license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['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: 2000 - mixed_precis... | c7ebb0288ddcf30ab7081d842d7aefdd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4569 | 0.25 | 500 | 0.8556 | 105.5427 | | 0.5478 | 0.5 | 1000 | 0.7056 | 86.3373 | | 0.2269 | 0.75 | 1500 | 0.6320 | 11... | 8739e08618e1ceebbce5246df4e5da06 |
apache-2.0 | ['question-generation', 'summarization'] | false | Introduction This model checkpoint is obtained by first fine-tuning the sshleifer/distilbart-cnn-6-6 summarization checkpoint on the SQuAD dataset. After this, the 6-6 fine-tuned model is distilled down to a 3-3 model which gives us the final checkpoint. [GitHub Link for training scripts.](https://github.com/darth-c0... | 7f3fadd33a1fa0e0f41cf195bc87a841 |
apache-2.0 | ['question-generation', 'summarization'] | false | Dataset The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scena... | 02022238c6a914d7b84bd9f55c6e391f |
apache-2.0 | ['question-generation', 'summarization'] | false | [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, ... | 7460fc619080b74edb2775cc3d8a439d |
apache-2.0 | ['question-generation', 'summarization'] | false | Stats **Original Dataset** | Split | Num Docs | Num Contexts | Ques w/ Ans | Ques w/o Ans | Num Unique Ans | | ----- | -------- | ------------ | ----------- | ------------ | -------------- | | Train | 442 | 19035 | 86821 | 43498 | 86821 | | Dev | 35 | 1204 | 5928 ... | 1e2e98b4824b8dbe0e0137e9492b1ff4 |
apache-2.0 | ['speech'] | false | SEW-D-base [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speake... | 2fdea65c6a5fae1fdb95895d011e143d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Bengali 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 bn dataset. It achieves the following results on the evaluation set: - Loss: 0.3377 - Wer: 14.4623 | 9c89d0ce8f9d5e11ccf2ec9af9bdd69e |
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: 4 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | f251012cee2057a483daf8bdc22ba154 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:-------:| | 0.2431 | 1.92 | 1000 | 0.2604 | 33.5683 | | 0.1403 | 3.83 | 2000 | 0.1703 | 23.7591 | | 0.0799 | 5.75 | 3000 | 0.1429 ... | 71701f6ae7e593dfa1c3b9e902ff5f7e |
apache-2.0 | ['generated_from_keras_callback'] | false | Digitalwitness/distilgpt2-finetuned-shakespeare 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: 2.0603 - Validation Loss: 2.2069 - Epoch: 19 | 63c5730755e9d0f1090f434249d0e5a0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.4056 | 3.1490 | 0 | | 3.1359 | 2.9958 | 1 | | 2.9970 | 2.9052 | 2 | | 2.9003 | 2.8363 | 3 | | 2.8192 | 2.7759 | 4 | | 2.7524 |... | c284cfda93cb4414641c9b98c70190b0 |
mit | [] | false | Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain) This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created an... | 0a81ce3c0a65cf9df4a60f58160e57e6 |
mit | [] | false | model = Wav2Vec2ForPreTraining.from_pretrained(model_path) model = model.to(device) model = model.half() model.eval() wav, sr = sf.read(wav_path) input_values = feature_extractor(wav, return_tensors="pt").input_values input_values = input_values.half() input_values = input_values.to(device) with torch.no_grad(): ... | 18c376ec237705fc761e3ccb2bdd066e |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-small-ruquad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gene... | b32cc4bd06a518c73490755f57e63931 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года") ``` - With `transformers`... | f3459c0c96e2dd215605f20c329408c3 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:-----... | 130e0f6f060434ed7278a79f41eb37ea |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 5 - b... | 3f688dad35c596bf5d549fe7f0de81d2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium 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: 0.3029 - Wer: 9.0355 | 9ad94532feab5f7f42488a2ad0c694f1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0392 | 3.03 | 1000 | 0.2023 | 10.1807 | | 0.0036 | 7.01 | 2000 | 0.2478 | 9.4409 | | 0.0013 | 10.04 | 3000 | 0.2791 | 9.1014... | 9ed9968f44d0ff012bd76c0e7cb660bd |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-finetuned-ner-per-v6 This model is a fine-tuned version of [BeardedJohn/bert-ner-wikiann](https://huggingface.co/BeardedJohn/bert-ner-wikiann) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0155 - Validation Loss: 0.0025 - Epoch: 0 | 7b3a772491c807e6819edd51517ecc7a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 313, 'end_learning_rat... | e54772a539b613c29c2fcf8c3e757128 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_gender_male-5_female-5_s263 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | b1413f70f35b84c82696eb5564331f35 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/msmarco-distilbert-base-dot-prod-v3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 7becf1deae424c7a53a64ef20962b068 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 7ab06b7b2c35c9f54bde564f7db2649c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-dot-prod-v3) | a94989d4d49dfd450faf023fe1857d0b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mo... | b7a29f7061b7978ba1e530bdc17134be |
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: 0.4908 - Matthews Correlation: 0.4468 | 6910e41d58d60535255464a960b66f53 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5214 | 1.0 | 535 | 0.4908 | 0.4468 | | 5d41c9877e2d9e27b67327b868dfdf5b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1661 - F1: 0.8557 | a6f22351d047714469a5329d3ded27c4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2935 | 1.0 | 715 | 0.1887 | 0.8216 | | 0.1476 | 2.0 | 1430 | 0.1625 | 0.8473 | | 0.0955 | 3.0 | 2145 | 0.1661 | 0.8557 | ... | 73ba482ad066b0302fe4508da3792223 |
openrail | [] | false | <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> <img src = 'https://images.unsplash.com/photo-1599623560574-39d485900c95?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'> | c256337326e142b7023d8b125ae762bf |
openrail | [] | false | Parameters Model was trained for 20 epochs, using params as follows. ``` per_gpu_train_batch_size: int = 2 self.per_gpu_eval_batch_size: int = 2 self.gradient_accumulation_steps: int = 1 self.learning_rate: float = 5e-5 self.weight_decay: float = 0.0 self.adam_epsilo... | d92b6fab902297583d864803f7870caa |
openrail | [] | false | Usage DialoGPT small version, finetuned on Buzz Scripts from Toy Story. Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-small-buzz-toy-story') model = AutoModelWit... | d929d6da47c312edd2c89e23ff32155a |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 180k (uncased) Seed 4 intermediate checkpoint 180k 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/goo... | dcab66f51e4f59c122e0e5ddab1e7ce2 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | 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-4-180k') model = BertModel.from_pretrained("multiberts-seed-4-180k") text = "Replace me by any text you'd like.... | 9b77b64c855e9c823f77b2d41674e566 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | 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 d5b5ec7b2e77bd3e10707141818b7e6c57ac6b3f pip install -e . cd egs2/amadeus/tts1 ./run.sh --skip_data_prep false --skip_train tru... | 6084b66ac64acff802e5a5dbb7fa9810 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/finetune_vits.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_amadeus_vits_finetune_from_jsut_32_sentence ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env... | ff2ffd7f44ea763897ad7f2a5578ffe0 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | 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 Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | d035b0a0703d91f78ea11952fc6259c5 |
apache-2.0 | ['generated_from_trainer'] | false | CR_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3663 - Accuracy: 0.9 | f495a26908515c34a9148adf9bc58d9b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6345 | 0.33 | 50 | 0.5656 | 0.66 | | 0.4704 | 0.66 | 100 | 0.3705 | 0.82 | | 0.3428 | 0.99 | 150 | 0.3186 | 0.... | 7a426d76ee261b76dd0f4f93e98bf5d8 |
mit | [] | false | Info >Model Used: Waifu Diffusion 1.2 >Steps: 3000 >Keyword: C.C (Use this in the prompt) >Class Phrase: 1girl_green_hair_yellow_eyes_anime  notebook. You can also... | 0604f4999871beea4133a88547f44e69 |
