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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cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-subjqa-vanilla-restaurants-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/asahi41... | e842af0915fa7ead39da0925d46924ea |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (restaurants) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question... | 3415bb99dff1445c42061d1653fae438 |
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/t5-small-subjqa-v... | 40886ca6fc99b088bea7f7d0b0360cd2 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-restaurants-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | | Score | Type | Dataset ... | 2ef631039a4d2007b2998263efb00bc7 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: restaurants - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 2 - bat... | 386c7b67178b2696d147e30e21ecda61 |
apache-2.0 | ['generated_from_trainer'] | false | smalldata-microsoft-deberta-base-eng-only-sentiment-single-finetuned-memes This model is a fine-tuned version of [jayantapaul888/twitter-data-microsoft-deberta-base-mnli-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-microsoft-deberta-base-mnli-sentiment-finetuned-memes) on the None dat... | e11e174c256f0f72f331bcf760d8b40b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 378 | 0.3307 | 0.8407 | 0.8682 | 0.8549 | 0.8541 | | 0.353 | 2.0 |... | fe355796c5899bd67a337a3eb34013a3 |
apache-2.0 | ['webgpt', 'regression', 'reward-model'] | false | Reward Model pretrained on openai/webgpt_comparison and humanfeedback summary. Unlike the other electra-large model this model is trained using rank loss with one more datasets. On validation dataset the result is much more stable than usual. You can refer to this [wandb](https://wandb.ai/theblackcat102/reward-model... | 64e8e5c0848b675dfe03600866c1eebb |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_accent_surpeninsular-8_nortepeninsular-2_s149 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_... | 37acb5933df3cd1b56b1ab718089956a |
apache-2.0 | ['generated_from_trainer'] | false | Goodreads_Books_Reviews_BERT_3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2441 | 60f7ecfc5e423ba921b08905c794a7aa |
apache-2.0 | ['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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 | 17a21f5b4d3081609c37eaa625621628 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4298 | 1.0 | 675 | 1.0408 | | 1.0215 | 2.0 | 1350 | 0.9826 | | 0.6131 | 3.0 | 2025 | 1.0458 | | 0.3825 | 4.0 | 2700 | 1.2441 ... | ceb3d60f8de50399f4bd96e6a939ae52 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 0fae8113d99d092e7cbe4bcc48f9361e7012cff2 pip install -e . cd egs2/slurp_mixture/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/Yen-Ju_Lu_spatilaizedslurp_asr_train_asr_conformer_transformer_valid.acc.best ``` <!-- Generated by... | 33d8f7e7983b69287f3260a07ddf6415 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Tue Mar 29 04:17:37 UTC 2022` - python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.9.0` - Git hash: `0fae8113d99d092e7cbe4bcc48f9361e7012cff2` - Commit date: `Thu Mar 24 07:54:19 2022 +0000` | 8d78cf147432e0c827ba6d6e90c08ff8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.best/devel|8690|109017|63.9|21.1|15.0|2.0|38.1|75.4| |inference_asr_model_valid.acc.best/test|6099|77315|69.0|17.4|13.5|1.8|32.8|68.9| |inference_asr_model_valid.acc.best/test_ineube|6099|77315|77.8|1... | eedef1f62558fe5d0f482d0855fa3e71 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.best/devel|8690|513265|79.6|9.3|11.0|3.6|23.9|75.4| |inference_asr_model_valid.acc.best/test|6099|362039|82.6|7.6|9.8|3.0|20.4|68.9| |inference_asr_model_valid.acc.best/test_ineube|6099|362039|87.5|4.... | aead8f01f4ef2c938e9d793737e15c36 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_raw_en_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 3 dist_rank: 0... | 0f2e8795ea45cb2ef11d0fdb1ecd84f0 |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | mt5-small-finetuned-multilingual-xlsum-new This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7673 - Rouge1: 9.1368 - Rouge2: 2.3893 - Rougel: 7.6599 - Rougelsum: 7.6873 | ae4d099f4e111810069033596c35dc1e |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | 3d2d83b456b25b0960338f55d5a011e5 |
