license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becas-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 5.9506 | 7d97fe2617fea9da49db4b3fe3d87025 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - 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: 10 | b8c458e2087451cc65b09287dd401098 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.9506 | | No log | 2.0 | 10 | 5.9506 | | No log | 3.0 | 15 | 5.9506 | | No log | 4.0 | 20 | 5.9506 ... | e3cf85d53f7168f7b196795a6bf52f2e |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-mrl This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0170 - Accuracy: 0.9967 - F1: 0.9967 | f3a8115881e6e5e2288e6d5721881316 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.1821851463909416e-05 - train_batch_size: 400 - eval_batch_size: 400 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 | e3b26a75182a32e0538ebbae227b04a4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.0265 | 0.9946 | 0.9946 | | No log | 2.0 | 96 | 0.0180 | 0.9962 | 0.9962 | | No log |... | 14ed1a8455ab37d2a44f6435ddb28a67 |
apache-2.0 | [] | false | PaddlePaddle/uie-x-base **Try out our space at [https://huggingface.co/spaces/PaddlePaddle/UIE-X](https://huggingface.co/spaces/PaddlePaddle/UIE-X)!** Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, na... | b4a6d6630affadcb3ac82d64ee5be342 |
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-... | ffc7144e51f489b179a6d1e506852558 |
apache-2.0 | [] | false | Performance on Multimodal Datasets** We experimented on the zero-shot performance of UIE-X on the in-house multi-modal test sets in three different domains of general, financial, and medical: <table> <tr><th ><th>General <th>Financial<th colspan='2'>Medical <tr><td>🧾🎓<b>uie-x-base (12L768H)</b><td>65.03<td>73.51<t... | ebeb05e5a2802505c6bd0c4c342b5035 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-contradiction This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://huggingface.co/domenicrosati/t5-small-finetuned-contradiction) on the snli dataset. It achieves the following results on the evaluation set: - Loss: 2.0458 - Rouge1: 34.4237 - Rouge2: 14.544... | 98b23e5728131e7b480900aebfa2dcd0 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 8 - mixed_precision_training: Native AMP | 0cdc77722ba75ef1a9c42c2479ce541e |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.8605 | 1.0 | 2863 | 2.0813 | 34.4597 | 14.5186 | 32.6909 | 32.7097 | | 1.9209 | 2... | 82458ef4390dcb16b8682576262ecb89 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s510 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) 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 th... | 4299f65fc0f9a9a1162f9861de39064a |
apache-2.0 | ['generated_from_trainer'] | false | whisper-medium-ft-cy This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4372 - Wer: 21.9753 | 8f56ef2e5e2325236b49225aead81181 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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... | 874e3df350b3fd238751f97c513016d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.098 | 2.47 | 1000 | 0.3386 | 25.2830 | | 0.0189 | 4.94 | 2000 | 0.3699 | 23.7659 | | 0.0017 | 7.41 | 3000 | 0.4079 | 22.436... | 69f3252a0b1cdcfcb9654039f8abc7a4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Files 3 files available (Best version is V2): -VCM07_style - 4000 steps (more focused on girl) -VCM07_style2 - 4000 steps (allowed to create animals) -Prompt_Blending Script (optional, used for prompt) | b6e11efdc57c565666d93134aca09bf6 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Prompt You need to use DeepDanBooru Tags (https://gigazine.net/gsc_news/en/20221012-automatic1111-stable-diffusion-webui-deep-danbooru/) Elysium_Anime_V2.ckpt (https://huggingface.co/hesw23168/SD-Elysium-Model) Prompt_blending script (https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/tree/main) Embedding w... | 75c9927ce541264063d87012a9841fb5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Human Example Positive Prompt: (VCM07_style2:1.0), (1girl:1.2), looking_at_viewer, (best quality), (masterpiece:1.2), (ultra-detailed),(official art),(an extremely delicate and beautiful), (attractive:1.2), (beautiful detailed eyes), (dynamic colours, vibrant colours), depth of field, god rays, dynamic lighting Neg... | a98662c12c3e7405c41301f76c08f99d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Animals Example For V2, embedding was trained with dogs, cats, foxes. Therefore, it is easier to get these animals. However, it is possible to get frogs, elephants, tigers, lions, etc... I used a method with blend prompt script then I described the anatomy of the animal: eyes, ears, nose, fur, etc... Positive Promp... | 35bf13f0352b69dd2972bf3c22c3b553 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_gender_male-0_female-10_s496 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using ... | 5aec4c836e4d46b0a1000ddc91c56bc0 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all 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: 0.1731 - F1: 0.8525 | 4a72c9a8366555cd2b5c3b83ee5ab2f2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2992 | 1.0 | 835 | 0.1936 | 0.8164 | | 0.1588 | 2.0 | 1670 | 0.1711 | 0.8466 | | 0.1022 | 3.0 | 2505 | 0.1731 | 0.8525 | ... | e634d6fdaf7cc9d3f7e58c84b288ce1d |
