No log | 2.0 | 426 | 0.2938 | 0.5236 | 0.3800 | 0.4404 | 0.9457 | | 0.0544 | 3.0 | 639 | 0.3055 | 0.5278 | 0.3957 | 0.4523 | 0.9462 | | 113f129353d8da620b85694228826e21 |
apache-2.0 | ['tapas'] | false | TAPAS base model fine-tuned on WikiTable Questions (WTQ) This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is: - `no_reset`, which corresponds to `tapas_wtq_wikisql_sqa_inter_masklm_base` (intermediate pre-training, absolute position embeddings). Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. | 239596fdb498add58ec12cde71c246e4 |
apache-2.0 | ['tapas'] | false | Results Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- LARGE | noreset | 0.5062 | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset) LARGE | reset | 0.5097 | [tapas-large-finetuned-wtq](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/main) **BASE** | **noreset** | **0.4525** | [tapas-base-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/no_reset) **BASE** | **reset** | **0.4638** | [tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/main) MEDIUM | noreset | 0.4324 | [tapas-medium-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/no_reset) MEDIUM | reset | 0.4324 | [tapas-medium-finetuned-wtq](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/main) SMALL | noreset | 0.3681 | [tapas-small-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/no_reset) SMALL | reset | 0.3762 | [tapas-small-finetuned-wtq](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/main) MINI | noreset | 0.2783 | [tapas-mini-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/no_reset) MINI | reset | 0.2854 | [tapas-mini-finetuned-wtq](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/main) TINY | noreset | 0.0823 | [tapas-tiny-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/no_reset) TINY | reset | 0.1039 | [tapas-tiny-finetuned-wtq](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/main) | ee6db502abb73696b6ef7f3cb25268b9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-tr-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: 1.1786 - Wer: 0.5933 | 8f079a427a5cfcb35de234179674f9b1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3421 | 14.81 | 400 | 1.1795 | 0.5922 | | 0.113 | 29.63 | 800 | 1.1786 | 0.5933 | | 0cb477d9ae7bb61c7ccc873a8b93816b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'endpoints-template'] | false | Fork of [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) > Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. > For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). For more information about the model, license and limitations check the original model card at [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4). | 7bfccc8cffb47407071dc2a6199768bc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'endpoints-template'] | false | License (CreativeML OpenRAIL-M) The full license can be found here: https://huggingface.co/spaces/CompVis/stable-diffusion-license --- This repository implements a custom `handler` task for `text-to-image` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints/blob/main/handler.py). There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints/blob/main/create_handler.ipynb) included, on how to create the `handler.py` | 60cec841587d0aeede009363078bf7e4 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'endpoints-template'] | false | helper decoder def decode_base64_image(image_string): base64_image = base64.b64decode(image_string) buffer = BytesIO(base64_image) return Image.open(buffer) def predict(prompt:str=None): payload = {"inputs": code_snippet,"parameters": parameters} response = r.post( ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json={"inputs": prompt} ) resp = response.json() return decode_base64_image(resp["image"]) prediction = predict( prompt="the first animal on the mars" ) ``` expected output  | 03ed7f2fe8800059bd7f528c9e184ff0 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-nl_s947 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (th)](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. | 122453428f0e1e29ee65aa58f8db96da |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | XLS-R-300M - Bulgarian 