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 results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2437 | 1.0 | 878 | 0.0708 | 0.9140 | 0.9188 | 0.9164 | 0.9807 | | 0.0545 | 2.0 |... | 486f240fe9fa40452187b14346543b7c |
mit | ['generated_from_trainer'] | false | finetuned-dem-patienten-in-der-ausubung-des-berufes-sicherheit-gewaehrleisten This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4416 - Accuracy: 0.7992 - F1: 0.7973 | 69a5cc6c3a482c23cca59aa45932d4a8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4756 | 1.0 | 1365 | 0.4355 | 0.7964 | 0.7919 | | 0.3686 | 2.0 | 2730 | 0.4416 | 0.7992 | 0.7973 | | 94d7ff9e3e3e2b215f28c8bfa196d70a |
cc-by-4.0 | [] | false | Mirror of OpenFold parameters as provided in https://github.com/aqlaboratory/openfold. Stopgap solution as the original download link was down. Updated based on the s3 bucket parameter update. All rights to the authors. OpenFold model parameters, v. 06_22. | ddd2d3e24e87a14294a020c2acb544ce |
cc-by-4.0 | [] | false | Training details: Trained using OpenFold on 44 A100s using the training schedule from Table 4 in the AlphaFold supplement. AlphaFold was used as the pre-distillation model. Training data is hosted publicly in the "OpenFold Training Data" RODA repository. To improve model diversity, we forked training after the init... | e6af854ee7ba2f0900fd02d8aeeee0bd |
cc-by-4.0 | [] | false | Parameter files: Parameter files fall into the following categories: initial_training.pt: OpenFold at the end of the initial training phase. finetuning_x.pt: Checkpoints in chronological order corresponding to peaks in the validation LDDT-Ca during the finetuning phase. Roughly even... | 18f4cae58b6fd7570c1a77e941144b3e |
apache-2.0 | ['generated_from_trainer'] | false | toy-qa 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: 0.2410 | 210ee23162e4ad388633868b4f9013f9 |
mit | ['generated_from_trainer'] | false | nbme-gpt2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3684 - Accuracy: 0.5070 | 70c9878221cb25be7a64b21df695d550 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - 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 - num_epoc... | 80ba29dba951fbdcd6329e3f6740cdf6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.99 | 101 | 2.5636 | 0.4809 | | No log | 1.99 | 202 | 2.4075 | 0.5018 | | No log | 2.99 | 303 | 2.3684 | 0.... | ec261e450c762cbe1f1f537dc0051838 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning'] | false | Evaluation on Common Voice FR Test ```python import re import torch import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation" model = Wav2Vec2ForCTC.from_pretrained(model_n... | c32478c464ac57cddac3f8344ef6aa88 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning'] | false | remove duplicates batch["target"] = re.sub('\.+', '.', batch["target"]) batch["target"] = re.sub('\?+', '?', batch["target"]) batch["target"] = re.sub('!+', '!', batch["target"]) batch["target"] = re.sub(',+', ',', batch["target"]) return batch result = ds.map(map_to_pred, batched=True, batch_size... | 00b2864e34f93e5403005a90dce74111 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning'] | false | Some results | Reference | Prediction | | ------------- | ------------- | | il vécut à new york et y enseigna une grande partie de sa vie. | il a vécu à new york et y enseigna une grande partie de sa vie. | | au classement par nations, l'allemagne est la tenante du titre. | au classement der nation l'allemagne est l... | 710184961dfb58e2a8e6f4b6e60e7cf7 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-sv_s703 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 1ab0ac1877d550349a42e3a185d97931 |
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: 0.6532 - Matthews Correlation: 0.5198 | 38e2ea40f49929d1742177722df1d632 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5228 | 1.0 | 535 | 0.5270 | 0.4212 | | 0.3448 | 2.0 | 1070 | 0.5360 | 0.5073 | | 0.2... | 642a160bdc0eb070f63b9f1cf44d9b12 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Phase Dreambooth model trained by Squirz with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusio... | 9a6a3e82cd5b545c6619e47d9abf994e |
mit | ['autogenerated-modelcard'] | false | Model Details **Model Description:** roberta-large-mnli is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://huggingface.co/datasets/multi_nli) corpus. The model is a pretrained model on English language text using a masked langua... | a051dd374fb6caf5ee5e62e9bc54abc1 |
mit | ['autogenerated-modelcard'] | false | How to Get Started with the Model Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so: ```python from transformers import pipeline classifier = pipeline('zero-shot-classification', model='roberta-large-mnli') ``` You can then use this pipelin... | fceefece883bd5b5abe54987358181c7 |
