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 | ['pytorch', 'causal-lm'] | false | HellaSwag (F1) | Model | params |n=0 | n=5 | n=10 | n=50 | |------------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| | [skt/ko-gpt-trinity-1.... | 9e72a704ee82bca344d79d5de8100525 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Limitations and Biases Polyglot-Ko has been trained to optimize next token prediction. Language models such as this are often used for a wide variety of tasks and it is important to be aware of possible unexpected outcomes. For instance, Polyglot-Ko will not always return the most factual or accurate response but the... | c62f97bf047404a44ca9663b0b3d65fd |
apache-2.0 | ['pytorch', 'causal-lm'] | false | BibTeX entry If you find our work useful, please consider citing: ```bibtex @misc{polyglot-ko, title = {{Polyglot-Ko: Open-Source Korean Autoregressive Language Model}}, author = {Ko, Hyunwoong and Yang, Kichang and Ryu, Minho and Choi, Taekyoon and Yang, Seungmu and Hyun, jiwung and Park, Sungho}, url = {https:... | eae37a1e62485627d26af742fcda66d6 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Licensing All our models are licensed under the terms of the Apache License 2.0. ``` Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required... | a41f49c288dbf17e6c7ab4fb4c830bdc |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgement This project was made possible thanks to the computing resources from [Stability.ai](https://stability.ai), and thanks to [TUNiB](https://tunib.ai) for providing a large-scale Korean dataset for this work. | d8cfdc0c136a680a53ab88e52710fcca |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_0010 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6371 - Train Accuracy: 0.0302 - Validation Loss: 0.7409 - Validation Accuracy: 0.0302 - Epoch: 9 | ef2bce25592994306cc1e8dc35d4f5e9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 | | 4.3149 | 0.0131 | 4.0521 | 0.0142 ... | 8f09294f849e68f9e3ae1cbcaf2605cc |
mit | ['spacy', 'token-classification'] | false | To Update [AUTHORS] "[PAPER NAME]". [PAPER DETAILS] [PAPER LINK] --- Indian Legal Named Entity Recognition(NER): Identifying relevant named entities in an Indian legal judgement using legal NER trained on [spacy](https://github.com/explosion/spaCy). | a712bd45fd8a82ed48a224fe5427ba28 |
mit | ['spacy', 'token-classification'] | false | Scores | Type | Score | | --- | --- | | **F1-Score** | **91.076** | | `Precision` | 91.979 | | `Recall` | 90.19 | | Feature | Description | | --- | --- | | **Name** | `en_legal_ner_trf` | | **Version** | `3.2.0` | | **spaCy** | `>=3.2.2,<3.3.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `t... | b1c4fc9e659c6f9047ed3a0073f74f84 |
mit | ['spacy', 'token-classification'] | false | Load Pretrained Model Install the model using pip ```sh pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl ``` Using pretrained NER model ```python | 4dfb4c14530464a951553a5c03a60ac5 |
mit | ['spacy', 'token-classification'] | false | Using spacy.load(). import spacy nlp = spacy.load("en_legal_ner_trf") text = "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984" doc = nlp(text) | 9835ab132c64b47e198a30f0594bab87 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (14 labels for 1 components)</summary> | ENTITY | BELONGS TO | | --- | --- | | `LAWYER` | PREAMBLE | | `COURT` | PREAMBLE, JUDGEMENT | | `JUDGE` | PREAMBLE, JUDGEMENT | | `PETITIONER` | PREAMBLE, JUDGEMENT | | `RESPONDENT` | PREAMBLE, JUDGEMENT | | `CASE_NUMBER` | J... | 1a5b51692e51af6042d78567f89a6198 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3645 - Accuracy: 0.8945 - F1: 0.8872 | d7aa8ea7ea21f9654bc5c4005726b156 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5816 | 0.8015 | 0.7597 | | 0.7707 | 2.0 | 250 | 0.3645 | 0.8945 | 0.8872 | | 8bf93e612831ff7da648db9506bd3eac |