apache-2.0 | ['multilingual model', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.7827 | 1.0 | 1687 | 2.8911 | 8.1314 | 1.9569 | 6.7927 | 6.8179 | | 3.6518 | 2.0 | 3374 ... | d98a31574db497ece8df42f80eb8d62b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-targin-final This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6307 - Accuracy: 0.6882 - Precision: 0.6443 - Recall: 0.6384 - F1: 0.640... | 9038c2da59ba74e521f493e74ad8b494 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5882 | 0.6854 | 0.6355 | 0.6182 | 0.6226 | | 0.5995 | 2.0 |... | 5a5fd238eda7455d96dd8ea1b3e73172 |
afl-3.0 | [] | false | This model is used to detect **abusive speech** in **Code-Mixed Malayalam**. It is finetuned on MuRIL model using Code-Mixed Malayalam abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive) LABEL_0 :-> Normal LABE... | 4c6f173118ca46be2ade338c6ea2a947 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Bg - Yonchevisky_tes2t 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: 0.7377 - Wer: 61.8352 | 66c04969ece468e04db0172395c63208 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 57891123f5053a3677cfc1b170007916 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.8067 | 0.37 | 100 | 1.6916 | 137.6897 | | 0.9737 | 0.73 | 200 | 1.1197 | 78.3571 | | 0.7747 | 1.1 | 300 | 0.9763 | 73... | 64bdc48dd61bed035dfabe28b5306604 |
apache-2.0 | ['generated_from_keras_callback'] | false | Gorenzelg/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7916 - Epoch: 0 | 15936db98a97cc36ba75c88cead73d41 |
apache-2.0 | ['generated_from_trainer', 'whisper-event', 'hf-asr-leaderboard'] | false | openai/whisper-small This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2195 - Wer: 19.56 | 17150e6ffd0a95a85bfe5cc05c5a8a11 |
apache-2.0 | ['generated_from_trainer', 'whisper-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - 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: 10000 - mixed_precisi... | 1aed0e10697b9a4f9c4c86cc02bf9a68 |
apache-2.0 | ['generated_from_trainer', 'whisper-event', 'hf-asr-leaderboard'] | false | Training results | Epoch | Step | Wer | |:-------------:|:-----:|:----:| | 0.1 | 1000 | 43.61 | | 0.2 | 2000 | 36.79 | | 0.3 | 3000 | 33.05 | | 0.4 | 4000 | 29.53 | | 0.5 | 5000 | 26.01 | | 0.6 | 6000 | 23.44 | | 0.7 | 7000 | 22.22 | | 0.8 | 8000 | 21.88 | | 0.9 | 9000 | 20.53 | | 1.0 | 10000 ... | 7019edda672b69d9964d520cc5e82aea |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'question-answering', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Classical Chinese texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from [bert-ancient-chinese](https://huggingface.co/Jihuai/bert-ancient-chinese) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDe... | c8cc4aa3d38baa92752158ceb71b2ffa |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head") qap=Question... | 10e404050dca10e182a839a814dc98ae |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD... | 2e9848b980ef482ae1b9ba20babba174 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0590 - Accuracy: 0.9830 | 1a56e4de9defbc18dc7bb68ffc022422 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2589 | 1.0 | 190 | 0.1036 | 0.9648 | | 0.1845 | 2.0 | 380 | 0.0707 | 0.9763 | | 0.1179 | 3.0 | 570 | 0.0590 | 0.... | 25c176cd498c4b995c0c24e869e30b83 |
cc-by-sa-4.0 | ['korean', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Korean texts for POS-tagging and dependency-parsing, derived from [roberta-base-korean-hanja](https://huggingface.co/KoichiYasuoka/roberta-base-korean-hanja) and [morphUD-korean](https://github.com/jungyeul/morphUD-korean). Every morpheme (형태소) is tagged by [UP... | ed8eee00ff71fbca4c7330afee191b5a |
cc-by-sa-4.0 | ['korean', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-korean-morph-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-korean-morph-upos") pipeline=Tok... | 7900f1bb074cde46c4d9c996321b1fba |
