license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Training information ``` --train_batch_size 1 --resolution 768,768 --learning_rate 1e-4 --max_train_steps 21100 --use_8bit_adam --xformers --mixed_precision fp16 --save_every_n_epochs 1 --network_module networks.lora --v2 --enable_bucket --max_token_length 225 --network_dim 32 --text_encoder_lr 5e-5 --lr_scheduler co... | d527e1fbae90eb305e4ef7a9447337f1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | PVC v1  This model is a latent diffusion model finetuned on [Waifu Diffusion v1.4 epoch 2](https://huggingface.co/hakurei/waifu-diffusion-v1-4) with [PVC figure images](https://huggingface.co/datasets/p1atdev/pvc) using LoRA method. You can... | a1871cb4ecf3e9f81485963524854fdd |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Model links - [**pvc-v1.safetensors**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1.safetensors) - [**pvc-v1.ckpt**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1.ckpt) - [pvc-v1.yaml](https://huggingface.co/p1atdev/pvc/blob/main/pvc-v1.yaml) (needed if you want to use the model in AUTOMATIC1111's ... | 7c8566084be2ec3ba33d6ad0ace9379b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Samples  ``` masterpiece, best quality, 1girl, green hair, sweater, beanie, turtleneck, looking at viewer, Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fi... | c6acdff0cbf9b24172dd378d09b09866 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Training information ``` --train_batch_size 1 --resolution 768,768 --learning_rate 1e-4 --max_train_steps 17740 --use_8bit_adam --xformers --mixed_precision fp16 --save_every_n_epochs 1 --network_module networks.lora --v2 --enable_bucket --max_token_length 225 --network_dim 16 --text_encoder_lr 5e-5 ``` | fc0f28ccc4c9894dc6a538bb1478e9e2 |
apache-2.0 | ['generated_from_trainer'] | false | fine-tune-Wav2Vec2-XLS-R-300M-Indonesia This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7924 - Wer: 0.4307 | b725ac8f3aecb7e0ee5ea9cb39136791 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | ec8293d865377c71566a0b7f84d54fe5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.8421 | 6.33 | 500 | 0.7758 | 0.7386 | | 0.1875 | 12.66 | 1000 | 0.6039 | 0.5712 | | 0.1138 | 18.99 | 1500 | 0.6463 | 0.5170 | |... | 31da5ee2558c772d4e490a88dc4e291f |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-med-term-conditional-masking-0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6688 - Rouge2 Precision: 0.694 - Rouge2 Recall: 0.4781 - Rouge2 Fmeasure: 0.5479 | c37e9a2300f0a8421b248f72e91cedeb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.9525 | 1.0 | 13915 | 0.8148 | 0.6657 | 0.4581 | 0... | f664820bc6172926857a8983e49988c8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becasv2-5 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0409 | ecd17a4ea7c34e88b432014727855923 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 5.3475 | | No log | 2.0 | 12 | 4.6045 | | No log | 3.0 | 18 | 4.1832 | | No log | 4.0 | 24 | 3.8223 ... | 5a76a4202a351ff457333a6556cc9922 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/wsj_e_branchformer` This model was trained by Yifan Peng using wsj recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Architectures... | 9c649fce05daf1f56c323b37e592e016 |
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 0aa06d0535323aabc1d8b057f8769da377f4d9ff pip install -e . cd egs2/wsj/asr1 ./run.sh --skip_data_prep false --skip_train true --... | c350d07d650e7ab7ddcf9a47c0ba9a4f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Dec 28 00:12:25 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `0aa06d0535323aabc1d8b057f8769da377f4d9ff` - Commit date: `Tue Dec 27 15:08:25 2022 -0600` | dffc627ed05d9447ae7a0e83903e73fd |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_dev93|503|8234|94.3|4.9|0.8|0.7|6.5|51.9| |decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_eval92|333|5643... | 89977e275140e31106566d157b6a1919 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_dev93|503|48634|97.8|1.0|1.1|0.6|2.8|58.3| |decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_eval92|333|333... | 1615b65624514c9bbd911e3a1a09931e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_e12_mlp1024_linear1024.