license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['bert'] | false | Introduction **MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning. Instead of masking with [MASK] token, which never appears in the fine-tuning stage, **we propose to use similar words for the masking purpose**. A si... | ec6dccc40d38700343b7b7ba2acf87ad |
apache-2.0 | ['bert'] | false | bility of the next word . | | **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . | | **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . | | **MLM as correction** | we use a text system to ca | 5aebcc8df78084758ce9e8a73be77a67 |
apache-2.0 | ['bert'] | false | bility of the next word . | Except for the new pre-training task, we also incorporate the following techniques. - Whole Word Masking (WWM) - N-gram masking - Sentence-Order Prediction (SOP) **Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architect... | e2f538a73fc844fbf170569f0ebbf358 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_model 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: 2.4769 - Accuracy: 0.5 | 0cca62f39c9722810a79ac5a281eb4ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 2.4849 | 0.5 | | No log | 2.0 | 2 | 2.4769 | 0.5 | | b56574ea021f8ab16c16082e0dfac8a4 |
mit | [] | false | Text Generation ```python >>> from transformers import AutoModel, AutoTokenizer, trainer_utils >>> >>> device = "cuda" >>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device) >>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese") >>> x_token = tokenizer.encode("織田信長は、", return_ten... | 2dadd73fa1bc5d422a70db5aed87d45e |
mit | [] | false | Text Generation with Prefix-LM model ```python >>> from transformers import AutoModel, AutoTokenizer, trainer_utils >>> >>> device = "cuda" >>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device) >>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese") >>> x_token = tokenizer.encode... | 64178cb35d6d7efaca9be6d769052d8d |
mit | [] | false | Masked Language Model And Text Generation ```python >>> from transformers import AutoModel, AutoTokenizer, trainer_utils >>> >>> device = "cuda" >>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device) >>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese") >>> x_token = tokenizer.e... | eaeaa30a8a48510544ad1a1a4bac408b |
mit | [] | false | Model Description Japanese language model using Switch Transformer. It has the same structure as the model introduced as `Prefix LM` in the T5 paper, and works with both Test Generation and Masked Language Model. - **Developed by:** Toshiyuki Sakamoto (tanreinama) - **Model type:** Switch Transformer - **Language(... | 8861a328db61d14ad4870758dba52a1e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | tl;dr Use istolemyownlora3.safetensors, it's the better one and gets 99% 1:1 my style, for better or worse, better than what I can do manually honestly. The other ones were trained first with less optimal settings | 8aa4669bde1ab5fff7a1b05cf4b2a25c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | How to use a LORA? Place it in "\stable-diffusion-webui\models\Lora". Don't see the folder? git pull Load any model, preferably an anime model. All these examples are made with Meadmix, I found it gives good results but any Anything based model should work. Use the purple icon under the generate button to bring up t... | 411d45ad6d900a454f1999b4c94a019c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | Examples for istolemyownlora3.safetensors (better settings, 5 repeats)  ... | fd87abebd4fd22e460788c6bfc7c939a |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | Examples for istolemyownlora2.safetensors (5 repeats)  ![00941-37511579... | ad5da44133e6743f1310228c0038abe6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | Examples for istolemyownlora.safetensors (2 repeats)  ![00646-710280603... | 1fa3ca5aafcec3490d744cbfe28e74a2 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | Terms and conditions >Does this give me and everyone else the rights over your entire art and dataset and all your future artworks both made manually and with AI? No >Can I use anything generated with this Lora for whatever I want though? Fair use and derivative works >Can I sell your LORAs? No >Can I merge the... | 3f12090d35cecd60af5d888e6ff887fa |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-etc-sym This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - Accuracy: 0.9997 - F1: 0.9997 | ada0c77b85ad686326e25daaa81fa70b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 262 | 0.0068 | 0.9987 | 0.9987 | | No log | 2.0 | 524 | 0.0005 | 0.9997 | 0.9997 | | 6f54d188ef4424d299ca471dfa6faa4f |
cc | ['token classification'] | false | Intended uses & limitations This model is intended to be used for named entity recoginition tasks. The model will identify entities of persons, locations, organisations, and miscellaneous. The model will predict lables based upon the CoNLL-2003 dataset. Note that the dataset and model may not be fully represetative ... | b5c49b352473f1ba4800ecf34e6bd43c |
cc | ['token classification'] | false | How to use Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003") model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll200... | 6c2744f34672ecbb4bfa2f8d46b0c1db |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-14-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 | a679e299c40beaf918cea01712f094f0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Thai Newmm Tokenized - Parinthapat Pengpun This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set: - eval_loss: 0.5888 - eval_wer: 67.3381 - eval_cer:... | 6e11e68f38eea25e787ca7637e7ca592 |
