license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['text-classification', 'zero-shot-classification'] | false | Training procedure DeBERTa-v3-base-mnli-fever-anli was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=3, | fca51cf3d479175942a4234bd01aec84 |
mit | ['text-classification', 'zero-shot-classification'] | false | Eval results The model was evaluated using the test sets for MultiNLI and ANLI and the dev set for Fever-NLI. The metric used is accuracy. mnli-m | mnli-mm | fever-nli | anli-all | anli-r3 ---------|----------|---------|----------|---------- 0.903 | 0.903 | 0.777 | 0.579 | 0.495 | b839d4600b0c8d812df0c1421f193d61 |
mit | ['text-classification', 'zero-shot-classification'] | false | Model Recycling [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.65&mnli_lp=nan&20_newsgroup=-0.61&ag_news=-0.01&amazon_reviews_multi=0.46&anli=0.84&boolq=2.12&cb=16.07&cola=-0.76&copa=8.60&dbpedia=-0.40&esnli=-0.29&financial_phrasebank=-1.98&imdb=-0.47&isear=-0.22&mnli=-0.21&m... | 48994a207bfbebcacc63231d2021428c |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_gender_male-8_female-2_s250 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 t... | 3df328cafce4df8dc198926d27123287 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-it_s515 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 03efbeb9c5f3da4ecb3ba4fa57ed80c7 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-el Neural machine translation model for translating from English (en) to Modern Greek (1453-) (el). 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 wor... | 6c7d3975d36c407afae34f4308936d2d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): eng * target language(s): ell * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](htt... | 7d48bab647604a4903eac92e5c164979 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "If I weren't broke, I'd buy it.", "I received your telegram." ] model_name = "pytorch-models/opus-mt-tc-big-en-el" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_p... | 2f10e639e468f49f7d89e7f4a4b98811 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Έλαβα το τηλεγράφημα σου. ``` 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-en-el") print(pipe("If I weren't broke, I'd buy it.")) | 250bc170cfaa292b15f04006bd7a2960 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ell/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 23d9050ac2038639c774eee44960969b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-ell | tatoeba-test-v2021-08-07 | 0.73660 | 55.4 | 10899 | 66884 | | eng-ell | flores101-devtest | 0.53952 | 27.4 | 1012 | 26615 | | ec50bad78fda70fc6c7ab74761abdf52 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-ner-kmeans This model is a fine-tuned version of [ArBert/bert-base-uncased-finetuned-ner](https://huggingface.co/ArBert/bert-base-uncased-finetuned-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1169 - Precision: 0.9084 - Recall: 0.9245 - F1: 0... | 180ee1bc20adca2ea591620059f60224 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.036 | 1.0 | 1123 | 0.1010 | 0.9086 | 0.9117 | 0.9101 | 0.9779 | | 0.0214 | 2.0 |... | 930156e40c57c9ad846e9cfe2f98ae8d |
apache-2.0 | ['translation'] | false | opus-mt-fi-iso * source languages: fi * target languages: iso * OPUS readme: [fi-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-iso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | c663d3b7fd3540c4ee9783dd5791565b |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_model 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: 0.2341 - Accuracy: 0.9314 | a152570d6c405728161fcb7f30bb1263 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2289 | 1.0 | 1563 | 0.2023 | 0.9219 | | 0.1513 | 2.0 | 3126 | 0.2341 | 0.9314 | | 599f16782185db5ef77993cf77b9fb74 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_age_teens-10_sixties-0_s613 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 t... | cdd8daff87a715593e18ccc58bfbb8d6 |
creativeml-openrail-m | ['stable-diffusion model', 'role-playing game'] | false | **Latest Update: Feb 5th, 2023** - Version 4.0 is live **[available here](https://huggingface.co/Anashel/rpg/tree/main/RPG-V4-Model-Download)** - New Prompt User Guide for RPG v4 **[Download Now](https://huggingface.co/Anashel/rpg/resolve/main/RPG-V4-Model-Download/RPG-Guide-v4.pdf)** | dbb436db817159e6eda387be749d023d |