creativeml-openrail-m | ['text-to-image'] | false | Rose Shield model Dreambooth model trained by sebrosen8 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the None base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/n... | 49ab58cf21defd4fb17a55d65bcae567 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny It 1 - Gianluca Ruberto 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: 0.711901 - Wer: 43.295896 | df132db683b03ed5a6df4a35ef3120aa |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5837 | 0.95 | 1000 | 0.789903 | 50.2149 | | 0.418 | 1.91 | 2000 | 0.730088 | 45.3411 | | 0.3144 | 2.86 | 3000 | 0.713151 | 44.370... | 17a9159952e9ed6e085db2d94bec7b81 |
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 | Fine-tuned XLSR-53 large model for speech recognition in Spanish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). When using this model, make sure t... | 1b9366e656d14888dc96b8effd9fafd4 |
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 [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-spanish") audio_paths = ["/... | bb044706917f03b30cbd2855d9a1d2cc |
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 | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs =... | 24446587512a481a435342c1431f23fd |
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 | Evaluation 1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-spanish --dataset mozilla-foundation/common_voice_6_0 --config es --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python... | 38901902ed46d1df73053afecab5aed7 |
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{grosman2021xlsr53-large-spanish, title={Fine-tuned {XLSR}-53 large model for speech recognition in {S}panish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish}}, year={2... | 8f5e500ea434edc219c60c25a8965deb |
other | ['generated_from_trainer'] | false | dalio-synthetic-io-1.3b This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-synthetic-io dataset. It achieves the following results on the evaluation set: - Loss: 2.4961 - Accuracy: 0.0636 | 8f1ef0068ba22bab0f91ab312e8a26f1 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6941 | 0.05 | 1 | 2.6543 | 0.0622 | | 2.6914 | 0.11 | 2 | 2.6543 | 0.0622 | | 2.6003 | 0.16 | 3 | 2.6016 | 0.... | faf502bc9cf8ebdeaa398f2de6c1d0a1 |
mit | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-lener_br This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [Luciano/lener_br_text_to_lm](https://huggingface.co/datasets/Luciano/lener_br_text_to_lm) dataset. It achieves the following results on the ... | 31504b34e0e92598301446176c554e6e |
mit | ['generated_from_trainer'] | false | Training hyperparameters (To update) The following hyperparameters were used during training: - learning_rate: 2e-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: 15 | 12f7b6cd25762f1d73c9f7e6086ec9b4 |
mit | ['generated_from_trainer'] | false | Training results (To update) | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3167 | 1.0 | 2079 | 1.1163 | | 1.1683 | 2.0 | 4158 | 1.0594 | | 1.0648 | 3.0 | 6237 | 1.0501 | | 1.0228 | 4.0 | 831... | 38939ff83728e8788616dc4d15de3290 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Swahili 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 sw dataset. It achieves the following results on the evaluation set: - Loss: 0.5597 - Wer: 27.6211 | bdce1db693d80da9c4d0e6e0f991b4d8 |
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: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - training_steps: 500 - mixed_precis... | 1106196c542aad5908d472e214c8da99 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | (https://linktr.ee/kukuhtw) Model Trained by (https://linktr.ee/kukuhtw) using fast-dreambooth (https://github.com/TheLastBen/fast-stable-diffusion) use keyword : <i>male kukuhtw person</i> sample prompt : <i>portrait of male kukuhtw person style studio ghibli</i> <i>male kukuhtw person . 3d model, unreal engine r... | 825410cff0afb233f31911ac097df0c2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_16_02_2022-14_20_41 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: 0.1722 - Precision: 0.3378 - R... | 3595c317a06da6ce653b7f445a127c9d |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_wav2vec2_s321 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 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | 33484eb80a189f9fe621f57fc0682c3f |