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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | c587998f2ef551fcd0c800c75d4ededf |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Odia - Sukanta Nanda with tips from Sanchit language None 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: 0.6090 - Wer: 59.0155 | b333e59c3618aab710086dd2a1d19897 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.006 | 20.0 | 1000 | 0.3898 | 60.6182 | | 0.0004 | 40.0 | 2000 | 0.4451 | 58.9010 | | 0.0001 | 60.0 | 3000 | 0.5533 | 57.469... | e407e18ac9f81f013a84601a14fb1523 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3185 - Accuracy: 0.8567 - F1: 0.8571 | 038ac04c5776a3065732433d991af3fb |
mit | [] | false | onzpo on Stable Diffusion This is the `<onzpo>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train you... | 9cb43608994d9cd18391f5c2a9c7caa1 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6515 - Accuracy: 0.1644 | 7fda9176723776dcc5bb1a0fb466932d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.5795 | 1.0 | 3 | 2.6515 | 0.1644 | | 2184596b9c032f7cc571a1eed3d140c7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Banano Chan - Anything v3.0 (banchan-anything-v3.0) A potassium rich latent diffusion model. [Anything V3.0](https://huggingface.co/Linaqruf/anything-v3.0) trained to the likeness of [Banano Chan](https://twitter.com/Banano_Chan/). The digital waifu embodiment of [Banano](https://www.banano.cc), a feeless and super ... | 148c3791be64f52f77800ba04a179733 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | banano-ai-art Discord channel](https://discord.com/channels/415935345075421194/991823100054355998) or [Community](https://huggingface.co/pbuyle/banchan-anything-v3-0/discussions) tab. Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main... | 6f4ee0311a48c9fd1162582a79f14a2a |
openrail | [] | false | Cloud4bert This model is a specialised version of the [BERT base model](https://huggingface.co/ultraleow/cloud4bert). The code for the training process will be uploaded [here](https://huggingface.co/ultraleow/cloud4bert/). This model is uncased: it does not make a difference between english and English. | e2a9b9d88d10177f77992ad8c36a4925 |
openrail | [] | false | Model description Cloud4bert is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots o... | 24ab637cabf66e4c8a17925c00c1a4d3 |
openrail | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> sentiment_analzyor = pipeline('text-classification', model='ultraleow/cloud4bert') >>> sentiment_analzyor("Sorry, I don't understand - are you saying you don't have the `paypal... | 6a8aaf9dcf004fe57874ddb57640aaf7 |
openrail | [] | false | LABEL_2 = positive ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ultraleow/cloud4bert") tokenizer = AutoTokenizer.from_pretrained("bert-b... | 375526335717f7641e919d133447cb87 |
openrail | [] | false | Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | Recall(Weighted) | Precision(Weighted) | f1(Weighted) | ACC | |:----:|:----:|:----:|:----:|:-----:| | | 94.03% | 94.06% | 94.02% | 94.03% | | 3e4bf685e623c418516b1de56b5794d8 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game scr... | 5fffbeac8bf9e522f0efc0670138727b |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | MahaSBERT A MahaBERT model (l3cube-pune/marathi-bert-v2) trained on the NLI dataset. <br> This is released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP <br> A better sentence similarity model(fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/marathi-sente... | 83c2071ac0a17f1ef847d436473dcab3 |
apache-2.0 | ['generated_from_trainer'] | false | SST2_ELECTRA_5E This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3431 - Accuracy: 0.9267 | e5a690e31de96e9b67883ed33c1f9f32 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.667 | 0.12 | 50 | 0.5772 | 0.8533 | | 0.4746 | 0.23 | 100 | 0.3421 | 0.9 | | 0.3104 | 0.35 | 150 | 0.2948 | 0.... | 961107bbd6f1adf1240195de8d81d176 |
apache-2.0 | ['generated_from_trainer'] | false | benchmark-finetuned-distilbert 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: 0.4592 - Accuracy: 0.8228 - F1: 0.8214 | 27d0c9bb95bb9341550a6dc87698209c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8561 | 1.0 | 48 | 0.6834 | 0.7288 | 0.7016 | | 0.5498 | 2.0 | 96 | 0.4948 | 0.8042 | 0.8036 | | 0.4184 |... | a47eaad26f33fa1cbfee59d6c292dd8a |
['cc0-1.0'] | ['graph neural networks'] | false | Keras Implementation of Graph Attention Networks for Node Classification 🕸 This repo contains the model and the notebook [to this Keras example on Graph Attention Networks for Node Classification](https://keras.io/examples/graph/gat_node_classification/). Full credits to: [Alexander Kensert](https://github.com/aken... | 8f0cbb8e91bcaa3451764e099dac1934 |
['cc0-1.0'] | ['graph neural networks'] | false | Background Information Graph neural networks is the preferred neural network architecture for processing data structured as graphs (for example, social networks or molecule structures), yielding better results than fully-connected networks or convolutional networks. This tutorial implements a specific graph neural n... | 17d70a0664db0dfbaffcd84f29ff5e95 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_all_16_02_2022-15_59_50 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. It achieves the following results on the evaluation set: - Loss: 0.1750 - Precision: 0.3286... | 4b4bd6c607a7dc3368204b2e766a119f |
