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 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'uk'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.0255 | 7.93 | 500 | 2.5514 | 0.9921 | 0.9047 | | 1.3809 | 15.86 | 1000 | 0.4065 | 0.5361 | 0.1201 | | 1.2355 | 23.8 |... | 016461f75c2a8c5abe75df0f2fae8a9a |
apache-2.0 | ['italian', 'sequence-to-sequence', 'efficient', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingfa... | 99eb753cfe45e389971328e63adacb94 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'efficient', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | Using the model The model is trained to generate a headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipeli... | 478e4885a9d7bea6f283da4be7bde3a2 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'efficient', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: 10.0 | 5632a909c1af83e446de729b7b4b32cf |
apache-2.0 | ['generated_from_trainer'] | false | testing 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: 0.6644 - Accuracy: 0.6814 - F1: 0.8105 - Combined Score: 0.7459 | c2d8ecad31d6e3d54193150207bbbc69 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 | 3b21c45bda476026dd69758f6e2eac0e |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion v2-1 Model Card This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2-1` model is fine-tuned from [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusi... | 058b12795f5acc18d000233184cfcf38 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in thi... | 28e5ebc1c3a61b96047097cc5f75fd82 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars"... | 95d7c3c84ec5a20528b88d3862591959 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from c... | 9f6a5d412dced42154ee3af42b3ba302 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](http... | 38d70d8e42c1c36a6b640e31dc1d4146 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validatio... | 9e153f16b9e44051aaea0dea8bd67491 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-hun-53h-colab 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.6027 - Wer: 0.4618 | 6afe1b199ff03bbb52d787287b73a706 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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... | 561d647123e877a494437da6dce9efc1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 13.4225 | 0.67 | 100 | 3.7750 | 1.0 | | 3.4121 | 1.34 | 200 | 3.3166 | 1.0 | | 3.2263 | 2.01 | 300 | 3.1403 | 1.0 | |... | e512e88a69953c586e0da0dd88b57bf3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Greek This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 1.3444 - Wer: 231.8841 | 6d13f9c143ae06fe66c1fa7a6f1c7480 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2 - mixed_precision... | bbe9a4d3b0fda7745cc25f10e85061b6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.5 | 2 | 1.3444 | 231.8841 | | 463cab3f131f713dde51cfd7972cb290 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2302 - Accuracy: 0.922 - F1: 0.9218 | 762bebe588eac919bbe7570e9c6fed57 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3344 | 0.903 | 0.9004 | | No log | 2.0 | 500 | 0.2302 | 0.922 | 0.9218 | | d3ebff51a1e9b42c585f08d61c824b49 |
mit | [] | false | Manga style on Stable Diffusion This is the `<manga>` 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 tra... | 0a3f207c69218bdad19f0e2e4cb3da17 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | 663754d63b6cd00654e54be2103b789c |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1000k'] | false | Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The inten... | 7bb9acb62fb4ed85f9d266d85bd2e909 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1000k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1000k') model = TFBertModel.from_pretrained("google/multib... | f80eca6ffa47f99fc3e15239892e912c |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1000k'] | false | Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and I... | 685504012ab6c0dc9bf83b989734addc |
mit | ['generated_from_trainer'] | false | multi-minilm-finetuned-amazon-review This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.2436 - Accuracy: 0.5422 - F1: 0.543... | ad2b1e5011275240cd8d438371d97e80 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | 54bc3f54fafdad8c1881fa09ca151998 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.0049 | 1.0 | 2500 | 1.0616 | 0.5352 | 0.5268 | 0.5347 | 0.5352 | | 0.9172 | 2.0 ... | 76f3f59162319186f9de450204492790 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper large-v2 zh-tw This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 zh-TW dataset. It achieves the following results on the evaluation set: - Loss: 1.1603 - Wer: 40.3946 - Cer: 41.1041 | 44f0853f74fa5de40bad3947f24ce9cf |