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: 0.8508 - Matthews Correlation: 0.5452 | f2d23526d4543232261e3512be9dd9aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5221 | 1.0 | 535 | 0.5370 | 0.4246 | | 0.3462 | 2.0 | 1070 | 0.5157 | 0.5183 | | 0.2... | b986ee12c287c8231726fe77f04499ca |
mit | ['fill-mask', 'japanese', 'albert'] | false | for PyTorch ```py from transformers import ( AutoModelForMaskedLM, AutoTokenizer ) tokenizer = AutoTokenizer.from_pretrained("ken11/albert-base-japanese-v1-with-japanese-tokenizer") model = AutoModelForMaskedLM.from_pretrained("ken11/albert-base-japanese-v1-with-japanese-tokenizer") text = "明日は明日の[MASK]が吹く" tok... | 768a833ec3a386ef48e5ef7f5ff26336 |
mit | ['fill-mask', 'japanese', 'albert'] | false | for TensorFlow ```py from transformers import ( TFAutoModelForMaskedLM, AutoTokenizer ) import tensorflow as tf tokenizer = AutoTokenizer.from_pretrained("ken11/albert-base-japanese-v1-with-japanese-tokenizer") model = TFAutoModelForMaskedLM.from_pretrained("ken11/albert-base-japanese-v1-with-japanese-tokenizer"... | 8037b6ea2b4d3a8ff8513d104812d478 |
mit | ['fill-mask', 'japanese', 'albert'] | false | Training Data 学習には - [日本語Wikipediaの全文](https://ja.wikipedia.org/wiki/Wikipedia:%E3%83%87%E3%83%BC%E3%82%BF%E3%83%99%E3%83%BC%E3%82%B9%E3%83%80%E3%82%A6%E3%83%B3%E3%83%AD%E3%83%BC%E3%83%89) を利用しています | 14ee0a0ea545324f967c8671c81f9825 |
apache-2.0 | ['generated_from_trainer'] | false | bert_uncased_L-2_H-128_A-2-finetuned-emotion-finetuned-tweet This model is a fine-tuned version of [muhtasham/bert_uncased_L-2_H-128_A-2-finetuned-emotion](https://huggingface.co/muhtasham/bert_uncased_L-2_H-128_A-2-finetuned-emotion) on the imdb dataset. It achieves the following results on the evaluation set: - Los... | ef905e80b741a5d4a76173e2498f70f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4751 | 1.28 | 500 | 0.3880 | 0.828 | 0.8277 | | 0.3453 | 2.56 | 1000 | 0.3282 | 0.8608 | 0.8607 | | 0.2973 |... | 3eb8fc68411b1b2c045dd4a0f911ea68 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_wnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6908 - Accuracy: 0.5634 | fbd8a2f4f461ab2b11059a41cc9b6090 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6936 | 1.0 | 5 | 0.6912 | 0.5634 | | 0.6932 | 2.0 | 10 | 0.6918 | 0.5634 | | 0.6931 | 3.0 | 15 | 0.6920 | 0.... | 8eccdfbddd2c98c068e6b56c6ac34b86 |
mit | [] | false | ScandiNLI - Natural Language Inference model for Scandinavian Languages This model is a fine-tuned version of [jonfd/electra-small-nordic](https://huggingface.co/jonfd/electra-small-nordic) for Natural Language Inference in Danish, Norwegian Bokmål and Swedish. We have released three models for Scandinavian NLI, of ... | 3ef3a07691bcd1dc4c3cd58ddf664c9d |
mit | [] | false | Quick start You can use this model in your scripts as follows: ```python >>> from transformers import pipeline >>> classifier = pipeline( ... "zero-shot-classification", ... model="alexandrainst/scandi-nli-small", ... ) >>> classifier( ... "Mexicansk bokser advarer Messi - 'Du skal bede til gud, om at je... | ed884d3f844ad2159842d1e12b175562 |
mit | [] | false | Scandinavian Evaluation The Scandinavian scores are the average of the Danish, Swedish and Norwegian scores, which can be found in the sections below. | **Model** | **MCC** | **Macro-F1** | **Accuracy** | **Number of Parameters** | | :-------- | :------------ | :--------- | :----------- | :----------- | | [`alexandr... | e94e43407e9edf23cb68156784eca2cd |
mit | [] | false | page=439) to evaluate the Danish performance of the models. The test split is generated using [this gist](https://gist.github.com/saattrupdan/1cb8379232fdec6e943dc84595a85e7c). | **Model** | **MCC** | **Macro-F1** | **Accuracy** | **Number of Parameters** | | :-------- | :------------ | :--------- | :----------- | :-... | 1084b5adda2eea0b79912bbbff3a4d66 |
mit | [] | false | Swedish Evaluation We use the test split of the machine translated version of the [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset to evaluate the Swedish performance of the models. We acknowledge that not evaluating on a gold standard dataset is not ideal, but unfortunately we are not aware of any NLI da... | c44b78033a83e0dda1e8a15b440991ba |
