license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | MAXIM pre-trained on Rain13k for image deraining MAXIM model pre-trained for image deraining. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first release... | d1c36ee3ba73608c523af6a377bbe565 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | Intended uses & limitations You can use the raw model for image deraining tasks. The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). | 255fb372851829adfb7881c402c88751 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | How to use Here is how to use this model: ```python from huggingface_hub import from_pretrained_keras from PIL import Image import tensorflow as tf import numpy as np import requests url = "https://github.com/sayakpaul/maxim-tf/raw/main/images/Deraining/input/55.png" image = Image.open(requests.get(url, stream=Tru... | 5ef5662baae766cce3fd660cc0884779 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mrpc_192 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: 0.5927 - Accuracy: 0.6887 - F1: 0.7784 - Combined Score: 0.7335 | 58d942f416ff143f620e7fe121b8dc73 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6412 | 1.0 | 15 | 0.6239 | 0.6838 | 0.8122 | 0.7480 | | 0.6281 | 2.0 | 30 | 0.62... | 716f043ac6de4e56839852b8a832f050 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody` ♻️ Imported from https://zenodo.org/record/5499050/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | b80a35fe64f253cb7e86bb8381ef8a9c |
apache-2.0 | ['translation'] | false | opus-mt-bcl-fi * source languages: bcl * target languages: fi * OPUS readme: [bcl-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | c1e7f4cd8cbed813ad238105e236c12d |
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: 0.1637 - F1: 0.8581 | 8d5fa9026fb641aa8d3795e7ad383a0b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Kuman-generator Dreambooth model trained by Kumar-kun with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get st... | 0965a0f1d5a369c2fdd2ae3c0799a2a1 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-squadshifts-reddit-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.com... | 5bc8cbf2260e495735fd0296ac1f3a4e |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (reddit) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://gith... | a86d119e827fb834a02a6149e5a2d1d6 |
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", "lmqg/t5-small-squadshifts-reddit-... | a2c03c146bd5e58156982d1cdfbfbfd7 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-reddit-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | Score | Type | Dataset ... | 29edde420ee0fbc3463265a70d989050 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: reddit - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoc... | cd7a0b4c10b21913bcf7fe1ee8ba5f80 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | t5-small-disfluent-fluent This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4839 - Bleu: 10.1823 | 33cbc4af2868844b35be3d94416cd32f |
apache-2.0 | ['vision', 'image-classification'] | false | ResNet-101 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been writ... | 2d7d7fe5f98796abb7e21e2779a07748 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image =... | 14c173d5c8b605dbe87617afd6ba5fc7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.5155 - Accuracy: 0.8002 | a03bd3e709c36561fce1ac8cde5035c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6904 | 1.0 | 4597 | 0.5155 | 0.8002 | | e4bdad5588d2df547368e19e19ede810 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Mannyv2 Dreambooth model trained by MannyD 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-diffus... | 577435cc3e6cc93e37fb47353660b0a0 |
apache-2.0 | ['trl', 'transformers', 'reinforcement-learning'] | false | TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. | 5d0f4eec21fb9ec37ea90fe8e4161cd3 |
apache-2.0 | ['trl', 'transformers', 'reinforcement-learning'] | false | Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="lewtun/dummy-trl-model") outputs = generator("Hello, my llama is cute") ``` ... | 048af2fda0b646582169aa297b760cad |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | aaureeliaav2 Dreambooth model trained by akahnn 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-d... | 1ad21ce66667360913922dc44e6ca2aa |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4272 - Wer: 31.6135 | 477f46d26c0324bee9afa49ab2e00187 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0879 | 2.44 | 1000 | 0.2916 | 33.7213 | | 0.0212 | 4.89 | 2000 | 0.3454 | 32.9679 | | 0.0016 | 7.33 | 3000 | 0.4065 | 31.744... | eb651ce4c29eee0b63a79d28a52a8686 |
apache-2.0 | ['vision', 'image-segmentation'] | false | SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and fir... | a5c8d2308a456031e9ea2e884dba5788 |
apache-2.0 | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor(reduce_lab... | b8324e2b12bad2e92d4721bb8824f016 |
creativeml-openrail-m | ['text-to-image'] | false | atmant_1_0_sd_1_5 Dreambooth model trained by clybrg with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/note... | 933b769d319b86cb368dc488f2206124 |
mit | ['aspect-based-sentiment-analysis', 'PyABSA'] | false | Note
This model is training with 30k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
| aeb2c55cdf0c3abace69b14edb209148 |
mit | ['aspect-based-sentiment-analysis', 'PyABSA'] | false | Training Model
This model is trained based on the FAST-LCF-BERT model with `microsoft/deberta-v3-large`, which comes from [PyABSA](https://github.com/yangheng95/PyABSA).