apache-2.0 | ['generated_from_trainer'] | false | TUS-swahili-news This model is a fine-tuned version of [eolang/TUS](https://huggingface.co/eolang/TUS) on the swahili_news dataset. It achieves the following results on the evaluation set: - Loss: 6.6283 | d53022085798ab2e280de02be119cfce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.1478 | 1.0 | 1010 | 6.8678 | | 6.8352 | 2.0 | 2020 | 6.6895 | | 6.7224 | 3.0 | 3030 | 6.6197 | | bc8903bab6364502ce7126b80fa412ab |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-finetuned-subj_v4_5Epoch This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3180 - Precision: 0.6640 - Recall: 0.5985 - F1: 0.6296 - Accuracy: 0.8... | 86bef5d9e5db352acff10bd7ba121029 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 32 | 0.4471 | 0.4951 | 0.1861 | 0.2706 | 0.8145 | | No log | 2.0 |... | b6debbde5d8e3dd1f6f37072c6ce016c |
apache-2.0 | ['Audio Visual to Text', 'Automatic Speech Recognition'] | false | Model Description These are model weights originally provided by the authors of the paper [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://arxiv.org/pdf/2201.02184.pdf). <figure> <img src="https://huggingface.co/vumichien/AV-HuBERT/resolve/main/HuBert.png" alt="Audio-vi... | 983d2e8fdc08bac45c0a970b08604687 |
apache-2.0 | ['Audio Visual to Text', 'Automatic Speech Recognition'] | false | Example <figure> <img src="https://huggingface.co/vumichien/AV-HuBERT/resolve/main/lipreading.gif" alt="Audio-Visual Speech Recognition"> <figcaption> Speech Recognition from visual lip movement </figcaption> </figure> | 4a0118658b6ac7b7b9fe7e4c001eec82 |
other | ['vision', 'image-segmentation'] | false | SegFormer (b0-sized) model fine-tuned on CityScapes SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this reposito... | 55b92b75fb830309e79ce7ecd900c43c |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretr... | d70bf0180fa5848861ab26cc01b5e646 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4789 - Rouge1: 28.2727 - Rouge2: 7.7068 - Rougel: 22.1993 - Rougelsum: 22.2071 - Gen Len: 18.8238 | 4f52b92d7da378ac24e64c8782807889 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7189 | 1.0 | 12753 | 2.4789 | 28.2727 | 7.7068 | 22.1993 | 22.2071 | 18... | a7d7333ddb30e4aa60b78c26e0d0ef1d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-qa This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1925 | e65dc3d0aaa96c0f00504a9ea6070081 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 | 2ab284838eeda7c0eba447e52efe9681 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | lineven Dreambooth model trained by Ape74 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusi... | 971ed9dbe0a9e65d4a3bea556ee4a452 |
cc-by-4.0 | [] | false |    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 your speec... | 8c94a4eb65b6c0a15088d1a4f53c7c44 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium French MLS This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the facebook/multilingual_librispeech French dataset. It achieves the following results on the evaluation set: - Loss: 0.1239 - Wer: 6.4972 | 15860833e9ac1653c501e311ec405efd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1424 | 1.0 | 1000 | 0.1239 | 6.4972 | | 94bbdeff424dddf1bab95395533090f2 |
mit | [] | false | Model description This model takes the XLM-Roberta-base model which has been continued to pre-traine on a large corpus of Twitter in multiple languages. It was developed following a similar strategy as introduced as part of the [Tweet Eval](https://github.com/cardiffnlp/tweeteval) framework. The model is furthe... | 0410f7cb9ccc573f40d2e1be784d5591 |
mit | [] | false | Intended Usage This model was developed to do Zero-Shot Text Classification in the realm of Hate Speech Detection. It is finetuned on the whole xnli train set containing 15 different languages like: **ar, bg ,de , en, el , es, fr, hi, ru, sw, th, tr, ur, vi, zh** Since the base model was pre-trained on 100 differ... | 3c898a3625ad21aae93559253855e57a |
mit | [] | false | Usage with Zero-Shot Classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="morit/xlm-t-roberta-base-mnli-xnli") ``` | 06e6a06f6da18572a89fcdb44d719244 |
mit | [] | false | Training This model was pre-trained on set of 100 languages, as described in the original paper. It was then fine-tuned on the task of NLI on the concatenated MNLI train set. Finally, it was trained for one additional epoch on only XNLI data where the translations for the premise and hypothesis are shuffled such tha... | 58bd1acb0ab4bc149f58604018e10e93 |