mit | [] | false | Norwegian Evaluation We use the test split of the machine translated version of the [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset to evaluate the Norwegian performance of the models. We acknowledge that not evaluating on a gold standard dataset is not ideal, but unfortunately we are not aware of any NL... | e6197feed396eaf34f9abe1bf0d521c0 |
mit | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4242 - gradient_accumulation_steps: 1 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 5aae8400dd4f2ed27ee6e8e0ef6519fb |
lgpl-3.0 | [] | false | t5_interpreter A rut5-based model for incomplete utterance restoration, spellchecking and text normalization for dialogue utterances. Read more about the task [here](https://huggingface.co/inkoziev/rugpt_interpreter). | 1705f012ac7aca77d9d6295d4635eba5 |
lgpl-3.0 | [] | false | Usage example ``` import torch from transformers import T5ForConditionalGeneration, T5Tokenizer model_name = 'inkoziev/t5_interpreter' tokenizer = T5Tokenizer.from_pretrained(model_name,) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained(model_na... | 130ec5f1121fd5890346af94e3cd69aa |
lgpl-3.0 | [] | false | ' input_ids = tokenizer(t5_input, return_tensors='pt').input_ids out_ids = model.generate(input_ids=input_ids, max_length=40, eos_token_id=tokenizer.eos_token_id, early_stopping=True) t5_output = tokenizer.decode(out_ids[0][1:]) print(t5_output) ``` | 70962921336ed9077bda48245063118e |
mit | [] | false | Base model: [roberta-base](https://huggingface.co/roberta-base) Fine tuned as a progression model (to predict the acceptability of a dialogue) on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019): Given a complete dialogue from (or in the style of) Persuasion For ... | d99a142637ecefdd6834b1c3f6ab3d24 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'rm-vallader', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-romansh-vallader This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RM-VALLADER dataset. It achieves the following results on the evaluation set: - Loss: 0.3155 - Wer: 0.3162 | 06a83e5d4dfb42a7af553d0e07ebacb0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'rm-vallader', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 1 - 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: 100.0 - mixed_precision_... | 238d695df0c6572e96ff5883841e2aa1 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'rm-vallader', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9556 | 15.62 | 500 | 2.9300 | 1.0 | | 1.7874 | 31.25 | 1000 | 0.7566 | 0.6509 | | 1.0131 | 46.88 | 1500 | 0.3671 | 0.3828 | |... | cb03db1ec2d9ad38bad8385875583560 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 40k (uncased) Seed 1 intermediate checkpoint 40k 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... | c571bd686a43fdbdbe3cc3c490afab05 |
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-1-40k') model = BertModel.from_pretrained("multiberts-seed-1-40k") text = "Replace me by any text you'd like." ... | e2142611c739cf1fdb6dd0f3ecf5df81 |
other | ['generated_from_trainer', 'text generation', 'stable diffusion', 'midjourney', 'text2image', 'text to image', 'prompt augment', 'prompt engineering'] | false | pszemraj/opt-350m-multiprompt <a href="https://colab.research.google.com/gist/pszemraj/bdd1238ee4b8330aeec6774a16f9a677/opt-350m-multiprompt-demo.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> Generate/augment your prompt with a model trained on a large & div... | c5697f0959bf2c7bc374e9d3050f8feb |
other | ['generated_from_trainer', 'text generation', 'stable diffusion', 'midjourney', 'text2image', 'text to image', 'prompt augment', 'prompt engineering'] | false | Example  <br> _The above example was created with [DALL-E 2](https://labs.openai.com/sc/YbiY2kkuQeODzHNwUHn4D5RN) but will of course work with any text2image model._ | 5dcf7249eef5352a1004a877297499c9 |
other | ['generated_from_trainer', 'text generation', 'stable diffusion', 'midjourney', 'text2image', 'text to image', 'prompt augment', 'prompt engineering'] | false | Intended uses & limitations - The model will generate augmentations that are biased towards the training data, i.e. what people already asked for in the SD/midjourney discords, etc. Creating a larger dataset was an attempt at mitigating this through more data from different datasets. | 90136ea3cbde6dc4c265601f1f40f269 |
