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 | openai/whisper-large-v2-welsh This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2947 - Wer: 18.0609 | 73d99b44af9f931332c2fd95fed7c4c1 |
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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - 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: 50 - training_steps: 500 | b38e3c9bf60e330224112d355e51a810 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4438 | 0.2 | 100 | 0.4208 | 27.3594 | | 0.3255 | 0.4 | 200 | 0.3633 | 23.6118 | | 0.2856 | 0.6 | 300 | 0.3248 | 20.7023 | | 0.1811 | 1.14 | 400 | 0.3011 | 18.5534 | | 0.1404 | 1.34 | 500 | 0.2947 | 18.0609 | | 96061719313f168f45f28c7ab8b1815e |
mit | ['Long documents', 'longformer', 'bertin', 'spanish'] | false | [Longformer](https://arxiv.org/abs/2004.05150) is a Transformer model for long documents. `longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint (**BERTIN** in this case) and pre-trained for *MLM* on long documents (from BETO's `all_wikis`). It supports sequences of length up to 4,096! **Longformer** uses a combination of a sliding window (*local*) attention and *global* attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations. This model was made following the research done by [Iz Beltagy and Matthew E. Peters and Arman Cohan](https://arxiv.org/abs/2004.05150). | a8be900447937a1d006824a9d0fe2711 |
mit | ['Long documents', 'longformer', 'bertin', 'spanish'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2022longformer-base-4096-spanish, title={Spanish LongFormer by Manuel Romero}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/longformer-base-4096-spanish}}, year={2022} } ``` | e90adfd86e581877338e3fb89a175a01 |
apache-2.0 | ['fleurs-asr', 'google/xtreme_s', 'generated_from_trainer'] | false | xtreme_s_xlsr_300m_fleurs_asr_western_european This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.ALL dataset. It achieves the following results on the evaluation set: - Cer: 0.2484 - Cer Ast Es: 0.1598 - Cer Bs Ba: 0.1749 - Cer Ca Es: 0.1655 - Cer Cy Gb: 0.2280 - Cer Da Dk: 0.3616 - Cer De De: 0.1287 - Cer El Gr: 0.6020 - Cer En Us: 0.1938 - Cer Es 419: 0.1288 - Cer Fi Fi: 0.2050 - Cer Fr Fr: 0.1811 - Cer Ga Ie: 0.4474 - Cer Gl Es: 0.1324 - Cer Hr Hr: 0.1555 - Cer Hu Hu: 0.3911 - Cer Is Is: 0.4646 - Cer It It: 0.1283 - Cer Kea Cv: 0.1818 - Cer Lb Lu: 0.2594 - Cer Mt Mt: 0.3628 - Cer Nb No: 0.2254 - Cer Nl Nl: 0.1790 - Cer Oci Fr: 0.2159 - Cer Pt Br: 0.2275 - Cer Sv Se: 0.3092 - Loss: 1.3089 - Loss Ast Es: 0.7715 - Loss Bs Ba: 0.7378 - Loss Ca Es: 0.7868 - Loss Cy Gb: 1.1441 - Loss Da Dk: 1.9130 - Loss De De: 0.5391 - Loss El Gr: 3.4904 - Loss En Us: 0.9632 - Loss Es 419: 0.6186 - Loss Fi Fi: 0.8953 - Loss Fr Fr: 0.9076 - Loss Ga Ie: 3.0217 - Loss Gl Es: 0.5788 - Loss Hr Hr: 0.6462 - Loss Hu Hu: 1.9029 - Loss Is Is: 2.6551 - Loss It It: 0.6052 - Loss Kea Cv: 0.9107 - Loss Lb Lu: 1.3705 - Loss Mt Mt: 2.3651 - Loss Nb No: 1.1518 - Loss Nl Nl: 0.8490 - Loss Oci Fr: 1.1421 - Loss Pt Br: 1.1641 - Loss Sv Se: 1.5910 - Wer: 0.6451 - Wer Ast Es: 0.4654 - Wer Bs Ba: 0.5443 - Wer Ca Es: 0.4979 - Wer Cy Gb: 0.5962 - Wer Da Dk: 0.8455 - Wer De De: 0.4221 - Wer El Gr: 0.9805 - Wer En Us: 0.4556 - Wer Es 419: 0.3928 - Wer Fi Fi: 0.8116 - Wer Fr Fr: 0.4690 - Wer Ga Ie: 0.8519 - Wer Gl Es: 0.4245 - Wer Hr Hr: 0.4895 - Wer Hu Hu: 0.9099 - Wer Is Is: 0.9960 - Wer It It: 0.4415 - Wer Kea Cv: 0.5202 - Wer Lb Lu: 0.7225 - Wer Mt Mt: 1.0096 - Wer Nb No: 0.6541 - Wer Nl Nl: 0.5257 - Wer Oci Fr: 0.5770 - Wer Pt Br: 0.6685 - Wer Sv Se: 0.8546 - Predict Samples: 20043 | 1318d79d8bcf1f3a5c9dad0d9e9f7a3b |
apache-2.0 | ['fleurs-asr', 'google/xtreme_s', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP | 7f4269305056c2c69789497ce2ef6cad |
apache-2.0 | ['fleurs-asr', 'google/xtreme_s', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 3.1411 | 0.49 | 500 | 3.1673 | 1.0 | 1.0 | | 0.6397 | 0.97 | 1000 | 0.9039 | 0.7171 | 0.2862 | | 0.4033 | 1.46 | 1500 | 0.8914 | 0.6862 | 0.2763 | | 0.3473 | 1.94 | 2000 | 0.8017 | 0.6505 | 0.2536 | | 0.3143 | 2.43 | 2500 | 0.8568 | 0.6566 | 0.2627 | | 0.3004 | 2.91 | 3000 | 0.8898 | 0.6640 | 0.2686 | | 0.282 | 3.4 | 3500 | 0.8489 | 0.6637 | 0.2571 | | 0.2489 | 3.88 | 4000 | 0.8955 | 0.6744 | 0.2691 | | 0.1706 | 4.37 | 4500 | 0.9190 | 0.6788 | 0.2688 | | 0.3336 | 4.85 | 5000 | 0.8915 | 0.6594 | 0.2572 | | 0.1426 | 5.34 | 5500 | 0.9501 | 0.6784 | 0.2686 | | 0.2301 | 5.83 | 6000 | 1.0217 | 0.6719 | 0.2735 | | 0.1325 | 6.31 | 6500 | 0.9578 | 0.6691 | 0.2655 | | 0.1145 | 6.8 | 7000 | 0.9129 | 0.6680 | 0.2593 | | 0.1202 | 7.28 | 7500 | 0.9646 | 0.6749 | 0.2619 | | 0.143 | 7.77 | 8000 | 0.9200 | 0.6554 | 0.2554 | | 0.1012 | 8.25 | 8500 | 0.9553 | 0.6787 | 0.2628 | | 0.1018 | 8.74 | 9000 | 0.9455 | 0.6445 | 0.2511 | | 0.1148 | 9.22 | 9500 | 1.0206 | 0.6725 | 0.2629 | | 0.0794 | 9.71 | 10000 | 0.9305 | 0.6547 | 0.2526 | | 0.2891 | 10.19 | 10500 | 1.0424 | 0.6709 | 0.2570 | | 0.1665 | 10.68 | 11000 | 0.9760 | 0.6596 | 0.2507 | | 0.1956 | 11.17 | 11500 | 0.9549 | 0.6340 | 0.2440 | | 0.0828 | 11.65 | 12000 | 0.9598 | 0.6403 | 0.2460 | | 0.059 | 12.14 | 12500 | 0.9972 | 0.6574 | 0.2531 | | 0.0505 | 12.62 | 13000 | 0.9836 | 0.6534 | 0.2525 | | 0.0336 | 13.11 | 13500 | 1.0619 | 0.6564 | 0.2519 | | 0.0435 | 13.59 | 14000 | 1.0844 | 0.6480 | 0.2543 | | 0.0216 | 14.08 | 14500 | 1.1084 | 0.6512 | 0.2521 | | 0.0265 | 14.56 | 15000 | 1.1152 | 0.6607 | 0.2563 | | 0.0975 | 15.05 | 15500 | 1.1060 | 0.6456 | 0.2471 | | 0.1396 | 15.53 | 16000 | 1.1100 | 0.6337 | 0.2418 | | 0.0701 | 16.02 | 16500 | 1.1731 | 0.6309 | 0.2415 | | 0.1171 | 16.5 | 17000 | 1.1302 | 0.6315 | 0.2396 | | 0.0778 | 16.99 | 17500 | 1.1485 | 0.6379 | 0.2447 | | 0.0642 | 17.48 | 18000 | 1.2009 | 0.6400 | 0.2464 | | 0.0322 | 17.96 | 18500 | 1.2028 | 0.6357 | 0.2425 | | 0.031 | 18.45 | 19000 | 1.2381 | 0.6285 | 0.2416 | | 0.0579 | 18.93 | 19500 | 1.2299 | 0.6265 | 0.2409 | | 0.0628 | 19.42 | 20000 | 1.2582 | 0.6277 | 0.2395 | | 0.074 | 19.9 | 20500 | 1.2572 | 0.6278 | 0.2394 | | f599c66f26882f9837b708526610623d |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v4_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2062 - Precision: 0.6464 - Recall: 0.6730 - F1: 0.6594 - Accuracy: 0.9315 | dffdb23ba0a393c943dda02dbd5f824d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 58 | 0.3048 | 0.3090 | 0.2978 | 0.3033 | 0.8852 | | No log | 2.0 | 116 | 0.2127 | 0.6096 | 0.6567 | 0.6323 | 0.9271 | | No log | 3.0 | 174 | 0.2062 | 0.6464 | 0.6730 | 0.6594 | 0.9315 | | ae5e9461715d8f0691a23545c64946e4 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | MVP-task-dialog The MVP-task-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). | fc101693d5c3a3c40e62bf80fb265f1a |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Model Description MVP-task-dialog is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled task-oriented system datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts. MVP-task-dialog is specially designed for task-oriented tasks, such as MultiWOZ. | 0285f9aed2f64154a40a46452474194c |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-task-dialog") >>> inputs = tokenizer( ... "Given the task dialog: System response [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest.", ... return_tensors="pt", ... ) >>> generated_ids = model.generate(**inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['What date and time would you like to go?'] ``` | abb2869f8974e081f7820efb9d87c36a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.9294 | 0.19 | 500 | 6.8136 | | 6.7692 | 0.39 | 1000 | 6.8006 | | 6.7567 | 0.58 | 1500 | 6.7770 | | 6.746 | 0.77 | 2000 | 6.7414 | | 6.7577 | 0.97 | 2500 | 6.7333 | | 6.7295 | 1.16 | 3000 | 6.7405 | | 6.7635 | 1.36 | 3500 | 6.7272 | | 6.7715 | 1.55 | 4000 | 6.7114 | | 6.7348 | 1.74 | 4500 | 6.7275 | | 6.719 | 1.94 | 5000 | 6.7322 | | 6.7427 | 2.13 | 5500 | 6.7242 | | 6.7136 | 2.32 | 6000 | 6.6852 | | 6.719 | 2.52 | 6500 | 6.7430 | | 6.7229 | 2.71 | 7000 | 6.7331 | | 6.7166 | 2.9 | 7500 | 6.7293 | | 814a17bd739a3f19b6bec0f3987601f3 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 10 - eval_batch_size: 16 - gradient_accumulation_steps: 4 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: constant - lr_warmup_steps: 0 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 | 6a2548309469cc7f3d158c06b2880148 |
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: 0.4915 - Wer: 25.5384 | 6b33214ea12083c63de175ad7df4d320 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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_scheduler_warmup_steps: 500 - training_steps: 6000 - mixed_precision_training: Native AMP | 27a26a8ef311d27c21a0c3624f22b2e9 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2107 | 1.3 | 1000 | 0.4673 | 34.0432 | | 0.0821 | 2.59 | 2000 | 0.4284 | 27.4152 | | 0.0378 | 3.89 | 3000 | 0.4210 | 25.3637 | | 0.0042 | 5.18 | 4000 | 0.4247 | 23.5541 | | 0.001 | 6.48 | 5000 | 0.4286 | 22.7770 | | 0.0106 | 7.77 | 6000 | 0.4915 | 25.5384 | | 4142f5025bda6b2adba54b92355c4b52 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9904 - Mae: 0.4867 | 8b53e47c8b354ace213437b3ea42d739 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.2067 | 1.0 | 308 | 1.0806 | 0.5575 | | 1.0182 | 2.0 | 616 | 0.9904 | 0.4867 | | dafd5017c6f32bdbec57127267d382d6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers'] | false | **Lomo Diffusion**  [*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/lomo-1.0.ckpt) - - - [*SAFETENSORS DOWNLOAD LINK*](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/lomo-1.0.safetensors) This is a dreambooth model trained on a diverse set of stylized photographs. Use the activation token **lomo style** in your prompt (I recommend at the start) This model is inspired by the Lomography movement, which embraces the imperfections and style of old LOMO cameras. The model excels at