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translation | transformers | ### MACHINE LEARNING APPROACH TO TRANSLATION OF ENGLISH TO LUGANDA
<!--
BY
SSEMWANGA MICHAEL TENDO
17/U/1120
SUPERVISED BY
AMBROSE SERUNJOGI
A DISSERTATION SUBMITTED TO THE SCHOOL OF STATISTICS AND PLANNING IN FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF
BACHELOR OF STATISTICS
AT
MAKERERE UNI... | {"tags": ["translation"]} | michaeltendo/luganda | null | [
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#transformers #pytorch #marian #text2text-generation #translation #autotrain_compatible #endpoints_compatible #region-us
| ### MACHINE LEARNING APPROACH TO TRANSLATION OF ENGLISH TO LUGANDA
* source languages: en
* target languages: lg
* dataset: self-generated
* model: transformer-align
* test set translations: eng\_lug\_2021-02-17\_test.txt
Benchmarks
----------
testset: URL, BLEU: 30.4, chr-F: 0.543
testset: URL, BLEU: 5.7, chr-F:... | [
"### MACHINE LEARNING APPROACH TO TRANSLATION OF ENGLISH TO LUGANDA\n\n\n* source languages: en\n* target languages: lg\n* dataset: self-generated\n* model: transformer-align\n* test set translations: eng\\_lug\\_2021-02-17\\_test.txt\n\n\nBenchmarks\n----------\n\n\ntestset: URL, BLEU: 30.4, chr-F: 0.543\ntestset:... | [
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fill-mask | transformers | distilbert trained on negative imdb reviews | {} | michalwilk123/distilbert-imdb-negative | null | [
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fill-mask | transformers | distilbert model trained on positive imdb reviews | {} | michalwilk123/distilbert-imdb-positive | null | [
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text-generation | transformers |
# Discord DialoGPT Model | {"tags": ["conversational"]} | michelleshx/DialoGPT-small-michelle-discord-bot | null | [
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# Discord DialoGPT Model | [
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null | null | This is a dummy model | {} | mickeyfromsd/dummy | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is a dummy model | [] | [
"TAGS\n#region-us \n"
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image-classification | transformers |
# dwarf-goats
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingp... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | micole66/dwarf-goats | null | [
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#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dwarf-goats
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### african pygmy goat
!african pygmy goat
#### nigerian dwarf goat
!nigerian dwarf goat | [
"# dwarf-goats\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
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"#### nigerian dwarf goat\n\n!nigerian dwarf g... | [
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fill-mask | transformers |
## MSR BiomedBERT (abstracts + full text)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedBERT (abstracts + full text)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fullt... | {"language": "en", "license": "mit", "tags": ["exbert"], "widget": [{"text": "[MASK] is a tumor suppressor gene."}]} | microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext | null | [
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## MSR BiomedBERT (abstracts + full text)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named "PubMedBERT (abstracts + full text)".
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext"... | [
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fill-mask | transformers |
## MSR BiomedBERT (abstracts only)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedBERT (abstracts)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract" or update your `transfo... | {"language": "en", "license": "mit", "tags": ["exbert"], "widget": [{"text": "[MASK] is a tyrosine kinase inhibitor."}]} | microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"2007.15779"
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] | TAGS
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|
## MSR BiomedBERT (abstracts only)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named "PubMedBERT (abstracts)".
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract" or update your 'transformer... | [
"## MSR BiomedBERT (abstracts only)\n\n<div style=\"border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;\">\n\n* This model was previously named \"PubMedBERT (abstracts)\".\n* You can either adopt the new model name \"microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract\" or update yo... | [
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text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | microsoft/DialoGPT-large | null | [
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| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
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] |
text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | microsoft/DialoGPT-medium | null | [
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| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] | [
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"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] |
text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | microsoft/DialoGPT-small | null | [
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| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
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"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!\n\... |
text-classification | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `depth` score |
| :------ | :------- | :------------: |
| I love NLP! | Can anyone recommend a nice review paper? | 0.724 |
| I love NLP! | ... | {} | microsoft/DialogRPT-depth | null | [
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"2009.06978"
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#transformers #pytorch #gpt2 #text-classification #arxiv-2009.06978 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Demo
====
Please try this Colab Notebook Demo (click me!)
The 'depth' score predicts how likely the response is getting a long follow-up discussion thread.
DialogRPT-depth
===============
### Dialog Ranking Pretrained Transformers
>
> How likely a dialog response is upvoted and/or gets replied ?
>
>
>
... | [
"### Dialog Ranking Pretrained Transformers\n\n\n\n> \n> How likely a dialog response is upvoted and/or gets replied ?\n> \n> \n> \n\n\nThis is what DialogRPT is learned to predict.\nIt is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback da... | [
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text-classification | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `human_vs_machine` score |
| :------ | :------- | :------------: |
| I love NLP! | I'm not sure if it's a good idea. | 0.000 |
| I love NLP!... | {} | microsoft/DialogRPT-human-vs-machine | null | [
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#transformers #pytorch #gpt2 #text-classification #arxiv-2009.06978 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Demo
====
Please try this Colab Notebook Demo (click me!)