mit | ['autogenerated-modelcard'] | false | Direct Use This fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (see the [GitHub repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for examples) and zero-shot sequence classification. | 6271f567ecad41cb227c3c3edd6d5a0a |
mit | ['autogenerated-modelcard'] | false | Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclan... | 03e295a1238e984ab5ac88d374a15b2d |
mit | ['autogenerated-modelcard'] | false | Training Data This model was fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. Also see the [MNLI data card](https://huggingface.co/datasets/multi_nli) for more information. As described in the [RoBERTa large model card](https://huggingface.co/roberta... | 16c178114d6162488ccb30a82a5d3811 |
mit | ['autogenerated-modelcard'] | false | Preprocessing As described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of > the model take pieces of 512 contiguous token that may span over documents. The beginning of... | 5b4817a8491a34aeb032d7f69b80fdb7 |
mit | ['autogenerated-modelcard'] | false | Pretraining Also as described in the [RoBERTa large model card](https://huggingface.co/roberta-large): > The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The > optimizer used is Adam with a learning rate of 4e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98... | e1cd2aea545d30cc3c610a114f6a8ff3 |
mit | ['autogenerated-modelcard'] | false | Testing Data, Factors and Metrics The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics: - **Dataset:** Part of [GLUE (Wang et al., 2019)](https://arxiv.org/pdf/1804.07461.pdf), the General Language Understanding Evaluation benchmark, a collection of 9... | 9b34e7f50a3351095987f1fb8a66d4d9 |
mit | ['autogenerated-modelcard'] | false | Results GLUE test results (dev set, single model, single-task fine-tuning): 90.2 on MNLI XNLI test results: | Task | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | |:----:|:--:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | |91.3... | 162cbe29b96ca367d2deb3ffb7bfaf73 |
mit | ['autogenerated-modelcard'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1907.11692.pdf). - **Hardware Type:** 1024 V100 GPUs - **Hours used:** 24 hours (one day) - **Cloud Provider:** Unknown - **Compute Region:*... | 346b1683d20c65d5fa44ba37ea5a11a8 |
mit | ['autogenerated-modelcard'] | false | Citation Information ```bibtex @article{liu2019roberta, title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach}, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin ... | 3897dedf0bbf426ff47b0175d675d1c4 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on accentcombinedlenous8ktq9 to apply classification on accent **Metrics of the best model:** accuracy 0.947980 recall_macro 0.749094 precision_macro 0.622545 f1_macro 0.656714 Name: LogisticRegression(C=1, class_weight='balanced', max_iter=1000), dtype: float6... | 944119bfaec639cdd055a4986d765e2e |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless word False False False ... False True False kana False False False ... False True False kind False False False .... | 1e0ccb70ac0138bf2320f0b603eb1dd4 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wra... | abc5a534fe85d6696b8bfd8ca123bf2d |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label sk-toggleable__label-arrow">Eas... | e7e33c4e21813a7fab01f1cdc09d98cd |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 - Accuracy: 0.9158 | e0f2e47ca59d2130d2a158ffb9d21293 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2883 | 1.0 | 318 | 3.2778 | 0.7390 | | 2.6185 | 2.0 | 636 | 1.8740 | 0.8232 | | 1.5423 | 3.0 | 954 | 1.1579 | 0.... | 176e8c972785c8ee9ceb08996873e0b2 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-ner-finetuned2-ner This model is a fine-tuned version of [StivenLancheros/roberta-base-bne-finetuned-ner](https://huggingface.co/StivenLancheros/roberta-base-bne-finetuned-ner) on the conll2002 dataset. It achieves the following results on the evaluation set: - Loss: 0.1067 - Precision: 0.8... | 49b2acb4351d847abb49c1fb5ac38fce |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 7d7ac702d065dee0acb6f5f81a022853 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0582 | 1.0 | 1665 | 0.0852 | 0.8697 | 0.8759 | 0.8728 | 0.9800 | | 0.0297 | 2.0 |... | 8787ff38b6d45d889ce5906faec96120 |
