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 | ['image-classification'] | false | resnet34 Implementation of ResNet proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) ``` python ResNet.resnet18() ResNet.resnet26() ResNet.resnet34() ResNet.resnet50() ResNet.resnet101() ResNet.resnet152() ResNet.resnet200() Variants (d) proposed in `Bag of Tricks f... | 3f716353386f6de51e5c809c6287af85 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-target-glue-sst2 This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4217 - Accuracy: 0.8716 | fcfcb9d33beb779a7bab97c7fb9523c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3912 | 0.24 | 500 | 0.3462 | 0.8406 | | 0.3049 | 0.48 | 1000 | 0.3246 | 0.8544 | | 0.2574 | 0.71 | 1500 | 0.3264 | 0.... | 257d44592b7b6f33e307431fdab760a5 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-sede-txt2sql This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the sede dataset. It achieves the following results on the evaluation set: - Loss: 1.1577 - Bleu Score: 0.5923 - Parsable Queries Accuracy: 0.0 - Partial Match F1: 0.0 - Partial Match F1 No ... | 5fc73e7c936a3e828813b699ccd9b426 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu Score | Parsable Queries Accuracy | Partial Match F1 | Partial Match F1 No Values | Partial Match Em | Partial Match No Values Em | |:-------------:|:-----:|:----:|:---------------:|:----------:|:-------------------------:|:----------------:|:--... | 6a2ecc36c3abaa44da69ae86ed7c3570 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | hegde- Dreambooth model trained by broidkhegde with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-di... | 0e9e5cff08bb9be9bd0a5f0437da2f4c |
apache-2.0 | ['automatic-speech-recognition', '../AI_Light_Dance.py', 'generated_from_trainer'] | false | ai-light-dance_singing_ft_wav2vec2-large-lv60-v2 This model is a fine-tuned version of [gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60](https://huggingface.co/gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60) on the ../AI_LIGHT_DANCE.PY - ONSET-SINGING dataset. It achieves the following results on the eva... | 3f7b6a7df2b638581e6d2eed2c6eb281 |
apache-2.0 | ['automatic-speech-recognition', '../AI_Light_Dance.py', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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 - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_... | b155dc7151ac0e67b9b6ccfc79c89f97 |
apache-2.0 | ['automatic-speech-recognition', '../AI_Light_Dance.py', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2775 | 1.0 | 1106 | 0.4372 | 0.2117 | | 0.2154 | 2.0 | 2212 | 0.4474 | 0.2044 | | 0.2023 | 3.0 | 3318 | 0.4372 | 0.192... | 7757d117b4820caaff8e618dede7b22a |
mit | ['generated_from_trainer'] | false | TweetEval_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.2770 - Accuracy: 0.9467 | fddbe5a97aadf0efbed7cf7c0ef5352d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5967 | 0.04 | 50 | 0.4851 | 0.7333 | | 0.4085 | 0.08 | 100 | 0.2177 | 0.9333 | | 0.3449 | 0.12 | 150 | 0.2164 | 0.... | 9239bc11a0eb84a4954c483287bc7a7e |
mit | ['ja', 'japanese', 'tokenizer'] | false | Japanese Dummy Tokenizer Repository containing a dummy Japanese Tokenizer trained on ```snow_simplified_japanese_corpus``` dataset. The tokenizer has been trained using Hugging Face datasets in a streaming manner. | 044fdaeb0d610750d212726ea02f3141 |
apache-2.0 | ['bert'] | false | Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples. | 86f2028fc1a79af892c509c665f2d063 |
apache-2.0 | ['bert'] | false | Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese', use_fast=False) model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese') text = '生活的真谛是[MA... | 76487b756c163847caa1918b030747ad |
apache-2.0 | ['bert'] | false | Finetune We present the dev results on some tasks. | Model | OCNLI | CMNLI | | ---------------------------------- | ----- | ------ | | RoBERTa-base | 0.743 | 0.7973 | | **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807 | | cc6555d1055cbb3adcfe3c1b8dc8534c |
apache-2.0 | ['bert'] | false | Citation If you find the resource is useful, please cite the following website in your paper. ``` @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2022}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ``` | 7be58359883a05a37f7dfe76f8d440bf |