The 'human\_vs\_machine' score predicts how likely the response is from a human rather than a machine.
DialogRPT-human-vs-machine
==========================
### Dialog Ranking Pretrained Transformers
>
> How likely a dialog response is upvoted and/o... | [
"### Dialog Ranking Pretrained Transformers\n\n\n\n> \n> How likely a dialog response is upvoted and/or gets replied ?\n> \n> \n> \n\n\nThis is what DialogRPT is learned to predict.\nIt is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback da... | [
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text-classification | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `human_vs_rand` score |
| :------ | :------- | :------------: |
| I love NLP! | He is a great basketball player. | 0.027 |
| I love NLP! | C... | {} | microsoft/DialogRPT-human-vs-rand | null | [
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#transformers #pytorch #gpt2 #text-classification #arxiv-2009.06978 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Demo
====
Please try this Colab Notebook Demo (click me!)
The 'human\_vs\_rand' score predicts how likely the response is corresponding to the given context, rather than a random response.
DialogRPT-human-vs-rand
=======================
### Dialog Ranking Pretrained Transformers
>
> How likely a dialog res... | [
"### Dialog Ranking Pretrained Transformers\n\n\n\n> \n> How likely a dialog response is upvoted and/or gets replied ?\n> \n> \n> \n\n\nThis is what DialogRPT is learned to predict.\nIt is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback da... | [
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text-classification | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `updown` score |
| :------ | :------- | :------------: |
| I love NLP! | Here’s a free textbook (URL) in case anyone needs it. | 0.613 |
| I... | {} | microsoft/DialogRPT-updown | null | [
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#transformers #pytorch #gpt2 #text-classification #arxiv-2009.06978 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Demo
====
Please try this Colab Notebook Demo (click me!)
The 'updown' score predicts how likely the response is getting upvoted.
DialogRPT-updown
================
### Dialog Ranking Pretrained Transformers
>
> How likely a dialog response is upvoted and/or gets replied ?
>
>
>
This is what DialogRPT ... | [
"### Dialog Ranking Pretrained Transformers\n\n\n\n> \n> How likely a dialog response is upvoted and/or gets replied ?\n> \n> \n> \n\n\nThis is what DialogRPT is learned to predict.\nIt is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback da... | [
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text-classification | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `width` score |
| :------ | :------- | :------------: |
| I love NLP! | Can anyone recommend a nice review paper? | 0.701 |
| I love NLP! | ... | {} | microsoft/DialogRPT-width | null | [
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"2009.06978"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-classification #arxiv-2009.06978 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Demo
====
Please try this Colab Notebook Demo (click me!)
The 'width' score predicts how likely the response is getting replied.
DialogRPT-width
===============
### Dialog Ranking Pretrained Transformers
>
> How likely a dialog response is upvoted and/or gets replied ?
>
>
>
This is what DialogRPT is ... | [
"### Dialog Ranking Pretrained Transformers\n\n\n\n> \n> How likely a dialog response is upvoted and/or gets replied ?\n> \n> \n> \n\n\nThis is what DialogRPT is learned to predict.\nIt is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback da... | [
"TAGS\n#transformers #pytorch #gpt2 #text-classification #arxiv-2009.06978 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Dialog Ranking Pretrained Transformers\n\n\n\n> \n> How likely a dialog response is upvoted and/or gets replied ?\n> \n> \n> \n\n\nThis is what Dia... |
text-classification | transformers |
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)".
Please find the information about preprocessing, ... | {"license": "mit", "tags": ["text-classification"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/MiniLM-L12-H384-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2002.10957",
"arxiv:1810.04805",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.10957",
"1810.04805"
] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #arxiv-2002.10957 #arxiv-1810.04805 #license-mit #endpoints_compatible #has_space #region-us
| MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
-----------------------------------------------------------------------------------
MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
Ple... | [
"### English Pre-trained Models\n\n\nWe release the uncased 12-layer model with 384 hidden size distilled from an in-house pre-trained UniLM v2 model in BERT-Base size.\n\n\n* MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base",
"#### Fine-tuning on NLU tasks\n\n... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #arxiv-2002.10957 #arxiv-1810.04805 #license-mit #endpoints_compatible #has_space #region-us \n",
"### English Pre-trained Models\n\n\nWe release the uncased 12-layer model with 384 hidden size distilled from an in-house pre-trained UniLM v2 model ... |
text-classification | transformers |
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)".