mit | ['generated_from_trainer'] | false | gpt2-largeseuss This model is a fine-tuned version of [derwahnsinn/gpt2-largeseuss](https://huggingface.co/derwahnsinn/gpt2-largeseuss) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1766 | a8f0ae867f6cd190bd1d48b3d2d06ae8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | c102c12f0bdb93c6fbe108b264cda175 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 1.8800 | | No log | 2.0 | 16 | 1.6786 | | No log | 3.0 | 24 | 1.5130 | | No log | 4.0 | 32 | 1.3828 ... | 47fd25b4402a6e1500b494d96f5bbf85 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'wits'] | false | IT5 Large for Wikipedia Summarization ✂️📑 🇮🇹 This repository contains the checkpoint for the [IT5 Large](https://huggingface.co/gsarti/it5-large) model fine-tuned on Wikipedia summarization on the [WITS](https://www.semanticscholar.org/paper/WITS%3A-Wikipedia-for-Italian-Text-Summarization-Casola-Lavelli/ad6c83122... | 803309b96138c99cbeb2991387bec9d2 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'wits'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines wikisum = pipeline("summarization", model='it5/it5-large-wiki-summarization') wikisum("Le dimensioni dell'isola sono di 8 km di lunghezz... | 9faa5821bf14182c8ed373548f86129f |
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: 0.5401 - Accuracy: 0.834 - F1: 0.8172 | d61271d020bf226f924539806c354c86 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 192 - eval_batch_size: 192 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 34d1b5607e00e7b0019eaa0e95875dc7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 84 | 0.7993 | 0.74 | 0.6827 | | No log | 2.0 | 168 | 0.5401 | 0.834 | 0.8172 | | e6779a0096cf55331149e224d6752279 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_tiny_rw_224.sw_in1k A timm specific MaxViT image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | 9e37aef10b57769b7b78f2ddb3c7e787 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 29.1 - GMACs: 5.1 - Activations (M): 33.1 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 8cd1d5fbcd88a28c3a143dff083b7992 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_tiny_rw_224.sw_in1k', pretrained=True) model = ... | 4516f2a8f50a2a4c4a3618569532c8a2 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_tiny_rw_224.sw_in1k', pretrained=Tru... | 4b136ea16bf79a1429175096e36b880d |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_tiny_rw_224.sw_in1k', pretrained=True, ... | b66f543e8799b1531869ca8e43eeef37 |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | Model Details - **Model Description:** 해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다. <br /> Wav2Vec2ConformerForCTC를 이용하여 KsponSpeech에 대한 Fine-Tuning 모델입니다. <br /> - Dataset use [AIHub KsponSpeech](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dat... | b4cc1a73cae3d461433816cd38589d50 |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | How to Get Started With the Model KenLM과 혼용된 Wav2Vec2ProcessorWithLM 예제를 보시려면 [42maru-kenlm 예제](https://huggingface.co/42MARU/ko-ctc-kenlm-spelling-only-wiki)를 참고하세요 ```python import librosa from pyctcdecode import build_ctcdecoder from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForCT... | d660a714dd21d38948b6e649cceed113 |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다. model = AutoModelForCTC.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s") feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s") tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s") beam... | 1b90eaecac50f6137cc2e8df640468fe |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1200k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1200k 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... | 7388f93180b311df60039148fed05a9c |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1200k'] | 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_1-step_1200k') model = TFBertModel.from_pretrained("google/multib... | abb56853272da6db7efe8e612115920c |