apache-2.0 | ['translation'] | false | opus-mt-efi-fi * source languages: efi * target languages: fi * OPUS readme: [efi-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/efi-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 51329d50f898cf9dfccf8a99fbb36bc2 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-nl_s423 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... | 77fc7fba0466c2f924670a6ebd649070 |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | MahaSBERT-STS A MahaSBERT model (l3cube-pune/marathi-sentence-bert-nli) fine-tuned on STS dataset. <br> This is released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP <br> More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.1118... | 4906c78d9fd59724105676bd61fa6252 |
apache-2.0 | ['generated_from_trainer'] | false | phishing-bert-base-uncased-finetuned-dsV0_10epochs 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: - eval_loss: 0.0387 - eval_accuracy: 0.9966 - eval_f1: 0.9630 - eval_precision: 0.9984 - ... | 1922035f6b44cbbbf2ab62a0631e80be |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-tw-gpt Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on zh-tw using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 81c58de2a227609402fd7fad4e2aeb5a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Usage [Colab trial](https://colab.research.google.com/drive/1e_z5jQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing) ``` import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, AutoTokenizer, AutoModelWithLMHead ) import torch import re im... | 042ba23d02543d0576b4a2b7e32d4a3e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name) tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") gpt_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(dev... | 98fe2396aa3511eca94d4e8fe90bb71f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the zh-tw test data of Common Voice. CER calculation refer to https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese env setup: ``` !pip install editdistance !pip install torchaudio !pip install datasets transformers ``` | 902ffa02f9318ca97462ed6978794a37 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation without LM: ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys from transformers import AutoTokenizer, AutoModelWithLMHead from datasets import Audio from math import log mode... | 2e5417da8033f0027cfea5ef895af49f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name... | 8f6b4eb14f7a3e3faa5a19ecc924b314 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation with GPT: ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys from transformers import AutoTokenizer, AutoModelWithLMHead from datasets import Audio from math import log model_... | 5b160c920c1a5c4a9478aaf7edd625a5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name... | 035e094e72e6b0045a3b1ea323f57f2b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation with GPT + beam search: ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys from transformers import AutoTokenizer, AutoModelWithLMHead from datasets import Audio from math impo... | fc1a75625f07a41574a9c5869c6cfbde |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" tokenizer = AutoTokenizer.from_pretrained("ckiplab/gpt2-base-chinese") lm_model = AutoModelWithLMHead.from_pretrained("ckiplab/gpt2-base-chinese").to(device) model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name... | 6239944a7f4d94bd7d9d024cb197ad1d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation with BERT: ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys from transformers import AutoTokenizer, AutoModelForMaskedLM model_name = "voidful/wav2vec2-large-xlsr-53-tw-gpt" ... | 061ff9b1ffafdb531c1123910ceb90dc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") lm_model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese").to(device) model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(processor_name) ds = load_da... | 2e8d8c6c5232f2f2ad9f121c2b7062a0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation with T-TA: setup ``` !git clone https://github.com/voidful/pytorch-tta.git !mv ./pytorch-tta/tta ./tta !wget https://github.com/voidful/pytorch-tta/releases/download/wiki_zh/wiki_zh.pt ``` ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForC... | d6ca2807e9332294a7313dda21ed1907 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | $%&()*+,\-.\:;<=>?@\[\]\\\/^_`{|}~]" tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") lm_model = TTALMModel("bert-base-chinese") tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") lm_model.load_state_dict(torch.load("./wiki_zh.pt",map_location=torch.device('cuda'))) lm_model.to('cuda') lm_m... | 933c8c02f656f8f682a8737cf61601b4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-kor-lr-5e-4 This model is a fine-tuned version of [teddy322/wav2vec2-large-xls-r-300m-kor-lr-5e-4](https://huggingface.co/teddy322/wav2vec2-large-xls-r-300m-kor-lr-5e-4) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6605 - eval_wer:... | bd57198cb851e78af90709ca6d2f4bc9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 78c0c15440e17a43c2d9a9c13cf19478 |