Please find the information about preprocessing, ... | {"language": ["multilingual", "en", "ar", "bg", "de", "el", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": "mit", "tags": ["text-classification"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/Multilingual-MiniLM-L12-H384 | null | [
"transformers",
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"tf",
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"text-classification",
"multilingual",
"en",
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"arxiv:2002.10957",
"arxiv:1809.05053",
"arxiv:1911.02116",
"arxiv:1910.07475",
"licens... | null | 2022-03-02T23:29:05+00:00 | [
"2002.10957",
"1809.05053",
"1911.02116",
"1910.07475"
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] | TAGS
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| MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
-----------------------------------------------------------------------------------
MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
Ple... | [
"### Multilingual Pretrained Model\n\n\n* Multilingual-MiniLMv1-L12-H384: 12-layer, 384-hidden, 12-heads, 21M Transformer parameters, 96M embedding parameters\n\n\nMultilingual MiniLM uses the same tokenizer as XLM-R. But the Transformer architecture of our model is the same as BERT. We provide the fine-tuning code... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #multilingual #en #ar #bg #de #el #es #fr #hi #ru #sw #th #tr #ur #vi #zh #arxiv-2002.10957 #arxiv-1809.05053 #arxiv-1911.02116 #arxiv-1910.07475 #license-mit #endpoints_compatible #has_space #region-us \n",
"### Multilingual Pretrained Model\n\n\n... |
fill-mask | transformers | Pretraining large natural language processing models such as BERT, RoBERTa, etc are now state of the art models in natural language understanding and processing tasks. However, these models are trained on a general corpus of articles from the web or from repositories like quora, wikipedia, etc which contain articles of... | {} | microsoft/SportsBERT | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| Pretraining large natural language processing models such as BERT, RoBERTa, etc are now state of the art models in natural language understanding and processing tasks. However, these models are trained on a general corpus of articles from the web or from repositories like quora, wikipedia, etc which contain articles of... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
image-segmentation | transformers |
# BEiT (base-sized model, fine-tuned on ADE20k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on [ADE20k](http://sceneparsing.csail.mit.edu/) (an important benchmark for semantic segmentation of images) at resolution 640x... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixture... | microsoft/beit-base-finetuned-ade-640-640 | null | [
"transformers",
"pytorch",
"beit",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #beit #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BEiT (base-sized model, fine-tuned on ADE20k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ADE20k (an important benchmark for semantic segmentation of images) at resolution 640x640. It was introduced in the paper BE... | [
"# BEiT (base-sized model, fine-tuned on ADE20k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ADE20k (an important benchmark for semantic segmentation of images) at resolution 640x640. It was introduced in the pa... | [
"TAGS\n#transformers #pytorch #beit #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BEiT (base-sized model, fine-tuned on ADE20k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million image... |
image-classification | transformers |
# BEiT (base-sized model, fine-tuned on ImageNet-22k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Trai... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-base-patch16-224-pt22k-ft22k | null | [
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BEiT (base-sized model, fine-tuned on ImageNet-22k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Train... | [
"# BEiT (base-sized model, fine-tuned on ImageNet-22k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper BEIT: BERT Pre... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BEiT (base-sized model, fine-tuned on ImageNet-22k) \n\nBEiT model pre-trained in a self-su... |
image-classification | transformers |
# BEiT (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Han... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-base-patch16-224-pt22k | null | [
"transformers",
"pytorch",
"jax",
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"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us
|
# BEiT (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and fi... | [
"# BEiT (base-sized model, pre-trained only) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BEiT (base-sized model, pre-trained only) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-2... |
image-classification | transformers |
# BEiT (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-T... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-base-patch16-224 | null | [
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BEiT (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Tr... | [
"# BEiT (base-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT ... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BEiT (base-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-sup... |
image-classification | transformers |
# BEiT (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [BEIT: BERT Pre-T... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-base-patch16-384 | null | [
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BEiT (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper BEIT: BERT Pre-Tr... | [
"# BEiT (base-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper BEIT: BERT ... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BEiT (base-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-sup... |
image-segmentation | transformers |
# BEiT (large-sized model, fine-tuned on ADE20k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on [ADE20k](https://huggingface.co/datasets/scene_parse_150) (an important benchmark for semantic segmentation of images) at r... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixture... | microsoft/beit-large-finetuned-ade-640-640 | null | [
"transformers",
"pytorch",
"safetensors",
"beit",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #safetensors #beit #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BEiT (large-sized model, fine-tuned on ADE20k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ADE20k (an important benchmark for semantic segmentation of images) at resolution 640x640. It was introduced in the paper B... | [
"# BEiT (large-sized model, fine-tuned on ADE20k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ADE20k (an important benchmark for semantic segmentation of images) at resolution 640x640. It was introduced in the p... | [
"TAGS\n#transformers #pytorch #safetensors #beit #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BEiT (large-sized model, fine-tuned on ADE20k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14... |
image-classification | transformers |
# BEiT (large-sized model, fine-tuned on ImageNet-22k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Tra... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-large-patch16-224-pt22k-ft22k | null | [
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BEiT (large-sized model, fine-tuned on ImageNet-22k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Trai... | [
"# BEiT (large-sized model, fine-tuned on ImageNet-22k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper BEIT: BERT Pr... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BEiT (large-sized model, fine-tuned on ImageNet-22k) \n\nBEiT model pre-trained in a self-s... |
image-classification | transformers |
# BEiT (large-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Ha... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-large-patch16-224-pt22k | null | [
"transformers",
"pytorch",
"jax",
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"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us
|
# BEiT (large-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and f... | [
"# BEiT (large-sized model, pre-trained only) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei... | [
"TAGS\n#transformers #pytorch #jax #safetensors #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BEiT (large-sized model, pre-trained only) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-... |
image-classification | transformers |
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-large-patch16-224 | null | [
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"vision",
"dataset:imagenet",
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] | null | 2022-03-02T23:29:05+00:00 | [
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|
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-T... | [
"# BEiT (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT... | [
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"# BEiT (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-su... |
image-classification | transformers |
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [BEIT: BERT Pre-... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-large-patch16-384 | null | [
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"endpoints_compatible",
"region:us"
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"2106.08254"
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|
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper BEIT: BERT Pre-T... | [
"# BEiT (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper BEIT: BERT... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BEiT (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fa... |
image-classification | transformers |
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 512x512. It was introduced in the paper [BEIT: BERT Pre-... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | microsoft/beit-large-patch16-512 | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 512x512. It was introduced in the paper BEIT: BERT Pre-T... | [
"# BEiT (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 512x512. It was introduced in the paper BEIT: BERT... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BEiT (large-sized model, fine-tuned on ImageNet-1k) \n\nBEiT model pre-trained in a self-su... |
null | transformers |
# Model Card for COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
# Model Details
## Model Description
This model card contains the COCO-LM model (**base++** version) pretrained models on GLUE and SQuAD 2.0 benchmarks.