mit | [] | false | Evaluation The model was evaluated on all the test sets of the xnli dataset resulting in the following accuracies: | ar | bg | de | el | en | es | fr | hi| ru | sw | th | tr |ur | vi | zh | |-----|-----|-----|----|----|----|----|----|----|----|----|----|----|----|----| |0.776|0.804|0.796|0.791|0.851|0.813|0.... | 9776d780e8e05c49f000d26465377ea9 |
apache-2.0 | ['generated_from_trainer'] | false | swin-base-patch4-window7-224-in22k-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0390 - Accuracy:... | 4b805f9ec9299e0bc0c0d8649f76c898 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1691 | 1.0 | 190 | 0.0693 | 0.9789 | | 0.1275 | 2.0 | 380 | 0.0419 | 0.9889 | | 0.1165 | 3.0 | 570 | 0.0390 | 0.... | 420ceed3a02a3ad7d8055b325507f529 |
apache-2.0 | ['generated_from_trainer'] | false | hasoc19-bert-base-multilingual-cased-targinsult1 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1105 - Accuracy: 0.6920 - Precision: 0.6630 - Recall: 0.652... | c49a069d831dcbad0a83dc45e1a65eb2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 263 | 0.5820 | 0.6820 | 0.6704 | 0.5890 | 0.5768 | | 0.5841 | 2.0 |... | 7adcd4b27f2fd3afd5b2a48c177cc551 |
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: 0.2154 - Accuracy: 0.9245 - F1: 0.9249 | e5377aaad2c7531e8cbd5d4d1bf80ac6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8175 | 1.0 | 250 | 0.3139 | 0.9025 | 0.8986 | | 0.2485 | 2.0 | 500 | 0.2154 | 0.9245 | 0.9249 | | 6e0b6b567a6cc902e2f5d112c1fb00f5 |
mit | [] | false | This model is ANCE-Tele trained on MS MARCO, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele. ANCE-Tele only trai... | 6800d6904cf5008ae3ff78a91559a462 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-zle Neural machine translation model for translating from English (en) 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 ... | 2c6ed381400b88c6187d7d71fd1a4744 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): eng * target language(s): bel rus ukr * valid target language labels: >>bel<< >>rus<< >>ukr<< * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * origina... | 60f4524668680ad9d4841612c85938ce |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>rus<< Are they coming as well?", ">>rus<< I didn't let Tom do what he wanted to do." ] model_name = "pytorch-models/opus-mt-tc-big-en-zle" tokenizer = MarianTokenizer.from_pretrained(model_name) mo... | f323125367a67d22cf95f905a5a030b9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Я не позволил Тому сделать то, что он хотел. ``` 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-en-zle") print(pipe(">>rus<< Are they coming as well?")) | f68ab4e7599bc2fd2ae4cf922b2cf386 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 0148df57c7d7209337c045c56bbeef17 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-bel | tatoeba-test-v2021-08-07 | 0.50345 | 24.9 | 2500 | 16237 | | eng-rus | tatoeba-test-v2021-08-07 | 0.66182 | 45.5 | 19425 | 134296 | | eng-ukr | tatoeba-test-v2021-08-07 | 0.60175 | 37.7 | 13127 | 80998 | | eng-bel | flores101-devtest | 0.42078 ... | e7a68f3329a61dd0fedc669ab4031027 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5039 - Rouge1: 80.8002 - Rouge2: 77.6863 - Rougel: 80.6746 - Rougelsum: 80.7336 - Gen Len: 14.1393 | 1b92fe5191e859b088cc2c4a5307e0b5 |
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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150 - mixed_precision_training: Native AMP | 0606083a465c2e7450a7e92eb04db859 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 113 | 0.7526 | 80.8695 | 77.9379 | 80.7636 | 80.7858 |... | 12012d99562ed1e46f8637ff7888e103 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 3.5139 | | No log | 2.0 | 12 | 2.8465 | | No log | 3.0 | 18 | 2.4698 | | No log | 4.0 | 24 | 2.2862 ... | baf7eb878a2005d7ac43638da94c974f |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mrpc-target-glue-stsb This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mrpc](https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9256 - Pearson: 0.8144 - Spearmanr: 0.8124 | 2c847a62b2be46da9c57d51531844288 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 2.9549 | 2.78 | 500 | 1.1776 | 0.7187 | 0.7620 | | 0.9497 | 5.56 | 1000 | 1.1286 | 0.7777 | 0.8045 | | 0.7413 ... | f1fbbbcd77d2ece2304e75757d10bb18 |
apache-2.0 | ['object-detection', 'vision'] | false | DETR (End-to-End Object Detection) model with ResNet-101 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first rel... | 5db3f62d9d7e6aa5000dd05263ded138 |
