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 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9721 | 1.8113 | 0 | | 1.6564 | 1.5052 | 1 | | 1.3640 | 1.2332 | 2 | | 1.1078 | 0.9996 | 3 | | 0.9158 | 0.8249 | 4 | | 0.7850 |... | f43718744ff5ac82a04a157d93b068e8 |
mit | ['generated_from_trainer'] | false | kobart_32_5e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6396 - Rouge1: 35.8418 - Rouge2: 12.983 - Rougel: 23.6913 - Bleu1: 29.8... | 77a8f0d7e068c5f11c71079492916f25 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 4b0c573f8e0d45225fec744036ab65a1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 1.6643 | 3.78 | 5000 | 2.6396 ... | 9c8776f95867e17339f5e677148d3f08 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Prompts to start with : close up portrait of beautiful young woman in a bus by hrrzg Negative prompt: low quality, fake, painting, greyscale, night Steps: 30, Sampler: DPM++ 2S a Karras, CFG scale: 7.5, Size: 896x768, Model hash: e93cb7f3, Denoising strength: 0.7, First pass size: 640x512 ------- beautifu... | 16ced46ce4f40e3ad4a769c5e3da3610 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Sample pictures of this concept: .png) .png) . The dataset consists of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm [here](https://github.com/nagadomi/lbpcascade_animeface). All... | e16d5b0363b6a3b53049e908bfbf58b1 |
apache-2.0 | ['dcgan'] | false | How to use this model You can use this model to generate new anime faces. If you want to continuously train, use with [discriminator](https://huggingface.co/merve/anime-faces-discriminator) using `tf.GradientTape()` as mentioned in the DCGAN tutorial. ``` from huggingface_hub import from_pretrained_keras model = fro... | 61c0813cf9db8ca5d3e988e611ad2179 |
mit | ['image-generation', 'gan', 'stylegan', 'stylegan3', 'nvidia'] | false | Anime Faces Generator (StyleGAN3 by NVIDIA) <img width="679" alt="Generated Faces" src="https://user-images.githubusercontent.com/35907066/161809457-e6467724-5942-4a89-b379-85ddfd6ac86c.png"> This is a [StyleGAN3 PyTorch](https://github.com/NVlabs/stylegan3) model trained on this [Anime Face Dataset](https://github.... | 0c9166728e0295a7fb82078ba0885d79 |
mit | ['image-generation', 'gan', 'stylegan', 'stylegan3', 'nvidia'] | false | Usage Demo on Spaces is not yet implemented. You can run the model pickle file locally using the instructions in this generator-script-only subset of the StyleGAN3 repo: - https://github.com/venture-anime/stylegan3-anime-faces-generator | e494dc7d465ced73c950ea8314297589 |
mit | ['image-generation', 'gan', 'stylegan', 'stylegan3', 'nvidia'] | false | Dataset & Model Details The [Anime Face Dataset](https://github.com/bchao1/Anime-Face-Dataset) was created by Mckinsey666. Training was done in [Paperspace Gradient](https://gradient.run/) on a free `RTX-5000` instance with the following parameters: - Configuration: `stylegan3-t` - GPUs: `1` - Batch Size: `8` - Gam... | 32bffeb96d980edcceddece46e69c5a3 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-from-scratch-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0244 | 4f4246677749e8a34a29335bc740e52f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.6626 | 0.4 | 500 | 6.7278 | | 6.5829 | 0.8 | 1000 | 6.5388 | | 6.4637 | 1.2 | 1500 | 6.3776 | | 6.3446 | 1.6 | 2000 | 6.2966 ... | 8d07d4e6531b3d9e1e6cc51df502dee3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 3.7517 | | No log | 2.0 | 12 | 3.6951 | | No log | 3.0 | 18 | 3.6769 | | e9fe0cf167fc1a5accd372cf7e0742eb |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e1 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8952 - Ro... | dfa48a60dda2d3e1ed430ff047c042f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.686 | 1.0 | 795 | 0.8952 | 53.0722 | 32.4229 | 34.8749 | 50.1772 | 14... | e626bb3d0e239e469647af43222922ab |
apache-2.0 | [] | false | Chinese Pre-Trained XLNet This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection. We welcome all experts and scholars to download and use this model. This project is based on CMU/Goo... | b264b42467fb409740dd854a563c5fe8 |
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: 0.8219 - Matthews Correlation: 0.5338 | 5f505a41633642e6f356d71046d63a2f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5223 | 1.0 | 535 | 0.5457 | 0.4195 | | 0.3485 | 2.0 | 1070 | 0.5099 | 0.4865 | | 0.2... | c9a3c9f8c26cd1e10161190968fb52ff |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9271 - Recall: 0.9381 - F1: 0.9326 - Accuracy: 0.9836 | cb379a7b99edd16c102e28d2a1338ac9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2324 | 1.0 | 878 | 0.0688 | 0.9146 | 0.9264 | 0.9205 | 0.9816 | | 0.0517 | 2.0 |... | c4acd202cf1065926740a7dad51a40d4 |