producing bright saturated colors as well as a variety of film artifacts that add to the illusion of a real photograph. When using most models, I typically use **blur haze** in my negative prompt. I encourage you to experiment and see what works well for you. Trained from 1.5 with VAE. Please see [this document where I share the parameters (prompt, sampler, seed, etc.) used for all example images.](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/paramets_for_samples.txt) You can [see here a non-cherrypicked batch of 49 images here.](https://i.imgur.com/cfIj3iq.jpg) And you can [see here a direct comparison between Analog Style and Lomo Style.](https://i.imgur.com/ugdFzPI.jpg)  | 3a60b079ffb45aa18df371c63e3f9d0f |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'CTC', 'Wav2vec2'] | false | Fine-tuned wav2vec2-FR-7K-large model for ASR in French <style> img { display: inline; } </style>    This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and validation splits of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french). When using the model make sure that your speech input is also sampled at 16Khz. | 23c8c7d186c1304e2982c6324725ab6e |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'CTC', 'Wav2vec2'] | false | Usage 1. To use on a local audio file with the language model ```python import torch import torchaudio from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").to(device) processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/asr-wav2vec2-french") model_sample_rate = processor_with_lm.feature_extractor.sampling_rate wav_path = "example.wav" | f5cd01e8a2016920ec0cbc0e6949f82d |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'CTC', 'Wav2vec2'] | false | normalize input_dict = processor_with_lm(waveform, sampling_rate=model_sample_rate, return_tensors="pt") with torch.inference_mode(): logits = model(input_dict.input_values.to(device)).logits predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0] ``` 2. To use on a local audio file without the language model ```python import torch import torchaudio from transformers import AutoModelForCTC, Wav2Vec2Processor device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").to(device) processor = Wav2Vec2Processor.from_pretrained("bhuang/asr-wav2vec2-french") model_sample_rate = processor.feature_extractor.sampling_rate wav_path = "example.wav" | b838dc13995aa833bbd93af2b1619c46 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'CTC', 'Wav2vec2'] | false | Evaluation 1. To evaluate on `mozilla-foundation/common_voice_11_0` ```bash python eval.py \ --model_id "bhuang/asr-wav2vec2-french" \ --dataset "mozilla-foundation/common_voice_11_0" \ --config "fr" \ --split "test" \ --log_outputs \ --outdir "outputs/results_mozilla-foundatio_common_voice_11_0_with_lm" ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py \ --model_id "bhuang/asr-wav2vec2-french" \ --dataset "speech-recognition-community-v2/dev_data" \ --config "fr" \ --split "validation" \ --chunk_length_s 30.0 \ --stride_length_s 5.0 \ --log_outputs \ --outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm" ``` | fc72a166fee548aa4d4bd65a5e180378 |
mit | [] | false | model by infoxixxx This your the Stable Diffusion model fine-tuned the Cat toy concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks toy** 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). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept:     | 04bcabb65588349e50ac811a51e5890b |
apache-2.0 | ['generated_from_trainer'] | false | deit-tiny-patch16-224-finetuned-eurosat This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1779 - Accuracy: 0.9192 | f03082a2aa20885edb61268c0037ceaf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.3528 | 0.8283 | | 0.5571 | 2.0 | 14 | 0.2141 | 0.8788 | | 0.197 | 3.0 | 21 | 0.1779 | 0.9192 | | ca889ac99d5112c65ed6c10d3d188122 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Afrikaans-AfriBooms | Feature | Description | | --- | --- | | **Name** | `af_udv25_afrikaansafribooms_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | | 80e223ecc7cc23f60282e1d254c6303d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (455 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `AOA`, `AOP`, `ASA`, `ASP`, `AVA`, `AVP`, `BO`, `BS`, `BV`, `KN`, `KO`, `LB`, `LO`, `NA`, `NEE`, `NM`, `NME`, `NSE`, `NSED`, `NSM`, `PA`, `PB`, `PDHEB`, `PDHEDP`, `PDHENP`, `PDHEW`, `PDMB`, `PDMP`, `PDMW`, `PDOENP`, `PDOEW`, `PDVEB`, `PDVEDP`, `PDVENP`, `PDVEW`, `PEEB`, `PEEDP`, `PEENP`, `PEMB`, `PEMP`, `PEMW`, `PO`, `PTEB`, `PTEDP`, `PTENP`, `PTEW`, `PTMP`, `PV`, `PW`, `RA`, `RK`, `RL`, `RO`, `RS`, `RSF`, `RV`, `RWD`, `SVS`, `THAB`, `THAO`, `THBB`, `THBO`, `THNB`, `THPB`, `THPO`, `TRAB`, `TRAO`, `TRBB`, `UPB`, `UPD`, `UPI`, `UPO`, `UPS`, `UPV`, `UPW`, `UXD`, `VTHOG`, `VTHOK`, `VTHOO`, `VTHOV`, `VTHSG`, `VTHSO`, `VTUOA`, `VTUOM`, `VTUOP`, `VUOT`, `VVHOG`, `VVHOK`, `VVHOO`, `VVUOM`, `VVUOP`, `ZE`, `ZM`, `ZPL`, `ZPR` | | **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP`, `AdjType=Attr\|Case=Nom\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Cop`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=NUM`, `POS=PART\|PartType=Inf`, `POS=VERB\|Subcat=Tran\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=PRON\|PronType=Rel`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Pas`, `POS=PUNCT`, `POS=CCONJ`, `POS=SCONJ`, `POS=VERB\|Subcat=Intr\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=VERB\|Subcat=Intr\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Pas`, `Degree=Pos\|POS=ADV`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Mod`, `POS=DET\|PronType=Ind`, `POS=X`, `Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Ind`, `POS=PART\|PartType=Neg`, `POS=VERB\|Subcat=Tran\|Tense=Past\|VerbForm=Part`, `AdjType=Pred\|Case=Nom\|Degree=Pos\|POS=ADJ`, `POS=DET\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART\|PartType=Gen`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|POS=ADV`, `Degree=Dim\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `AdjType=Attr\|Case=Nom\|Degree=Sup\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `POS=VERB\|Subcat=Prep\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Aux`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Rcp`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Cop`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `AdjType=Attr\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Sup\|POS=ADJ` | | **`parser`** | `ROOT`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `punct`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `8`, `10`, `12`, `14`, `16`, `18`, `21`, `24`, `26`, `28`, `31`, `32`, `34`, `37`, `39`, `40`, `42`, `44`, `46`, `47`, `49`, `51`, `53`, `54`, `56`, `57`, `58`, `59`, `61`, `64`, `66`, `68`, `69`, `72`, `74`, `75`, `77`, `78`, `81`, `83`, `84`, `85`, `86`, `87`, `90`, `92`, `94`, `96`, `99`, `101`, `103`, `105`, `108`, `110`, `113`, `116`, `117`, `118`, `121`, `123`, `124`, `125`, `127`, `128`, `129`, `133`, `136`, `138`, `141`, `143`, `145`, `147`, `151`, `153`, `154`, `156`, `158`, `159`, `160`, `162`, `164`, `165`, `167`, `168`, `170`, `172`, `174`, `176`, `178`, `179`, `180`, `181`, `183`, `185`, `189`, `190`, `191`, `192`, `194`, `195`, `197`, `198`, `201`, `202`, `203`, `204`, `206`, `207`, `209`, `213`, `214`, `216`, `217`, `218`, `220`, `221`, `222`, `223`, `225`, `226`, `228`, `229`, `231`, `233`, `234`, `236`, `238`, `240`, `241`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `255`, `256`, `257`, `258`, `261`, `262`, `263`, `265`, `267`, `269`, `270`, `271`, `273`, `275`, `276`, `278`, `279`, `281`, `283`, `285`, `287`, `289`, `291`, `294`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `305`, `306`, `307`, `309`, `310`, `311`, `313`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `326`, `327`, `328`, `329`, `330`, `332`, `333`, `335`, `336`, `337`, `338`, `339`, `340`, `341`, `343`, `344`, `347`, `348`, `349`, `351`, `353`, `355`, `357`, `359`, `360`, `361`, `362`, `365`, `366`, `367`, `369`, `371`, `373`, `374`, `375`, `377`, `379`, `381`, `383`, `386`, `388`, `390`, `392`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `403`, `405`, `406`, `408`, `409`, `411`, `412`, `414`, `417`, `215`, `418`, `419`, `420`, `421`, `422`, `424`, `425`, `426`, `427`, `429`, `431`, `432`, `433`, `434`, `436`, `438`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `450`, `452` | </details> | 32252bcd5d5c6dfef6f95058b09c716c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.92 | | `TOKEN_P` | 99.89 | | `TOKEN_R` | 99.94 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 100.00 | | `SENTS_P` | 100.00 | | `SENTS_R` | 100.00 | | `TAG_ACC` | 96.01 | | `POS_ACC` | 98.52 | | `MORPH_ACC` | 97.52 | | `DEP_UAS` | 90.78 | | `DEP_LAS` | 87.50 | | `LEMMA_ACC` | 97.87 | | 3140633c4d1f7f421202abbfcf26bfba |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout ad91279f0108d54bd22abe29671b376f048822c5 pip install -e . cd egs2/chime4/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/chime4_conformer_e12_linear2048 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 20bb7e10483ed4622b6ab7d4eb9f11b9 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Dec 28 20:41:40 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `ad91279f0108d54bd22abe29671b376f048822c5` - Commit date: `Wed Dec 28 20:15:42 2022 -0500` | aa4eb9e536e1c1c4051b8ef7fee15f56 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|93.3|5.4|1.3|0.5|7.3|55.6| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|27120|91.7|6.7|1.6|0.9|9.1|62.0| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|21409|89.2|8.9|1.9|1.1|12.0|64.5| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|21416|87.8|9.6|2.6|1.4|13.6|68.1| | b4e590363a00f5e642158d8027ee95fe |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|97.2|1.5|1.3|0.7|3.5|55.6| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|160400|96.3|2.0|1.7|1.0|4.7|62.0| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|126796|95.1|2.8|2.1|1.2|6.1|64.6| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|126812|94.0|3.1|3.0|1.6|7.7|68.1| | cbeebbd52935c79d4a87b89a25aa304e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e12_linear2048.