apache-2.0 | ['object-detection', 'vision'] | false | Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queri... | 8740abf66811a2bba5cda8583ac4ae09 |
apache-2.0 | ['object-detection', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import DetrFeatureExtractor, DetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = Detr... | 62b462c89e19bf278935a78ed879f1bf |
apache-2.0 | ['object-detection', 'vision'] | false | convert outputs (bounding boxes and class logits) to COCO API target_sizes = torch.tensor([image.size[::-1]]) results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist... | c0973d666b94745cd1aab531d786a5e9 |
apache-2.0 | ['object-detection', 'vision'] | false | let's only keep detections with score > 0.9 if score > 0.9: print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output (something along the lines of): ``` Detected cat with confidence 0.998... | 3b3f3c8b462d145dc89c0225959d05dd |
apache-2.0 | ['object-detection', 'vision'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pix... | bc1b289504b131c8a7282a0e4b7a12f2 |
apache-2.0 | ['object-detection', 'vision'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2005-12872, author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, title = {End-to... | a6bf7626b7b036a375ff084d1b9c6598 |
apache-2.0 | ['translation'] | false | opus-mt-ig-fr * source languages: ig * target languages: fr * OPUS readme: [ig-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ig-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 459e3f3de7b86721398d650c8ced26fe |
apache-2.0 | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** SummerSigh - **Model type:** DeBERTav3 - **Language(s) (NLP):** English - **License:** apache-2.0 - **Finetuned from model:** https://huggingface.co/sileod/deberta-v3-base-tasksource-nli | 3dfa3e08f75b9e9e7f470124c796f5c3 |
apache-2.0 | [] | false | Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Given an input text, this model will output "KEPT" (0) or "BROKE" (1). KEPT indicates that the text keeps the policies finetuned in mind, while BROKE means that i... | d3fd75869ea8e8faeb3a0542deacdb54 |
apache-2.0 | ['generated_from_trainer'] | false | serverless-roomsort This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0394 - Accuracy: 0.9892 | 6b9c06c1e95ad70f57deebda7727934e |
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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 | ebbf02f4dadda3622aa892821f85b1ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7844 | 1.0 | 762 | 0.0608 | 0.9791 | | 0.0361 | 2.0 | 1524 | 0.0626 | 0.9830 | | 0.0149 | 3.0 | 2286 | 0.0468 | 0.... | ac7018246113e729af29a6e29785a0cf |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-distilbert-base-uncased-5000-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: 0.1147 - Accuracy: 0.982 - F1: 0.9904 | 56a67051c05a5a5e40a0600e71ea1ff6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper large persian This model is a fine-tuned version of [anuragshas/whisper-large-v2-hi](https://huggingface.co/anuragshas/whisper-large-v2-hi) on the mozilla-foundation/common_voice_11_0 fa dataset. It achieves the following results on the evaluation set: - Loss: 0.3047 - Wer: 26.3679 | fff40dcc18f5f2021540d3071088c1a3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_sc... | 26ccceace525fe99fa5f52c6c3eec7dd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2948 | 0.19 | 250 | 0.4258 | 35.6023 | | 0.2443 | 0.39 | 500 | 0.3650 | 30.9747 | | 0.1956 | 0.58 | 750 | 0.3228 | 28.019... | ea3feb973a7433e7a8b8ecabdc6fe9cb |
mit | ['vision', 'image-classification'] | false | NAT (base variant) NAT-Base trained on ImageNet-1K at 224x224 resolution. It was introduced in the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). | b90d53df83b15e9b343c54d7905b63d8 |
mit | ['vision', 'image-classification'] | false | Model description NAT is a hierarchical vision transformer based on Neighborhood Attention (NA). Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels. NA is a sliding-window attention patterns, and as a result is highly flexib... | 333daca85a76a8e8c6f679a5e690bef4 |
mit | ['vision', 'image-classification'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=nat) to look for fine-tuned versions on a task that interests you. | 21e8d1b1c69b1e0c2cedef8a31539e48 |
mit | ['vision', 'image-classification'] | false | Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, NatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = ... | 4e0fe6ad666abb3133624a5f5b3168ce |