mit | ['generated_from_trainer'] | false | m2m100_418M-finetuned-ko-to-en4-finetuned-ko-to-en5 This model is a fine-tuned version of [inhee/m2m100_418M-finetuned-ko-to-en4](https://huggingface.co/inhee/m2m100_418M-finetuned-ko-to-en4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2863 - Bleu: 87.4185 - Gen Len: 9.7107... | 4b2dfe6cffcfb183888621c3626ff975 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_... | 28c22be19e14c33ef9bac8b661522219 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 105 | 0.3571 | 78.7464 | 9.5775 | | No log | 2.0 | 210 | 0.3410 | 81.9462 | 9.6505 | | No log |... | 0f48d30d53eb5f4b96740ef7154893ee |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium nan-tw 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 nan-tw dataset. It achieves the following results on the evaluation set: - Loss: 0.9100 - Wer: 42.0709 - Cer: 22.3681 | 833300e5e7c3f196f80470d1c4af8020 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precisio... | ec297150fb6e197c54849c69912441fc |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.0568 | 5.0 | 1000 | 0.7769 | 48.2706 | 26.0890 | | 0.0057 | 10.0 | 2000 | 0.8438 | 44.0722 | 23.9270 | | 0.0041 |... | 08b1f0e3168a4ed11696c2552b89ef13 |
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 None dataset. It achieves the following results on the evaluation set: - Loss: 2.9423 | 6b3f87c65d3306fb5eb7ca51ae79d52a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 65 | 3.3894 | | No log | 2.0 | 130 | 3.0268 | | No log | 3.0 | 195 | 2.9423 | | 693249d3b1b853588637645ce2145291 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Catalan 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. | d13b2da681c5e91e6a8a5e2e8eb4b57a |
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-ca") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ca") ``` | 316b1d6c74317cc3a8e0eb4a0b316634 |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | Chinese Prompt(prompt-t5-base-chinese) Model 中文NLP的Prompt模型[shibing624/prompt-t5-base-chinese](https://huggingface.co/shibing624/prompt-t5-base-chinese)，One model For All nlp task(OFA) 1. 在[ClueAI/PromptCLUE-base](https://huggingface.co/ClueAI/PromptCLUE-base)预训练模型上fine-tuned 了[pCLUE中文prompt数据集](https://github.com/C... | dbab5e4adeb95ab271f664246935c60a |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | Dataset) 2. 模型用[textgen](https://github.com/shibing624/textgen)的`T5Model`训练，复现脚本:[training_zh_prompt_model_demo.py](https://github.com/shibing624/textgen/blob/main/examples/T5/training_zh_prompt_model_demo.py) `prompt-t5-base-chinese` evaluate public test data： The overall performance of T5 on `pCLUE_test_public.jso... | f5871edcd8fbec0ce9e7e9899a57e8a6 |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | Feature PromptCLUE：大规模多任务Prompt预训练中文开源模型。 千亿中文token上大规模预训练，累计学习1.5万亿中文token，支持几十个不同类型的NLP任务，具有较好的零样本学习能力和少样本学习能力。针对理解类任务，如分类、情感分析、抽取等，可以自定义标签体系；针对生成任务，可以进行多样性的文本生成。 中文上的三大统一：统一模型框架，统一任务形式，统一应用方式： - 统一模型框架：采用Text-to-Text的生成式预训练模型进行统一建模。 - 统一任务形式：Prompt统一不同的NLP任务间的差异，转化为统一的text-to-text数据形式。 - 统一应用方式：对目标任务形成拿来即用的模型，下游... | 0e885fd489a6a33faec4d36682d61cb7 |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | Usage 本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持T5模型，通过如下命令调用： Install package: ```shell pip install -U textgen ``` ```python from textgen import T5Model model = T5Model("t5", "shibing624/prompt-t5-base-chinese") r = model.predict(["中文改错：为了让人们遵守交通规律，警查叔叔不分昼夜在忙碌。"]) print(r) | e11045d67a336c08cd25269983300cc2 |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | Usage (HuggingFace Transformers) Without [textgen](https://github.com/shibing624/textgen), you can use the model like this: First, you pass your input through the transformer model, then you get the generated sentence. Install package: ``` pip install transformers ``` ```python from transformers import T5ForCondi... | 9058054d9a5ca7c3a285eb02424de92a |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | 新闻分类(classify) ```bash Input: 分类任务： 折价率过低遭抛售基金泰和跌7.15%，证券时报记者 朱景锋本报讯 由于折价率在大盘封基中处于最低水平，基金泰和昨日遭到投资者大举抛售，跌幅达到7.15%，远超大盘。盘面显示，基金泰和随大盘高开，之后开始震荡走低，午后开始加速下行，几乎没有像样反弹。截至收盘时，在沪深300指数仅下跌2.56%的情况下，基金泰和收盘跌幅高达7.15%，在所有封基中跌幅最大，而昨日多数封基跌幅在2%左右。 选项：财经，娱乐，时政，股票 答案： Model output: 财经 ``` | 0854cf45c3dd6d23b638fdf70e68bcbd |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | 阅读理解(generate) ```bash Input: 阅读文章，给出答案： 段落： 港汇指数，全称港元实际汇兑指数（Effective Exchange Rate Index for the Hong Kong Dollar）是由香港政府统计处编制的一项指数，以反映港元与香港主要贸易伙伴之货币的名义有效汇率加权平均数的变动情况。加权比重是按1999年至2000年平均贸易模式所制定，但政府并未有公布详细的计算公式。旧港汇指数基准日为2000年1月1日，基数为100点。由2012年1月3日起，新系列港汇指数 (包括15种货币及以2010年1月 = 100) 已取代旧港汇指数系列。港汇指数的作用，主要是用于反映香港的货品及服务的价... | 83d37fa081491553b08dec8ecbe1fd80 |