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e12_linear2048_raw_en_char_sp ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45069 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 15000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_char_sp/train/speech_shape - exp/asr_stats_raw_en_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_en_char_sp/valid/speech_shape - exp/asr_stats_raw_en_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr05_multi_noisy_si284_sp/wav.scp - speech - kaldi_ark - - dump/raw/tr05_multi_noisy_si284_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dt05_multi_isolated_1ch_track/wav.scp - speech - kaldi_ark - - dump/raw/dt05_multi_isolated_1ch_track/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - <space> - E - T - A - N - I - O - S - R - H - L - D - C - U - M - P - F - G - Y - W - B - V - K - . - X - '''' - J - Q - Z - ',' - '-' - '"' - <NOISE> - '*' - ':' - ( - ) - '?' - '&' - ; - '!' - / - '{' - '}' - '1' - '2' - '0' - $ - '8' - '9' - '6' - '3' - '5' - '7' - '4' - '~' - '`' - _ - <*IN*> - <*MR.*> - \ - ^ - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_char_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202211' distributed: true ``` </details> | 09ed408991177fb00c7bcf935e90740f |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | luke-japanese-large **luke-japanese** is the Japanese version of **LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained _knowledge-enhanced_ contextualized representation of words and entities. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Please refer to our [GitHub repository](https://github.com/studio-ousia/luke) for more details and updates. This model contains Wikipedia entity embeddings which are not used in general NLP tasks. Please use the [lite version](https://huggingface.co/studio-ousia/luke-japanese-large-lite/) for tasks that do not use Wikipedia entities as inputs. **luke-japanese**は、単語とエンティティの知識拡張型訓練済み Transformer モデル**LUKE**の日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。詳細については、[GitHub リポジトリ](https://github.com/studio-ousia/luke)を参照してください。 このモデルは、通常の NLP タスクでは使われない Wikipedia エンティティのエンベディングを含んでいます。単語の入力のみを使うタスクには、[lite version](https://huggingface.co/studio-ousia/luke-japanese-large-lite/)を使用してください。 | acc9de80c08ae9b2408d155636bfd1d7 |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | Experimental results on JGLUE The experimental results evaluated on the dev set of [JGLUE](https://github.com/yahoojapan/JGLUE) is shown as follows: | Model | MARC-ja | JSTS | JNLI | JCommonsenseQA | | ----------------------------- | --------- | ------------------- | --------- | -------------- | | | acc | Pearson/Spearman | acc | acc | | **LUKE Japanese large** | **0.965** | **0.932**/**0.902** | **0.927** | 0.893 | | _Baselines:_ | | | Tohoku BERT large | 0.955 | 0.913/0.872 | 0.900 | 0.816 | | Waseda RoBERTa large (seq128) | 0.954 | 0.930/0.896 | 0.924 | **0.907** | | Waseda RoBERTa large (seq512) | 0.961 | 0.926/0.892 | 0.926 | 0.891 | | XLM RoBERTa large | 0.964 | 0.918/0.884 | 0.919 | 0.840 | The baseline scores are obtained from [here](https://github.com/yahoojapan/JGLUE/blob/a6832af23895d6faec8ecf39ec925f1a91601d62/README.md). | 12bd45adc27fab323b6757c6404511ad |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | Citation ```latex @inproceedings{yamada2020luke, title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, booktitle={EMNLP}, year={2020} } ``` | 70d9fd324d8555888143de90af02c863 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum-wei0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.6289 - Rouge1: 25.7398 - Rouge2: 6.1361 - Rougel: 19.8262 - Rougelsum: 19.8284 - Gen Len: 18.7984 | 64f48745fbe2ac2f12aec32f73505d90 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | 748c8c25ac10cff05b6823089925b30f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.858 | 1.0 | 1701 | 2.6289 | 25.7398 | 6.1361 | 19.8262 | 19.8284 | 18.7984 | | f427a67917bce89045c183d4cc8615ee |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY (Deep-Narrow version) T5-Efficient-TINY is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | 877bfc09a34a35e82c954f124dca2928 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny** - is of model type **Tiny** with no variations. It has **15.58** million parameters and thus requires *ca.* **62.32 MB** of memory in full precision (*fp32*) or **31.16 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | b3492a8f162f35a5cf185b97346a5842 |