apache-2.0 | ['translation'] | false | opus-mt-tn-en * source languages: tn * target languages: en * OPUS readme: [tn-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tn-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | 82c9deb43dfe239e79a47ef7a3d79dc0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-IMDB_disbert1 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: 2.0461 | f92b075adb3e04d171d401fee8a86540 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 | 7267e77231c17c33593df0f9c4e5a5ed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.5838 | 1.0 | 10000 | 2.3985 | | 2.4367 | 2.0 | 20000 | 2.3194 | | 2.349 | 3.0 | 30000 | 2.2716 | | 2.2764 | 4.0 | 40000 | 2... | 4cac31b95d3e369044fee1e6fd18f428 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4772 - Wer: 0.2821 | 8267891538075369b4abbdcb78e03283 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6949 | 0.87 | 500 | 2.4599 | 0.9999 | | 0.9858 | 1.73 | 1000 | 0.5249 | 0.4674 | | 0.4645 | 2.6 | 1500 | 0.4604 | 0.390... | 2b3217b65416620af99be39cef20f05c |
mit | [] | false | Model for generating custom Magia Record unit designs, inspired by [this reddit post](https://www.reddit.com/r/magiarecord/comments/x63rm9/ive_got_a_fun_little_game_who_is_ready_to_make_a/). Made with GPT-2 retrained with an extremely small dataset (<= 250 entries, contains official characters in the game and the cus... | 509eb60c0457c607957ed165de2de23a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-1b-ka This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/KA/NOIZY_STUDENT_2/ - KA dataset. It achieves the following results on the evaluation set: - Loss: 0.1022 - Wer: 0.1527 - Cer: 0.0221 | 446316e8803252242b067da66ef4b5a9 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sch... | bbeb7f0805a33e643ab701065c7e98af |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.2839 | 6.45 | 400 | 0.2229 | 0.3609 | 0.0557 | | 0.9775 | 12.9 | 800 | 0.1271 | 0.2202 | 0.0317 | | 0.9045 | 19.35 |... | 6b7e9f586e9dac34c96aadc0dfd428cb |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Whisper Small Punjabi 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: 0.5991 - Wer: 39.0469 | 1bcc4470e6f37cf3d6855eb051768a2b |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 - mixed_precision_training: Native AMP | 1bc1a049c5bb8782257923afce6b7af6 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4346 | 5.01 | 50 | 0.3902 | 49.6797 | | 0.0728 | 11.0 | 100 | 0.3811 | 40.7379 | | 0.009 | 16.02 | 150 | 0.4924 | 39.508... | 21cba49213e87c5285fe055aa3944c8f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper large-v2 nan-tw This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 nan-tw dataset. It achieves the following results on the evaluation set: - Loss: 0.7525 - Wer: 42.5930 - Cer: 23.2970 | d7922514a0621ef388cfe2ea775faba9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.4781 | 1.04 | 1000 | 0.7256 | 52.4690 | 28.7583 | | 0.1881 | 2.08 | 2000 | 0.7346 | 50.2067 | 26.6389 | | 0.0429 |... | cd581687ecf78c2d6ce2432677ee2e00 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings + 4-gram This model is identical to [Facebook's wav2vec2-conformer-rope-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rope-large-960h-ft), but is augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams]... | 0f7faffb1ff06c765ccace38f7e990f0 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate **patrickvonplaten/wav2vec2-conformer-rope-large-960h-ft-4-gram** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torch from jiwer import wer model_id = "p... | da6ba09d6ea0f62d5356376bebeb3bab |
mit | ['roberta-base', 'roberta-base-epoch_42'] | false | RoBERTa, Intermediate Checkpoint - Epoch 42 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | d5606f3f9e75a9b9ef30483a2e913c15 |
mit | [] | false | model by MvsSrs This your the Stable Diffusion model fine-tuned the Mau cat concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks cat** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.... | 1f52436630f1ea89d00ebb87edd1a0dc |
apache-2.0 | ['t5-small', 'text2text-generation', 'dialog state tracking', 'conversational system', 'task-oriented dialog'] | false | t5-small-dst-multiwoz21 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21). Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. | ad1186c537a9bae1786682f152f807a9 |
apache-2.0 | ['audio-to-audio', 'audio-source-separation', 'Source Separation', 'Speech Separation', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | SepFormer trained on WHAMR! (16k sampling frequency) This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) dataset with 16k sampling frequency, w... | 8e2c64add56135b67391faa7de437ef3 |
