license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
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creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Description This is a Stable Diffusion model fine-tuned on the infamous blobfish (often remarked as the ugliest animal in the world) for the DreamBooth Hackathon 🔥 animal theme. To participate or learn more, visit [this page](https://huggingface.co/dreambooth-hackathon). To generate blobfish images, use **a photo o... | 80f27b996707e1ef2fb55bcdafd132d5 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Examples *a photo of blofi fish wearing a beautiful flower crown.*  *a photo of blofi fish in nerdy glasses.*  *a photo of blofi fish at the Arctic in a fluffy hat.*  ... | d2a723d75eef0f3aeb908a7648d47511 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout b757b89d45d5574cebf44e225cbe32e3e9e4f522 pip install -e . cd egs2/chime6/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 973696439d5a759963a54cac55356ba5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Tue May 3 16:47:10 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.10.1` - Git hash: `b757b89d45d5574cebf44e225cbe32e3e9e4f522` - Commit date: `Mon May 2 09:21:08 2022 -0400` | d3963d39c701ac3460fd703453bdce57 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_asr_model_1epoch/dev_gss_multiarray|7437|58881|66.5|21.3|12.2|8.8|42.3|77.4| |decode_asr_transformer_asr_model_2epoch/dev_gss_multiarray|7437|58881|68.6|20.7|10.6|8.4|39.8|77.5| |decode_asr_transformer_asr_m... | 412936efa484f4336291235936324612 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_asr_model_1epoch/dev_gss_multiarray|7437|280767|78.1|7.7|14.1|9.1|31.0|77.9| |decode_asr_transformer_asr_model_2epoch/dev_gss_multiarray|7437|280767|80.0|7.6|12.5|8.7|28.8|78.1| |decode_asr_transformer_asr_m... | 09ab56bac76239b383dd636af1f659af |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_asr_model_1epoch/dev_gss_multiarray|7437|92680|65.8|18.8|15.4|8.7|42.9|78.0| |decode_asr_transformer_asr_model_2epoch/dev_gss_multiarray|7437|92680|67.9|18.1|13.9|8.2|40.3|78.2| |decode_asr_transformer_asr_m... | 4ea55846bbe184e2f645955a34d3c30a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_... | 2d2bdab22edfc1acb28d61209348bcd3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ' - ǎ - î - ü - ǐ - ñ - â - ç - ']' - é - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram1000/bpe.model non_linguistic_sym... | 7e1c96b48d0d5310c4bb91e26d22e4e7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0620 - Precision: 0.9267 - Recall: 0.9371 - F1: 0.9319 - Accuracy: 0.9838 | c9105765b4245a3f45186c74b4633e57 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2462 | 1.0 | 878 | 0.0714 | 0.9052 | 0.9223 | 0.9137 | 0.9803 | | 0.0535 | 2.0 |... | f96e1b905900bfa3a7850a26c74953b7 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss... | f4e7eb27105098b9c37995a9957dc6f4 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I... | 300f192285dbd0d9b527b0f4d6f812d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0624 | 1.0 | 4078 | 0.1844 | 0.8002 | 0.7923 | 0.7963 | 0.9607 | | 0.0284 | 2.0 ... | 1fbd2b63f491089cabac673dc11fa7e6 |
apache-2.0 | ['generated_from_trainer'] | false | muril-base-cased-finetuned-TRAC-DS This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1894 - Accuracy: 0.6838 - Precision: 0.6534 - Recall: 0.6513 - F1: 0.6522 | d36c9a94abcc979cda001ff2707c83a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0109 | 1.99 | 612 | 0.9284 | 0.5948 | 0.4327 | 0.5193 | 0.4509 | | 0.8635 | 3.99 |... | d19ec9762fd0c826d97d700f9a5e8552 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/temp-colab-upload-test4 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0000 - Validation Loss: 0.0000 - Epoch: 1 | edf2a1d21bfcdbf590a860180f1d0d22 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Breton This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 br dataset. It achieves the following results on the evaluation set: - Loss: 0.8486 - Wer: 41.6117 | 43b2cf1ee82c864b53ad49ffd4245b3d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - train... | 4d39e27171d6b67173f6fbcfc817256a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0602 | 5.03 | 1000 | 0.7324 | 43.6957 | | 0.0036 | 10.05 | 2000 | 0.8486 | 41.6117 | | 0.001 | 15.08 | 3000 | 0.9033 | 42.045... | 10cfea221fe078c2bbc2d19eac2d8d01 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-subjqa-vanilla-movies-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: movies) via [`lmqg`](https://github.com/asahi417/lm-quest... | a0248f69d33163a91c599e408568debd |