To track state-of-the-art models, please see [PyASBA](https://github.com/yangheng95/PyABSA).
| f2ccada9e33bc2eab0058d4438e96c36 |
mit | ['aspect-based-sentiment-analysis', 'PyABSA'] | false | Usage
```python3
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-large-absa-v1.1")
model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-large-absa-v1.1")
```
| 94f833632a4ca0da980c1707cfdd05ef |
mit | ['aspect-based-sentiment-analysis', 'PyABSA'] | false | Datasets
This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files:
```
loading: integrated_datasets/apc_datasets/SemEval/laptop14/Laptops_Train.xml.seg
loading: integrated_datasets/apc_datasets/SemEval/restaurant14/Restaurants_Train.xml.seg
loading: inte... | e94c659ba99596dcec8a3c47c5bdd6d8 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_vp-es_s506 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) 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 you... | 2b3a865b43cb81c106645daf80290727 |
apache-2.0 | ['generated_from_trainer'] | false | van-base-finetuned-eurosat-imgaug This model is a fine-tuned version of [Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-base) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0379 - Accuracy: 0.9885 | cc38b0dc34f20aa1201635001bdcf377 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0887 | 1.0 | 190 | 0.0589 | 0.98 | | 0.055 | 2.0 | 380 | 0.0390 | 0.9878 | | 0.0223 | 3.0 | 570 | 0.0379 | 0.... | 239899176937f14ae26983f1c19d3b20 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1792 - Pearson: 0.1721 - Spearmanr: 0.1790 - Combined Score: 0.1755 | 2b31b30053a80634e7cefc6674d610a1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 1.6404 | 1.0 | 23 | 1.2916 | 0.0486 | 0.0549 | 0.0518 | | 1.0137 | 2.0 | 46 ... | 4e030aef92beabd2bc6d7dfbc66e9b2f |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a truncated [PubMed Summarization](https://huggingface.co/datasets/ccdv/pubmed-summarization) dataset. It achieves the following results on the evaluation set: - Loss: 2.7252 - Rouge1: 19.4457 - Rouge2: 3.12... | 10202edbb99958c0a2bf788c1ae847ff |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.2735 | 1.0 | 13 | 2.9820 | 18.745 | 3.7918 | 15.7876 | 15.8512 | | 3.0428 | 2.0 |... | c1ad624f6325cdd717aef991c11b7d63 |
mit | ['generated_from_trainer'] | false | finetuned-im-rahmen-der-rechtlichen-und-ethischen-bestimmungen-arbeiten 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: 0.2919 - Accuracy: 0.8970 - F1: 0.8843 | 50d247a492f42d97f249f2634be493d1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2946 | 1.0 | 1365 | 0.2791 | 0.8992 | 0.8829 | | 0.2204 | 2.0 | 2730 | 0.2919 | 0.8970 | 0.8843 | | 6563d217ccc57cd32df1da481fb7969c |
apache-2.0 | ['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 [shivaniNK8/... | d1f7040347d6ef8d0bac20d9a4f628d5 |
apache-2.0 | ['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/t5-small-finetuned-cnn-news-int8-dynamic', ) ``` | 39690ce6490c43fb0593f7c4c62e6978 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb 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: 2.3114 | 8750b4ad4088df7d2e9eddbb7ae33406 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5561 | 1.0 | 782 | 2.3738 | | 2.4474 | 2.0 | 1564 | 2.3108 | | 2.4037 | 3.0 | 2346 | 2.3017 | | 9011513be875678bfc059f70c9b61bf4 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | stable-diffusion-kiminonawa-v1.2 Dreambooth model trained by Thaweewat 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/T... | dbfd3ffa5dbd0344f2ccb7598f527b9d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | Overview This folder contains a fully trained German speech recognition pipeline consisting of an acoustic model using the new wav2vec 2.0 XLS-R 1B **TEVR** architecture and a 5-gram KenLM language model. For an explanation of the TEVR enhancements and their motivation, please see our paper: [TEVR: Improving Speech ... | 1a5ddd94b21488e8acd2d8a9c4a6f2e9 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | Citation If you use this ASR pipeline for research, please cite: ```bibtex @misc{https://doi.org/10.48550/arxiv.2206.12693, doi = {10.48550/ARXIV.2206.12693}, url = {https://arxiv.org/abs/2206.12693}, author = {Krabbenhöft, Hajo Nils and Barth, Erhardt}, keywords = {Computation and Language (cs.CL), Sound (... | 2efcd2b5ab293aba57bdbfbd85b29d9f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | TEVR Tokenizer Creation / Testing See https://huggingface.co/fxtentacle/tevr-token-entropy-predictor-de for: - our trained ByT5 model used to calculate the entropies in the paper - a Jupyter Notebook to generate a TEVR Tokenizer from a text corpus - a Jupyter Notebook to generate the illustration image in the paper | 1fc39886800bfefd2f3f696771aa10c8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | Evaluation To evalue this pipeline yourself and/or on your own data, see the `HF Eval Script.ipynb` Jupyter Notebook or use the following python script: ```python !pip install --quiet --root-user-action=ignore --upgrade pip !pip install --quiet --root-user-action=ignore "datasets>=1.18.3" "transformers==4.11.3" li... | b1cd11458274f4b5f5647c8896dafa36 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | replace invisible characters with space allchars = list(set([c for t in testing_dataset['sentence'] for c in list(t)])) map_to_space = [c for c in allchars if unicodedata.category(c)[0] in 'PSZ' and c not in 'ʻ-'] replacements = ''.maketrans(''.join(map_to_space), ''.join(' ' for i in range(len(map_to_space))), '\'ʻ')... | 342e45156ca2bc678dac91858e4a9bc2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | load processor class HajoProcessor(Wav2Vec2ProcessorWithLM): @staticmethod def get_missing_alphabet_tokens(decoder, tokenizer): return [] processor = HajoProcessor.from_pretrained("fxtentacle/wav2vec2-xls-r-1b-tevr") | 4d51285dabf611999bb11ccd018ebe7b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard'] | false | print results print('WER', load_metric("wer").compute(predictions=all_predictions['prediction'], references=all_predictions['groundtruth'])*100.0, '%') print('CER', load_metric("cer").compute(predictions=all_predictions['prediction'], references=all_predictions['groundtruth'])*100.0, '%') ``` WER 3.64333990425232... | 98f9b3bdbb2b78ce65b6b638d1bc1037 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en` This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/). | 9b9aab11f03579161feebae95e493207 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Aug 6 11:44:39 JST 2021` - python version: `3.7.9 (default, Apr 23 2021, 13:48:31) [GCC 5.5.0 20171010]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.7.0` - Git hash: `0f7558a716ab830d0c29da8785840124f358d47b` - Commit date: `Tue Jun 8 15:33:49 2021 -0400` | 766f7e87c516e6c67c776cef51e41aad |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.5|1.3|0.2|0.2|1.7|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|... | dcc86b5f61ab268ce4dde07673f926dc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864... | 8b090eaf3aa29de9735570bf4419fe8a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|98.2|1.3|0.5|0.4|2.2|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|... | e2761908299c574a15828df474e0833d |
apache-2.0 | ['multiple-choice', 'int8', 'Intel® Neural Compressor', 'PostTrainingStatic'] | false | Post-training static 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 [thyagosme/be... | 6204f85da65de7fe3eb4b446770a922d |
apache-2.0 | ['multiple-choice', 'int8', 'Intel® Neural Compressor', 'PostTrainingStatic'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForMultipleChoice int8_model = IncQuantizedModelForMultipleChoice.from_pretrained( 'Intel/bert-base-uncased-finetuned-swag-int8-static', ) ``` | 25d6edaa75e39cb05bde352edc9218c7 |