apache-2.0 | ['generated_from_trainer'] | false | BioLinkBERT-base-finetuned-ner This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1226 - Precision: 0.8760 - Recall: 0.9185 - F1: 0.8968 - Accuracy: 0.9647 | 7b778b2707b88a0734820b1d124fdaa1 |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations The goal was to have a drug tag printed immediately for a particular sentence, but it has the disadvantage of being marked as LABEL LABEL0 : irrelevant text LABEL1,2 : Drug LABEL3,4 : condition | b09b4e4ea46bdd82083d6a8b578127ee |
apache-2.0 | ['generated_from_trainer'] | false | Training procedure Reference Code: SciBERT Fine-Tuning on Drug/ADE Corpus (https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb) | 45393fbdf042a5dc75539fd2b1d418a9 |
apache-2.0 | ['generated_from_trainer'] | false | How to use from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") | 8f526102200c084371b75743de313d56 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 20a72e883be1c6c5941fb2987088a982 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1099 | 1.0 | 201 | 0.1489 | 0.8415 | 0.9032 | 0.8713 | 0.9566 | | 0.1716 | 2.0 | 402 | 0.1318 | 0.8456 | 0.9135 | 0.8782 | 0.9597 | | 0.1068 | 3.0 | 603 | 0.1197 | 0.8682 | 0.9110 | 0.8891 | 0.9641 | | 0.0161 | 4.0 | 804 | 0.1219 | 0.8694 | 0.9157 | 0.8919 | 0.9639 | | 0.1499 | 5.0 | 1005 | 0.1226 | 0.8760 | 0.9185 | 0.8968 | 0.9647 | | d0e728e76c87bbd221d76367c1ba7a0b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. | f838e688b6b99e513bee69d84c3c3c1d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` | 3407068942390f64a76620a3e2ff88b1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F | 80340aac08ec2138ced4e68cef2840a1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2) ------ | 034907bbfdc98479ab40a59616b50998 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. | 794d0236398aa32a809324e398fbd1ec |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. | 75a7878f90b8b0898b1c4f8120d6f492 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. | 9c4d0dc1e5714d73c0789ac2a9728fd1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. | 45085ab854e8f521d47c36830cf8cbc9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. | 0af92f1c43cc9a1f2807672fa83d0935 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa | e1a58c534c6ff1d2047f97aaa81d64ab |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/ | 00e5c6a8019ba8108abdd34e59780c99 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** | | 5ab61e3127e57090df32a90a3d683423 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8107 - Matthews Correlation: 0.5422 | 4926f6a765a6be2909633818db2cdcfc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.522 | 1.0 | 535 | 0.5193 | 0.4152 | | 0.3451 | 2.0 | 1070 | 0.4942 | 0.5166 | | 0.2335 | 3.0 | 1605 | 0.5490 | 0.5291 | | 0.179 | 4.0 | 2140 | 0.7727 | 0.5150 | | 0.1314 | 5.0 | 2675 | 0.8107 | 0.5422 | | 254315debbb2e1135b77ee8fa7128c34 |
apache-2.0 | ['generated_from_trainer'] | false | distiled_flip_model_emotion_alpha_0.8_epoch7_v1 This model is a fine-tuned version of [ArafatBHossain/distill_bert_fine_tuned_emotion_dataset](https://huggingface.co/ArafatBHossain/distill_bert_fine_tuned_emotion_dataset) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1583 - Accuracy: 0.9435 | 6ab11a43efa0489e721e7dbc2625d679 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 7 - mixed_precision_training: Native AMP | d8e277c276b382112b37bd520f20508b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2013 | 1.0 | 2000 | 0.2653 | 0.9355 | | 0.1625 | 2.0 | 4000 | 0.2537 | 0.9365 | | 0.1262 | 3.0 | 6000 | 0.1934 | 0.935 | | 0.1048 | 4.0 | 8000 | 0.1813 | 0.9435 | | 0.0777 | 5.0 | 10000 | 0.1500 | 0.941 | | 0.0614 | 6.0 | 12000 | 0.1591 | 0.944 | | 0.0465 | 7.0 | 14000 | 0.1583 | 0.9435 | | 95578d6fe60151d7048acf3ae970695d |
mit | ['generated_from_trainer'] | false | bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc4chemd_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0641 - Precision: 0.8944 - Recall: 0.8777 - F1: 0.8860 - Accuracy: 0.9908 | 9df780f81f50b080e0cefaa1b33065cc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.006 | 1.0 | 1918 | 0.0310 | 0.8697 | 0.8510 | 0.8602 | 0.9894 | | 0.0097 | 2.0 | 3836 | 0.0345 | 0.8855 | 0.8637 | 0.8745 | 0.9898 | | 0.0058 | 3.0 | 5754 | 0.0359 | 0.8733 | 0.8836 | 0.8784 | 0.9902 | | 0.0014 | 4.0 | 7672 | 0.0440 | 0.8723 | 0.8842 | 0.8782 | 0.9903 | | 0.0005 | 5.0 | 9590 | 0.0539 | 0.8862 | 0.8673 | 0.8766 | 0.9903 | | 0.0001 | 6.0 | 11508 | 0.0558 | 0.8939 | 0.8628 | 0.8781 | 0.9904 | | 0.0001 | 7.0 | 13426 | 0.0558 | 0.8846 | 0.8729 | 0.8787 | 0.9903 | | 0.0012 | 8.0 | 15344 | 0.0635 | 0.8935 | 0.8696 | 0.8814 | 0.9905 | | 0.0 | 9.0 | 17262 | 0.0624 | 0.8897 | 0.8831 | 0.8864 | 0.9908 | | 0.0002 | 10.0 | 19180 | 0.0641 | 0.8944 | 0.8777 | 0.8860 | 0.9908 | | baf6ab74eac7dc1f972796885fcbdb18 |