- **Developed by:** Microsoft
- **Shared by [Optional]:** ... | {} | microsoft/cocolm-base | null | [
"transformers",
"pytorch",
"arxiv:2102.08473",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.08473",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #arxiv-2102.08473 #arxiv-1910.09700 #endpoints_compatible #region-us
| Model Card for COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
================================================================================================
Model Details
=============
Model Description
-----------------
This model card contains the COCO-LM model (base++ versi... | [
"### Preprocessing\n\n\nThe model devloeprs note in the associated paper:\n\n\n\n> \n> We employ three standard settings, base, base++, and large++. Base is the BERTBase training configuration: Pretraining on Wikipedia and BookCorpus (16 GB of texts) for 256 million samples on 512 token sequences (125K batches with... | [
"TAGS\n#transformers #pytorch #arxiv-2102.08473 #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe model devloeprs note in the associated paper:\n\n\n\n> \n> We employ three standard settings, base, base++, and large++. Base is the BERTBase training configuration: Pretraining on W... |
null | transformers | # COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
This model card contains the COCO-LM model (**large++** version) proposed in [this paper](https://arxiv.org/abs/2102.08473). The official GitHub repository can be found [here](https://github.com/microsoft/COCO-LM).
# Citation
If you f... | {} | microsoft/cocolm-large | null | [
"transformers",
"pytorch",
"arxiv:2102.08473",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.08473"
] | [] | TAGS
#transformers #pytorch #arxiv-2102.08473 #endpoints_compatible #region-us
| # COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
This model card contains the COCO-LM model (large++ version) proposed in this paper. The official GitHub repository can be found here.
If you find this model card useful for your research, please cite the following paper:
| [
"# COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining\n\nThis model card contains the COCO-LM model (large++ version) proposed in this paper. The official GitHub repository can be found here.\n\nIf you find this model card useful for your research, please cite the following paper:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2102.08473 #endpoints_compatible #region-us \n",
"# COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining\n\nThis model card contains the COCO-LM model (large++ version) proposed in this paper. The official GitHub repository can be found here.\n\nIf... |
fill-mask | transformers | ## CodeBERT-base-mlm
Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155).
### Training Data
The model is trained on the code corpus of [CodeSearchNet](https://github.com/github/CodeSearchNet)
### Training Objective
This model is initialized wi... | {} | microsoft/codebert-base-mlm | null | [
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"roberta",
"fill-mask",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.08155"
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#transformers #pytorch #tf #jax #rust #roberta #fill-mask #arxiv-2002.08155 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## CodeBERT-base-mlm
Pretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.
### Training Data
The model is trained on the code corpus of CodeSearchNet
### Training Objective
This model is initialized with Roberta-base and trained with a simple MLM (Masked Language Model) objective... | [
"## CodeBERT-base-mlm\nPretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.",
"### Training Data\nThe model is trained on the code corpus of CodeSearchNet",
"### Training Objective\nThis model is initialized with Roberta-base and trained with a simple MLM (Masked Language ... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #roberta #fill-mask #arxiv-2002.08155 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## CodeBERT-base-mlm\nPretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.",
"### Training Data\nThe model is trained ... |
feature-extraction | transformers | ## CodeBERT-base
Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155).
### Training Data
The model is trained on bi-modal data (documents & code) of [CodeSearchNet](https://github.com/github/CodeSearchNet)
### Training Objective
This model is i... | {} | microsoft/codebert-base | null | [
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"jax",
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"roberta",
"feature-extraction",
"arxiv:2002.08155",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [
"2002.08155"
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#transformers #pytorch #tf #jax #rust #roberta #feature-extraction #arxiv-2002.08155 #endpoints_compatible #has_space #region-us
| ## CodeBERT-base
Pretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.