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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precisio... | 76375e219230dae04cdf8eb071a2c340 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 2.87 | 0.2 | 1000 | 3.0804 | 192.9556 | 192.6466 | | 2.6143 | 0.4 | 2000 | 2.4951 | 96.5525 | 96.6443 | | 1.863 ... | 75f4059d4e46e6d5d654a5b795f45590 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from Italic languages (itc) to Basque (eu). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mod... | 7612b6b36e0418561ea414f5403d3bdf |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language model... | 5a4c21be213dacd549787f07770873e5 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Il est riche.", "¿Correcto?" ] model_name = "pytorch-models/opus-mt-tc-big-itc-eu" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretr... | 93ff36cbc074af3eeb383aaaa2bba7d4 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Zuzena? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-eu") print(pipe("Il est riche.")) | 2d988be64f63fab3e824c1f5e5479c27 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-e... | 0d33ec0cd09f9b53a21c48a666a1f05a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/it... | 3e1abb231861236ef0c7b63982e58400 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Citation Information * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)... | 6dee266f23ec16e2c3d434ece0ed394b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_toy_train_data_fast_10pct This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6983 - Wer: 0.5026 | 54a8b74be10e31d97dc85cde9645b54c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3619 | 1.05 | 250 | 3.4334 | 1.0 | | 3.0818 | 2.1 | 500 | 3.4914 | 1.0 | | 2.3245 | 3.15 | 750 | 1.6483 | 0.9486 | |... | d60ecbf1048247cca5ebadcaa4164613 |
other | ['PyTorch'] | false | Diffusion GANというコードを使ってつくりました https://github.com/Zhendong-Wang/Diffusion-GAN つかいかた 試してないので動かなかったらごめんなさい - 環境をととのえる - 最近のNVIDIA製GPUがついたパソコンにLinuxを入れることをおすすめします - PytorchをCUDAありでインストールしてください - https://pytorch.org/get-started/locally/ - conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytor... | e70f1501bc1886ab920d93dad06c8dc5 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-1-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4797 - Accuracy: 0.28 | 58a2eda4898f64be526b9b0dda21065f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7721 | 1.0 | 1 | 2.6529 | 0.1889 | | 2.2569 | 2.0 | 2 | 2.5866 | 0.2333 | | 1.9837 | 3.0 | 3 | 2.5340 | 0.... | 9e2ff25e49a9f8d99dde92691919d23b |
creativeml-openrail-m | ['text-to-image'] | false | quino Dreambooth model trained by machinelearnear 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/noteboo... | f3840c1945ec05b978f363253fcbd74a |
mit | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | Cross English & German RoBERTa for Sentence Embeddings This model is intended to [compute sentence (text) embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html) for English and German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki/C... | 650b8cf0dc0aa41fe373986ed6b32056 |
mit | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | How to use To use this model install the `sentence-transformers` package (see here: <https://github.com/UKPLab/sentence-transformers>). ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('T-Systems-onsite/cross-en-de-roberta-sentence-transformer') ``` For details of usage and... | 7154ec1ff11cfc3880febae515024dd6 |
mit | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | Training The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). This model has been further trained by [Nils Reimers](https://www.nils-reimers.de/) on a large scale paraphrase dataset for 50+ languages. [Nils Reimers](https://www.nils-reimers.de/) about this [on GitHub](https://github.com/UKPLa... | fc5341f48407bc6269c4c3c38d4a438d |
mit | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | issuecomment-712243280): >A paper is upcoming for the paraphrase models. > >These models were trained on various datasets with Millions of examples for paraphrases, mainly derived from Wikipedia edit logs, paraphrases mined from Wikipedia and SimpleWiki, paraphrases from news reports, AllNLI-entailment pairs with in-b... | e1237cff7cb63e83fe912aff58076eea |
mit | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | Evaluation The evaluation has been done on English, German and both languages crossed with the STSbenchmark test data. The evaluation-code is available on [Colab](https://colab.research.google.com/drive/1gtGnKq_dYU_sDYqMohTYVMVpxMJjyH0M?usp=sharing). As the metric for evaluation we use the Spearman’s rank correlation ... | f2ff1356441e1f416b5e5516fe2c68b4 |