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 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.2473 - Wer: 0.3002 | e87b4b19142e8b6d82fd3156b4b9ae57 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 50.0 - mixed_precision_training: Native AMP | fda23bee2b297b6c2945cc20009f8bf0 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1589 | 3.48 | 400 | 3.0830 | 1.0 | | 2.8921 | 6.96 | 800 | 2.6605 | 0.9982 | | 1.3049 | 10.43 | 1200 | 0.5069 | 0.5707 | | 1.1349 | 13.91 | 1600 | 0.4159 | 0.5041 | | 1.0686 | 17.39 | 2000 | 0.3815 | 0.4746 | | 0.999 | 20.87 | 2400 | 0.3541 | 0.4343 | | 0.945 | 24.35 | 2800 | 0.3266 | 0.4132 | | 0.9058 | 27.83 | 3200 | 0.2969 | 0.3771 | | 0.8672 | 31.3 | 3600 | 0.2802 | 0.3553 | | 0.8313 | 34.78 | 4000 | 0.2662 | 0.3380 | | 0.8068 | 38.26 | 4400 | 0.2528 | 0.3181 | | 0.7796 | 41.74 | 4800 | 0.2537 | 0.3073 | | 0.7621 | 45.22 | 5200 | 0.2503 | 0.3036 | | 0.7611 | 48.7 | 5600 | 0.2477 | 0.2991 | | 8216f6314e7faa898b33760fda2cbc07 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset mozilla-foundation/common_voice_8_0 --config bg --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` | 273a5c86905efd326108929a2f089952 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "anuragshas/wav2vec2-large-xls-r-300m-bg" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "bg", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits transcription = processor.batch_decode(logits.numpy()).text | c2171c254589333ec81c5eb18e479a78 |
apache-2.0 | ['synthesis', 'speech', 'speech synthesis'] | false | Welcome to Crust 🍕⭕ Crust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improves the performance of the model and makes it be able to synthesize comparable results with only 1 minute of data. The results are surprisingly good and because of the lower dataset, batch size can be lowered and the model is generally faster than other models. | 63628ddcf8a253de5bdddc5c21ba8464 |
apache-2.0 | ['synthesis', 'speech', 'speech synthesis'] | false | What is a multispeaker model? A multispeaker model is a model that has been trained on multiple speakers, the model first generates an "average" voice of all of the speakers and then tunes the different speakers on that average voice. If you have a lot of speakers, individual results won't be that great, as the model only has ~250+ mb to work with, but this is great for finetuning different voices on it because the model has learned an "average" voice. This average voice has the knowledge of all voices included in the dataset. Core: A multispeaker model is a model trained on multiple speakers. | 0d0ed9b5bdda1b3fbd6751f975406fbb |
apache-2.0 | ['synthesis', 'speech', 'speech synthesis'] | false | How does this make training possible with 1 minute of training data? The model has been trained on 168 datasets, ~20 hours of data, or ~19.8 thousand audio files. This is smaller than LJ speech but it has way more variety in voices, which LJ speech doesn't have. this variety allows the model to learn speech in different genders, accents, pitches, and other important factors, meaning that it knows a lot more in terms of voices. Finetuning this on 1 minute of data is possible because it already has a decently close match of your voice somewhere in its latent space. Core: The multispeaker has more knowledge of multiple people speaking, making it surprisingly good at training on low-minute datasets. | d4d6375e7bb13a03cfdb52dc23b38c4b |
apache-2.0 | ['synthesis', 'speech', 'speech synthesis'] | false | What are the downsides? **-Training time.** Training time sadly does still take a while, but considering you might only be training using 1 minute of data, it would take shorter than training it on the Lj-speech model, but would not come close to corentj's realtime voice cloning, it would be more accurate. **-Clean datasets.** We still doubt if the model would be able to be trained on datasets that have loud noise in them or have background music in them, realistically, it would not be able to be trained on these kinds of datasets, so before you train, please use a clean dataset. **-Inference.