mit | [] | false | Shenhe Genshin Impact on Stable Diffusion This is the `<shenhe-genshin-impact>` 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) ... | a95341f226e6fbd07a16a7d3a2420980 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-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: 0.2597 - Accuracy: 0.9477 | b159520fc7133b880225c77b38c0a194 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2126 | 1.0 | 318 | 3.1503 | 0.7529 | | 2.395 | 2.0 | 636 | 1.5569 | 0.8581 | | 1.1586 | 3.0 | 954 | 0.7708 | 0.... | 871828f1f1818298b0270f02701b028b |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-fr_s91 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) 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... | ebcbb853bdfaa3060b20c77a30494a82 |
apache-2.0 | ['generated_from_trainer'] | false | results 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: 0.1194 | 0b3bae1c8456e7276ce0761d2fd57e2b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 050de47b98182e00ee664fedc85a52e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1428 | 1.0 | 510 | 0.1347 | | 0.0985 | 2.0 | 1020 | 0.1189 | | 0.0763 | 3.0 | 1530 | 0.1172 | | 0.0646 | 4.0 | 2040 | 0.1194 ... | 46f698ae2f654ee3d33186ba21db07b2 |
apache-2.0 | ['longformer'] | false | XLM-R Longformer Model XLM-R Longformer is a XLM-R model, that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/con... | 9d89c392a67b46a26e995e20e42699f0 |
apache-2.0 | ['longformer'] | false | xlm-r). Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps. | 88ee81249f9dfa09ced63ce43cff3c1d |
apache-2.0 | ['longformer'] | false | How to Use The model can be used as expected to fine-tune on a downstream task. For instance for QA. ```python import torch from transformers import AutoModel, AutoTokenizer MAX_SEQUENCE_LENGTH = 4096 MODEL_NAME_OR_PATH = "markussagen/xlm-roberta-longformer-base-4096" tokenizer = AutoTokenizer.from_pretrain... | 3a1c4c90b1d7b239ca32a6e719d2f3b9 |
apache-2.0 | ['longformer'] | false | Training Procedure The model have been trained on the WikiText-103 corpus, using a **48GB** GPU with the following training script and parameters. The model was pre-trained for 6000 iterations and took ~5 days. See the full [training script](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer/blob/... | 4759c3e316a3df6b23f600762c718f8f |
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: 0.2111 - Accuracy: 0.9225 - F1: 0.9223 | ff2d945aa61b51b32feb9c3520270e8f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8274 | 1.0 | 250 | 0.3054 | 0.912 | 0.9096 | | 0.2409 | 2.0 | 500 | 0.2111 | 0.9225 | 0.9223 | | 057fc1a10eb679dade765eb55d912513 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small TW on Chinese base This model is a fine-tuned version of [Jingmiao/whisper-small-chinese_base](https://huggingface.co/Jingmiao/whisper-small-chinese_base) on the mozilla-foundation/common_voice_11_0 zh-TW dataset. It achieves the following results on the evaluation set: - Loss: 0.2601 - Wer: 41.9038 | 3f050a5d9993c9858a84c16021eb9200 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0071 | 6.02 | 1000 | 0.2364 | 42.6407 | | 0.0008 | 13.02 | 2000 | 0.2601 | 41.9038 | | 0.0004 | 20.01 | 3000 | 0.2771 | 42.395... | 90fee96944927e92a7eb5029df1e87b0 |
wtfpl | [] | false | Pokemon Classifier
This repo is a part of my study in deep learning with [fast.ai](https://www.fast.ai), this app uses this template [repo](https://github.com/render-examples/fastai-v3). thanks to them for the starter code and the [fast ai MOOC](https://course.fast.ai/) for making it easy to build deep learning mo... | 8fc624d86cbcfdb62ef8885292c1464c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-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: 0.1842 - Accuracy: 0.9307 | 50ad2d502794f980e86e00201e3ec0ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.217 | 1.0 | 1563 | 0.1842 | 0.9307 | | 2a0f3a62b0923b1e420185dd60d51faf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__subj__train-8-6 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: 0.6075 - Accuracy: 0.7485 | 6b9bd2f93aa86df57a8c9f3650365575 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7163 | 1.0 | 3 | 0.6923 | 0.5 | | 0.6648 | 2.0 | 6 | 0.6838 | 0.5 | | 0.6329 | 3.0 | 9 | 0.6747 | 0.... | e222dc835bbe31aa97958b99285da3e7 |
gpl-3.0 | [] | false | rachael-scai
Generation model (Pegasus fine-tuned with QReCC) used in the participation of group Rachael for SCAI 2021.