apache-2.0 | [] | false | Model Description This model is fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) . The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_rubert_tiny2/fine_tune_rubert_ti... | 73f18a7770e4f0b711460bb48849e9a6 |
apache-2.0 | [] | false | How to Get Started With the Model You can use the model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/MedRuBertTiny2') >>> pipeline("У пациента [MASK] боль в грудине.") [{'score': 0.4527082145214081, ... | c301c9988445fde6e0984f82164bbab1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Ca This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0, the Fleurs, the SLR69, the tb3_parla and the parlament_parla datasets. It achieves the following results on the evaluation set: - eval_loss: 0.1905 - eval_wer: 10.003... | 3cc45c77520d29db000b2c09a451d7cc |
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: 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_sche... | c5303da8d2049e6899d5ed7090161255 |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep60 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8314 - Epoch: 59 | c4bda79126afb5659157aaf49e323c75 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.8539 | 0 | | 3.0645 | 1 | | 3.0225 | 2 | | 3.0128 | 3 | | 3.0023 | 4 | | 2.9834 | 5 | | 2.9859 | 6 | | 2.9814 | 7 | | 2.9729 | 8 | | 2.9736 | 9 | | 2.9687 | 10 | | ... | ff350a6800fcf4085ebdf62e548eba5e |
apache-2.0 | ['generated_from_trainer'] | false | bert-mlm-feedback 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: - Loss: 1.0646 | 2bcae837b6c5cc0cebb582208cd60044 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2248 | 1.0 | 350 | 1.5091 | | 2.0629 | 2.0 | 700 | 1.2582 | | 2.0031 | 3.0 | 1050 | 1.4637 | | 6459d7ecf3a6755093f7c5a19e973fb0 |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-squad-seed-9002 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9743 | 99b7ba5dab4158fa436c5549eba67a03 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8517 | 1.0 | 8248 | 0.8737 | | 0.6243 | 2.0 | 16496 | 0.8350 | | 0.4289 | 3.0 | 24744 | 0.9743 | | ccddf8fde52015d534f1be05c600e6ef |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | bp500-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets: - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distribu... | fb59ff640fe45433cac4ec177b8edb72 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | bp\_500... | 9dee5e7fb1b170dcb06adee9310840d1 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Transcription examples | Text | Transcription | |------------------------------------------------------------------------... | 6c195bc0038c1e27235a974b110dfb14 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.05159097808687998 | e8da3b6404579e05fd72286fc8f49ec7 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.13659981509705973 | 410536a5e0130d9c78770b06038e5dbb |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.03196969696969697 | 17a957b922d5beb70b0f09a9602c00d5 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.1178481066463896 | 34b9360ccbf52de9b29f0b0c4e0de5c8 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.09544588416964224 | df4d72abd98b613de741eac940f7b4e3 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.24868046340420813 | 47587fdef96d43476908936446cafbc6 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.08246076839826841 | ccbe8b340dbe1ee57d2beff230968680 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Cetuc ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.03222801788375573 | 1209c7a09590088ae1d600c6a950814b |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.09713866021093655 | 8f16ec75f09985f32dd61e6b7f16b0d0 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.022310606060606065 | 873585cb7903091bc5708ceedddaa683 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.11408590958696524 | 5bdbee2d3f2076e893c4bc1269140213 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.12502797252979136 | 8dda4d50bea6ffbb35ae5fd3aaf630c2 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.24603179403904793 | 8ced96b7b8aa772d716147a700d5ede1 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.06542207792207791 | 7b4800967105d1123738c27aa70deef8 |
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 fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca pip install -e . cd egs2/americasnlp22/asr1 ./run.sh \ --skip_data_prep false \ ... | 5938163e96636c2e0f8b61586950cacc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Jun 5 04:51:42 CEST 2022` - python version: `3.9.13 (main, May 18 2022, 00:00:00) [GCC 11.3.1 20220421 (Red Hat 11.3.1-2)]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0+cu115` - Git hash: `d55704daa36d3dd2ca24ae3162ac40d81957208c` - Commit date: `Wed Jun 1 02:33:09... | 1b7b39905ce6dce025f63bc41efc03a6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_raw_quy_bpe100_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// d... | d997b0d9873e5637a0b470cd8318c731 |
gpl-2.0 | ['corenlp'] | false | Core NLP model for chinese CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentimen... | 8800b97bc06cf8c675db90ed6d17e45c |