** Even though this model can be trained on 1 minute of data, we still recommend training it on more, we can't promise good results if the model doesn't have sufficient data, this would ideally be measured in syllables or phonemes, but minutes is a lot easier. **-Audio quality.** Sadly, the model has only been trained on 22050 hz and mono audio files, while this still sounds good when there's a Hi-Fi Gan vocoder, It's still going to not have stereo sound (which would not be that useful) or 44100 hz audio quality on its own. Sadly the Hi-Fi Gan vocoder does also bring in artifacts into the wav files which makes synthesis not as realistic. We used [**Uberduck's TTS Pipeline on github**](https://github.com/uberduck-ai/uberduck-ml-dev) To train our model. | bcbf1569acea05ad70649ab2349696bc |
apache-2.0 | ['generated_from_trainer'] | false | german_trained This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-german) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9367 - Wer: 1.0 | 709bdd579e04de3c5682714e9f10750b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 30 | 3fefb9a0f588969d606bae781f386738 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 12.0352 | 5.0 | 5 | 12.6165 | 1.0 | | 4.0249 | 10.0 | 10 | 6.6453 | 1.0 | | 2.6661 | 15.0 | 15 | 5.7873 | 1.0 | | 2.4123 | 20.0 | 20 | 4.3250 | 1.0 | | 1.9481 | 25.0 | 25 | 3.9899 | 1.0 | | 1.7533 | 30.0 | 30 | 3.9367 | 1.0 | | cb3cc4613a9822d8a278ec645ac72c09 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | luigisaetta/whisper-tiny2-it This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4686 - Wer: 25.9110 | a170c0ba64a4f6b204915d896df52c8e |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5765 | 2.01 | 1000 | 0.5728 | 32.2181 | | 0.3726 | 4.02 | 2000 | 0.5035 | 28.4606 | | 0.2789 | 6.04 | 3000 | 0.4861 | 26.7894 | | 0.2996 | 8.05 | 4000 | 0.4694 | 26.0279 | | 0.2925 | 10.06 | 5000 | 0.4686 | 25.9110 | | 4a2ee018b97623d0fd88a4712e2ac58c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8489 | b0b85b68b9266b60dd46d4616ea6c47f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7098 | 1.0 | 5681 | 2.3952 | | 2.3633 | 2.0 | 11362 | 1.9956 | | 2.1293 | 3.0 | 17043 | 1.8489 | | dcb41ba3cc3361e45e944ea94de145bd |
mit | ['russian', 'ukrainian'] | false | A little about the model The model is trained to answer questions about health topics (Open-book question answering-comprehend). cointegrated/rut5-base-multitask For training, a compact T5 model was used: cointegrated/rut5-base-multitask The training was conducted on a small set out of 220 thousand pairs of question-answer sentences, so it still does not work as correctly as we would like. The model is not a medical application and it is strongly discouraged to use the model for medical purposes! | 0373fde253497d79ee22312b71ef2aed |
apache-2.0 | ['speech'] | false | Wav2Vec2-Large-Tedlium The Wav2Vec2 large model fine-tuned on the TEDLIUM corpus. The model is initialised with Facebook's [Wav2Vec2 large LV-60k](https://huggingface.co/facebook/wav2vec2-large-lv60) checkpoint pre-trained on 60,000h of audiobooks from the LibriVox project. It is fine-tuned on 452h of TED talks from the [TEDLIUM](https://huggingface.co/datasets/LIUM/tedlium) corpus (Release 3). When using the model, make sure that your speech input is sampled at 16Khz. The model achieves a word error rate (WER) of 8.4% on the dev set and 8.2% on the test set. [Training logs](https://wandb.ai/sanchit-gandhi/tedlium/runs/10c85yc4?workspace=user-sanchit-gandhi) document the training and evaluation progress over 50k steps of fine-tuning. See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how this model was fine-tuned. | 8eff744f88730f244be2446c4013ee60 |
apache-2.0 | ['speech'] | false | Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch | 4067db0b54c6014888374d6576fbd471 |
apache-2.0 | ['speech'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium") model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium") | 7737b98b22d19f96dd9d86340dc0c0e6 |
apache-2.0 | ['speech'] | false | take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) print("Target: ", ds["text"][0]) print("Transcription: ", transcription[0]) ``` | bb63909c9fb5a6758d62f18768eb1578 |