apache-2.0 | ['translation'] | false | opus-mt-en-sm * source languages: en * target languages: sm * OPUS readme: [en-sm](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sm/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 74a454ee13f66bdd21b61cfac280eed8 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.6956 - Accuracy: 0.7262 - Macro-f1: 0.7053 - Weighted-macro-f1: 0.7... | 234c33eab00749001e339a9cf455518f |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | 5ddb4eba057ab11cbd5658fa932c5ce2 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-nonstrict-epoch-4' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSeque... | ff54dcc0002b33307953a34233615710 |
apache-2.0 | ['translation'] | false | opus-mt-en-bem * source languages: en * target languages: bem * OPUS readme: [en-bem](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-bem/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | c49e67da386311b8d4b3cc1c304eee10 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Spanish Added custom language model to https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice). Wh... | 546bb82be4cb221c44eed7d1446e89ed |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows... Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library: ```python from asrecognition import ASREngine asr = ASREngine("es", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-spanish") audio_paths = ["/path/to... | edeb26d8cccd7265803a4cdf037bfe30 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021wav2vec2-large-xlsr-53-spanish, title={XLSR Wav2Vec2 Spanish by Jonatas Grosman}, author={Grosman, Jonatas}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav... | 73af57646337521fa4f5ac898696a94b |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Introduction mMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection. In the version v1, the datasets were translated us... | 7a47c32fba06df024cf7357e994e5643 |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Usage ```python from transformers import AutoTokenizer, AutoModel model_name = 'unicamp-dl/mMiniLM-L6-v2-mmarco-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` | 7a9a248ad4354ed93ab8958ecbc725d2 |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Citation If you use mMiniLM-L6-v2-mmarco-v1, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo... | 540de832d47d4a369bd0326c7b31e726 |
apache-2.0 | ['generated_from_trainer'] | false | nli-distilroberta-base-finetuned-cola This model is a fine-tuned version of [cross-encoder/nli-distilroberta-base](https://huggingface.co/cross-encoder/nli-distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8280 - Matthews Correlation: 0.4957 | 5146f0cc4ca552b416e2a26910bf502f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5646 | 1.0 | 535 | 0.6462 | 0.3422 | | 0.4267 | 2.0 | 1070 | 0.5672 | 0.4422 | | 0.3... | 0c09a061569fdb669e0b041cf170868a |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2 This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2364 - Accuracy: 0.926... | 64e17885aa237d2fdf456346fa0cc0d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4176 | 1.0 | 527 | 0.2978 | 0.9197 | | 0.1807 | 2.0 | 1054 | 0.2951 | 0.9174 | | 0.1163 | 3.0 | 1581 | 0.2749 ... | 1236efa40982fdd48d3c185bc48578eb |
apache-2.0 | ['generated_from_trainer'] | false | english-filipino-wav2vec2-l-xls-r-test-02 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4561 - Wer: 0.2632 | 9cc07e9cd537709d4806909aaacf0aa3 |
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 - lr_sche... | 87b52d5a6adcf2fbe3908401fe10996f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1707 | 2.09 | 400 | 0.8006 | 0.8224 | | 0.4801 | 4.19 | 800 | 0.3363 | 0.4329 | | 0.2541 | 6.28 | 1200 | 0.3365 | 0.3676 | |... | ebf7c521f91d4dd7284120eb842e0376 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Nepali This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ne-NP dataset. It achieves the following results on the evaluation set: - Loss: 1.5835 - Wer: 231.7073 | 6398199e008b7a8f2be8e4f4ab2065b4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0 | 999.0 | 1000 | 1.5835 | 231.7073 | | 0.0 | 1999.0 | 2000 | 1.9067 | 231.7073 | | 0.0 | 2999.0 | 3000 | 2.1258 ... | 22cda70c29f6d10ecfe77afb0975a277 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-target-glue-qnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-stsb](https://huggingface.co/muhtasham/small-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3477 - Accuracy: 0.8547 | 8d8c59531175a13a51e4a9123b73df81 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4913 | 0.15 | 500 | 0.3941 | 0.8287 | | 0.4468 | 0.31 | 1000 | 0.3872 | 0.8303 | | 0.4246 | 0.46 | 1500 | 0.3619 | 0.... | 38fe0f388ced6d29ba6a0c507ea5b968 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Thai - Parinthapat Pengpun This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set: - eval_loss: 0.1875 - eval_wer: 17.5807 - eval_cer: 8.9942 - ... | 5f2e0f5a4fcd6df137707a80361ff3b7 |