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_e12_mlp1024_linear1024_raw_en_char ngpu: 1 seed: 0 num_workers: 4 num_att_... | e7eb7f34388e89e091bf86123000e14f |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-base_mini This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3938 - Accuracy: 0.9076 - F1: 0.9516 | 0017bb7a1cea83af5f549371bd1829fa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.354 | 2.55 | 500 | 0.2300 | 0.9116 | 0.9538 | | 0.2086 | 5.1 | 1000 | 0.3182 | 0.8815 | 0.9370 | | 0.1401 |... | 869780f9205ca8b4a7e5de2f9d298c79 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-sentiment-2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5882 - Accuracy: 0.7614 - F1: 0.7... | 67fd1b8a872ef1242b438f82b7e348d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0... | a0ed877aa3bd105e1a65f96ab9627701 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3110 - Accuracy: 0.8833 - F1: 0.8845 | 7e5ab2fb56ab305ddde6b58d0fac23fb |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-nl_s418 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 46db583b9a3557bda42df07570dbdb0b |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on an unknown dataset. It achieves the following results o... | 392e20259fa5f1e7c63d24fc271b874b |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | Training hyperparameters 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: 16 - mixed_precision_training: Native AMP | f76c3ef884a79db89d62bc184ad592a3 |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.3777 | 1.0 | 1353 | 0.3168 | | 0.2358 | 2.0 | 2706 | 0.1564 | | 0.1372 | 3.0 | 4059 | 0.1149 | | 0.1046 | 4.0 | 5412 | 0.0956 ... | e78461a8eeac55bebc47767834a9cc9b |
apache-2.0 | ['classification'] | false | Erlangshen- MacBERT-110M-BinaryClassification-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) | 5e0ad55659ccfa4d5b997a8d6f5fce80 |
apache-2.0 | ['classification'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | MacBERT | 110M | Chinese | | 618a035bb4bbbc2bd4bc68cf008ef8f7 |
apache-2.0 | ['classification'] | false | 模型信息 Model Information 为了提高模型在二分类任务上效果，我们收集了大量开源二分类数据并使用meta- learning方法对其增量预训练。 To improve the model performance on the binary classification task, we collected numerous binary classification datasets for incremental pre-training based on meta-learning methods. | 3016f1b5390bd4183e044e4f38cb64d3 |
apache-2.0 | ['classification'] | false | 下游效果 Performance 在EPRSTMT任务上的效果： The results on EPRSTMT: | Model | EPRSTMT | | --------------------------------------------- | ------ | | MacBERT | 74.96 | | **Erlangshen- MacBERT-110M-BinaryClassification-Chinese** | 88.56 | | 991b83189c832b2857df478449d9740b |
apache-2.0 | ['classification'] | false | 使用 Usage ```python3 from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese") model = AutoModelForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese") ``` | 617c108834bf3a556fee4cce033be32d |
mit | ['generated_from_trainer'] | false | BERiT_2000_ls_1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.7791 | 3db581adc67d38d3573bd651dd1b69f6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - label_smoothing_factor: 0.1 | 4997c660c4199437d5501971700d3820 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.9212 | 0.19 | 500 | 6.8322 | | 6.8009 | 0.39 | 1000 | 6.8173 | | 6.8004 | 0.58 | 1500 | 6.7916 | | 6.7725 | 0.77 | 2000 | 6.8005 ... | 2fa6c528c63350ecad5f32edfb86d879 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | Details  This is a Dreamboothed Stable Diffusion model trained on pictures of mosaic art. The total dataset is made of 46 pictures. V2 was trained on [Stable diffusion 2.1 