apache-2.0 | ['audio-to-audio', 'audio-source-separation', 'Source Separation', 'Speech Separation', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | Perform source separation on your own audio file ```python from speechbrain.pretrained import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-whamr16k", savedir='pretrained_models/sepformer-whamr16k') | 3508a08bcf246c5c4e4fef2d124bc2cb |
apache-2.0 | ['audio-to-audio', 'audio-source-separation', 'Source Separation', 'Speech Separation', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-whamr16k/test_mixture16k.wav') torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 16000) torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 16000) ``` The system expects input recording... | 3b2a8bcb54b6349c3343489d609e6a65 |
apache-2.0 | ['audio-to-audio', 'audio-source-separation', 'Source Separation', 'Speech Separation', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (fc2eabb7). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/WHAM... | 59f4101cb9a19daad53f3b4d13b8e30e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Assamese- Drishti Sharma 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: 0.4330 - Wer: 20.9168 | 9f8f04e1bf34581d41f73ee16678c9ef |
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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 - mixed_precisio... | b66c9bb6464a76de668ed88208d4ce94 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0 | 30.86 | 5000 | 0.4330 | 20.9168 | | 6ffc1f9d6898e115fea6e204f1cd8411 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-NL2ModelioMQ-FR This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge2 Precision: 0.9788 - Rouge2 Recall: 0.6055 - Rouge2 Fmeasure: 0.7295 | 94b87ac99c7ae36f5a0d1c51a072ec77 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 3 - mixed_precision_training: Native AMP | 4e1ed7fc58cee50ceb00064bc4fc2730 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.0087 | 1.0 | 4449 | 0.0002 | 0.9787 | 0.6054 | 0.72... | 45f85058c54eadacfa1159056050e15f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9797 | 1.0 | 69 | 5.1736 | | 4.8746 | 2.0 | 138 | 5.1852 | | 4.7168 | 3.0 | 207 | 5.2026 | | ffaf7c17e14176b2d0a02926a07f55c5 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_wav2vec2_s379 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | 1738807a0494f698aaa237155a439cc1 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-lega... | 9b374e3cdf13626037d5c8132236b554 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', '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 = ["Isto é um exemplo", "Isto ... | 3575764d0a646656609f66a5edcfc4dd |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0') | cb4fd2709d2f41572607d8cb13163906 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-8-50-0.01 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9219 - Matthews Correlation: 0.0 | c29a55a1497c62deed141d3320eb2239 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - 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: 50 | 17f4203e0c809faa0676fe7e1789facb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | No log | 1.0 | 400 | 0.9219 | 0.0 | | 1.2047 | 2.0 | 800 | 1.8168 | 0.0 | |... | 33f8e4cf1daf4dce0d322a7535062033 |
mit | ['vision', 'image-segmentation'] | false | OneFormer OneFormer model trained on the COCO dataset (large-sized version, Dinat backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer).... | 6b18d13dba08cb15570f69b00c8754ef |
mit | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.jpeg" image = Image.open(requests.get(url, stream=True).raw) | cbae1bb73b0310402d7841f4598d45d2 |
mit | ['vision', 'image-segmentation'] | false | Loading a single model for all three tasks processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_coco_dinat_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_dinat_large") | 8b02a7b19312a61ce86b565f437a05a3 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-8_nortepeninsular-2_s507 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this ... | 77921c1ee5e08092ec68224bb336ceac |