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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 15000 - mixed_preci... | 81715c0e99dbf4eedbdad237a335a285 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-ca-finetuned-mnli This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4137 - Accuracy: 0.8778 | a3a87fd5236b9af8b951898ea9f571ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3699 | 1.0 | 1255 | 0.3712 | 0.8669 | | 0.3082 | 2.0 | 2510 | 0.3401 | 0.8766 | | 0.2375 | 3.0 | 3765 | 0.4137 | 0.... | 888be37fcc78c3856b0f8d1cf072842f |
mit | [] | false | model by alxdfy This your the Stable Diffusion model fine-tuned the noggles_glasses_1200 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of a person wearing sks glasses** You can also train your own concepts and upload them to the library by using [this... | fee52744aeaed226d12b2a80c1c6e21c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hy', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | 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/HY/NOIZY_STUDENT_4/ - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.1693 - Wer: 0.2373 - Cer: 0.0429 | 5de493b6ec2414525e8d3206935c54b2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hy', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 842 - 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_sc... | 045cf0b351b5c7011e20ca4a16f7d917 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hy', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.255 | 7.24 | 500 | 0.2978 | 0.4294 | 0.0758 | | 1.0058 | 14.49 | 1000 | 0.1883 | 0.2838 | 0.0483 | | 0.9371 | 21.73 |... | 046b22af9f1df42e3644fc7db1db91eb |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-imdb This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5540 | 355bd13645f2cc96cab8b18465af3f88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.2358 | 0.16 | 500 | 3.8225 | | 4.1206 | 0.32 | 1000 | 3.7793 | | 4.0857 | 0.48 | 1500 | 3.7520 | | 4.0699 | 0.64 | 2000 | 3.7277 ... | 7362d50fd4c33f01c2e9b0ad8ffbf6ce |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-squad-qg-no-paragraph` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/... | 68d5ece1298648cf3ca1a7dad0737e0a |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-squad-q... | 1d0ea686c5a9c76e02172a887dcb88dc |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 286778d96b7d154b61e90b258beec858 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 128 - max_length_output: 32 - epoch: 3 - b... | 524510572cd39d1a8045289745fdef23 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_unispeech-sat_s459 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition on English 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 you... | 53ef484e01ce2eb4920b362a00b74807 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.9728 | 0.19 | 500 | 8.6854 | | 8.7387 | 0.39 | 1000 | 8.7712 | | 8.6739 | 0.58 | 1500 | 8.7362 | | 8.786 | 0.77 | 2000 | 8.7816 ... | 0295613f741e216ea1c1f2885182583f |
apache-2.0 | ['generated_from_keras_callback'] | false | tomthekkan/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1138 - Validation Loss: 3.3816 - Epoch: 7 | 5076e9b10075d1c8fb9dbd65bd305485 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.9822 | 4.2802 | 0 | | 5.9654 | 3.7811 | 1 | | 5.2343 | 3.6557 | 2 | | 4.8190 | 3.5433 | 3 | | 4.5149 | 3.4695 | 4 | | 4.3105 |... | f1ac31996c8314c1e6bc8f599aa66289 |
creativeml-openrail-m | ['text-to-image'] | false | Open Potion Bottle v2 Dreambooth model trained by [piEsposito](https://twitter.com/piesposi_to) with open weights, configs and prompts (as it should be) - Concept: `potionbottle` You can run this concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/bl... | 6d7bc6b29fc269169a3c0c98282d63d5 |