other | ['generated_from_trainer', 'text generation', 'stable diffusion', 'midjourney', 'text2image', 'text to image', 'prompt augment', 'prompt engineering'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - total_eval_batch_size: 8 - optimizer: Adam wi... | eb59bbccb09623002875630932d37490 |
other | ['generated_from_trainer', 'text generation', 'stable diffusion', 'midjourney', 'text2image', 'text to image', 'prompt augment', 'prompt engineering'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1677 | 1.0 | 990 | 2.0888 | | 1.856 | 2.0 | 1980 | 1.8215 | | 1.6864 | 3.0 | 2970 | 1.6935 | | 1.6228 | 4.0 | 3960 | 1.6670 ... | 254532b9f35aaf026cfbe23c8319f366 |
apache-2.0 | ['generated_from_trainer'] | false | korean-aihub-learning-2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9945 - Wer: 0.9533 | 4006355afa21a52f0a5cbfadd858f27b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 35 | 46.3840 | 1.0 | | No log | 1.99 | 70 | 26.0949 | 1.0 | | 37.1581 | 2.99 | 105 | 19.0168 | 1.0 | |... | f1194c3735f27c69ba98fa1df751d008 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the bongodog concept trained by dacquaviva on the dacquaviva/bongodog dataset. This is a Stable Diffusion model fine-tuned on the bongodog concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of bongodog dog** This model was created as part of the DreamBooth Hack... | 78121fe7d245fc90674fbf208c2cb8e0 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Photo of my dog Bongo: <img src="https://drive.google.com/uc?export=view&id=1m5heLYYzQIxDeyNoxxtB6X7bwNhxqG9v" alt="bongodog" width="200"/> <img src="https://drive.google.com/uc?export=view&id=1nP3JqAYEZSlTFAgduhhFC6S7XYKo8Nz9" alt="bongodog" width="200"/> | 20f2a4c6ff19ab597385509a8786c65d |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Examples of generated images: <img src="https://drive.google.com/uc?export=view&id=1DaJUXJP2nQy0_TVQpf3QAaJd6rBfbOlo" alt="bongodog" width="200"/> <img src="https://drive.google.com/uc?export=view&id=1ybeN5vg0OYuSalOenQX8AB8yaYBRW3O0" alt="bongodog" width="200"/> <img src="https://drive.google.com/uc?export=view&id=1-... | d60a178aeb65552834adf822e209d2ed |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1900k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1900k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | 1ab13639c6a53a74e22b9b9d2feedd69 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1900k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_1900k') model = TFBertModel.from_pretrained("google/multib... | 838c6411fc41605df4bad3070dd2d2ca |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_qqp_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5126 - Accuracy: 0.7888 - F1: 0.7301 - Combined Score: 0.7595 | 845c04adf4cb0bc0fe3782ab6a0fc237 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.3952 | 1.0 | 29671 | 0.5126 | 0.7888 | 0.7301 | 0.7595 | | 0.2233 | 2.0 | 59342... | 86178b4f761049433e2e4003043a9958 |
apache-2.0 | ['generated_from_trainer'] | false | wspr-sm-ar4 This model is a fine-tuned version of [Seyfelislem/wspr-sm-ar3](https://huggingface.co/Seyfelislem/wspr-sm-ar3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3664 - Wer: 58.7933 | aba697071ec714d2932408f6f9da6d6a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 150 - mixed_precisi... | 608fca8257aacd7a9a19670b3360ddb5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0972 | 1.0 | 150 | 0.3664 | 58.7933 | | 3dcb3beb28490165541e9b7e7adcedd2 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-extraction-cnndm_fs0.05-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6598 | df35622a8dd712a7b86b899e5390c04e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0976 | 0.23 | 200 | 1.7854 | | 1.9321 | 0.45 | 400 | 1.7458 | | 1.891 | 0.68 | 600 | 1.7279 | | 1.8488 | 0.9 | 800 | 1.7043 ... | 05deb665bad04832dddba1807befc1df |
apache-2.0 | ['translation'] | false | vie-fra * source group: Vietnamese * target group: French * OPUS readme: [vie-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-fra/README.md) * model: transformer-align * source language(s): vie * target language(s): fra * model: transformer-align * pre-processing: normalization + Se... | 66abfe31adc9357ab8d61401608386fe |
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