apache-2.0 | ['translation'] | false | opus-mt-ru-fr * source languages: ru * target languages: fr * OPUS readme: [ru-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-26.zip](https://... | 2a649bac0bbce691786b1dea86f27b2c |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.ru.fr | 18.3 | 0.497 | | newstest2013.ru.fr | 21.6 | 0.516 | | Tatoeba.ru.fr | 51.5 | 0.670 | | 13e32c98cd301e53e3b89d878be61b77 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'photorealistic', 'photoreal', 'diffusers'] | false | If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW. You can add **photo** to your prompt to make your gens look more photorealistic. Non-square aspect ratios work be... | de45c7c523debc05da49a5258b930830 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'photorealistic', 'photoreal', 'diffusers'] | false | Examples <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview2.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.c... | bfeaaa3a0251c43cb5d0a5a17681656a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'photorealistic', 'photoreal', 'diffusers'] | false | dreamlike.art You can use this model for free on [dreamlike.art](https://dreamlike.art/)! <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/> | 3761650cfeee8141f263c01b1c4c34ff |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'photorealistic', 'photoreal', 'diffusers'] | 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 Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "dreamlik... | 21e28dbc81a6cfc4fcb38f5f544850d2 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'photorealistic', 'photoreal', 'diffusers'] | false | License This model is licesed under a **modified** CreativeML OpenRAIL-M license. - **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at contact@dreamlike.art** - **You are free to host the model card and files (Without an... | 6a9fbd86d64a081edc75f7cfcc8c9adc |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Norwegian Wav2Vec2 Model - 1B Bokmål This model is finetuned on top of feature extractor [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-1b) from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language... | 0e8d46b15b74970ae90950be205c52ba |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Model description This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores: | Model | Final WER | | ... | d514498a571dac5ded2b3f52a40c225b |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Dataset In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training. | d58364471a7d3f2798cca5046a72d707 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Code We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automat... | 17d7d8865a2876a29dca9706b20eefa9 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Training procedure To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event | 85b27512258a5b5b02990d0269fc87c3 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | talks) to train a simple Swedish model. When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our res... | 62336c8fbda3124605346aeb80c9ab67 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Language Model As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance ... | 9e7c0dd49fca9691549d50b6cb8e17fe |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', False, 'nb', 'nb-NO'] | false | Parameters The final model was run using these parameters: ``` --dataset_name="NbAiLab/NPSC" --model_name_or_path="facebook/wav2vec2-xls-r-1b" --dataset_config_name="16K_mp3_bokmaal" --output_dir="./" --overwrite_output_dir --num_train_epochs="40" --per_device_train_batch_size="12" --per_device_eval_batch_size="12" -... | 2cdcee360c2919b7fcf336fbe1d762b3 |
apache-2.0 | ['object-detection', 'license-plate-detection', 'vehicle-detection'] | false | YOLOS (small-sized) model The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [thi... | ba1efb752fe9b3085e5c729382027e63 |
apache-2.0 | ['object-detection', 'license-plate-detection', 'vehicle-detection'] | false | Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). | 047aaf890de7143c92f1c0519f6b7b95 |
apache-2.0 | ['object-detection', 'license-plate-detection', 'vehicle-detection'] | false | How to use Here is how to use this model: ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection from PIL import Image import requests url = 'https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = YolosF... | 43b0d5d239c05c0143019933d4747dd9 |
apache-2.0 | ['object-detection', 'license-plate-detection', 'vehicle-detection'] | false | Evaluation results This model achieves an AP (average precision) of **47.9**. Accumulating evaluation results... IoU metric: bbox Metrics | Metric Parameter | Location | Dets | Value | ---------------- | --------------------- | ------------| ------------- | ----- | Average Precision | (... | 1f18fbeb56dc737799e8440dc50b0a3e |