### Training Data
The model is trained on bi-modal data (documents & code) of CodeSearchNet
### Training Objective
This model is initialized with Roberta-base and trained with MLM+RTD objective (cf. the paper)... | [
"## CodeBERT-base\nPretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.",
"### Training Data\nThe model is trained on bi-modal data (documents & code) of CodeSearchNet",
"### Training Objective\nThis model is initialized with Roberta-base and trained with MLM+RTD objective... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #roberta #feature-extraction #arxiv-2002.08155 #endpoints_compatible #has_space #region-us \n",
"## CodeBERT-base\nPretrained weights for CodeBERT: A Pre-Trained Model for Programming and Natural Languages.",
"### Training Data\nThe model is trained on bi-modal data ... |
text-classification | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "widget": [{"text": "[CLS] I love you. [SEP] I like you. [SEP]"}]} | microsoft/deberta-base-mnli | null | [
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"2006.03654"
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"en"
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.\n\n\n\nIf you find DeBERTa useful for your work, please cite the following paper:"
] | [
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"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.\n\n\n\nIf you find D... |
fill-mask | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-base | null | [
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.\n\n\n\nIf you find DeBERTa useful for your work, please cite the following paper:"
] | [
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"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.\n\n\n\nIf you find DeBERTa useful for your work, please cite t... |
text-classification | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "widget": [{"text": "[CLS] I love you. [SEP] I like you. [SEP]"}]} | microsoft/deberta-large-mnli | null | [
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI... | [
"TAGS\n#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\... |
fill-mask | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-large | null | [
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"2006.03654"
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#transformers #pytorch #tf #deberta #deberta-v1 #fill-mask #en #arxiv-2006.03654 #license-mit #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI... | [
"TAGS\n#transformers #pytorch #tf #deberta #deberta-v1 #fill-mask #en #arxiv-2006.03654 #license-mit #endpoints_compatible #has_space #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following... |
text-classification | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "widget": [{"text": "[CLS] I love you. [SEP] I like you. [SEP]"}]} | microsoft/deberta-v2-xlarge-mnli | null | [
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
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"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n... |
fill-mask | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposit... | {"language": "en", "license": "mit", "tags": ["deberta", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-v2-xlarge | null | [
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"2006.03654"
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] | TAGS
#transformers #pytorch #tf #deberta-v2 #deberta #fill-mask #en #arxiv-2006.03654 #license-mit #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
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"#### Notes.\n\n\n* 1 Following ... |
text-classification | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repos... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "widget": [{"text": "[CLS] I love you. [SEP] I like you. [SEP]"}]} | microsoft/deberta-v2-xxlarge-mnli | null | [
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
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"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n... |
fill-mask | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-v2-xxlarge | null | [
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
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"#### Notes.\n\n\n* 1 Following ... |
fill-mask | transformers |
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority ... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-v3-base | null | [
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| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
----------------------------------------------------------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wit... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.\n\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers\n\n\nIf you find DeBERTa useful for your work, please cite the following papers:"
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"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.\n\n\n\nWe present the dev results on... |
fill-mask | transformers |
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority ... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-v3-large | null | [
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| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
----------------------------------------------------------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wit... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers\n\n\nIf you find DeBERTa useful for your work, please cite the following papers:"
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"#### Fine-tuning with HF transformers... |
fill-mask | transformers |
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority ... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-v3-small | null | [
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| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
----------------------------------------------------------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wit... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers\n\n\nIf you find DeBERTa useful for your work, please cite the following papers:"
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"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers... |
fill-mask | transformers |
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority ... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-v3-xsmall | null | [
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| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
----------------------------------------------------------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wit... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers\n\n\nIf you find DeBERTa useful for your work, please cite the following papers:"
] | [
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"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers\n\n\nIf yo... |
text-classification | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "widget": [{"text": "[CLS] I love you. [SEP] I like you. [SEP]"}]} | microsoft/deberta-xlarge-mnli | null | [
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| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
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"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n... |
null | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli)
| {"language": "en", "license": "mit", "tags": "deberta", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-xlarge-v2-mnli | null | [
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"license:mit",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use DeBERTa-V2-XLarge-MNLI
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null | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)
| {"language": "en", "license": "mit", "tags": "deberta", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-xlarge-v2 | null | [
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"deberta",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #deberta #en #license-mit #endpoints_compatible #has_space #region-us