apache-2.0 | ['translation'] | false | eng-bnt * source group: English * target group: Bantu languages * OPUS readme: [eng-bnt](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-bnt/README.md) * model: transformer * source language(s): eng * target language(s): kin lin lug nya run sna swh toi_Latn tso umb xho zul * model: trans... | 8aa1ec8fb1033f03bbf7d43a1704c3cc |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-kin.eng.kin | 12.5 | 0.519 | | Tatoeba-test.eng-lin.eng.lin | 1.1 | 0.277 | | Tatoeba-test.eng-lug.eng.lug | 4.8 | 0.415 | | Tatoeba-test.eng.multi | 12.1 | 0.449 | | Tatoeba-test.eng-nya.eng.nya ... | e2b1ca1575f822091ec166751c4cb768 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-bnt - source_languages: eng - target_languages: bnt - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-bnt/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'sn', 'zu', 'rw', 'lg', 'ts', 'ln', 'ny', 'xh',... | 625fcc0884c1f469a7a64569b23e515a |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.005-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2752 - Rmse: 0.5246 - Mse: 0.2752 - Mae: 0.4184 | b617a447e46952226fa2ffac930032ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2773 | 0.5266 | 0.2773 | 0.4296 | | 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 ... | 4d8cdf267cb048367b712bed05b08e18 |
apache-2.0 | ['finnish', 'gpt2'] | false | GPT-2 large for Finnish Pretrained GPT-2 large model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://... | bce00e83115f6d2f0f4f380ca7cd731c |
apache-2.0 | ['finnish', 'gpt2'] | false | How to use You can use this model directly with a pipeline for text generation: ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='Finnish-NLP/gpt2-large-finnish') >>> generator("Tekstiä tuottava tekoäly on", max_length=30, num_return_sequences=5) [{'generated_text': ... | 4a081c9a4a237873ecb16b50dbf24b5e |
apache-2.0 | ['finnish', 'gpt2'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 640k steps (a bit over 1 epoch, 64 batch size). The optimizer used was a AdamW with learning rate 4e-5, learning rate warmup for 4000 steps and cosine decay of the learning rate... | fcada85c3b4d90a93209bc94465e5919 |
apache-2.0 | ['finnish', 'gpt2'] | false | perplexity-for-language-models) (smaller score the better) as the evaluation metric. As seen from the table below, this model (the first row of the table) performs better than our smaller model variants. | | Perplexity | |------------------------------------------|------------|... | 081888c56b86c70c2051797630a83203 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Vietnamese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 262610dc5cf6fa3483393f1196a31968 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "vi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretr... | 5b6af548d9cff2ba3e8c65ffa4986980 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Vietnamese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "vi", split="test") wer ... | ba1adf958b9eb755c500d74538d92120 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | 05d284f2446ad8f2798c3e899a82fda7 |
apache-2.0 | ['speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-common_voice-tr-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3856 - Wer: 0.3556 | 5c9c3ae0a4d6ddaf3c954173a2795715 |
apache-2.0 | ['speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7391 | 0.92 | 100 | 3.5760 | 1.0 | | 2.927 | 1.83 | 200 | 3.0796 | 0.9999 | | 0.9009 | 2.75 | 300 | 0.9278 | 0.8226 | |... | 8cd2ba5e58c805f794f05e029a82f67a |
mit | [] | false | noggles on Stable Diffusion This is the `<noggles>` 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 also train... | a801431f9fda078e7925e294c8141f9d |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-jm-finetuned-panx-all_hub 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: 0.1725 - F1: 0.8551 | 4127f088a9a4ef742a709049543c7e08 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3036 | 1.0 | 835 | 0.1925 | 0.8094 | | 0.1537 | 2.0 | 1670 | 0.1792 | 0.8392 | | 0.0995 | 3.0 | 2505 | 0.1725 | 0.8551 | ... | bf895349897642bd894bbe36a22d738e |