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) (movies) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-gene... | 9254fac0334340ede9a2130c8dbd827c |
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... | 5e5552e03c179bd4c66088b54664dfe2 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-movies-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | | Score | Type | Dataset ... | 1539da4a6f0695dc797ddfcb4218736f |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: movies - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 3... | 8cf9d0b2e7c249910a6e8989332f9c9b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1561 | a8f3865c46cb76883a1a4232624f9284 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2353 | 1.0 | 5533 | 1.1740 | | 0.9722 | 2.0 | 11066 | 1.1192 | | 0.7677 | 3.0 | 16599 | 1.1561 | | 2e026e5d86631c61e91294a9831fd4b5 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-9e-05 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 image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8481 - Accur... | b923d8c37148e2e9e62a80220112c0ed |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 121fb973be1edd5fe0248c225f32fba8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1839 | 1.0 | 224 | 1.0266 | 0.6120 | | 1.0333 | 2.0 | 448 | 0.9063 | 0.6608 | | 0.9655 | 3.0 | 672 | 0.8481 | 0.... | e0e3820e46d88c86071ae8203b4d3ca2 |
mit | ['generated_from_trainer'] | false | roberta-large-unlabeled-gab-semeval2023-task10-9000sample This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0541 | 91cc7b183840eead2745809674160de0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.515 | 1.0 | 563 | 2.3288 | | 2.2807 | 2.0 | 1126 | 2.1769 | | 2.0351 | 3.0 | 1689 | 2.0541 | | 6513003b43053d7cb9775f7c3eb8688c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | **Wednesday Diffusion** This is the fine-tuned Stable Diffusion 1.4 model trained on promotional pictures of Jenna Ortega as Wednesday Addams on Netflix's adaptation. Use the tokens **_WednesdayAdJO_** in your prompts for the effect. This model was trained using the diffusers based dreambooth training by ShivamShrir... | a8c7e273514f9bb67c6fba0b3349bf7e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-hindi-epochs60-colab 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: 1.7322 - Wer: 0.9188 | f35b554e941ba8e6104b5134113d75bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.2832 | 44.42 | 400 | 1.7322 | 0.9188 | | 1bb528a9495eb2af757d0be510815f46 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-English-to-BASH This model is a fine-tuned version of [kevinum/t5-small-finetuned-English-to-BASH](https://huggingface.co/kevinum/t5-small-finetuned-English-to-BASH) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7624 - Bleu: 15.8119 - Gen Len: 7.75 | 880a330b05cffddc4f6ac373d15bff60 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 36 | 2.4759 | 9.4129 | 12.8472 | | No log | 2.0 | 72 | 2.2581 | 14.8612 | 9.7639 | | No log |... | f4274f71e430660a7a9a54027bd05109 |
apache-2.0 | ['generated_from_trainer'] | false | esci-us-bert-base-uncased 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: 1.1785 - Accuracy: 0.7499 | a796f52767b7bcb780f9923882f16be3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - total_eval_batch_size: 128 - optimizer: Adam... | 4e7f2faf4dca1ba4e5e436023ddc94f1 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | UL2-mini-nl8 for Finnish Pretrained T5 model on Finnish language using a UL2 (Mixture-of-Denoisers) objective. T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The UL2 objective was introd... | 1dd5721c8086c7ab065e9ec170d2691f |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. Finnish T5 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which... | 8be50e07bc1c2c34bcc1414ab99a0503 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on self-superv... | 321e38b9c7479045edf9c6c680a43fca |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | UL2 pretraining objective This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given inpu... | 26375b61c09e59ecad8d495c903e8fe9 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Intended uses & limitations This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. **Note:** You most likely need to fine-tune these... | 395cfc9556ecfe5bd0f19d67369781f2 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/ul2-mini-nl8-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/ul2-mini-nl8-finnish") ``` and in TensorFlow: ... | 6789dfddbebe16d2d751dde14b4fd51a |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 500K steps with a batch size of 256 (in total 66B tokens). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and ... | dc1b42f5c744b40e595da4622bb7c6ec |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length... | 2d3aeb94c21e3f687630283b7c001c23 |
apache-2.0 | [] | false | Descripción do modelo Modelo de (~) 67M de parámetros, adestrado e afinado desde cero, usando un dataset en galego de 305MB obtido da wikipedia en galego. No contexto da Resolución do 22 de decembro de 2021 da Secretaría Xeral de Educación e Formación Profesional pola que se convocan premios para o desenvolvemento d... | a2172f25bf1794ed199088b9dc2c1796 |
apache-2.0 | [] | false | Hyperparametros de entrenamento - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.08113086280077723,0.8857246592117177) and epsilon=5.264065162059701e-07 - lr_scheduler_type: linear - num_epochs:... | 7151ec0507984fefd1c1974b088909cc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_rte_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Accuracy: 0.5271 | 6b82356d19822d4f56c3b71239eb3624 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6951 | 1.0 | 10 | 0.6927 | 0.5271 | | 0.6935 | 2.0 | 20 | 0.6925 | 0.5271 | | 0.692 | 3.0 | 30 | 0.6931 | 0.... | 901836f9f1ef8e23f6dbc7a527faa9cd |
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 | TCBert | 110M | Chinese | | 00d8e94df7331a16d44a1308c2b62d4a |
apache-2.0 | ['classification'] | false | 下游效果 Performance 我们为每个数据集设计了两个prompt模板。 We customize two prompts templates for each dataset. 第一个prompt模板： For ***prompt template 1***: | Dataset | Prompt template 1 | |---------|:------------------------:| | TNEWS | 下面是一则关于__的新闻： | | CSLDCP | 这一句描述__的内容如下： | | IFLYTEK | 这一句描述__的内容如下： | 第一个prompt模板的微调实验结果：... | 8f7733ac749427e31f2caeebc75087c3 |
apache-2.0 | ['classification'] | false | Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese") | 2283f250246a2e63926335505058ee0a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [ali221000262/wav2vec2-base-timit-demo-colab](https://huggingface.co/ali221000262/wav2vec2-base-timit-demo-colab) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2161 - Wer: 1.0 | d1f13608e591a2ca4de06e35bb48cdb6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_tra... | 5cbdbbf21cb1306684c34dfac66386b2 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1367 - F1: 0.8633 | 5883d632e684cf3f56f9d8850042fe04 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2582 | 1.0 | 525 | 0.1653 | 0.8238 | | 0.1301 | 2.0 | 1050 | 0.1417 | 0.8439 | | 0.0841 | 3.0 | 1575 | 0.1367 | 0.8633 | ... | a91bba085397da228402fb0509639bf9 |
apache-2.0 | [] | false | CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型） Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding. We have seen rapid progress on building multilingual PLMs in recent year. However, there is a lack of ... | feed77bef08d68013c922800ca09308d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 838407533a3df21025bf255ab693f99f |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, ... | 31cc931bd0bb08e3261de7b6d0ce6b95 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup-3-8 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4132 - Rouge1: 49.6606 - Rouge2: 28.4044 - Rougel: 31.5419 - Rougelsum: 46.2463... | 27163edd6410e97cf41d72b9f89e5e58 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 8 - mixed_precision_training: Native AMP | 6e7c100da366afb5e2d81191a9ff2bba |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 258 | 1.2686 | 48.8513 | 28.7007 | 31.1199 | 45.7318 | ... | 