|
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use DeBERTa-V2-XLarge
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"## This model is DEPRECATED, please use DeBERTa-V2-XLarge"
] |
fill-mask | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
Please ... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-xlarge | null | [
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"has_space",
"region:us"
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"2006.03654"
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#transformers #pytorch #tf #deberta #deberta-v1 #fill-mask #en #arxiv-2006.03654 #license-mit #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB tra... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
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"#### Notes.\n\n\n* 1 Following ... |
null | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli)
| {"language": "en", "license": "mit", "tags": "deberta", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-xxlarge-v2-mnli | null | [
"transformers",
"pytorch",
"deberta-v2",
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"endpoints_compatible",
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"en"
] | TAGS
#transformers #pytorch #deberta-v2 #deberta #en #license-mit #endpoints_compatible #region-us
|
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use DeBERTa-V2-XXLarge-MNLI
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"## DeBERTa: Decoding-enhanced BERT with Disentangled Attention",
"## This model is DEPRECATED, please use DeBERTa-V2-XXLarge-MNLI"
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null | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)
| {"language": "en", "license": "mit", "tags": "deberta", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/deberta-xxlarge-v2 | null | [
"transformers",
"pytorch",
"deberta-v2",
"deberta",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #deberta #en #license-mit #endpoints_compatible #region-us
|
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use DeBERTa-V2-XXLarge
| [
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"## This model is DEPRECATED, please use DeBERTa-V2-XXLarge"
] |
fill-mask | transformers | ## GraphCodeBERT model
GraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence ... | {} | microsoft/graphcodebert-base | null | [
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"jax",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.08366"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2009.08366 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## GraphCodeBERT model
GraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence ... | [
"## GraphCodeBERT model\n\nGraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum se... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2009.08366 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## GraphCodeBERT model\n\nGraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-... |
fill-mask | transformers | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
b9d214025837... | {} | microsoft/infoxlm-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.07834"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2007.07834 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # InfoXLM
InfoXLM (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
MD5
BibTeX
| [
"# InfoXLM\n\nInfoXLM (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.\n\nMD5\n\n\n\nBibTeX"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2007.07834 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# InfoXLM\n\nInfoXLM (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.\n\nMD5\n\n\n\nBibTeX"
] |
fill-mask | transformers | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
05b95b7d9774... | {} | microsoft/infoxlm-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.07834"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2007.07834 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # InfoXLM
InfoXLM (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
MD5
BibTeX
| [
"# InfoXLM\n\nInfoXLM (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.\n\nMD5\n\n\n\nBibTeX"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2007.07834 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# InfoXLM\n\nInfoXLM (NAACL 2021, paper, repo, model) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.\n\nMD5\n\n\n\nBibTeX"
] |
null | transformers | # LayoutLM
**Multimodal (text + layout/format + image) pre-training for document AI**
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document ... | {} | microsoft/layoutlm-base-cased | null | [
"transformers",
"pytorch",
"layoutlm",
"arxiv:1912.13318",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.13318"
] | [] | TAGS
#transformers #pytorch #layoutlm #arxiv-1912.13318 #endpoints_compatible #region-us
| # LayoutLM
Multimodal (text + layout/format + image) pre-training for document AI
Microsoft Document AI | GitHub
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt und... | [
"# LayoutLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding a... | [
"TAGS\n#transformers #pytorch #layoutlm #arxiv-1912.13318 #endpoints_compatible #region-us \n",
"# LayoutLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layo... |
null | transformers |
# LayoutLM
**Multimodal (text + layout/format + image) pre-training for document AI**
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document... | {"language": "en", "license": "mit"} | microsoft/layoutlm-base-uncased | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"layoutlm",
"en",
"arxiv:1912.13318",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.13318"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #layoutlm #en #arxiv-1912.13318 #license-mit #endpoints_compatible #has_space #region-us
|
# LayoutLM
Multimodal (text + layout/format + image) pre-training for document AI
Microsoft Document AI | GitHub
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt un... | [
"# LayoutLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding a... | [
"TAGS\n#transformers #pytorch #tf #safetensors #layoutlm #en #arxiv-1912.13318 #license-mit #endpoints_compatible #has_space #region-us \n",
"# LayoutLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLM is a simple but e... |
null | transformers | # LayoutLM
Multimodal (text + layout/format + image) pre-training for document AI
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document imag... | {} | microsoft/layoutlm-large-uncased | null | [
"transformers",
"pytorch",
"tf",
"layoutlm",
"arxiv:1912.13318",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.13318"
] | [] | TAGS
#transformers #pytorch #tf #layoutlm #arxiv-1912.13318 #endpoints_compatible #has_space #region-us
| # LayoutLM
Multimodal (text + layout/format + image) pre-training for document AI
Microsoft Document AI | GitHub
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt und... | [
"# LayoutLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding a... | [
"TAGS\n#transformers #pytorch #tf #layoutlm #arxiv-1912.13318 #endpoints_compatible #has_space #region-us \n",
"# LayoutLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLM is a simple but effective pre-training method o... |
null | transformers |
# LayoutLMv2
**Multimodal (text + layout/format + image) pre-training for document AI**
The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutlmv2).
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/... | {"language": "en", "license": "cc-by-nc-sa-4.0"} | microsoft/layoutlmv2-base-uncased | null | [
"transformers",
"pytorch",
"layoutlmv2",
"en",
"arxiv:2012.14740",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.14740"
] | [
"en"
] | TAGS
#transformers #pytorch #layoutlmv2 #en #arxiv-2012.14740 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# LayoutLMv2
Multimodal (text + layout/format + image) pre-training for document AI
The documentation of this model in the Transformers library can be found here.