apache-2.0 | ['speech'] | false | Evaluation This code snippet shows how to evaluate **Wav2Vec2-Large-Tedlium** on the TEDLIUM test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer tedlium_eval = load_dataset("LIUM/tedlium", "release3", split="test") model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium").to("cuda") processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = tedlium_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` | cfee5d1704319ebfa647bfe327fa7cbc |
mit | ['zul', 'fill-mask', 'pytorch', 'roberta', 'masked-lm'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_zul_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_zul_roberta") ``` | 25a34ec112db495d8cfcfaff9fe82179 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab-1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9634 - Wer: 0.4398 | d6c2d81a9a53c43e091658f75dccb8ff |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 50 - mixed_precision_training: Native AMP | e19519a20c8e7f355ba7a73cfd6713b2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8991 | 5.26 | 500 | 1.4319 | 0.7522 | | 0.8555 | 10.53 | 1000 | 0.7895 | 0.5818 | | 0.4584 | 15.79 | 1500 | 0.7198 | 0.5211 | | 0.3096 | 21.05 | 2000 | 0.7983 | 0.5118 | | 0.2165 | 26.32 | 2500 | 0.7893 | 0.4745 | | 0.163 | 31.58 | 3000 | 0.8779 | 0.4589 | | 0.1144 | 36.84 | 3500 | 0.9256 | 0.4540 | | 0.0886 | 42.11 | 4000 | 0.9184 | 0.4530 | | 0.0668 | 47.37 | 4500 | 0.9634 | 0.4398 | | 6f1fa521948eff9a4d434fd99576cc3c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1636 - F1: 0.8567 | ce4f589fecf7308f0b2948593eb1def9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2905 | 1.0 | 715 | 0.1810 | 0.8263 | | 0.1477 | 2.0 | 1430 | 0.1561 | 0.8488 | | 0.095 | 3.0 | 2145 | 0.1636 | 0.8567 | | a1083e2187d88e4c25b027af922859f6 |
mit | ['generated_from_trainer'] | false | Model description This model was trained using [pile-detoxify](https://huggingface.co/datasets/tomekkorbak/pile-detoxify), which is data from [The Pile](https://huggingface.co/datasets/the_pile), annotated based on toxicity detected by [Detoxify](https://github.com/unitaryai/detoxify). | 56b3d7db3854dfe9c47e324f718188d1 |
mit | ['generated_from_trainer'] | false | Intended uses & limitations This model has been trained to generate text that receives a low score for toxicity from [Detoxify](https://github.com/unitaryai/detoxify). While we have promising results with the methods used to avoid toxic text, we cannot guarantee that it will output text that is fully aligned with non-toxicity in every situation. This model and its associated datasets are intended for research purposes only and should not be deployed anywhere. Please take care to avoid misusing the datasets used to train this model (where toxicity and personal identifiable information are annotated) or putting anybody in danger by publicizing their information. | 23ab4af651665d790e97b88711737068 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'goofy_pasteur', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | ff6e6c55261d4e8012a9f5b5714e2a50 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-chaii This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4651 | 5f867ae0f4f17fae07b37f82266d5069 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.92 | 1.0 | 899 | 0.4482 | | 0.8055 | 2.0 | 1798 | 0.3225 | | 0.7485 | 3.0 | 2697 | 0.4651 | | 9736d47c255087a80dec36b1e2a8bc22 |
apache-2.0 | ['generated_from_trainer'] | false | STT_Model_4 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2311 - Wer: 0.1373 | 5a8276b69884fc31ff7c47c70dd382ca |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 | 2866d8d0ae03afdd5dad3a8799de31be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4196 | 5.68 | 500 | 0.9866 | 0.6983 | | 0.3696 | 11.36 | 1000 | 0.8788 | 0.4010 | | 0.1182 | 17.05 | 1500 | 0.2187 | 0.1947 | | 0.0658 | 22.73 | 2000 | 0.2578 | 0.1757 | | 0.0421 | 28.41 | 2500 | 0.2178 | 0.1609 | | 0.0346 | 34.09 | 3000 | 0.2038 | 0.1584 | | 0.0285 | 39.77 | 3500 | 0.2187 | 0.1594 | | 0.0228 | 45.45 | 4000 | 0.2114 | 0.1445 | | 0.0262 | 51.14 | 4500 | 0.2201 | 0.1631 | | 0.0162 | 56.82 | 5000 | 0.2078 | 0.1424 | | 0.0135 | 62.5 | 5500 | 0.1989 | 0.1393 | | 0.0128 | 68.18 | 6000 | 0.2118 | 0.1410 | | 0.0104 | 73.86 | 6500 | 0.2158 | 0.1361 | | 0.0081 | 79.55 | 7000 | 0.2154 | 0.1348 | | 0.0067 | 85.23 | 7500 | 0.2107 | 0.1358 | | 0.0067 | 90.91 | 8000 | 0.2161 | 0.1373 | | 0.0056 | 96.59 | 8500 | 0.2311 | 0.1373 | | c4449ad5fc0bea89ae9731cc954d2a8a |