768](https://huggingface.co/stabilityai/stable-diffusion-2-1). I u... | b0eb160d79a417155da57c87a3d75139 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | Model v1  [CKPT v1](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v1.ckpt) [CKPT v1 with ema weights](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v1_ema.ckpt) [Dataset](https://huggingface.co/Guizmu... | f7886ea6a0dad2fec1a5f6b640da3bca |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 96433ea824a0b37eabff83bb0de3e01e |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | fin5 This model is a fine-tuned version of [nlpaueb/sec-bert-shape](https://huggingface.co/nlpaueb/sec-bert-shape) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0752 - Precision: 0.9243 - Recall: 0.9243 - F1: 0.9243 - Accuracy: 0.9909 | c744aaee879cb1b00b89fa1f0cf28884 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.0825 | 0.8327 | 0.8924 | 0.8615 | 0.9811 | | No log | 2.0 |... | f64670760ab2e768dd124f5e76cc4cb1 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the silicone dataset. It achieves the following results on the evaluation set: - Loss: 0.9647 - Accuracy: 0.7111 - Micro-precision: 0.7111 - Micro-recall: 0.7111 - Micro-f1: 0.7111 - Macro-preci... | 734c910acb06ca98684ae97d5d8ab844 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:---... | e4ab54a494e695ba459d50c41c0bf5df |
mit | ['generated_from_trainer'] | false | xlnet-base-cased_fold_8_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5333 - F1: 0.8407 | 9c6149f17941d8d6c99c022319ecaf78 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.3866 | 0.8172 | | 0.4299 | 2.0 | 580 | 0.4215 | 0.8246 | | 0.4299 | 3.0 | 870 | 0.4765 | 0.8238 | |... | 891b625c9c767a8bb5903e849ee7bf1d |
apache-2.0 | ['translation'] | false | eng-tut * source group: English * target group: Altaic languages * OPUS readme: [eng-tut](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-tut/README.md) * model: transformer * source language(s): eng * target language(s): aze_Latn bak chv crh crh_Latn kaz_Cyrl kaz_Latn kir_Cyrl kjh kum m... | fbff39c401716d63443369bd59efe8e6 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-entr-engtur.eng.tur | 10.4 | 0.438 | | newstest2016-entr-engtur.eng.tur | 9.1 | 0.414 | | newstest2017-entr-engtur.eng.tur | 9.5 | 0.414 | | newstest2018-entr-engtur.eng.tur | 9.5 | 0.415 | | Tatoeba-t... | fde8a16500e908b3ddaa84f18dd2bf91 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-tut - source_languages: eng - target_languages: tut - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-tut/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'tut'] - src_constituents: {'eng'} - tgt_cons... | b7bb3aaee939fbc0692e995c3a42943b |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-Turkish-Tr-small This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4375 - Wer: 0.5050 | d9a82dddc566f9b65e08eb88c7b8541d |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8735 | 4.21 | 400 | 2.8173 | 1.0002 | | 1.0073 | 8.42 | 800 | 0.4981 | 0.6717 | | 0.3395 | 12.63 | 1200 | 0.4470 | 0.5866 | |... | da0e923cc290b50b6b5b977a764aa1e5 |
apache-2.0 | [] | false | CANINE-s (CANINE pre-trained with subword loss) Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) and first released in [... | f654513d4793f52b3953944c54082c6d |
apache-2.0 | [] | false | :~:text=For%20Unicode%2C%20the%20particular%20sequence,forming%20a%20self%2Dsynchronizing%20code.). This means that input processing is trivial and can typically be accomplished as: ``` input_ids = [ord(char) for char in text] ``` The ord() function is part of Python, and turns each character into its Unicode code... | 3f6f5f9be0f4e6e7a442a2b17f162562 |
apache-2.0 | [] | false | Model description CANINE is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion, similar to BERT. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic pro... | 8e7f259b256feb8958dbd5c0458c0ee0 |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=canine) to look for fine-tuned versions on a task that interests you. Note th... | 45ba070e05fa8c0765f2017fbee3b392 |