Microsoft Document AI | GitHub
## Introduction
LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among te... | [
"# LayoutLMv2\nMultimodal (text + layout/format + image) pre-training for document AI\n\nThe documentation of this model in the Transformers library can be found here.\n\nMicrosoft Document AI | GitHub",
"## Introduction\nLayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the inter... | [
"TAGS\n#transformers #pytorch #layoutlmv2 #en #arxiv-2012.14740 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# LayoutLMv2\nMultimodal (text + layout/format + image) pre-training for document AI\n\nThe documentation of this model in the Transformers library can be found here.\n\nMicro... |
null | transformers |
# LayoutLMv2
**Multimodal (text + layout/format + image) pre-training for document AI**
## Introduction
LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves new s... | {"language": "en", "license": "cc-by-nc-sa-4.0"} | microsoft/layoutlmv2-large-uncased | null | [
"transformers",
"pytorch",
"layoutlmv2",
"en",
"arxiv:2012.14740",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.14740"
] | [
"en"
] | TAGS
#transformers #pytorch #layoutlmv2 #en #arxiv-2012.14740 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# LayoutLMv2
Multimodal (text + layout/format + image) pre-training for document AI
## Introduction
LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves new state... | [
"# LayoutLMv2\nMultimodal (text + layout/format + image) pre-training for document AI",
"## Introduction\nLayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves... | [
"TAGS\n#transformers #pytorch #layoutlmv2 #en #arxiv-2012.14740 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# LayoutLMv2\nMultimodal (text + layout/format + image) pre-training for document AI",
"## Introduction\nLayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to mo... |
null | transformers |
# LayoutXLM
**Multimodal (text + layout/format + image) pre-training for document AI**
LayoutXLM is a multilingual variant of LayoutLMv2.
The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutxlm).
[Microsoft Document AI](https://www... | {"license": "cc-by-nc-sa-4.0"} | microsoft/layoutxlm-base | null | [
"transformers",
"pytorch",
"layoutlmv2",
"arxiv:2104.08836",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08836"
] | [] | TAGS
#transformers #pytorch #layoutlmv2 #arxiv-2104.08836 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# LayoutXLM
Multimodal (text + layout/format + image) pre-training for document AI
LayoutXLM is a multilingual variant of LayoutLMv2.
The documentation of this model in the Transformers library can be found here.
Microsoft Document AI | GitHub
## Introduction
LayoutXLM is a multimodal pre-trained model for multilin... | [
"# LayoutXLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nLayoutXLM is a multilingual variant of LayoutLMv2.\n\nThe documentation of this model in the Transformers library can be found here.\n\nMicrosoft Document AI | GitHub",
"## Introduction\nLayoutXLM is a multimodal pre-trained m... | [
"TAGS\n#transformers #pytorch #layoutlmv2 #arxiv-2104.08836 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# LayoutXLM\nMultimodal (text + layout/format + image) pre-training for document AI\n\nLayoutXLM is a multilingual variant of LayoutLMv2.\n\nThe documentation of this model in the... |
null | transformers |
# MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extr... | {"language": ["en"]} | microsoft/markuplm-base | null | [
"transformers",
"pytorch",
"markuplm",
"en",
"arxiv:2110.08518",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08518"
] | [
"en"
] | TAGS
#transformers #pytorch #markuplm #en #arxiv-2110.08518 #endpoints_compatible #has_space #region-us
|
# MarkupLM
Multimodal (text +markup language) pre-training for Document AI
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. ... | [
"# MarkupLM\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information ... | [
"TAGS\n#transformers #pytorch #markuplm #en #arxiv-2110.08518 #endpoints_compatible #has_space #region-us \n",
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"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for v... |
null | transformers |
# MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extr... | {"language": ["en"]} | microsoft/markuplm-large | null | [
"transformers",
"pytorch",
"markuplm",
"en",
"arxiv:2110.08518",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08518"
] | [
"en"
] | TAGS
#transformers #pytorch #markuplm #en #arxiv-2110.08518 #endpoints_compatible #region-us
|
# MarkupLM
Multimodal (text +markup language) pre-training for Document AI
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. ... | [
"# MarkupLM\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information ... | [
"TAGS\n#transformers #pytorch #markuplm #en #arxiv-2110.08518 #endpoints_compatible #region-us \n",
"# MarkupLM\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-ric... |
fill-mask | transformers |
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority ... | {"language": ["multilingual", "en", "ar", "bg", "de", "el", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": "mit", "tags": ["deberta", "deberta-v3", "mdeberta", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/mdeberta-v3-base | null | [
"transformers",
"pytorch",
"tf",
"deberta-v2",
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"license:mit",
"end... | null | 2022-03-02T23:29:05+00:00 | [
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"ru",
"sw",
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"ur",
"vi",
"zh"
] | TAGS
#transformers #pytorch #tf #deberta-v2 #deberta #deberta-v3 #mdeberta #fill-mask #multilingual #en #ar #bg #de #el #es #fr #hi #ru #sw #th #tr #ur #vi #zh #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #endpoints_compatible #has_space #region-us
| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