43e60072e459a670c425f276a5fd57d3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mrpc_256 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: 0.5996 - Accuracy: 0.6814 - F1: 0.8105 - Combined Score: 0.7459 | b63790153121fd90953570aab2c5dfe6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6343 | 1.0 | 15 | 0.6246 | 0.6838 | 0.8122 | 0.7480 | | 0.6276 | 2.0 | 30 | 0.62... | bfba06bc41cedbf1568cc43e180866b9 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Sanskrit This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 241bf9e2e7b9e3d64fd28c7135039f9a |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa") ``` | 566cddfc7edbefb170626bd227a681fa |
apache-2.0 | ['generated_from_trainer', 'Summarization'] | false | distilbart-podimo-data-5 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.1325 | 5499d0c8bfb3378ebfbaca0c3779e709 |
apache-2.0 | ['generated_from_trainer', 'Summarization'] | false | Model description model | rouge1 | rouge2 | rougeL | rougeLsum --- | --- | --- | --- |--- sshleifer/distilbart-cnn-12-6 | 0.202654 | 0.025766 | 0.123072 | 0.130183 emmyapi/distilbart-podimo-data-3 | 0.235147 | 0.047087 | 0.151535 | 0.161782 emmyapi/distilbart-podimo-data-4 | 0.236926 | 0.048327 | 0.153539 | 0.165026... | 7b045f2e1243c058f02ef808e6e60942 |
apache-2.0 | ['generated_from_trainer', 'Summarization'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 97f9869bb9e28d93edbb2b414248598a |
apache-2.0 | ['generated_from_trainer', 'Summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3477 | 3.33 | 500 | 3.7027 | | 2.6286 | 6.66 | 1000 | 3.6995 | | 2.0718 | 10.0 | 1500 | 3.8868 | | 1.7806 | 13.33 | 2000 | 4.1325 ... | 354d458f3f3bd304ab85a8b40f6dbec1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Hindi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 hi dataset. It achieves the following results on the evaluation set: - Loss: 0.3191 - Wer: 11.3039 | 4d9406e70878ae1a26ea8e1e5d0b8d72 |
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 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | 61dd3fd5cac79789df9a6569630bec8d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0479 | 2.06 | 200 | 0.2189 | 12.3226 | | 0.0081 | 5.06 | 400 | 0.2649 | 11.5740 | | 0.001 | 8.06 | 600 | 0.2998 | 11.425... | eb138b4f3da78686ec9f28da5bd05e1f |
apache-2.0 | ['generated_from_trainer'] | false | Article_250v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2531 - Precision: 0.6347 - Recall: 0.6342 - F1: 0.6345 - Accuracy: 0.... | ab3e5646b4b91fc2961eb62ddbc47cf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 82 | 0.2668 | 0.5478 | 0.5370 | 0.5424 | 0.9064 | | No log | 2.0 |... | 0a77c3a924b2740da269e4d172e6d1aa |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Small Stable Diffusion Model Card 【Update 2023/02/07】 Recently, we have released [a diffusion deployment repo](https://github.com/OFA-Sys/diffusion-deploy) to speedup the inference on both GPU (\~4x speedup, based on TensorRT) and CPU (\~12x speedup, based on IntelOpenVINO). Integrated with this repo, small-stable-di... | 4d27f4767efb97fac1d500d6869fbf35 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run small-stable-diffusion-v0: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f254630253946254134253937253230487... | 92839d4e7d9967919418070769d4480c |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Example *Use `Diffusers` >=0.8.0, do not support lower versions.* ```python import torch from diffusers import StableDiffusionPipeline model_id = "OFA-Sys/small-stable-diffusion-v0/" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "an apple, 4k" ... | 0838cddad6bb3774712684b8c320938e |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Initialization This model is initialized from stable-diffusion v1-4. As the model structure is not the same as stable-diffusion and the number of parameters is smaller, the parameters of stable diffusion could not be utilized directly. Therefore, small stable diffusion set `layers_per_block=1` and select the first l... | 6083df8bd1f50970f900c607e2036d83 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Training Procedure After the initialization, the model has been trained for 1100k steps in 8xA100 GPUS. The training progress consists of three stages. The first