mit | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more examples, please refer to the [documentation](https://huggingface.co/transformers/model_doc/nat.html | 09eb3edf70fe38010900541ab64e8392 |
mit | ['vision', 'image-classification'] | false | BibTeX entry and citation info ```bibtex @article{hassani2022neighborhood, title = {Neighborhood Attention Transformer}, author = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2204.07143}, eprint = {2204.... | 973f50fc2a16f919b07be9b0f9d5ba2b |
apache-2.0 | ['generated_from_trainer'] | false | HateXplain-majority-annotator This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3362 - Accuracy: 0.6735 | 09bde05f08e53e5244e9f60def8ed182 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qnli-target-glue-mnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7907 - Accuracy: 0.6507 | 9329206f9d1c86a6f5f16e3224394c02 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0753 | 0.04 | 500 | 1.0327 | 0.4677 | | 1.0084 | 0.08 | 1000 | 0.9655 | 0.5434 | | 0.962 | 0.12 | 1500 | 0.9232 ... | a0d4c0ef0f8f2d568714e9b15a6d1d69 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-it_s250 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | f6ec3280a699ddf38ac975059b6b94d3 |
apache-2.0 | ['translation'] | false | opus-mt-kqn-en * source languages: kqn * target languages: en * OPUS readme: [kqn-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kqn-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | d3111d7f37477623a233f51307143035 |
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: 0.0722 - Precision: 0.9032 - Recall: 0.9220 - F1: 0.9125 - Accuracy: 0.9800 | 780b97cf920d57dcc7c1c047b22fde2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 220 | 0.0974 | 0.8663 | 0.8865 | 0.8763 | 0.9735 | | No log | 2.0 |... | 98515b6207b14065a75a4cb2d094032d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | whisper-base-fine_tuned-ru This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the [common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) dataset. It achieves the following results on the evaluation set: - Loss: 0.4553 - Wer: 41... | 3e52d1f5d1c0e649d7dc1900638a1992 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model description Same as original model (see [whisper-base](https://huggingface.co/openai/whisper-base)). ***But! This model has been fine-tuned for the task of transcribing the Russian language.*** | 3565f7df26ff37b427574eb08eec0f7d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 4a844f2f43cf51c0acbb7a4ded147f3f |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.702 | 0.25 | 500 | 0.8245 | 71.6653 | | 0.5699 | 0.49 | 1000 | 0.6640 | 55.7048 | | 0.5261 | 0.74 | 1500 | 0.6127 | 5... | 5d8f8e24da1850a9d2b3c274e4a05b17 |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'polarity'] | false | Danish BERT fine-tuned for Sentiment Analysis with `senda` This model detects polarity ('positive', 'neutral', 'negative') of Danish texts. It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/se... | eac2a5aeb19633846b3238b9bd2a396b |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'polarity'] | false | Performance The `senda` model achieves an accuracy of 0.77 and a macro-averaged F1-score of 0.73 on a small test data set, that [Alexandra Institute](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md | 93421b26349a70c26be9b37d3ab3245e |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'polarity'] | false | twitter-sentiment) provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the [`senda`](https://github.com/ebanalyse/senda) package to do this. | b8257447620fc51d0d197ebcf4266975 |
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 the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3236 - Accuracy: 0.8667 - F1: 0.8667 | 16f2f8bb6514d425538f10904992dde4 |
apache-2.0 | ['generated_from_trainer'] | false | claim-spotter 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: 0.3266 - F1: 0.8709 | 94e8065d9e8f268afcd237938b8a8e5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3697 | 1.0 | 830 | 0.2728 | 0.8589 | | 0.1475 | 2.0 | 1660 | 0.3266 | 0.8709 | | ab6ca5ce04d6ea8008fb79c96b2ff878 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2-agu This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1869 | db9c2e6befe46b7ac06593c2aa893deb |
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