mit | ['generated_from_keras_callback'] | false | nouman10/robertabase-finetuned-claim-ltp-full-prompt This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0233 - Validation Loss: 0.0231 - Epoch: 4 | 8b579182d65ea73a18e5537461377e69 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -425, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | f69623b208690fa1145e64788306f224 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1965 | 0.0452 | 0 | | 0.0321 | 0.0231 | 1 | | 0.0232 | 0.0231 | 2 | | 0.0232 | 0.0231 | 3 | | 0.0233 | 0.0231 | 4 | | e8432283e94e4903593b6707214e4185 |
mit | ['exbert'] | false | ColD Fusion BERT uncased model Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). | c40f0f1c8cda4ee881365a8861ac117d |
mit | ['tapex', 'table-question-answering'] | false | TAPEX (large-sized model) TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). | f3c2d856f365874422eb7a907dc82a5b |
mit | ['tapex', 'table-question-answering'] | false | Model description TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. This model is the `tapex-base` model fine-tuned on the [Tabfact](https://huggingface.co/datasets/tab_fact) dataset. | 2f84db3f690d17ba38c0a62ddade19df |
mit | ['tapex', 'table-question-answering'] | false | How to Use Here is how to use this model in transformers: ```python from transformers import TapexTokenizer, BartForSequenceClassification import pandas as pd tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact") model = BartForSequenceClassification.from_pretrained("microsoft/tapex-large-finetuned-tabfact") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) | 8a44ecd9a3adad24cd64d4b3fe1aec9a |
mit | ['tapex', 'table-question-answering'] | false | tapex accepts uncased input since it is pre-trained on the uncased corpus query = "beijing hosts the olympic games in 2012" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model(**encoding) output_id = int(outputs.logits[0].argmax(dim=0)) print(model.config.id2label[output_id]) | 0a02c124410590c014401de768701fb1 |
mit | ['tapex', 'table-question-answering'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ liu2022tapex, title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor}, author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=O50443AsCP} } ``` | 6dd025e47e791366846deac4cf78da32 |
apache-2.0 | ['vision', 'image-classification'] | false | Swin Transformer v2 (tiny-sized model) Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team. | 1ba914a384dfbf193e7ed076c892a1f7 |
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 21k ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits | 09a3642e87185149edfec11b7a11f4c4 |
apache-2.0 | ['generated_from_trainer'] | false | small_finetune_M01 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2363 - Wer: 1.0 | 0737836edebec7ac64310660f06a8cb9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 20 - 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: 4000 - mixed_precision_training: Native AMP | 7ebd1e86626e273f88fd04e757d62be9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:---:| | 121.7217 | 200.0 | 800 | 3.1742 | 1.0 | | 2.066 | 400.0 | 1600 | 2.8390 | 1.0 | | 1.7019 | 600.0 | 2400 | 2.8359 | 1.0 | | 1.5282 | 800.0 | 3200 | 2.8655 | 1.0 | | 1.4089 | 1000.0 | 4000 | 2.8933 | 1.0 | | 1.3123 | 1200.0 | 4800 | 2.9047 | 1.0 | | 1.2361 | 1400.0 | 5600 | 2.9677 | 1.0 | | 1.1758 | 1600.0 | 6400 | 3.0008 | 1.0 | | 1.1241 | 1800.0 | 7200 | 3.0795 | 1.0 | | 1.0816 | 2000.0 | 8000 | 3.1214 | 1.0 | | 1.0497 | 2200.0 | 8800 | 3.1518 | 1.0 | | 1.0349 | 2400.0 | 9600 | 3.1584 | 1.0 | | 1.0058 | 2600.0 | 10400 | 3.1876 | 1.0 | | 0.9983 | 2800.0 | 11200 | 3.1843 | 1.0 | | 0.9863 | 3000.0 | 12000 | 3.1914 | 1.0 | | 0.9776 | 3200.0 | 12800 | 3.2005 | 1.0 | | 0.9647 | 3400.0 | 13600 | 3.2245 | 1.0 | | 0.9586 | 3600.0 | 14400 | 3.2352 | 1.0 | | 0.9521 | 3800.0 | 15200 | 3.2398 | 1.0 | | 0.9537 | 4000.0 | 16000 | 3.2363 | 1.0 | | 9a841f8927196069b9a44e1c52046064 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504 pip install -e . cd egs2/librispeech_100/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_librispeech_100_conformer_char ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | eae142ee0dbf3151edf60ce1bd50f864 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Feb 24 17:47:04 KST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `82a0a0fa97b8a4a578f0a2c031ec49b3afec1504` - Commit date: `Wed Feb 23 08:06:47 2022 +0900` | 96191862725682afa7f1ed67b28011ae |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|93.9|5.6|0.5|0.7|6.8|57.1| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|82.5|15.7|1.8|1.9|19.3|82.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|93.8|5.7|0.6|0.7|6.9|58.4| |decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|82.2|15.9|2.0|1.7|19.5|83.6| | cc376fb5f6e9b745fbdad2ccfde8dacc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|98.3|1.0|0.7|0.7|2.4|57.1| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|93.3|4.1|2.6|1.9|8.7|82.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|98.3|1.0|0.7|0.6|2.3|58.4| |decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|93.2|4.1|2.7|1.8|8.6|83.6| | 3d810c174aa8d8e9db48ece79165493a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_char.