gpl-3.0 | ['pytorch', 'token-classification', 'albert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ws')... | 35eae35dfc52e8a05c70dd5c2203fc83 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | sci-fi-landscape- Dreambooth model trained by Joeythemonster with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/... | 0c43248ace3f1b3acb13f86fa52dd82a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.3618 | 1.0 | 18539 | 0.3174 | | 0.2826 | 2.0 | 37078 | 0.2560 | | 0.2633 | 3.0 | 55617 | 0.2315 | | a9c9441349ae03d0fa24cb454906e9d5 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_qqp_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5069 - Accuracy: 0.7558 - F1: 0.6391 - Combined Score: 0.6975 | dc0eceae58f1a3abb3ce49152778d652 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.6505 | 1.0 | 2843 | 0.6497 | 0.6321 | 0.0012 | 0.3166 | | 0.6473 | 2.0 | 5686 | ... | 329b49027332625587ddf69724991faa |
apache-2.0 | ['generated_from_trainer'] | false | model_broadclass_onSet4 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1340 - 0 Precision: 1.0 - 0 Recall: 0.9615 - 0 F1-score: 0.9804 - 0 Support: 2... | bddaae848be24ad12bb6fc6fffa4534c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall ... | b210746037d98120bb4307e0e51a0bf9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-tamil-commonvoice This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6145 - Wer: 0.8512 | ebe72b61805ff0ed94b5720b7dba8c20 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | d629bebd72c5f3d7a7bba4de99d21cd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.0478 | 1.05 | 100 | 3.3867 | 1.0 | | 3.2522 | 2.11 | 200 | 3.2770 | 1.0 | | 3.1689 | 3.16 | 300 | 3.1135 | 1.0039 | |... | 28056e65eb5b73560d23fbeeb2adc090 |
mit | ['vision', 'image-segmentation'] | false | UperNet, ConvNeXt tiny-sized backbone UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al. Combining UperNet with a ConvNeXt backbone was introduced in the... | e6b539f9ed0ea5ea6e57a81aac0c5b92 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_sst2 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5408 - Accuracy: 0.7901 | 3c80ab801adbe58b508ff0bc984fd56d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3509 | 1.0 | 8748 | 0.5408 | 0.7901 | | 0.2706 | 2.0 | 17496 | 0.5891 | 0.7718 | | 0.2265 | 3.0 | 26244 | 0.6881 ... | f8dd6426545aa5dae0a2887c2657d6cf |
apache-2.0 | ['tapas'] | false | TAPAS base model fine-tuned on Sequential Question Answering (SQA) This model has 4 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was ... | 33ae12ee6334e498c375ce2f5829bc8a |
mit | ['generated_from_trainer'] | false | This repository is the submission for the final project for BF510 [Institutional Racism in Health and Science](http://irhs.bu.edu/) for Shariq Madha. To see Jupyter detailing how this model was produced, as well as the motivation behind it, go [here](https://github.com/ssmadha/BF510-final-project/). To try this out ... | eb57115ad7e1cc763d3eb7802e7ad766 |
mit | ['generated_from_trainer'] | false | gpt2-finetuned-scientific-articles This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on scientific articles about algorithmic bias. It achieves the following results on the evaluation set: - Loss: 2.3793 | a4345b7b4890038d7b65efa8ae899061 |
mit | ['generated_from_trainer'] | false | Model description This model is a casual language modeling GPT2 fine-tuned on scientific articles about algorithmic bias, in an attempt to showcase an example about correcting for algorithmic bias. | 1372621a1b2326557c97c57fb88193a0 |
mit | ['generated_from_trainer'] | false | Training and evaluation data This model is trained on fully freely accessible articles obtained from a PubMed Central search on algorithmic bias. The pmc_result_algorithmicbias.txt file contains the list of PMC's used. Due to technical and time limitations, only fine-tuned on the introduction sections, but training o... | 85adb718a60296ff44eadc87c68bcc95 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5293 | 1.0 | 1071 | 2.3892 | | 2.4821 | 2.0 | 2142 | 2.3793 | | 620ebb2b413da29afb960cd4832274fd |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Core ML Converted Model: - This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br> - Provide the... | c7f4afd474a195ce47709916dc91d46c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5279 - Accuracy: 0.1549 | 8ac83fbe7ee747a197d4d7fdb6fc41e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3422 | 1.0 | 218 | 0.5279 | 0.1549 | | 0.305 | 2.0 | 436 | 0.5961 | 0.1268 | | 0.291 | 3.0 | 654 | 0.6364 | 0.... | d2aa9f79e6fa44f9f889716b01b5d745 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-sv_s884 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that ... | 8da157331f9f4edb32e96e4cd08fffb4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | No log | 1.0 | 46 | 1.4873 | 29.6133 | 26.9081 | | f495c31f22e3e382e744371b59c807f3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_5_binary 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: 0.5093 - F1: 0.7801 | 72b69ab81799d584d880f3af122b80cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4760 | 0.7315 | | 0.3992 | 2.0 | 576 | 0.4428 | 0.7785 | | 0.3992 | 3.0 | 864 | 0.5093 | 0.7801 | |... | 3573bc4ea72f46b1600ced42d0d63cdd |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_r-wav2vec2_s911 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | ffbebf2e6a9047cf784e2e8f72acefa6 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-0_england-10_s729 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 6ac616d62163ec70d1fcef3f3615931a |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_250v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3186 - Precision: 0.5548 - Recall: 0.4939 - F1: 0.5226 - Accura... | e00c5c33a26dec779e6cc2b2e99c2eba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 95 | 0.4132 | 0.3646 | 0.2008 | 0.2590 | 0.8504 | | No log | 2.0 |... | 9fefecc8cfbcc4533da30b639098b879 |
mit | [] | false | arcane-cyberpunk-random-new on Stable Diffusion This is the `<arcane-cyberpunk-random>` 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... | 7b814ea2512a6d7d33cc1f6adb39b37e |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov6', 'pypi'] | false | Yolov6 Inference ```python from yolov6 import YOLOV6 model = YOLOV6(weights='kadirnar/yolov6n-v2.0', device='cuda:0',hf_model=True) model.classes = None model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(source='data/images',yaml='data/coco.yaml', img_size=640) ``` | b21b5b91bc89326672f065bde8b9f5d5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 2.6623 | 9420a4cd8639ed56219bebc3818dd2ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3993 | 1.0 | 2051 | 1.8058 | | 1.0467 | 2.0 | 4102 | 1.9564 | | 0.8304 | 3.0 | 6153 | 2.6623 | | 946782676ac349c9fe781c6e1c5a8551 |
apache-2.0 | ['translation'] | false | Model Details - **Developed by:** İlhami SEL - **Model type:** Turkish-English Machine Translation -- Transformer Based(6 Layer) - **Language:** Turkish - English - **Resources for more information:** Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Comp... | df12c6e1f1ad5bb9fc9d64c47d840281 |
apache-2.0 | [] | false | Swin Transformer model HPU configuration This model only contains the `GaudiConfig` file for running the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to spe... | 33ce2f88a85fcbeae5bb8fe18dc24819 |
apache-2.0 | [] | false | Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification exampl... | a02e9bdc3ac66459f1675cd7295daac5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncasedreference-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0408 - Matthews Correlation: 0.2397 | b1e3b56e921bfa14f99c44b58a1d4dfa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 23 | 1.0818 | 0.0 | | No log | 2.0 | 46 | 1.0870 | 0.0 | | No ... | c93a573e2385ee87babb97ddeab88f2e |
apache-2.0 | ['translation'] | false | epo-swe * source group: Esperanto * target group: Swedish * OPUS readme: [epo-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-swe/README.md) * model: transformer-align * source language(s): epo * target language(s): swe * model: transformer-align * pre-processing: normalization + Se... | 3487a34db530c71f783d15db79f5f41d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: epo-swe - source_languages: epo - target_languages: swe - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-swe/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'sv'] - src_constituents: {'epo'} - tgt_const... | 120a50549cf831c36676d6a92ee78374 |
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: 0.5608 - Matthews Correlation: 0.5062 | 3a5fa3e89268b90db65535ba3e095b63 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 134 | 0.4851 | 0.4301 | | No log | 2.0 | 268 | 0.4619 | 0.4891 | | No ... | 1840d51e2a7375320a581007c42e134f |
mit | [] | false | Frank Frazetta on Stable Diffusion This is the `frank franzetta` 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 c... | 8e0a7e93f81cdd5aaf345e16dbd8a156 |
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