creativeml-openrail-m | ['text-to-image'] | false | Usage examples with `potionbottle` - Prompt: fantasy dragon inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbot... | aeac67eb67c80bfa44765094641712f6 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-sst2-target-glue-cola This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5598 - Matthews Correlation: 0.3885 | 48d3b16e50083d934c9ec524c56f6aa5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5397 | 1.87 | 500 | 0.6364 | 0.2396 | | 0.3514 | 3.73 | 1000 | 0.7722 | 0.3110 | | 0.2... | 6151dec8e706a477d5ee7d6b37c54ec6 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-vi-190322 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on the mt_eng_vietnamese dataset. It achieves the following results on the evaluation set: - Loss: 1.2652 - Bleu: 37.2837 | ff2d198eef69d481b7820691fbcbd679 |
apache-2.0 | [] | false | Model description **CAMeLBERT-CA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model. For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset. Our fine... | 7b311b230fd4442ab233e4d9db0ae678 |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf') >>> text = 'شلونك ؟ شخبارك ؟' >>> pos(text) [{'entity': 'noun', 'score': 0.99572617, 'index': 1, 'word': 'شلون', ... | 15e515217a0f8d8723a859105f9c4b6b |
apache-2.0 | [] | false | ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.9999661, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.99286526, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.9983397, 'index': 5, 'word': ' | ff944d6f8bc9300f3446ee85aeafa74e |
apache-2.0 | [] | false | ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999668, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. | 7eb509f0bf62ccdf749972b17b2ede94 |
mit | ['generated_from_trainer'] | false | amh_xlmr This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1295 | 66fadbc319d937a305fa5131bd627697 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_imtiaz This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1956 - eval_wer: 0.2202 - eval_runtime: 574.912 - eval_samples_per_second: 8.697 - eval... | a66dae6dcca5b441070eb34dda38887d |
apache-2.0 | ['generated_from_trainer', 'text-generation', 'opt', 'non-commercial', 'dialogue', 'chatbot'] | false | pszemraj/opt-peter-1.3B This model is a fine-tuned version of [pszemraj/opt-peter-1.3B-1E](https://huggingface.co/pszemraj/opt-peter-1.3B-1E) on 80k Whatsapp/iMessages (mine). It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_): - eval_los... | 09731b72dc5f5a82436cdb8ffbe3eea3 |
apache-2.0 | ['generated_from_trainer', 'text-generation', 'opt', 'non-commercial', 'dialogue', 'chatbot'] | false | Intended uses & limitations - OPT has a license that does not allow for commercial use, see original for details - **any statements or claims made by this model do not reflect actual claims/statements by me** | 9090f042ce852fe25dcacd479268ff14 |
apache-2.0 | ['generated_from_trainer', 'text-generation', 'opt', 'non-commercial', 'dialogue', 'chatbot'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_sc... | 541aeef70eb367857bc275a68051fcc4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/sentence-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from... | c4bd6adb311e8de2453e1e383506352f |
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 = ["This is an... | 310e4a1ac5f326f987515a55781d509b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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/sentence-t5-base)
| eb9958cd1606e2fecf20c67a79d77eb7 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1433 - Accuracy: 0.3333 | f9590bf513eed63f73c952431128dc94 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.67 | 1 | 1.5398 | 0.1667 | | No log | 1.67 | 2 | 1.1394 | 0.5556 | | No log | 2.67 | 3 | 1.1433 | 0.... | 14bc42bafedb0c1799a1b6cfe46d3f02 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-subjqa-vanilla-books-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.co... | a3cf10c37365f8ce97299f4796788f5e |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi4... | 74fecb9ed9edb4a67a09fbb9fc1d95e2 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-subjqa-... | d726d399a16a04993f4fb5da68596024 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-subjqa-vanilla-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset ... | aaf64a5676a4b4ab5f9d49ec1c9abc72 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 1 -... | 701f5625c60807a280b4f38003f4ea36 |