apache-2.0 | ['generated_from_trainer'] | false | vit_for_dfl This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the dfl dataset. It achieves the following results on the evaluation set: - Loss: 0.1771 - F1: 0.2453 | 4520df0caacd4bfaecf346ceb91497f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | fe951cbd811117adc28155667738dc91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0836 | 1.0 | 358 | 0.1841 | 0.2453 | | 0.207 | 2.0 | 716 | 0.1835 | 0.2453 | | 0.2325 | 3.0 | 1074 | 0.1771 | 0.2453 | ... | c49ada06c276103c85dbbbdb16d1db0d |
apache-2.0 | ['sagemaker', 'bart', 'summarization'] | false | `bart-large-cnn-samsum` This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container. For more information look at: - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html) - [Example Notebooks](https://github.com/huggingface/notebooks/tr... | 0d212a71b772991af046a01ba87cd2b1 |
apache-2.0 | ['sagemaker', 'bart', 'summarization'] | false | Hyperparameters { "dataset_name": "samsum", "do_eval": true, "do_predict": true, "do_train": true, "fp16": true, "learning_rate": 5e-05, "model_name_or_path": "facebook/bart-large-cnn", "num_train_epochs": 3, "output_dir": "/opt/ml/model", "per_device_eval_batch_size": 4, ... | 9cb0644f469407c945f0896672cdc8d7 |
apache-2.0 | ['sagemaker', 'bart', 'summarization'] | false | Usage from transformers import pipeline summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum") conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? Philipp: Sure you can use the new Hugging Face Deep Learning Container. Jeff: ok. Jeff: and... | 0ecbb03eee9ee3dd7c7a606d2b229c10 |
apache-2.0 | ['sagemaker', 'bart', 'summarization'] | false | Results | key | value | | --- | ----- | | eval_rouge1 | 42.059 | | eval_rouge2 | 21.5509 | | eval_rougeL | 32.4083 | | eval_rougeLsum | 39.0015 | | test_rouge1 | 40.8656 | | test_rouge2 | 20.3517 | | test_rougeL | 31.2268 | | test_rougeLsum | 37.9301 | | ea37bc39bc31ba4070de6c1da05c048d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner | Feature | Description | | --- | --- | | **Name** | `hu_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `loo... | 5918dd55a80a4ff0ce9a78bef27bd08f |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1209 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` | | **`morphologizer`** | `Definite=Def\|P... | 13256e0377174abb31760fd03e47cbf0 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.99 | | `TOKEN_P` | 99.86 | | `TOKEN_R` | 99.93 | | `TOKEN_F` | 99.89 | | `SENTS_P` | 98.00 | | `SENTS_R` | 98.00 | | `SENTS_F` | 98.00 | | `TAG_ACC` | 96.76 | | `POS_ACC` | 96.62 | | `MORPH_ACC` | 93.54 | | `MORPH_MICRO_P` | 96.68 | | `MORPH_MICRO_R` | 96.24 ... | 265c46b7d4783b229843b061461b0554 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-de-zle Neural machine translation model for translating from German (de) to East Slavic languages (zle). 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 w... | 8ff5d03a429a428d6302f68a25735d0c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-23 * source language(s): deu * target language(s): bel rus ukr * valid target language labels: >>bel<< >>rus<< >>ukr<< * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original m... | 11c78e8ee4470502133e8d30247b21d5 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ukr<< Der Soldat hat mir Wasser gegeben.", ">>ukr<< Ich will hier nicht essen." ] model_name = "pytorch-models/opus-mt-tc-big-de-zle" tokenizer = MarianTokenizer.from_pretrained(model_name) model =... | b900a390cca2e9f04cc1d57ef724f329 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Я не хочу тут їсти. ``` 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-de-zle") print(pipe(">>ukr<< Der Soldat hat mir Wasser gegeben.")) | 29f8921b8764623449d3d7899de81a06 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-zle/opusTCv20210807_transformer-big_2022-03-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/de... | b24709674bc0311d2cc1d8af17de7127 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | deu-bel | tatoeba-test-v2021-08-07 | 0.53128 | 29.5 | 551 | 3601 | | deu-rus | tatoeba-test-v2021-08-07 | 0.67143 | 46.1 | 12800 | 87296 | | deu-ukr | tatoeba-test-v2021-08-07 | 0.62737 | 40.7 | 10319 | 56287 | | deu-rus | flores101-devtest | 0.54152 | 2... | 1f9a319edd5659189d2b3152e7afb1a2 |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. It was trained with a SmoothL1 loss with Beta = 1 (aka same as Huber Loss). | 0f11d1928d82f2c74d4bd2a99189bbcc |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | Usage To use this LoRA you have to download the file, as well as drop it into the "\stable-diffusion-webui\models\Lora" folder To use it in a prompt, please refer to the extra networks panel in your Automatic1111 webui. I highly recommend using it at around 0.8 strength for the best results. If you'd like to support... | fea96c7f90fcd0021d5d974642749b23 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | Example Pictures <table> <tr> <td><img src=https://i.imgur.com/96fultD.png width=50% height=100%/></td> </tr> <tr> <td><img src=https://i.imgur.com/y66xA99.png width=50% height=100%/></td> </tr> <tr> <td><img src=https://i.imgur.com/btwOjyJ.png width=50% height=100%/></td> </tr> </table> | 2ceb4cc72e642f151d08bf7d9e57ac01 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script da... | de6742d7a741be47a4fe1a9cd339442c |