apache-2.0 | [] | false | How to use Here is how to use this model: ```python from transformers import CanineTokenizer, CanineModel model = CanineModel.from_pretrained('google/canine-s') tokenizer = CanineTokenizer.from_pretrained('google/canine-s') inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."] encodi... | 82fcada7411a6b55530b7090b48edb51 |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-06874, author = {Jonathan H. Clark and Dan Garrette and Iulia Turc and John Wieting}, title = {{CANINE:} Pre-training an Efficient Tokenization-Free Encoder for Language... | 988c278b6d1c9522331c8d8ee9f53b28 |
mit | ['deberta-v3', 'deberta-v2`', 'deberta-mnli'] | false | DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the [official repository]... | 0d9306f081f2d3da5151ca55faf01da2 |
mit | ['deberta-v3', 'deberta-v2`', 'deberta-mnli'] | false | Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B | |---------------------------|-----------|-----------|-------------|-------|------|------|-----... | dd6f00ab8d18f2ac1248c3bc78412c60 |
mit | ['deberta-v3', 'deberta-v2`', 'deberta-mnli'] | false | Notes. - <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xl... | a4a3a4235b5264dab7ce273f4ea1f906 |
mit | ['deberta-v3', 'deberta-v2`', 'deberta-mnli'] | false | Download the deepspeed config file wget https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/ds_config.json -O ds_config.json export TASK_NAME=mnli output_dir="ds_results" num_gpus=8 batch_size=8 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \\ run_glue.py \\ --model_name_or_path micr... | f8eab9577afb265d94fc059d620b0f86 |
mit | ['deberta-v3', 'deberta-v2`', 'deberta-mnli'] | false | Citation If you find DeBERTa useful for your work, please cite the following paper: ``` latex @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Repr... | fb52d351d215fe65e7add9c8c6d1d487 |
mit | [] | false | Bangla News Article Doc2Vec model Bengali News Article doc2vec model trained on [this](https://www.kaggle.com/datasets/ebiswas/bangla-largest-newspaper-dataset) datasets with 8 JSONS and vector size 100. This model is trained for the [bnlp](https://github.com/sagorbrur/bnlp) library. | 2189c90b893263cc020194f19ffc393c |
mit | [] | false | keep other .npy model files also in same folder document = "রাষ্ট্রবিরোধী ও উসকানিমূলক বক্তব্য দেওয়ার অভিযোগে গাজীপুরের গাছা থানায় ডিজিটাল নিরাপত্তা আইনে করা মামলায় আলোচিত ‘শিশুবক্তা’ রফিকুল ইসলামের বিরুদ্ধে অভিযোগ গঠন করেছেন আদালত। ফলে মামলার আনুষ্ঠানিক বিচার শুরু হলো। আজ বুধবার (২৬ জানুয়ারি) ঢাকার সাইবার ট্রাইব্... | 0c29f43368e867233afcacda14a5b12c |
mit | [] | false | keep other .npy model files also in same folder article_1 = "রাষ্ট্রবিরোধী ও উসকানিমূলক বক্তব্য দেওয়ার অভিযোগে গাজীপুরের গাছা থানায় ডিজিটাল নিরাপত্তা আইনে করা মামলায় আলোচিত ‘শিশুবক্তা’ রফিকুল ইসলামের বিরুদ্ধে অভিযোগ গঠন করেছেন আদালত। ফলে মামলার আনুষ্ঠানিক বিচার শুরু হলো। আজ বুধবার (২৬ জানুয়ারি) ঢাকার সাইবার ট্রাইব... | 2740d38605a038907417453b5f6c8f90 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036268/ This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 435acc93dd0231231eb3498451ae66ac |
mit | ['generated_from_trainer'] | false | suspicious_mestorf This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-... | de966079035a133869c3806cc48d8493 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 7f289985da0cad474f4b9b619897a08f |
gpl | [] | false | <h2> GPT2 Model for German Language </h2> Model Name: Tanhim/gpt2-model-de <br /> language: German or Deutsch <br /> thumbnail: https://huggingface.co/Tanhim/gpt2-model-de <br /> datasets: Ten Thousand German News Articles Dataset <br /> | 2c3c7636494d97c081595835750f77de |