creativeml-openrail-m | ['text-to-image'] | false | model by kingery This your the Stable Diffusion model fine-tuned the hyc_01_sdv1-5_2e_6_1500_man_ddim concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of yangguangkechuang man** You can also train your own concepts and upload them to the library by using... | 81d1197697be5e1acc6a25da9c66b657 |
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 8ee35df7260008e9a8a20d9a9b64773a02f706ef pip install -e . cd egs2/tedlium2/asr1 ./run.sh --skip_data_prep false --skip_train tr... | 330af2749e6bfa4c1ad15f090c78595b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sat Dec 17 04:27:41 CST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.12.1` - Git hash: `26f432bc859e5e40cac1a86042d498ba7baffbb0` - Commit date: `Fri Dec 9 02:16:01 2022 +0000` | 225b98f704784bbcb27c20ca0aaf5ee9 |
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|466|14671|93.5|4.1|2.5|1.0|7.5|70.0| |decode_asr_asr_model_valid.acc.ave/test|1155|27500|93.4|4.0|2.6|1.0|7.6|64.2| | 6e4ff7fe0c27f5de106d75e6beecd2c7 |
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|466|78259|97.0|0.8|2.1|0.8|3.8|70.0| |decode_asr_asr_model_valid.acc.ave/test|1155|145066|97.0|0.9|2.2|0.9|4.0|64.2| | 703e8f12bb23d182a84f55ee69259731 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev|466|28296|95.0|2.8|2.2|0.8|5.9|70.0| |decode_asr_asr_model_valid.acc.ave/test|1155|52113|95.1|2.5|2.4|0.9|5.8|64.2| | 5e7f71621e43085f3e09057c162c7ce1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e15.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e15_raw_en_bpe500_sp ngpu: 1 seed: 2022 num_workers: 6 num_att_plot: 3 dist_backend: nccl dist_init_met... | f3af97f757f0dfe9d765b342472b55fc |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 80k (uncased) Seed 1 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/googl... | bb198938bb29e0633a9139ea9973be00 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-80k') model = BertModel.from_pretrained("multiberts-seed-1-80k") text = "Replace me by any text you'd like." ... | c3c5e98c7e59ae15b4ee12d136676073 |
mit | ['fill-mask', 'alloys', 'metallurgy'] | false | Abstract: Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously. This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property. Efficiency of tasks such as *Alloy Property Predicti... | 8a88c225c0dc98765e251b79bc963b35 |
mit | ['fill-mask', 'alloys', 'metallurgy'] | false | Footnote: Work done via [MLDMM Lab](https://sites.google.com/view/mldmm-lab/home)  | 3c5fd6ee7a26930339e3ae02ece22b75 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-arabic-gpu-colab-similar-to-german-bigger-warm-up 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: 0.6370 - Wer: 0.4146 | bfe423418e7093dbabd2d8d3aad18352 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 7f99cacb65c656e87b0497694323eec0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.4958 | 2.83 | 400 | 3.4822 | 1.0 | | 3.2281 | 5.67 | 800 | 2.9404 | 1.0 | | 2.942 | 8.51 | 1200 | 2.8690 | 1.0 | |... | 2d5730de9a654dd01bea606f3686e4bd |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/distilbert-base-nli-stsb-quora-ranking 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. | a5f2e996e3da0f475441da86f88e3701 |
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 = ["This is an example sen... | cbb6f1ec24a311bcb0c5499baaccb1e4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-quora-ranking') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-quora-ranking') | 938f5e4e604916d0a53b916dc13e5ee1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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/distilbert-base-nli-stsb-quora-ranking) | 3e98b8c5a474f7cc3e20715663a7dde8 |
mit | [] | false | Retro-Girl on Stable Diffusion This is the `<retro-girl>` 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... | 37392625606e6acb5f812a49f7bb78c4 |
mit | ['stable-diffusion', 'text-to-image'] | false | Usage To use this model you have to download the .ckpt file as well as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder To use it in a prompt: ```"Rebecca girl"``` for highest strength or just "Rebecca" To increase the strength put "Rebecca girl" in () brackets To decrease the strength put "... | c2efde139b9ccdded48dbf147fce7c27 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.