GitHub repository can be found in: [gonced8/rachael-scai](https://github.com/gonced8/rachael-scai)
Gonçalo Raposo
| 8673bedcf64f514af7c439ba24bf888f |
gpl-3.0 | [] | false | Cite
```bibtex
@InProceedings{Raposo2022,
author = {Gonçalo Raposo and Rui Ribeiro and Bruno Martins and Luísa Coheur},
booktitle = {44th European Conference on Information Retrieval},
title = {Question rewriting? Assessing its importance for conversational question answering},
year = {20... | 576c979f95fc9a6c15fe9cdcdabe90ff |
apache-2.0 | [] | false | Model description **CAMeLBERT-DA POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model. For the fine-tuning, we used the ARZTB dataset . Our fine-tuning procedure and the hyperparameters we used can... | 94344fc398036235b16a41d073a5c5ad |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy') >>> text = 'عامل ايه ؟' >>> pos(text) [{'entity': 'adj', 'score': 0.99843216, 'index': 1, 'word': 'عامل', 'start'... | eec6433904d943cef11a4f866a16585b |
mit | ['generated_from_trainer'] | false | indobert-finetune-tydiqa-transfer-indoqa This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the tydiqa dataset. It achieves the following results on the evaluation set: - Loss: 2.3210 | 7db02f36c5a16f196974b7e13124beee |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | ecaa9b3d5059f4a1bfef9099506c0eee |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.162 | 1.0 | 241 | 3.2961 | | 1.9671 | 2.0 | 482 | 2.4509 | | 1.457 | 3.0 | 723 | 2.3005 | | 1.2349 | 4.0 | 964 | 2.2628 ... | 95b225358f8c677e49874fde0b775841 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0884 - Matthews Correlation: 0.2439 | e5f16e97f1fb95fdb611989c3a91a905 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 23 | 1.1535 | 0.0 | | No log | 2.0 | 46 | 1.1430 | 0.0 | | No ... | 15014c4045087d6e76641bcbae8b5b45 |
apache-2.0 | ['generated_from_trainer'] | false | Redex-t5-small-finetuned-en-to-regex This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0813 - Semantic-accuracy: 0.916 - Gen Len: 10.476 | 5ce429ee08e8d194fba4dc9850aa6dc2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Semantic-accuracy | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:-------:| | 0.2376 | 1.0 | 563 | 0.0826 | 0.918 | 10.478 | | 0.0817 | 2.0 | 1126 | 0.0691 | 0.9... | ad4c1bce114d60c1fefbcf3276cfe5fd |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-rte-target-glue-qnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3460 - Accuracy: 0.8565 | d384e7bcde8ec3d4df9b3a65f837c12b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.484 | 0.15 | 500 | 0.3909 | 0.8314 | | 0.4419 | 0.31 | 1000 | 0.3811 | 0.8373 | | 0.4211 | 0.46 | 1500 | 0.3588 | 0.... | f8722dd34a6d0e2a3b9e2c7922daf468 |
cc0-1.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer', 'robust-speech-event'] | false | wav2vec2-large-voxrex-npsc This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 | 44d1495b69cc967adcc305e353ff2295 |
cc0-1.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - 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_sc... | 4a045b5c013760ca1b2468e2a4dbcc3f |
cc0-1.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.9728 | 0.32 | 500 | 2.9449 | 1.0 | | 2.5099 | 0.64 | 1000 | 1.8492 | 0.9910 | | 0.7872 | 0.97 | 1500 | 0.4467 | 0.377... | 743ab629ee968161e2a4a4dbae433dc3 |
mit | ['generated_from_keras_callback'] | false | Sushant45/Heresy-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1942 - Train End Logits Accuracy: 0.9549 - Train Start Logits Accuracy:... | bff207ab4055ac8a26613f082e565be6 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 0f2b7f104ee25e4bff7f1389ee31152f |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | luke-japanese-large-lite **luke-japanese** is the Japanese version of **LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained _knowledge-enhanced_ contextualized representation of words and entities. LUKE treats words and entities in a given text as independent tokens, and out... | 833e7bdd8da296ca72dc8418ef680d0c |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-4front-1body-0rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4643 - Wer: 0.1751 - Mer: 0.1690 - Wil: 0.2562 - Wip: 0.7438 - Hits: 55598 - S... | 89db4b8d4e99900224fc5e4e120defee |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6492 ... | 0de77742922357324397961c4dd8cff0 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_transport-roberta-large-v1-4-3 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with con... | 7ec587409eef1d6b156bdfff252e1b8d |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-ner-kmeans This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0592 - Precision: 0.9559 - Recall: 0.9615 - F1: 0.9587 | dd3c96c621a12b8719ca176fe066fc86 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.0248 | 1.0 | 878 | 0.0609 | 0.9507 | 0.9561 | 0.9534 | | 0.0163 | 2.0 | 1756 | 0.0640 | 0.9515 ... | 8b917ea3598e14b80cd9124ab404d15c |
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: 0.7570 - Matthews Correlation: 0.5335 | 4916a6548635de1eabbbdc6da487c1b2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5315 | 1.0 | 535 | 0.5214 | 0.4009 | | 0.354 | 2.0 | 1070 | 0.5275 | 0.4857 | | 0.2... | 407acce332708d0935c1f4ab2d4091e8 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Uyghur 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. | 00d697a2cd8d76a49b45066049c13bbc |
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-ug") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ug") ``` | 1ff96b15d216e0978afc42924158de5d |
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: 0.7813 - Matthews Correlation: 0.5429 | 460f94ef6f0dae8f6f6bdc62e7570586 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5255 | 1.0 | 535 | 0.5432 | 0.4004 | | 0.3527 | 2.0 | 1070 | 0.5086 | 0.4889 | | 0.2... | 789690e99871374603a581156f7aa496 |
creativeml-openrail-m | ['text-to-image'] | false | [](https://huggingface.co/spaces/Duskfallcrew/photography-and... | f3234001e615eecebb5feca7686752b6 |
creativeml-openrail-m | ['text-to-image'] | false | Photography And Landscapes Dreambooth model trained by Duskfallcrew 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/h... | 9cf1809601c68419f86a583e2a05a161 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4278363/ This model was trained by kamo-naoyuki using reverb/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 835f88863a157caec39acddc1db1621f |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-med-term-mlm This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4736 - Rouge2 Precision: 0.7731 - Rouge2 Recall: 0.5541 - Rouge2 Fmeasure: 0.6251 | fecd1b8bed162b4faf2a8caba98a0f22 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6498 | 1.0 | 15827 | 0.5480 | 0.7629 | 0.5457 | 0.61... | 25357f34faaddb63b112515d038423c4 |
apache-2.0 | ['translation'] | false | bul-epo * source group: Bulgarian * target group: Esperanto * OPUS readme: [bul-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md) * model: transformer-align * source language(s): bul * target language(s): epo * model: transformer-align * pre-processing: normalization + ... | ebdc508e9995789b3c24afd278ab96e7 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: bul-epo - source_languages: bul - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['bg', 'eo'] - src_constituents: {'bul', 'bul_Latn'} ... | 087b782a14146e4647bdfe3a5d4fae5d |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_unispeech-sat_s496 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) 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 that you... | cf6664d12c279b9394f6acea8ed9864f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-nia12_phone-ipa_english This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the TIMIT dataset. It achieves the following results on the evaluation set: - Loss: 0.1531 - Per: 0.0638 | eb899eed1ef5692679459cac02ffc513 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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: 1000 - num_epochs: 100 - mixed_precision... | f66948cee275011ceb0727e697fc6bf7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Step | Validation Loss | Per | |:-------------:|:----:|:---------------:|:------:| | 2.0846 | 500 | 0.1810 | 0.0991 | | 0.1857 | 1000 | 0.1411 | 0.0691 | | 0.0948 | 1500 | 0.1345 | 0.0666 | | 0.0646 | 2000 | 0.1444 ... | 216228a8d6aef58be376dd9c1f697ccd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | 4fde1654cd4f58514cdd2ad9a4b12d56 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Inkpunk-Diffusion: [  | f75ea0d5ef1ff09afc5d74f2d9921d20 |