gpl | [] | false | How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, I set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generation= pipeline('text-generation', model='Tanhim/gpt2-model-de', tokenizer='Tanhim/gpt2-mo... | e7637e223a37221a33ad0a66f320e84c |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | ✨ xlm-roberta-capitalization-punctuation This a [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model finetuned for Vietnamese punctuation restoration on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset. The model predicts the punctuation and upper-casing of plain, lower-cased ... | e7a5ecb4f3f2f0d850900fd5490bb4b0 |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | 🚋 Usage **Below is a quick way to get up and running with the model.** 1. Download files from hub ```python import os import shutil import sys from huggingface_hub import snapshot_download cache_dir = "./capu" def download_files(repo_id, cache_dir=None, ignore_regex=None): download_dir = snapshot_download(repo... | 3098650a550016d997294081e5b35d73 |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | ['Theo đó, Thủ tướng dự kiến tiếp Bộ trưởng Nông nghiệp Mỹ Tom Wilsack, Bộ trưởng Thương mại Mỹ Gina Raimondo, Bộ trưởng Tài chính Janet Yellen, gặp gỡ Thượng nghị sĩ Patrick Leahy và một số nghị sĩ Mỹ khác.'] model("những gói cước năm g mobifone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời so với mạ... | 526b5b72aab851c83600cc6ca7a6bf16 |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | ['Những gói cước 5G MobiFone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời. So với mạng 4G thì tốc độ truy cập mạng 5G MobiFone được Nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần.'] ``` **This model can work on arbitrarily large text in Vietnamese language.** ------------------------------... | a80e1476ecf582b4a2f01ee374663f5c |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | 📡 Training data Here is the number of product reviews we used for fine-tuning the model: | Language | Number of text samples | | --- | --- | | Vietnamese | 5,600,000 | ----------------------------------------------- | 069048cd91dabeeec3ab390d364a23b0 |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | 🎯 Accuracy Below is a breakdown of the performance of the model by each label on 10,000 held-out text samples: | label | precision | recall | f1-score | support | | --- | --- | --- | --- | --- | | **Upper** | 0.89 | 0.90 | 0.89 | 56497 | | **Complex-Upper** | 0.93 | ... | 6e503a36e54cdbe2db6525f7e01221db |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4455 - Accuracy: 0.8073 | adc82d037da9f73335edd8b7a3878be0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4404 | 1.0 | 264 | 0.5503 | 0.7477 | | 0.2565 | 2.0 | 528 | 0.6115 | 0.7580 | | 0.2067 | 3.0 | 792 | 0.4455 | 0.... | 09b215b4c40081674f3d9cc37da6a6d4 |
apache-2.0 | ['translation'] | false | opus-mt-niu-es * source languages: niu * target languages: es * OPUS readme: [niu-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | d27deb721950817eed1f62a0c48276ae |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becas-7 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 4.3059 | 754796a08bf3f2bef4bfc510f32df618 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.4980 | | No log | 2.0 | 10 | 5.0383 | | No log | 3.0 | 15 | 4.6244 | | No log | 4.0 | 20 | 4.2090 ... | 488501a329d44ade13653e5784da2616 |
mit | ['russian'] | false | This is the [rut5-base](https://huggingface.co/cointegrated/rut5-base) model, with the decoder fine-tuned to recover (approximately) Russian sentences from their [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) embeddings. Details are [here](https://habr.com/ru/post/677618/) (in Russian). It can be used, f... | 4cf6562c4c1ea455b234cf978b034caa |
mit | ['russian'] | false | encode some texts into vectors embeddings = encode([ "4 декабря 2000 года", "Давно такого не читала, очень хорошо пишешь!", "Я тогда не понимала, что происходит, не понимаю и сейчас.", "London is the capital of Great Britain.", ]) print(embeddings.shape) | a58da71a4f166e417569b7757fd770a2 |