apache-2.0 | ['generated_from_trainer'] | false | bert-mini-mlm-finetuned-emotion This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0041 | 5d9e0723d60342a707e99bff747a4b22 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.3034 | 22.73 | 500 | 3.0423 | | 3.0612 | 45.45 | 1000 | 3.0242 | | 2.9507 | 68.18 | 1500 | 2.9806 | | 2.8609 | 90.91 | 2000 | 3.0442 ... | ee6c7f70b1bbecce96943590f1706844 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | MAXIM pre-trained on RESIDE-Indoor for image dehazing MAXIM model pre-trained for image dehazing. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first rel... | 4f0456d03a7269e869033fed89bfa3e2 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | Intended uses & limitations You can use the raw model for image dehazing tasks. The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). | 6be28a8e08969e5ba0f0df17858a62ad |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | How to use Here is how to use this model: ```python from huggingface_hub import from_pretrained_keras from PIL import Image import tensorflow as tf import numpy as np import requests url = "https://github.com/sayakpaul/maxim-tf/raw/main/images/Dehazing/input/1440_10.png" image = Image.open(requests.get(url, stream... | 296a71ccb4b7cbcebc6d6fa17bb45135 |
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: 0.2086 - Accuracy: 0.9255 - F1: 0.9257 | 69329f20e1aad7f34c2025b8d281f38b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8249 | 1.0 | 250 | 0.3042 | 0.9085 | 0.9068 | | 0.2437 | 2.0 | 500 | 0.2086 | 0.9255 | 0.9257 | | 35f3b98b24b58eb46635852716a2af15 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-stsb-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-stsb](https://huggingface.co/muhtasham/tiny-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5105 - Accuracy: 0.7970 | 1ed36c062bfbf1dac501fd2112b3c7ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5917 | 0.24 | 500 | 0.4991 | 0.7649 | | 0.4448 | 0.48 | 1000 | 0.4693 | 0.7787 | | 0.3934 | 0.71 | 1500 | 0.4471 | 0.... | 6220e1bd57f0f03887f23dea4d5ee03b |
mit | ['exbert'] | false | CXR-BERT-general [CXR-BERT](https://arxiv.org/abs/2204.09817) is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization, and text augmentations. The resulting model demonstrates improved performance on radiology natural language ... | 36f78e5bf57ee1faaf8ca8062866f78e |
mit | ['exbert'] | false | Further information Please refer to the corresponding paper, ["Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing", ECCV'22](https://arxiv.org/abs/2204.09817) for additional details on the model training and evaluation. For additional inference pipelines with CXR-BERT, please refer to... | bd23f3bc1023c473dc251a8ff0961744 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-cnn-news This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.8412 - Rouge1: 24.7231 - Rouge2: 12.292 - Rougel: 20.5347 - Rougelsum: 23.4668 | 3296dad2fc3347d8519d025cacd97b14 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00056 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 2a58eb6ccb6c7da149e2275a60b67133 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.0318 | 1.0 | 718 | 1.8028 | 24.5415 | 12.0907 | 20.5343 | 23.3386 | | 1.8307 | 2.0 ... | 6f7821ef06071eb23e5313e5d94ba869 |
mit | ['generated_from_trainer'] | false | compassionate_hypatia This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the ... | 33bde23a9ea1e6a31149f9d70ec1877d |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 6108c53dde75a0187d9ab10fb90709f5 |
creativeml-openrail-m | ['text-to-image'] | false | Zlikwid Dreambooth model trained by Zlikwid 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-diffu... | 587856bb22cdc1ee685d5a9ac24be6f1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Hindi- Drishti Sharma 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: 0.3655 - Wer: 11.7663 | d50b137a31256446806f97e23232671d |
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: 100 - training_steps: 10000 - mixed_precisi... | 89eb27f4fa3413ff004ad96e2b8aa1ae |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0 | 12.22 | 10000 | 0.3655 | 11.7663 | | 005c166a918c0cc35e0a5ac4df4efc80 |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | xtreme_s_xlsr_300m_minds14 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.ALL dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9033 - Accuracy Cs-cz: 0.9164 - Accuracy De-de: 0.94... | 327baa6da86b7fcb4afe9951600efb1e |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=... | f2c5ca8e2a6fa02856a0380e8064dcd4 |
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