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 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Northern Sámi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5559 - Wer: 24.9143 | b5ff9f78f12ad285a25a8559d9e5aebd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 6 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - traini... | d07f17f63c27bf2570d91000af4ca824 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:-------:| | 0.4665 | 58.0 | 1000 | 0.8572 | 54.5143 | | 0.3041 | 117.0 | 2000 | 0.6711 | 44.1143 | | 0.2671 | 176.0 | 3000 | 0.5794 ... | cf9e9de3c12f939426fb69cb34712361 |
apache-2.0 | ['generated_from_keras_callback'] | false | oscarth_54321 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5784 - Validation Loss: 4.5266 - Epoch: 1 | 06e3bc8eed061d3e674d1da12b0fce6b |
apache-2.0 | ['generated_from_trainer'] | false | Millad_Customer_RN 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: 4.5635 - Wer: 0.8113 - Cer: 0.4817 | 9c8ec4fe16256de4928b09a8d83f6cd2 |
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: 4000 - num_epochs: 600 - mixed_precision_t... | 550b78c3e9e1194d91cd9eaf43ff8c56 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | 1.9257 | 13.33 | 2000 | 2.0606 | 0.9767 | 0.5500 | | 1.4828 | 26.67 | 4000 | 2.1161 | 0.9019 | 0.4932 | | 1.2582 |... | 8c9f8483980278df2986934b9b852c8e |
other | [] | false | This is the model trained for this video: https://www.youtube.com/watch?v=OEPL5Tm3mmQ Due to hardware limitations, I trained this model with only a batch size of 2. (I know this isn't ideal). The quality of the model may be affected. After training was complete, the best model according to a hold-out set was used.... | 581189302d92b64262ccba320892537b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst-2-english-zero-shot-sentiment-model This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. | 328c3eb3b35b159c9693411510966274 |
apache-2.0 | ['translation'] | false | spa-eng * source group: Spanish * target group: English * OPUS readme: [spa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md) * model: transformer * source language(s): spa * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (s... | 541904b893c5fa3a02de8f396415c2e9 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-spaeng.spa.eng | 30.6 | 0.570 | | news-test2008-spaeng.spa.eng | 27.9 | 0.553 | | newstest2009-spaeng.spa.eng | 30.4 | 0.572 | | newstest2010-spaeng.spa.eng | 36.1 | 0.614 | | newstest2011-spaeng.s... | 52e9bb83d41abe1daa8969fadf9f7f4e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-eng - source_languages: spa - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'en'] - src_constituents: {'spa'} - tgt_const... | 3e5c303eb532837727197c7a2dee2a29 |
apache-2.0 | ['generated_from_trainer'] | false | XLSR_Fine_Tuned_Urdu_V2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_8_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8023 - Wer: 0.4382 | 81b692be21f846077d7c1d4e799311fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.424 | 3.25 | 1000 | 2.9777 | 1.0 | | 1.4315 | 6.49 | 2000 | 0.8493 | 0.5896 | | 0.6938 | 9.74 | 3000 | 0.7438 | 0.4978 | |... | d3a5f964a254352bb97dd59ea58261d7 |
apache-2.0 | ['translation'] | false | opus-mt-fr-ase * source languages: fr * target languages: ase * OPUS readme: [fr-ase](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ase/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | f62c487db19a485b84e860d04c26c361 |
apache-2.0 | ['generated_from_keras_callback'] | false | pedramyamini/distilbert-base-multilingual-cased-finetuned-mobile-banks-cafebazaar This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5059 -... | ee9fab977e55dbf9b99ecc9d858faf91 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.5075 | 0.7437 | 0 | | 0.5074 | 0.7437 | 1 | | 0.5079 | 0.7437 | 2 | | 0.5086 | 0.7437 | 3 | | 0.5059 | 0.7437 | 4 | | d3a3418ace7ab37f5a327deb624d28ec |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | ECHR_test_2 Task A This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.1998 - Macro-f1: 0.5295 - Micro-f1: 0.6157 | 5f1d7d11729e9fe444ee234125b73d6c |