mit | ['russian'] | false | now try to recover the texts from the vectors out = decoder.generate( encoder_outputs=BaseModelOutput(last_hidden_state=embeddings.unsqueeze(1)), max_length=256, repetition_penalty=3.0, ) for tokens in out: print(dec_tokenizer.decode(tokens, skip_special_tokens=True)) | 21e54866c2b61d4c8b02dc512b6bb784 |
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: 0.1895 - Accuracy: 0.94 - F1: 0.9401 | 7274efa858fc65d5e2d4ace74356997b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4628 | 1.0 | 2000 | 0.2334 | 0.9315 | 0.9312 | | 0.1579 | 2.0 | 4000 | 0.1895 | 0.94 | 0.9401 | | 09e4e6ccbab3eb54639020d3867ecf1b |
apache-2.0 | ['generated_from_trainer'] | false | platzi-distilroberta-base-mrpc-glue-tommasory This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7098 - Accuracy: 0.8309 - F1: 0.8734 | ed7639db4903aa06188a51c294b44081 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5196 | 1.09 | 500 | 0.5289 | 0.8260 | 0.8739 | | 0.3407 | 2.18 | 1000 | 0.7098 | 0.8309 | 0.8734 | | 8aaa9cb5d35ee399a72858cedeb76b49 |
apache-2.0 | ['generated_from_trainer'] | false | mBERT-base-cased-NER-CONLL (EN-ES) This model is a fine-tuned version of [bert-base-multilingual-cased ](https://huggingface.co/bert-base-multilingual-cased) on the conll2003 and conll2002 datasets. Training was performed separately. It achieves the following results on the evaluation set: Connll2003: - Loss: 0.0585... | 213d0891ad8d8a5fd7e469374c27749d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | d7e1c5f136477bd199c9beaceb9c5907 |
apache-2.0 | ['generated_from_trainer'] | false | Training results Conll2003: | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1739 | 1.0 | 878 | 0.0741 | 0.9246 | 0.9181 | 0.9213 | 0.9823 | | 0.045 ... | 3292b7043ca953516c1b13144fc39449 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_mrpc_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6239 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | c4fdfba6504197a311738c32d5e603cb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6686 | 1.0 | 15 | 0.6467 | 0.6838 | 0.8122 | 0.7480 | | 0.6433 | 2.0 | 30 | 0.63... | e449ab0020fc32b8cf635ff48ed83717 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best` ♻️ Imported from https://zenodo.org/record/5590204 This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 534bb242963b66ea85381535daf07324 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ES... | dca54df1e80e51d8e7f5c1f19d394aae |
mit | [] | false | Model Description A CLIP ViT-bigG/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Mitchell Wortsman on the [stability.ai](https://stability.ai/) cluster. The license for this model is ... | 6a81642dcd3e9b491ccdbeb4ef8fa480 |
mit | [] | false | Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). Fine-tuning was also partially done on LAION-A, a 900M subset of LAION-2B filtered with aesthetic V2 4.5+ and phash deduplicated. **IMPORTANT NOTE:** The motivation behind dataset creation is... | 78bc33c9285d2fa48943f803cd8ad6d9 |
mit | [] | false | Results The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, and will soon be visible at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. ... | 1320b9b9e47d485478c26f19d9ca0bd3 |
mit | [] | false | Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman... | d705de1c7691c6a57e3842e704f39a07 |
mit | ['text-classification'] | false | Multi2ConvAI-Quality: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model type... | 937d600e464e20b235444db213d63c29 |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-en-bert")
````
| 38d2d9ac04603e624355076f26a6ad87 |
cc-by-sa-4.0 | ['belarusian', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on [CC-100](https://data.statmt.org/cc-100/). You can fine-tune `roberta-small-belarusian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-belarusian-upos), dependency-parsing, and so on. | b68d34ba443afae769c2ca0d3f1e8fc3 |
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