mit | ['generated_from_trainer'] | false | kobart_8_5.6e-5_min30_lp5_sample_beams2 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8283 - Rouge1: 35.819 - Rouge2: 12.1658 - Rougel: 23.3058 - Bleu1: 29.6395 - Ble... | 7e5ba22e840bae06a6fc104393db0e61 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 154506bb4e7aba04443159951481eee1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:------:|:-------:| | 2.527 | 0.19 | 1000 | 3.0014 ... | 6ab0038339f44e1729b0045efc62a122 |
apache-2.0 | ['generated_from_keras_callback'] | false | andreiliphdpr/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: 0.0015 - Train Accuracy: 0.9995 - Validation Loss: 0.0570 -... | 9fcb4ec7acea506436adaa257f235c84 |
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': 43750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 3b6a711fd31476f14eea5db60f0c84b8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0399 | 0.9870 | 0.0281 | 0.9908 | 0 | | 0.0182 | 0.9944 | 0.0326 | 0.9901 ... | 57bbbe4425599de56f5d5d13d407edbc |
apache-2.0 | ['summarization'] | false | How To Use ```PYTHON from transformers import BartForConditionalGeneration, BartTokenizer model = BartForConditionalGeneration.from_pretrained("NTUYG/ComFormer") tokenizer = BartTokenizer.from_pretrained("NTUYG/ComFormer") code = ''' public static void copyFile( File in, File out ) throws IOException... | 9eadfd17ad04328f87886a4dcbf98a99 |
apache-2.0 | ['summarization'] | false | can find in https://github.com/NTDXYG/ComFormer input_text = code_seq + sbt input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=256, truncation=True) summary_text_ids = model.generate( input_ids=input_ids, bos_token_id=model.config.bos_token_id, eos_token_id=model.config.eos_token_id,... | 52fd78ee975207d60685b4d00f2afef4 |
apache-2.0 | ['summarization'] | false | BibTeX entry and citation info ``` @misc{yang2021comformer, title={ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation}, author={Guang Yang and Xiang Chen and Jinxin Cao and Shuyuan Xu and Zhanqi Cui and Chi Yu and Ke Liu}, year={2021}, eprint=... | 3b4d132c59e439ac767944392792af99 |
cc-by-sa-4.0 | ['sentence-transformers', 'sentence-bert', 'feature-extraction', 'sentence-similarity'] | false | This is a Japanese+English sentence-BERT model. 日本語+英語用Sentence-BERTモデルです。 [日本語のみバージョン](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)と比べて、手元の非公開データセットでは日本語の精度が0.8pt低く、英語STSbenchmarkでは精度が8.3pt高い（Cosine-Similarity Spearmanが79.11%）結果が得られました。 事前学習済みモデルとして[cl-tohoku/bert-base-japanese-whole-word-ma... | 33576cac0d83096b0ee87a060559d679 |
cc-by-sa-4.0 | ['sentence-transformers', 'sentence-bert', 'feature-extraction', 'sentence-similarity'] | false | 使い方 ```python from transformers import BertJapaneseTokenizer, BertModel import torch class SentenceBertJapanese: def __init__(self, model_name_or_path, device=None): self.tokenizer = BertJapaneseTokenizer.from_pretrained(model_name_or_path) self.model = BertModel.from_pretrained(model_name_or_pa... | 18f4d686f33663e6449b99bc7e318b80 |
cc-by-sa-4.0 | ['sentence-transformers', 'sentence-bert', 'feature-extraction', 'sentence-similarity'] | false | First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) @torch.no_grad() def encode(self... | b51675c8a3e0f3bf2edc55ecbaccc12a |
cc-by-sa-4.0 | ['sentence-transformers', 'sentence-bert', 'feature-extraction', 'sentence-similarity'] | false | return torch.stack(all_embeddings).numpy() return torch.stack(all_embeddings) MODEL_NAME = "sonoisa/sentence-bert-base-ja-en-mean-tokens" model = SentenceBertJapanese(MODEL_NAME) sentences = ["暴走したAI", "暴走した人工知能"] sentence_embeddings = model.encode(sentences, batch_size=8) print("Sentence embeddings:", sen... | 5e385e9add2454d381ae684a3ecb0ec6 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 300k (uncased) Seed 2 intermediate checkpoint 300k 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... | f2445d016118625724372fdb32df9fe0 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | 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-2-300k') model = BertModel.from_pretrained("multiberts-seed-2-300k") text = "Replace me by any text you'd like.... | a70eaa66b61d79a3508a385297357e83 |
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