apache-2.0 | ['vision', 'image-classification'] | false | Dataset ```python DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 329 }) validation: Dataset({ features: ['image', 'label'], num_rows: 56 }) }) ``` 36 Break and 293 Normal in train 5 Break and 51 Normal in validation | 14c26f9bd3104866d55530bf9ef94d3a |
apache-2.0 | ['vision', 'image-classification'] | false | Load image import torch from transformers import ViTFeatureExtractor, ViTForImageClassification,AutoModel from PIL import Image import requests url='https://datasets-server.huggingface.co/assets/ShihTing/IsCausewayOffset/--/ShihTing--IsCausewayOffset/validation/0/image/image.jpg' image = Image.open(requests.get(url, stream=True).raw) | d0ea7ea6e9c1bded9dd7d5bb8d8087f4 |
apache-2.0 | ['vision', 'image-classification'] | false | Load model from transformers import AutoFeatureExtractor, AutoModelForImageClassification device = torch.device('cpu') extractor = AutoFeatureExtractor.from_pretrained('ShihTing/PanJuOffset_TwoClass') model = AutoModelForImageClassification.from_pretrained('ShihTing/PanJuOffset_TwoClass') | b36f1bb1ce0fa1a5429f3fe57faeec67 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT-zero-shot-finetuned-50cad This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1475 - Accuracy: 0.5 - F1: 0.6667 | 4ce4a0e157f9e2badfd1e423a69f1ae8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3775 | 0.06 | 500 | 0.0302 | | 0.0207 | 0.11 | 1000 | 0.0188 | | 0.0182 | 0.17 | 1500 | 0.0179 | | 0.0171 | 0.22 | 2000 | 0.0152 | | 0.0178 | 0.28 | 2500 | 0.0161 | | 0.0147 | 0.33 | 3000 | 0.0150 | | 0.0157 | 0.39 | 3500 | 0.0137 | | 0.0137 | 0.44 | 4000 | 0.0126 | | 0.0133 | 0.5 | 4500 | 0.0137 | | 0.012 | 0.56 | 5000 | 0.0120 | | 0.0122 | 0.61 | 5500 | 0.0117 | | 0.0129 | 0.67 | 6000 | 0.0118 | | 0.0113 | 0.72 | 6500 | 0.0114 | | 0.0106 | 0.78 | 7000 | 0.0109 | | 0.0119 | 0.83 | 7500 | 0.0108 | | 0.0122 | 0.89 | 8000 | 0.0102 | | 0.0105 | 0.94 | 8500 | 0.0101 | | 0.0094 | 1.0 | 9000 | 0.0098 | | 0.01 | 1.06 | 9500 | 0.0097 | | 0.0097 | 1.11 | 10000 | 0.0096 | | 63beb5c6a1b62387326ac60377e4533f |
apache-2.0 | ['generated_from_trainer'] | false | T5-summarizer-simple-wiki-v2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0866 | 2281908ea7b5e4eab419bc3258264e4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2575 | 1.0 | 14719 | 2.1173 | | 2.2663 | 2.0 | 29438 | 2.0926 | | 2.2092 | 3.0 | 44157 | 2.0866 | | 5bcbcb5c95f6e84c6118c2989757de74 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1894 - Accuracy: 0.9448 | ef8401576ed24d59d1bdd151eeab2209 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6133 | 1.0 | 318 | 1.0679 | 0.7290 | | 0.8231 | 2.0 | 636 | 0.5164 | 0.8652 | | 0.4289 | 3.0 | 954 | 0.3019 | 0.9168 | | 0.2722 | 4.0 | 1272 | 0.2336 | 0.9335 | | 0.214 | 5.0 | 1590 | 0.2117 | 0.94 | | 0.1914 | 6.0 | 1908 | 0.2007 | 0.9445 | | 0.1785 | 7.0 | 2226 | 0.1947 | 0.9435 | | 0.1716 | 8.0 | 2544 | 0.1919 | 0.9468 | | 0.1674 | 9.0 | 2862 | 0.1901 | 0.9452 | | 0.1659 | 10.0 | 3180 | 0.1894 | 0.9448 | | 447e2b66dc72ee80dc9a76f9363b8e25 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | kyoto_marian_mod_3_5 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2](https://huggingface.co/Hoax0930/kyoto_marian_mod_2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8052 - Bleu: 18.4305 | decb86f3ca0d1d1acdcee72b590f7cd0 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP | b64fc47a0260b8a8e23114b645685021 |
mit | ['generated_from_trainer'] | false | finetuning-insult-model-deberta This model is a fine-tuned version of [yangheng/deberta-v3-base-absa-v1.1](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9472 - Accuracy: 0.7458 - F1: 0.7630 - Precision: 0.7332 - Recall: 0.7953 | 20aa5ad8f160f02c612eb9aac1187200 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.02-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8160 - Bleu: 7.448 - Gen Len: 44.2241 | 1b2132a9ddadcd025ca190aaaa629ba8 |
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