stage is a simple pre-training precedure. In the last two stages, the original stable diffusion was utilized to distill knowledge to small model as a teach... | cbb67a1c412f3978ea4dd9ca9ad3400e |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Training Data The model developers used the following dataset for training the model: 1. [LAION-2B en aesthetic](https://huggingface.co/datasets/laion/laion2B-en-aesthetic) 2. [LAION-Art](https://huggingface.co/datasets/laion/laion-art) 3. [LAION-HD](https://huggingface.co/datasets/laion/laion-high-resolution) | d25c7b5f4f659a254ac30087846f2494 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Citation ```bibtex @article{Lu2022KnowledgeDO, title={Knowledge Distillation of Transformer-based Language Models Revisited}, author={Chengqiang Lu and Jianwei Zhang and Yunfei Chu and Zhengyu Chen and Jingren Zhou and Fei Wu and Haiqing Chen and Hongxia Yang}, journal={ArXiv}, year={2022}, volume={abs/2206... | 0b54614405c7ea2cf83b4f1e341a1f94 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other art... | e1115a656b78b826bed256fb85d3e2f5 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates histori... | 63460af1ecc1b0561bb54d2fff7f23f4 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from ... | fcc0f070fadfa3b3433bce712a6f9ed2 |
openrail | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hid... | 8561410c0f3ee6959d819008d10a362f |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_g... | 7848bc8c777441f43ab9807f8f91fdb4 |
mit | ['generated_from_trainer'] | false | TExAS-SQuAD-da This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-da dataset. It achieves the following results on the evaluation set: - Exact match: 63.96% - F1-score: 68.40% In comparison, the `jacobshein/danish-bert-botxo-qa-squad` model achieves 3... | 79d0576af9b120ac3c08b345899618f3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6438 | 1.0 | 4183 | 1.4711 | | 1.4079 | 2.0 | 8366 | 1.4356 | | 1.2532 | 3.0 | 12549 | 1.4509 | | 4d7eae3e3c3f8502f2f54b765f6bc33a |
apache-2.0 | ['generated_from_keras_callback'] | false | EdBianchi/GPT-2-finetuned-papers This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.4718 - Validation Loss: 2.2371 - Epoch: 0 | ae4fdf20dc694582fbb7ba23a223a26d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1':... | 8c075d7a5a18b7e64cdc687e6f654972 |
mit | ['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_ratio: 0.1 - num_epochs: 10 | f2ffd8d67447f44c5a6db4d3cb69a5e2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_toy_train_data_random_low_pass 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: 0.6572 - Wer: 0.4973 | a9c89f22379aa6c93d9f817eb09f7ba4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0834 | 2.1 | 500 | 3.4478 | 1.0 | | 1.0735 | 4.2 | 1000 | 0.9113 | 0.7815 | | 0.5516 | 6.3 | 1500 | 0.7035 | 0.6081 | |... | 265ce160d52bffdca778d4fdfeb117fb |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Precision: 0.9410 - Recall: 0.9529 - F1: 0.9469 - Accuracy: 0.9862 | f3d82ee0641ef3a0ce72585706982c4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0863 | 1.0 | 1756 | 0.0673 | 0.9231 | 0.9335 | 0.9283 | 0.9827 | | 0.0329 | 2.0 |... | 3d738c447f52c742ed6c5481c7eefbca |
apache-2.0 | ['speechbrain', 'embeddings', 'Commands', 'Keywords', 'Keyword Spotting', 'pytorch', 'xvectors', 'TDNN', 'Command Recognition', 'audio-classification'] | false | Command Recognition with xvector embeddings on Google Speech Commands This repository provides all the necessary tools to perform command recognition with SpeechBrain using a model pretrained on Google Speech Commands. You can download the dataset [here](https://www.tensorflow.org/datasets/catalog/speech_commands) Th... | cfcb9bd79271f01299086e7e5fdf9662 |
apache-2.0 | ['speechbrain', 'embeddings', 'Commands', 'Keywords', 'Keyword Spotting', 'pytorch', 'xvectors', 'TDNN', 'Command Recognition', 'audio-classification'] | false | Pipeline description This system is composed of a TDNN model coupled with statistical pooling. A classifier, trained with Categorical Cross-Entropy Loss, is applied on top of that. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampli... | cf7e7d63e29ddcf748965edf310403cd |
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