cc-by-sa-4.0 | ['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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 50d36146b088eae2d96621bbde221b97 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.2142 | 0.44 | 500 | 0.2887 | 0.2391 | 0.4263 | | 0.172 | 0.89 | 1000 | 0.2672 | 0.2908 | 0.4628 | | 0.1... | e25201bac377a79ec959c84ef8fb8770 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-code-snippet-quality-scoring This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4070 - Accuracy: 0.8568 | 218e3323e12c25c03343d662411fcbd5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5353 | 0.13 | 1000 | 0.5110 | 0.7574 | | 0.4686 | 0.26 | 2000 | 0.4339 | 0.7859 | | 0.4517 | 0.39 | 3000 | 0.4240 ... | 00166018a7615254ed186f48a5d3dc89 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | KerasCV Stable Diffusion in Diffusers 🧨🤗 The pipeline contained in this repository was created using [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers). The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/hugg... | a52ca4c2ce07e10e18a7d271d5c73b58 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_english This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2620 - Wer: 0.1916 | 071c19cfb02dada368d852a9a5095e46 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 4677b05e5367462b389cd3ef36b57db2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0506 | 0.12 | 250 | 3.0206 | 0.9999 | | 1.4381 | 0.25 | 500 | 1.0267 | 0.6323 | | 1.0903 | 0.37 | 750 | 0.5841 | 0.370... | b62719cfb381d672ec830ef02e831e4b |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3500 - Wer: 19.5235 | 09efafccba3b43975612442c437c056b |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | b4f2912d742d1a4aecc02ab1134dc2be |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1391 | 1.3 | 1000 | 0.2981 | 21.5939 | | 0.049 | 2.59 | 2000 | 0.2954 | 20.5614 | | 0.0198 | 3.89 | 3000 | 0.3049 | 19.956... | 72e42ce04b62b2e887c1150cd86660a4 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Accuracy: 0.926 - F1: 0.9260 | 6810500f397f09eedb70be57f547d688 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8794 | 1.0 | 250 | 0.3392 | 0.8985 | 0.8948 | | 0.2663 | 2.0 | 500 | 0.2315 | 0.926 | 0.9260 | | a6fc71d5f5f9980655f3a0e26dea9f0a |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model Description Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size. | Hyperparameter | Value | |-------------------|--------| | n_parameters | 6,053,381,344 | | n_layers | 28* | | d_model ... | 323da22a209283aacac094f828f6c52f |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable. For now, you need to use this fork: [Fork](https://github.com/finetuneanon/transformers) to install with pip: ```bash pip install git+https://gi... | 6b09078d31fa82f6abcad5ba3ab7fa63 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgements This project was possible because of the compute provided by the [TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B. Thanks to everyone who contributed to this project! - [Aero](https://github.com/AeroScripts) - [Finetune... | 841b108aa981d4cf7645fee362d06a11 |
mit | ['mbart-50'] | false | mBART-50 mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. | 53fbc78aedb63a92e37f8cddb0416689 |
mit | ['mbart-50'] | false | Model description mBART-50 is a multilingual Sequence-to-Sequence model. It was introduced to show that multilingual translation models can be created through multilingual fine-tuning. Instead of fine-tuning on one direction, a pre-trained model is fine-tuned on many directions simultaneously. mBART-50 is created us... | 4549538090e9ade4fa1d4cefa7853eea |
mit | ['mbart-50'] | false | Intended uses & limitations `mbart-large-50` is pre-trained model and primarily aimed at being fine-tuned on translation tasks. It can also be fine-tuned on other multilingual sequence-to-sequence tasks. See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for fine-tuned versions. | daf4f15a98ca790a9765965e6a9c7a07 |
mit | ['mbart-50'] | false | Training As the model is multilingual, it expects the sequences in a different format. A special language id token is used as a prefix in both the source and target text. The text format is `[lang_code] X [eos]` with `X` being the source or target text respectively and `lang_code` is `source_lang_code` for source tex... | f7296c1302de62e9779760bf81083758 |
mit | ['mbart-50'] | false | Languages covered Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP),... | 36da7dc6013b47ceee396de6f77f3b73 |
mit | ['mbart-50'] | false | BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, e... | ec129e0757c01f55010de18052587bf4 |
mit | ['spacy', 'token-classification'] | false | en_core_web_lg English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `en_core_web_lg` | | **Version** | `3.4.1` | | **spaCy** | `>=3.4.0,<3.5.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `... | c0bc49092a03c75c34744ac58f68a0aa |