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: 0.7335 - Matthews Correlation: 0.5356 | b2166185ea93c6f724954a4a9fe9fe62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5309 | 1.0 | 535 | 0.5070 | 0.4239 | | 0.3568 | 2.0 | 1070 | 0.5132 | 0.4913 | | 0.2... | 54a652e12e2f48a62fa4910a72e4aa5b |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_age_teens-2_sixties-8_s769 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | 9b3c36f10257899e12d6d1153d9b0194 |
apache-2.0 | ['generated_from_trainer'] | false | eng-hin-translator This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4143 - Bleu Score: 34.2532 | 8c7d995557f326e5046eaf373410880e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu Score | |:-------------:|:-----:|:----:|:---------------:|:----------:| | 1.7332 | 1.0 | 548 | 1.5131 | 31.6167 | | 1.3588 | 2.0 | 1096 | 1.4463 | 33.0225 | | 1.1651 | 3.0 | 1644 | 1.4209 ... | b8e90ce57fb1f2b3dd375b4991a2e2b3 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 1000k (uncased) Seed 4 intermediate checkpoint 1000k 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/g... | 9b1ae8c5c7b3a96f8f27cb5678beb674 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-1000k') model = BertModel.from_pretrained("multiberts-seed-4-1000k") text = "Replace me by any text you'd lik... | 5e4a95360735ca8df765351fb3c8ec3e |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3023 - Accuracy: 0.8767 - F1: 0.8771 | ff6e0363e439fee6ecc815483dcf5f14 |
apache-2.0 | ['dialogue-summarization'] | false | Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having a... | 52292e8114c82e713324451b9bb51eda |
apache-2.0 | ['dialogue-summarization'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-4 - train_batch_size: 4 - eval_batch_size: 4 - 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: 100.0 - label_smoothing_fa... | 0fb4a5edceee2a92e12bb8f6d5e6d219 |
apache-2.0 | ['dialogue-summarization'] | false | Results on Test Set - predict_gen_len = 480.0 - predict_rouge1 = **46.8707** - predict_rouge2 = **10.1337** - predict_rougeL = **19.3386** - predict_rougeLsum = **43.6989** - predict_samples = 6 - predict_samples_per_sec... | 89500070598916c81197ef19f5261f90 |
apache-2.0 | ['dialogue-summarization'] | false | Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning... | 5df74d8fe0dcd61b005e3c62a3c5967a |
apache-2.0 | ['generated_from_keras_callback'] | false | ParulChaudhari/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an SQUAD dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3927 - Validation Loss: 1.1305 - Epoch: 0 | 45cf3b9b2b543f0ca0f0582e14242fdd |
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': 177048, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'be... | 1a126246035c3cb5632062791cf0ff0c |
apache-2.0 | ['generated_from_trainer'] | false | albert-offensive-lm-tapt-finetuned This model is a fine-tuned version of [k4black/albert-offensive-lm-tapt](https://huggingface.co/k4black/albert-offensive-lm-tapt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4680 - F1: 0.7765 | aa799aa79b1b85a2644baba519e92911 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 0bfd6278a0d4a0b7d49ca96836f88cfa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6585 | 0.1 | 100 | 0.6663 | 0.3932 | | 0.6308 | 0.2 | 200 | 0.5807 | 0.5746 | | 0.5161 | 0.29 | 300 | 0.5005 | 0.7366 | |... | 9377b33150c63d43ee95fe7c09c47199 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_vp-sv_s738 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | ab3d929e521b0f90e443b6ae44ad67c9 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-distilbert-trial2 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: 0.5119 - Accuracy: 0.9323 - F1: 0.9339 | b9d7668cb499c58bc6496547691b6976 |
apache-2.0 | ['LABEL-0 = NONE', 'LABEL-1 = B-DATE', 'LABEL-2 = I-DATE', 'LABEL-3 = B-TIME', 'LABEL-4 = I-TIME', 'LABEL-5 = B-DURATION', 'LABEL-6 = B-DURATION', 'LABEL-7 = B-SET', 'LABEL-8 = B-SET'] | false | roberta-base-biomedical-clinical-es-finetuned-ner-timebank This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0162 - ... | bc61b9bcf92378eb9c8f89c53c000c87 |
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