mit | [] | false | This model (extreme learning machine, a shallow neural net) was trained in R, to reproject sentence transformers ('all-mpnet-base-v2') into wikidata5m knowledge graph embeddings (rotate version). It is stored with fastsave (https://github.com/barkasn/fastSave_, depends on the library elmNNRcpp (https://cran.r-projec... | e275a14fe135b1c3e5a593ea0390947a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny PT This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6077 - Wer: 29.9844 | b4d7c584b4174664147d48ae91c5a77f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4143 | 1.04 | 500 | 0.5325 | 32.7399 | | 0.2693 | 3.03 | 1000 | 0.4718 | 29.4867 | | 0.1724 | 5.01 | 1500 | 0.4758 | 28.721... | 72f2cad5f5454371c872eb7f5d5dcdbf |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | lewispons/Email-classifier-v2 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2.... | 5770a1a05fb045038cf2fe95d86499a4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | emrecan/bert-base-turkish-cased-mean-nli-stsb-tr This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of [NLI](ht... | e0e34a310f423de56d6d874089a9fc79 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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 = ["Bu örnek bir cümle", "... | 76acfc82dfeee5c09502528db0025038 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr') model = AutoModel.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr') | 111cee758132bf7e83ee049601d9c960 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results Evaluation results on test and development sets are given below: | Split | Epoch | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman | |------------|-------|----------------|-----------------|-------... | 3dacf713c467ca9f0ed4a4b19991c3f0 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Training Training scripts [`training_nli_v2.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py) and [`training_stsbenchmark_continue_training.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_trai... | e551f8634484ac84b684420c587cea87 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean... | 56944b8dc52ff3063cea37816b4b1170 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-csa-10-rev3 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: 3.5869 - Wer: 1.0 | 568f89166f5336a1c4a1e9c4973cf770 |
other | ['generated_from_trainer'] | false | distilroberta-offensive This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4526 - Acc: 0.8975 | 0533c65b2cb5dad18ce69ebc34b141ac |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 12345 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 16 - num_epochs: 20 - mixed_precision_... | b865e8b10344d0e2645e6a14faec3bae |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Acc | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2321 | 1.0 | 1030 | 0.2404 | 0.9044 | | 0.2539 | 2.0 | 2060 | 0.2139 | 0.9098 | | 0.1997 | 3.0 | 3090 | 0.2561 | 0.9090 | |... | 524067d51859808f774bb92c46ec9642 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_mnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.7834 - Accuracy: 0.3295 | d80e744c2a3e0ecedc4b5f693266a4b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.8865 | 1.0 | 3068 | 1.7940 | 0.3274 | | 1.8864 | 2.0 | 6136 | 1.7939 | 0.3274 | | 1.8864 | 3.0 | 9204 | 1.7943 ... | 9f562511f4abcc28fa16f783a41664e4 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-sv_s116 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | b3e3c1ae4cff7c4d7d8b44707b28da2f |
mit | ['generated_from_keras_callback'] | false | botModel77k_synthetic_weightDecay 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: nan - Train Accuracy: 0.0009 - Train Perplexity: 388470.5 - Validation Loss: 0.6799 - Validation Accuracy: 0.0007 - ... | 1ff62edaf3d467dc0f500a599c8a7906 |
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': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps... | a9051f90546968f60048daacbaab9e0a |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Perplexity | Validation Loss | Validation Accuracy | Validation Perplexity | Epoch | |:----------:|:--------------:|:----------------:|:---------------:|:-------------------:|:---------------------:|:-----:| | nan | 0.0401 | 530613.75 | 0.67... | 5a749678ff5463dce72b0ce60b397acb |
mit | ['generated_from_trainer'] | false | mBART_slang_to_standard_augmented This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0284 - Bleu: 96.9375 - Gen Len: 41.456 | 1cb1cf1cc77cb29c153c6eeec45053ab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.8419 | 1.0 | 3366 | 0.2810 | 76.5247 | 47.684 | | 1.0628 | 2.0 | 6732 | 0.0525 | 93.5929 | 44.108 | | 0.7883 ... | 53b2a56acecb391ecbd6651b0ea0a352 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_qnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5054 | 27383d00524935fd85aed8ce74cfcb7d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6934 | 1.0 | 819 | 0.6932 | 0.4939 | | 0.6933 | 2.0 | 1638 | 0.6933 | 0.4946 | | 0.6932 | 3.0 | 2457 | 0.6931 | 0.... | caf4e24a5ab5e5a6a9b8969004b25ce7 |
mit | ['generated_from_keras_callback'] | false | sachinsahu/Adult_contemporary_music-clustered This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3264 - Train End Logits Accuracy: 0.9271 - Train Sta... | 9ff637059ff87c031ac2ae9bbddd080e |
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