apache-2.0 | ['xlm-roberta-large'] | false | Model description This model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported. You can use hosted inference API to infer a label for a headli... | 496e946278c7979811878226192c3c0e |
apache-2.0 | ['xlm-roberta-large'] | false | How to use ```python from tqdm.notebook import tqdm from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline def get_batch(data, batch_size): start_index = 0 while start_index < len(data): end_index = start_index + batch_size batch = data[start_index:end_index] ... | 2279dfe1c6418c2283a162ce9ea765db |
apache-2.0 | ['xlm-roberta-large'] | false | Training data * HuggingFace dataset: [IlyaGusev/headline_cause](https://huggingface.co/datasets/IlyaGusev/headline_cause) * GitHub: [IlyaGusev/HeadlineCause](https://github.com/IlyaGusev/HeadlineCause) | a79fcc795e65120599662d4bb32866b7 |
apache-2.0 | ['xlm-roberta-large'] | false | Training procedure * Notebook: [HeadlineCause](https://colab.research.google.com/drive/1NAnD0OJ0TnYCJRsHpYUyYkjr_yi8ObcA) * Stand-alone script: [train.py](https://github.com/IlyaGusev/HeadlineCause/blob/main/headline_cause/train.py) | 43d89cf9fe22307a77ae9e6b7956a65e |
apache-2.0 | ['xlm-roberta-large'] | false | BibTeX entry and citation info ```bibtex @misc{gusev2021headlinecause, title={HeadlineCause: A Dataset of News Headlines for Detecting Causalities}, author={Ilya Gusev and Alexey Tikhonov}, year={2021}, eprint={2108.12626}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | ec544ac3a60e84789c6b9c85ab22ddae |
mit | ['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingDynamic'] | false | Post-training dynamic quantization This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [facebook/ba... | 323ab26d9593a2088acc3081ae559fba |
mit | ['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingDynamic'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained( 'Intel/bart-large-cnn-int8-dynamic', ) ``` | c2d10f9031e19111ef00e47e27ec9467 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0106 | f554460f19eccf40161ae2e258a413be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0626 | 1.0 | 5533 | 1.0308 | | 0.8157 | 2.0 | 11066 | 1.0106 | | 21eb500bd42374ceceb59124199be3e9 |
mit | ['roberta-base', 'roberta-base-epoch_78'] | false | RoBERTa, Intermediate Checkpoint - Epoch 78 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 2a167345acd36ad74d62874f0e85c981 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | dyc0001 Dreambooth model trained by anmol-chawla with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-... | c306864b785104edd2b48e19f2cd252b |
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: 0.5268 - Accuracy: 0.838 - F1: 0.8228 | 25e90c300ed463a06e442dea4d7f8b47 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.9225 | 1.0 | 250 | 0.5268 | 0.838 | 0.8228 | | 713365461cf2cf2fe34e07e3dd0c758d |
apache-2.0 | ['generated_from_trainer'] | false | paraphrase-MiniLM-L12-v2-CoLA This model is a fine-tuned version of [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4636 - Matthews Correlation: 0.5057 | 78e0e7dcc48ea94ec31cee3a88c09b24 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 30198 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - ... | 28dcba9e9bc4479656140cdb407fb501 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5747 | 1.0 | 67 | 0.5394 | 0.3455 | | 0.5025 | 2.0 | 134 | 0.4999 | 0.4270 | | 0.3... | c206f05b1eb707eb2f313ab9635d4364 |
apache-2.0 | ['translation'] | false | mkd-spa * source group: Macedonian * target group: Spanish * OPUS readme: [mkd-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mkd-spa/README.md) * model: transformer-align * source language(s): mkd * target language(s): spa * model: transformer-align * pre-processing: normalization + S... | 1d2b6f81b9b707fb957f71fa65f25830 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: mkd-spa - source_languages: mkd - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mkd-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['mk', 'es'] - src_constituents: {'mkd'} - tgt_const... | aef77fd2ce7e6f7e8d26ab82978c2c9f |
apache-2.0 | ['image-classification', 'vision'] | false | Data2Vec-Vision (large-sized model, pre-trained only) BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.o... | 982e6d70c10ae093ff0f17acad416e00 |
apache-2.0 | ['image-classification', 'vision'] | false | Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 1 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance. | 5b471f8003f558225c2de31b902418c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0... | 328ccc475d32e8cf5376fbf5b6562447 |