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_conformer_lr2e-3_warmup15k_amp_nondeterministic_char ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 70 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: 400 use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1600000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_char_sp/train/speech_shape - exp/asr_stats_raw_en_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_en_char_sp/valid/speech_shape - exp/asr_stats_raw_en_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_clean_100_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_clean_100_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - <space> - E - T - A - O - N - I - H - S - R - D - L - U - M - C - W - F - G - Y - P - B - V - K - '''' - X - J - Q - Z - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_char_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> | bced6343473e708d0325df5037b5b795 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_vp-100k_s334 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 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | da9d27b2b4261dac204b10cc5cf83012 |
apache-2.0 | ['generated_from_keras_callback'] | false | food-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0092 - Validation Loss: 0.0323 - Epoch: 2 | b14a0cd07034e5d2fde2475ec7ec9d0d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1035, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | fd73ba206625cb02972f4acdf0d0ee67 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0808 | 0.0284 | 0 | | 0.0193 | 0.0286 | 1 | | 0.0092 | 0.0323 | 2 | | a180e442620ea8ff579a97c283d3dad6 |
apache-2.0 | ['translation'] | false | en-he * source group: English * target group: Hebrew * OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md) * model: transformer * source language(s): eng * target language(s): heb * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-10-04.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.zip) * test set translations: [opus-2020-10-04.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.test.txt) * test set scores: [opus-2020-10-04.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.eval.txt) | 5deef9b8bb1c4ae8ca937e5df8ae6ca0 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: en-he - source_languages: eng - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'he'] - src_constituents: ('English', {'eng'}) - tgt_constituents: ('Hebrew', {'heb'}) - src_multilingual: False - tgt_multilingual: False - long_pair: eng-heb - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.test.txt - src_alpha3: eng - tgt_alpha3: heb - chrF2_score: 0.602 - bleu: 37.9 - brevity_penalty: 1.0 - ref_len: 60359.0 - src_name: English - tgt_name: Hebrew - train_date: 2020-10-04 00:00:00 - src_alpha2: en - tgt_alpha2: he - prefer_old: False - short_pair: en-he - helsinki_git_sha: 61fd6908b37d9a7b21cc3e27c1ae1fccedc97561 - transformers_git_sha: d99ed7ad618037ae878f0758157ed0764bd7f935 - port_machine: LM0-400-22516.local - port_time: 2020-10-15-16:31 | 83265448c95360442258958ab3bfb46a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-law This model is a fine-tuned version of [ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt](https://huggingface.co/ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt) on the None dataset. | 9561d22dc3915660070e0422b94108c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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 - num_epochs: 10 - mixed_precision_training: Native AMP | 2a0e7b09bd6c64dd94592aead5de3440 |
apache-2.0 | ['translation'] | false | opus-mt-fr-mos * source languages: fr * target languages: mos * OPUS readme: [fr-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-mos/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-mos/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mos/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mos/opus-2020-01-20.eval.txt) | 8e39b56342f5a8ca5761f77a905951b2 |
mit | ['audio-generation'] | false | !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/honk-140k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios | 93c39f8e2de3c97ff92222b61d1cc1cc |
mit | ['audio-generation'] | false | !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/honk-140k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios | 9bd5e97d997706eb84a0acf0d1175a19 |
apache-2.0 | ['toxicity', 'portuguese', 'hate speech', 'offensive language', 'generated_from_trainer'] | false | dougtrajano/toxic-comment-classification Toxic Comment Classification is a model that detects if the text is toxic or not. This BERT model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the [OLID-BR dataset](https://huggingface.co/datasets/dougtrajano/olid-br). | d3b97facc1ed15f68bf7feab0295b085 |
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