mit | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | License Copyright (c) 2020 Philip May, T-Systems on site services GmbH Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License by reviewing the file [LICENSE](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sent... | b271202e195ba1dd44f3e9915638b6ef |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_mrpc 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: 0.6028 - Accuracy: 0.6961 - F1: 0.8171 - Combined Score: 0.7566 | 05d5ad5aae397b0bcfd3552850772287 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_tra... | 5b1552610fffa9e59a4c266dd398e5a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6617 | 1.0 | 15 | 0.6507 | 0.6838 | 0.8122 | 0.7480 | | 0.6412 | 2.0 | 30 | 0.62... | 2a02d5334e20bfadf7364c59ef3ed06a |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_xls-r_s250 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | 6fbce3ad387ae268d8916586cd8a8133 |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2 This model is a fine-tuned version of [tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2](https://huggingface.co/tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2) on an unknown dataset. It achieves the following... | 276818c05ba690e8d96f31453b223bf0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 | a044e0025e7a14c1c3768e83c5229cac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 33 | 0.3078 | 0.7675 | 0.5943 | 0.6699 | 0.8842 | | No log | 2.0 |... | 012e1c8932d7b835fe95b09590e2d5cd |
apache-2.0 | ['generated_from_trainer'] | false | Training and evaluation data Training Data - Data Name: NIA13 ASIA - Num. of Samples: 9,634 - Audio Length: 9H 42M Evaluation Data - Data Name: NIA13 ASIA - Num. of Samples: 3,707 - Audio Length: 3H 37M Test Data - Data Name: NIA13 ASIA (Same as the Evaluation Data) - Num. of Samples: 3,707 - Audio Length: 3H 37M | 3e5176cbf22d939fe7e565b8a143f9ef |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Swedish -3000 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: 0.2974 - Wer: 19.6042 | 1f12c03c4559d0c17caf138f63bceb17 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precis... | b92415ee1d34c2e13430a8176a41b855 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1448 | 1.29 | 1000 | 0.2953 | 21.4245 | | 0.0188 | 2.59 | 2000 | 0.2879 | 20.0882 | | 0.0233 | 3.88 | 3000 | 0.2974 | 19.604... | b5c2f34d309982558166a0af65dde22d |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_qqp_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.8027 - Accuracy: 0.7596 - F1: 0.6364 - Combined Score: 0.6980 ... | 042c6896e715837bc1a81d766757d5ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 1.2838 | 1.0 | 2843 | 1.2200 | 0.6318 | 0.0 | 0.3159 | | 1.0184 | 2.0 | 5686 | ... | 732cc98b142d8c4dd9455d3142e70237 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4157 - F1: 0.8636 | b35c4bdc3c5f9304463916a3bd7c1b71 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0847 | 1.0 | 191 | 0.4066 | 0.8524 | | 0.0574 | 2.0 | 382 | 0.4025 | 0.8570 | | 0.0333 | 3.0 | 573 | 0.4157 | 0.8636 | ... | 220b132c8d049d43ebbef59c74243ac3 |
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: 0.7818 - Matthews Correlation: 0.5492 | 3226b3be195374351546b01b8b6c5a5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5257 | 1.0 | 535 | 0.5238 | 0.4004 | | 0.3516 | 2.0 | 1070 | 0.5173 | 0.5206 | | 0.2... | aaa063f0bbd8beb1f6a7a52e32b82049 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_20k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 20k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different r... | 74b4e27fdfd685c989c3a8f85a1603c4 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_20k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_20k') model = TFBertModel.from_pretrained("google/multiber... | 3780bf43602f5e5a93591403e99125cb |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-extraction-cnndm_fs0.1-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6225 | 82bad0ea938930d154945bd9ee06535b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 48 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | b79790c45192847a5883dbb75a444adb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0798 | 0.11 | 200 | 1.7813 | | 1.8704 | 0.23 | 400 | 1.7363 | | 1.8398 | 0.34 | 600 | 1.7100 | | 1.8068 | 0.45 | 800 | 1.6951 ... | ebcfc92b3ca1704dac967e0f1aecf348 |
bsd-3-clause | ['summarization'] | false | Citation ``` @misc{https://doi.org/10.48550/arxiv.2110.07166, doi = {10.48550/ARXIV.2110.07166}, url = {https://arxiv.org/abs/2110.07166}, author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema}, keywords = {Computation and Langu... | 06aee945e943c984ae202d96f09879b0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-as-g1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AS dataset. It achieves the following results on the evaluation set: - Loss: 1.3327 - Wer: 0.5744 | d3fd57468b27cd26384bbc3cd6522424 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Assamese ... | d46760e8025c72c669ab0dca2369829c |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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... | cdf6ed5b42764bc24933623cd90248f8 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 14.1958 | 5.26 | 100 | 7.1919 | 1.0 | | 5.0035 | 10.51 | 200 | 3.9362 | 1.0 | | 3.6193 | 15.77 | 300 | 3.4451 | 1.0 ... | f7ddb135c74d693bc6d20dcb97f57aaf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | 95b9a005c4fd6e3d671b4a3bd4ddc0dc |