apache-2.0 | ['generated_from_keras_callback'] | false | qp321/distilbert-base-uncased-finetuned-cola 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: - Train Loss: 0.1122 - Validation Loss: 0.6352 - Train Matthews Correlation: 0.52... | 77f193567c6e955e0e2fc8233f461271 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.3241 | 0.4856 | 0.5251 | 0 | | 0.1893 | 0.5330 | 0.5158 | 1 | | 0.1122 | 0.6352... | 63a06a585ef0dc0ccb3fffb874869bee |
mit | ['spacy', 'text-classification'] | false | Text statistics including readability and formality. | Feature | Description | | --- | --- | | **Name** | `en_statistics` | | **Version** | `0.0.1` | | **spaCy** | `>=3.1.1,<3.2.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `syllables`, `formality`, `readability` | | **C... | bc278048ea9a32e54dbc4ac08090354d |
mit | ['spacy', 'text-classification'] | false | Label Scheme <details> <summary>View label scheme (96 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT... | 1d4c3cf42b2024d450e2629a2385d35e |
apache-2.0 | [] | false | Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-smiles2caption", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-smiles2caption') input_text = 'C1=CC2=C(C(=C1)[O-])N... | a99b6ea7a0ee326e22ab15ed8efd16a4 |
apache-2.0 | [] | false | Poets The model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the [fine-tuning notebook](https://colab.research.google.com/github/hooshvare/parsgpt/blob/master/notebooks/Persian_Poetry_FineTuning.ipynb). ```text <s>رودک... | 948b826a702127836465146b5bba2b14 |
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: 0.1658 - Accuracy: 0.928 - F1: 0.9284 | b0bee3e6e4996538ca00bc0e4f1a61b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2188 | 1.0 | 250 | 0.1809 | 0.925 | 0.9246 | | 0.1383 | 2.0 | 500 | 0.1658 | 0.928 | 0.9284 | | 988d44f6af8832f76d0107f117c4dc65 |
mit | [] | false | RD paintings on Stable Diffusion This is the `<rd-painting>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can ... | 88fa97dec0560d8803b206b9de9ff43d |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_xls-r_s662 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 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | 0d6716ef1d56301bf174a8b0bfa287ed |
mit | [] | false | model by AlbertoTrunk This your the Stable Diffusion model fine-tuned the Zombie head concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks zombie** You can also train your own concepts and upload them to the library by using [this notebook](https://co... | a62eddbb53fa75ec45144824fd0bcf50 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-qqp This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.2260 - Accuracy: 0.9067 - F1: 0.8714 - Combined Score: 0.8891 | 828e17fa01eec4c84f62b38ea8d05af4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.2922 | 1.0 | 2843 | 0.2523 | 0.8943 | 0.8604 | 0.8773 | | 0.1837 | 2.0 | 5686 | ... | efb316d93c303bcc888bf0999708fc1e |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the cybercity concept trained by lzghades. This is a Stable Diffusion model fine-tuned on the cybercity concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of cybercity city** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation... | f31eb41d9e3eea83900d7e65446d0609 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on `city` images for the wildcard theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. | ac34674757cc95703a2dd18cd9aa3c9c |
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: 0.2227 - Accuracy: 0.9255 - F1: 0.9255 | 65685b2e2094fc46bfe1eb44b3b3b61d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8417 | 1.0 | 250 | 0.3260 | 0.9045 | 0.9006 | | 0.2569 | 2.0 | 500 | 0.2227 | 0.9255 | 0.9255 | | 01bdf967f7a30cf0b4309bc2b4f0d647 |
mit | [] | false | Isabell Schulte pviii - 4 tiles - 1 lr - 3000 steps - Style on Stable Diffusion This is the `<isabell-schulte-p8-4tiles-1lr-300s-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/bl... | e74e95f3f9e46dc1c8cd9c115bfb4214 |
mit | ['deberta-v1', '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 repositor... | c2b407a02302116c80612ad498150611 |
mit | ['deberta-v1', '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... | eb8fde1a78c407a0046a83378ac5cc0d |
mit | ['generated_from_trainer'] | false | indobert-squad-trained This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8025 | 86ab30fbd9df4635fa8fd009409f3917 |
mit | ['generated_from_trainer'] | false | IndoBERT [IndoBERT](https://huggingface.co/indolem/indobert-base-uncased) is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources: - Indonesian Wikipedia (74M words) - news articles from Kompas, Tempo (Tala et al., 2003), and Liputan6 (55M words in total) ... | 8ebeac71c10e1a38e304e703a709ec33 |
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