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: 0.0596 - Precision: 0.9279 - Recall: 0.9378 - F1: 0.9328 - Accuracy: 0.9840 | dd0500331cf047f81348f7d665b4d906 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2377 | 1.0 | 878 | 0.0717 | 0.9140 | 0.9205 | 0.9172 | 0.9800 | | 0.0498 | 2.0 |... | d31a022ef85aa7af703ee729254a3b48 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5112 - Wer: 0.9988 | 6031c0e8896f996bf9f4c884a8955a18 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5557 | 1.0 | 500 | 1.6786 | 1.0 | | 0.8407 | 2.01 | 1000 | 0.5356 | 0.9988 | | 0.4297 | 3.01 | 1500 | 0.4431 | 0.998... | 1c65ce0891cdc8f7c5bcee625a6f2d45 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Description: This model was trained by Manuel Pariente using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `sep_clean` task of the WHAM! dataset. | 4c9ffe97ab302b11d6714341cc452194 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Training config: ```yaml data: n_src: 2 mode: min nondefault_nsrc: None sample_rate: 8000 segment: 3 task: sep_clean train_dir: data/wav8k/min/tr/ valid_dir: data/wav8k/min/cv/ filterbank: kernel_size: 16 n_filters: 512 stride: 8 main_args: exp_dir: exp/wham gpus: -1... | b23451c2419a01085728b9f604220686 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Results: ```yaml si_sdr: 16.21326632846293 si_sdr_imp: 16.21441705664987 sdr: 16.615180021738933 sdr_imp: 16.464137807433435 sir: 26.860503975131923 sir_imp: 26.709461760826414 sar: 17.18312813480803 sar_imp: -131.99332048277296 stoi: 0.9619940905157323 stoi_imp: 0.2239480672473015 ``` | be2064602dd12d5ed1266f68c091ecdd |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | License notice: This work "ConvTasNet_WHAM!_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A) by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only). "ConvTas... | fb00aca23e65bc7261e016d2c0e4ef13 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-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: 2.3611 | 71c63b76efed4568f367ef60d2ef0ba9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | dyc0003 Dreambooth model trained by anmol-chawla 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-... | 01dfbb4b7358e22f01ea9ed9094f6423 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need for fine-tuning. Whisper was proposed in the paper [Robust Speech... | 29e505e2f4fcbedb6b50d9eabc977a30 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing th... | d81e309896a363d7b3929d76477f909a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> | f684cd45b16d44922167bb17bf0a5500 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> model.config.forced_decoder_ids = None >>> | 749bfc1e74f822088b2e4353e108fe57 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>'] >>> transcription = processor.batch_decode(pr... | e9b2b36302c2b72751b2f5295f5ca8df |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> | 8240a2ad326a5bd58327cb60d4eb4313 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> | 15f4ce545beb13a5b4517b1637ea2b42 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["samplin... | 6d83c1ed398f216ad608a4c736c6f7ee |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai trav... | 02ec5421d99de4789e88487bf67bd647 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> | 1ca4c56099ced93f6c099a3754c6a8a1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load... | b8d013f4dfd4803bd12261aa80761d23 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. It can also be extended to predict utterance level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from dat... | 56eaa17b51f606c1ad73c8cf26d3d114 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-extraction-cnndm_fs0.1-all This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7159 | ce10743925085b44016dc93e3bef4fcc |
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: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | cecc440e9e6383d4aa183e37711bfc72 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2503 | 0.45 | 200 | 1.8495 | | 1.9367 | 0.9 | 400 | 1.7930 | | 1.8669 | 1.35 | 600 | 1.7704 | | 1.8371 | 1.81 | 800 | 1.7481 ... | 9b34ab6d14a0faf29fbc948ee1768e54 |
apache-2.0 | ['generated_from_trainer'] | false | my_ASR_model This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2180 - Wer: 0.2546 | c6445e11676364e3eaf570acec41bec7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 36ac324d7190bc96c8555edee121f319 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.6732 | 20.0 | 100 | 1.5134 | 0.4502 | | 1.1618 | 40.0 | 200 | 1.4121 | 0.3838 | | 0.8533 | 60.0 | 300 | 1.2672 | 0.3616 | |... | e1d37ddcd4684c20cdbca0f4419a4287 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-ro-fp16_off This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.4078 - Bleu: 7.3056 - Gen Len: 18.2556 | 993e48b7f9dc3efaffaf5b1dcd4256ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 0.6037 | 1.0 | 7629 | 1.4078 | 7.3056 | 18.2556 | | ec65fb02fc9b5997017f425d36f6d286 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4436450/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | e24ef57d2e867438c2e94d3d74db8f57 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-base-wikinewssum-english-100 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.6225 - Rouge1: 3.909 - Rouge2: 0.9312 - Rougel: 3.3835 - Rougelsum: 3.7786 | 133b8c3cf31ec0cbb0437a2bae560c33 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 0.96 | 12 | 