apache-2.0 | [] | false | PaddlePaddle/uie-medium Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general I... | 57ed924116eee15fa50e94cf4f045ab2 |
apache-2.0 | [] | false | Available Models | Model Name | Usage Scenarios | Supporting Tasks | | :----------------------------------------------------------: | :----------------------------------------... | e5074936d9a45e8a2a4e665e2b5be038 |
apache-2.0 | [] | false | Performance on Text Dataset We conducted experiments on the in-house test sets of the three different domains of Internet, medical care, and finance: <table> <tr><th row_span='2'><th colspan='2'>finance<th colspan='2'>healthcare<th colspan='2'>internet <tr><td><th>0-shot<th>5-shot<th>0-shot<th>5-shot<th>0-shot<th>5-... | 456a14f0795c3954a10b866e27117214 |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | BLIP-2, Flan T5-xl, fine-tuned on COCO BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by ... | d7c6361ddd19aabbd7dc6258ded3539c |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is ... | 916040e942e44f51845fff4820f0c96b |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | Intended uses & limitations You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. | 1170b21d5c398c39a09050bb199d8d1a |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-small-finetuned-xum-test 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: - Train Loss: 2.9733 - Validation Loss: 2.6463 - Epoch: 0 | 324dcb50651e33e1f2470cc9b616e58e |
mit | [] | false | Sherhook Painting v2 on Stable Diffusion This is the `<sherhook>` 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 ... | fdf073844472c80106d307642fb12070 |
other | [] | false | This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC The model was fine-tuned based off [the CamemBERT language model](https://huggingface.co/camembert-base). The accuracy is 93% on the test split during training and 79% on a manually picked (and thus harder) sample of 200 senten... | 37c282df6596195b14e265ecec62712a |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0621 - Precision: 0.9357 - Recall: 0.9507 - F1: 0.9432 - Accuracy: 0.9865 | 6fe8a39a0a7095391e9b60b18a12cc02 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0861 | 1.0 | 1756 | 0.0695 | 0.9142 | 0.9293 | 0.9217 | 0.9811 | | 0.0341 | 2.0 |... | 4fbeebd86abd46d6e0228d2874846a01 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-eng This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5047 - Wer: 0.2233 | ca8e5fe2f6376532b0caef22792160fc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 1000 - num_epochs: 30 - mixed_precision_tr... | 839e987969afbd6b51474211410cff80 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5485 | 1.0 | 500 | 1.9954 | 1.0042 | | 0.9068 | 2.01 | 1000 | 0.6418 | 0.4572 | | 0.4398 | 3.01 | 1500 | 0.4586 | 0.362... | f0d6d5b61ecaa0db3770e2e7830e9a65 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-fine-tuned-on-clinc_oos-dataset This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 1.2811 - Accuracy Score: 0.9239 - F1 Score: 0.9213 | db53d3f5062a088c70aae70b1e3e7771 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 4e244064201925fa1d9aada6c0de922d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:--------:| | 4.4271 | 1.0 | 239 | 3.5773 | 0.6116 | 0.5732 | | 3.0415 | 2.0 | 478 | 2.4076 | 0.8390 ... | 9f85bbe304f7290a2224506cc9721fe9 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s728 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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make s... | 15901d00ec4ff8d78f168d5dc4a9f13a |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Samples I hope it gives you an idea of what kind of styles can be created with this model. <img src="https://huggingface.co/Froddan/frost/resolve/main/frostography_nature_1.png" width="256px"/> <img src="https://huggingface.co/Froddan/frost/resolve/main/frostography_nature_2.png" width="256px"/> <img src="https://hug... | 0f10eec03bba6ef92c5bfb5bbf6f0768 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). | ba6c2504449d29214cc4a3e7332f10d8 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_g... | b2bbbe7e07c2a61b6bc433e76bed6316 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 400k (uncased) Seed 4 intermediate checkpoint 400k 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... | fa36dd579a82d66a2c32087f095cfb93 |
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