apache-2.0 | ['t5', 'pytorch', 'prompt', 'zh', 'Text2Text-Generation'] | false | 中文Prompt数据集 - 数据：[pCLUE中文prompt数据集](https://github.com/CLUEbenchmark/pCLUE) - 相关内容 - [Huggingface](https://huggingface.co/) - [PromptCLUE-base Model](https://huggingface.co/ClueAI/PromptCLUE-base) - [textgen](https://github.com/shibing624/textgen) 数据格式： ```text {"input": "哪个类别最好的描述了这篇新闻？扣篮王拉文：精彩暴扣表演！炸\n选... | 9821e4d3e24ee16812f4e46f312b95b6 |
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: 0.3222 - Accuracy: 0.87 - F1: 0.8704 | bfdccf8d0dffda071b299033b0f83549 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Towards Robust Named Entity Recognition for Historic German Based on [our paper](https://www.aclweb.org/anthology/W19-4312/) we release a new model trained on the LFT dataset. **Note:** We use BPEmbeddings instead of the combination of Wikipedia, Common Crawl and character embeddings (as used in the paper), so save ... | de854c5ef9ea8b34df15cb0021dd96fb |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Results | Dataset \ Run | Run 1 | Run 2 | Run 3† | Avg. | ------------- | ----- | ----- | --------- | ------------ | Development | 76.32 | 76.13 | **76.36** | 76.27 | Test | 77.07 | 77.35 | 77.20 | 77.21 Paper reported an averaged F1-score of 77.51. † denotes that this model is selected for upload... | acac725873e859dacf5be16d43dcfa06 |
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: 0.7550 - Matthews Correlation: 0.5265 | 5dadcb5471ab0a5bf888777ffd62d770 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5296 | 1.0 | 535 | 0.5144 | 0.4215 | | 0.3504 | 2.0 | 1070 | 0.4903 | 0.5046 | | 0.2... | a3d61b96a0951f340572100a65ad0133 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_rte_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6926 - Accuracy: 0.5271 | c1f92e7bf8b713bfb85362b2648b169f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6935 | 1.0 | 20 | 0.6926 | 0.5271 | | 0.6934 | 2.0 | 40 | 0.6930 | 0.5271 | | 0.6931 | 3.0 | 60 | 0.6932 | 0.... | 60a050fc264e2bf8bdeb315376e5c99d |
apache-2.0 | ['generated_from_trainer'] | false | bert-uncased-keyword-discriminator This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1296 - Precision: 0.8439 - Recall: 0.8722 - Accuracy: 0.9727 - F1: 0.8578 - Ent/precision: 0.... | c3e33cc3748dd7f569137332a31d79e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:----... | 330510e48630feaeb0691bb20dae01ea |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_no-pretraining_s6 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been ... | 99b412e23a23fde68efad62c4996eb32 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | ja_core_news_sm Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `ja_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parse... | fb2baaae1ceef084521e303280a9643c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (65 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=VERB`, `POS=SCONJ`, `POS=AUX`, `POS=PUNCT`, `POS=PART`, `POS=DET`, `POS=NUM`, `POS=ADV`, `POS=PRON`, `POS=ADJ`, `POS=PROPN`, `POS=CCONJ`, ... | d41780e83cf8677ec598d074c967c56e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.37 | | `TOKEN_P` | 97.65 | | `TOKEN_R` | 97.90 | | `TOKEN_F` | 97.77 | | `POS_ACC` | 96.09 | | `MORPH_ACC` | 0.00 | | `MORPH_MICRO_P` | 34.01 | | `MORPH_MICRO_R` | 98.04 | | `MORPH_MICRO_F` | 50.51 | | `SENTS_P` | 98.63 | | `SENTS_R` | 99.21 | | `SENTS_F` | 9... | a59f81aca8ac2420618d8c556cc6d6b4 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4039194/ This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 50f1a5f637d733cf1feb063c811d3a54 |
apache-2.0 | ['translation'] | false | opus-mt-sv-bzs * source languages: sv * target languages: bzs * OPUS readme: [sv-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-bzs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | bdb690f8e4ffaefb763003fd35edafb8 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0419 - Accuracy: 0.9834 | 0d37bb2516c8d5a64962d73059decedc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.352 | 1.0 | 527 | 0.2383 | 0.9065 | | 0.2104 | 2.0 | 1054 | 0.1154 | 0.9562 | | 0.1764 | 3.0 | 1581 | 0.0837 | 0.... | 1c7c05e9311f1e87bf249a3531788cf1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 1000 | 522e9491ec55bf5e05a4511ff31c2901 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-mbert-ours-run-5 This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2437 - Accuracy: 0.665 - Precision: 0.6223 - Recall: 0.5991 - F1: 0.6039 | ecd74c9886b436602f0f7ebc4dbba00c |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 112235565f77dbddc2b75339a4fc0fde |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9643 | 1.0 | 100 | 0.7996 | 0.69 | 0.6596 | 0.6593 | 0.6521 | | 0.6951 | 2.0 |... | 94cdc9c6115f304def626a5f8338be21 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.4130 - F1: 0.6851 | 1a8eb32d735df7358a19262fedc444b7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1435 | 1.0 | 50 | 0.5604 | 0.5493 | | 0.513 | 2.0 | 100 | 0.4557 | 0.6504 | | 0.3744 | 3.0 | 150 | 0.4130 | 0.6851 | ... | 2b6c8bceb83a06b5c6b20278d156be49 |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/bert-base-uncased-mlm-electronics-attribute-correction This model is a fine-tuned version of [ksabeh/bert-base-uncased-mlm-electronics](https://huggingface.co/ksabeh/bert-base-uncased-mlm-electronics) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0524 - Validat... | 1d66931e657c19b1cd33aa1d7cd1ab37 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36848, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | ad4c1875e5110d3ca36852d666bc5cf7 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'pytorch', 'NeMo', 'QuartzNet', 'QuartzNet15x5', 'faroese', 'faroe islands'] | false | stt_fo_quartznet15x5_sp_ep163_100h **NOTE! This model was trained with the NeMo version: nemo-toolkit==1.10.0** The "stt_fo_quartznet15x5_sp_ep163_100h" is an acoustic model created with NeMo which is suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model ["QuartzNet15x5Base... | 0a3fabd6614527beb33e854d2df68ebb |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'pytorch', 'NeMo', 'QuartzNet', 'QuartzNet15x5', 'faroese', 'faroe islands'] | false | Acknowledgements Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Mi... | 609d4e40609c6125cec0e7f335fb1569 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-weaksup-100-NOpad-early 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: 2.0768 - Rouge1: 28.7908 - Rouge2: 10.6989 - Rougel: 20.534 - Rougelsum: 24.129... | 25652ba9fc8d885b385ee1447961d850 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 100 | 1.8905 | 31.1534 | 13.7074 | 21.6489 | 27.0709 | 64... | 95536480773eabe321a740b2d48de1fd |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_wav2vec2_s515 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 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | bd193a873c67752feb894f435b2c1944 |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | 9c5e6963cd6c03c62a8fc7a3df9f5654 |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-NL 2B) was pre-trained on [the Pile](https://github.com/EleutherAI/the-pile), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai/). Parts of the dataset include code data. | 6777f0298ae9fae52dd56855e5dbd68b |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-2B-nl") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-2B-nl") text = "def... | d636fb647454fce69c82cbe07634b74f |
mit | ['exbert'] | false | Overview **Language model:** gelectra-base-germanquad **Language:** German **Training data:** GermanQuAD train set (~ 12MB) **Eval data:** GermanQuAD test set (~ 5MB) **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 | 5d368867a49b79af66f6be27c98ae662 |
mit | ['exbert'] | false | Details - We trained a German question answering model with a gelectra-base model as its basis. - The dataset is GermanQuAD, a new, German language dataset, which we hand-annotated and published [online](https://deepset.ai/germanquad). - The training dataset is one-way annotated and contains 11518 questions and 11518 ... | a7d634ff09a65730a7980d8986174f17 |
mit | ['exbert'] | false | Performance We evaluated the extractive question answering performance on our GermanQuAD test set. Model types and training data are included in the model name. For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset. The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned... | 86b38c7f8ceef9ec3770a49f3148a1ba |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | d75c2ebfdd1b573c08bc8eca4685bf2f |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-nonstrict-epoch-3' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSeque... | 165cfe77a3b0e15c85272ed35f79dbe5 |
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: 0.7629 - Matthews Correlation: 0.5556 | 84f4eb6b0e47598520ccb42527dd23ca |
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