14.4949 | 2.7398 | 0.7181 | 2.491 | 2.6561 | | No log | 1.96 | 24 ... | bb16431fabb2c79efd0307d04de11db9 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: North Sami This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | fe7d0e794ca1012af904dc006adf9129 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme") ``` | 00f5a434ba5e964da5970617d35e212d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab-test 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: 0.4283 - Wer: 0.3356 | 019313029ca12eac22079030dce67583 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7386 | 4.0 | 500 | 2.2419 | 1.0 | | 0.9366 | 8.0 | 1000 | 0.4789 | 0.4807 | | 0.3118 | 12.0 | 1500 | 0.4197 | 0.3973 | |... | 92cf555bfb10335dfbe9abc81f763b5e |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 350.2 - GMACs: 179.2 - Activations (M): 169.0 - Image size: 384 x 384 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt... | a33adef38cf77ea872d9c1ce5149a625 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_xlarge.fb_in22k_ft_in1k_384', pretrained=True... | b600c77b1d3c152f61e0a88c352063e8 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_xlarge.fb_in22k_ft_in1k_384', pret... | 9cb7f007bc3af718e3ff2dc3bc1ea9f4 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_xlarge.fb_in22k_ft_in1k_384', pretrained... | 7118eeb7b1915b2e7a130988abab0ca1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.3183 | 1.0 | 318 | 3.3075 | 0.7416 | | 2.633 | 2.0 | 636 | 1.8792 | 0.8384 | | 1.5339 | 3.0 | 954 | 1.1514 | 0.... | 897ee535c9622fe60e8a6a4b1499bab7 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | e6192943e6ca9fc24166855de52053e9 |
mit | ['generated_from_keras_callback'] | false | Ashraf-kasem/gpt2_frame_text_predictor This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.9203 - Validation Loss: 8.7222 - Epoch: 0 | 8e7e47de40c64a2b0ffb3f336e766912 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'LinearWarmup', 'config': {'after_warmup_lr_sched': {'initial_learning_rate': 5e-05, 'decay_steps': 16, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None},... | c8794b18e72408307227bded43ed7be4 |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Electronic | a2dee9b6280900e9c95311243f5e48b4 |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Usage ```python from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("johnowhitaker/Electronic_test") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) ``` | 35d1a6d12714fa3bf4e46ab337361329 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | datasets) This model transcribes speech into lowercase Cyrillic alphabet including space, and is trained on around 1636 hours of Russian speech data. It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters. See the [model architecture]( | 3030175cc3fb0253850e9653add950b1 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_ru_conformer_transducer_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | aa153db34984a98926c4ebddbe799436 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/a... | 04032f3df65508651e67022e48705379 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Datasets All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of more than a thousand hours of Russian speech: - Mozilla Common Voice 10.0 (Russian) - train subset [28 hours] - Golos - crowd [1070 hours] and fairfield [111 hours] subsets - Russian LibriSpeech (RuLS) [92 hours]... | 671cfbdf4e0b7a634a9f3f2e732b2c20 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | MCV 10.0 dev | MCV 10.0 test | GOLOS-crowd test | GOLOS-farfield ... | 70df0dd0ca62c964cb50985ac2b5c1c9 |
mit | [] | false | This model has been pretrained on MS MARCO passages first, then fine-tuned on the MS MARCO training set following the approach described in the paper **Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**. The model can be used to reproduce the experimental results associated GitHub repo... | 8670c99395a0aa38bf16a8838faf385f |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------------------------|---------------------------... | 77e56c6b32e99817aaf56a78eb9a3e5e |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;, max_iter=300))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wra... | c916967bf681f9435240a880a87e2486 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;, max_iter=300))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-tog... | 4a8aa6fda0c8560e534cc50d6b1628af |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-tog... | 545407e18687fbfea1e9680c1592e516 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-t... | 20e3f13620896c6bff6644b9efa1bd18 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | x27;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggle... | 59c2bf50765d4e9afc93988473b5d1bf |
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