----------------------------------------------------------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wit... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on XNLI with zero-shot cross-lingual transfer setting, i.e. training with English data only, test on other languages.",
"#### Fine-tuning with HF transformers\n\n\nIf you find DeBERTa useful for your work, please cite the following papers:"
] | [
"TAGS\n#transformers #pytorch #tf #deberta-v2 #deberta #deberta-v3 #mdeberta #fill-mask #multilingual #en #ar #bg #de #el #es #fr #hi #ru #sw #th #tr #ur #vi #zh #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #endpoints_compatible #has_space #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev... |
text2text-generation | transformers |
## prophetnet-large-uncased-cnndm
Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on summarization task CNN/DailyMail.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a... | {"language": "en", "datasets": ["cnn_dailymail"]} | microsoft/prophetnet-large-uncased-cnndm | null | [
"transformers",
"pytorch",
"rust",
"prophetnet",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"arxiv:2001.04063",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.04063"
] | [
"en"
] | TAGS
#transformers #pytorch #rust #prophetnet #text2text-generation #en #dataset-cnn_dailymail #arxiv-2001.04063 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## prophetnet-large-uncased-cnndm
Fine-tuned weights(converted from original fairseq version repo) for ProphetNet on summarization task CNN/DailyMail.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet ... | [
"## prophetnet-large-uncased-cnndm\nFine-tuned weights(converted from original fairseq version repo) for ProphetNet on summarization task CNN/DailyMail. \nProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. \nProp... | [
"TAGS\n#transformers #pytorch #rust #prophetnet #text2text-generation #en #dataset-cnn_dailymail #arxiv-2001.04063 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## prophetnet-large-uncased-cnndm\nFine-tuned weights(converted from original fairseq version repo) for ProphetNet on summariza... |
text2text-generation | transformers |
##
prophetnet-large-uncased-squad-qg
Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on question generation
SQuAD 1.1.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with... | {"language": "en", "datasets": ["squad"]} | microsoft/prophetnet-large-uncased-squad-qg | null | [
"transformers",
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"en",
"dataset:squad",
"arxiv:2001.04063",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.04063"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #prophetnet #text2text-generation #en #dataset-squad #arxiv-2001.04063 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
##
prophetnet-large-uncased-squad-qg
Fine-tuned weights(converted from original fairseq version repo) for ProphetNet on question generation
SQuAD 1.1.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNe... | [
"## \nprophetnet-large-uncased-squad-qg\nFine-tuned weights(converted from original fairseq version repo) for ProphetNet on question generation \nSQuAD 1.1. \nProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. \n... | [
"TAGS\n#transformers #pytorch #safetensors #prophetnet #text2text-generation #en #dataset-squad #arxiv-2001.04063 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## \nprophetnet-large-uncased-squad-qg\nFine-tuned weights(converted from original fairseq version repo) for ProphetNet on quest... |
text2text-generation | transformers |
## prophetnet-large-uncased
Pretrained weights for [ProphetNet](https://arxiv.org/abs/2001.04063).
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future tokens with a n-strea... | {"language": "en"} | microsoft/prophetnet-large-uncased | null | [
"transformers",
"pytorch",
"rust",
"safetensors",
"prophetnet",
"text2text-generation",
"en",
"arxiv:2001.04063",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.04063"
] | [
"en"
] | TAGS
#transformers #pytorch #rust #safetensors #prophetnet #text2text-generation #en #arxiv-2001.04063 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## prophetnet-large-uncased
Pretrained weights for ProphetNet.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementati... | [
"## prophetnet-large-uncased\nPretrained weights for ProphetNet. \nProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. \nProphetNet is able to predict more future tokens with a n-stream decoder. The original imple... | [
"TAGS\n#transformers #pytorch #rust #safetensors #prophetnet #text2text-generation #en #arxiv-2001.04063 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## prophetnet-large-uncased\nPretrained weights for ProphetNet. \nProphetNet is a new pre-trained language model for sequence-to-sequenc... |
summarization | transformers |
# SSR-base
SSR-base model as in EMNLP 2021 paper [Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting](https://arxiv.org/abs/2101.00416).
| {"language": ["en"], "tags": ["summarization", "text2text-generation"], "datasets": ["c4"]} | microsoft/ssr-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"en",
"dataset:c4",
"arxiv:2101.00416",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00416"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #en #dataset-c4 #arxiv-2101.00416 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SSR-base
SSR-base model as in EMNLP 2021 paper Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting.
| [
"# SSR-base\n\nSSR-base model as in EMNLP 2021 paper Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #en #dataset-c4 #arxiv-2101.00416 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SSR-base\n\nSSR-base model as in EMNLP 2021 paper Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriti... |
image-classification | transformers |
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first relea... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | microsoft/swin-base-patch4-window12-384-in22k | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer... | [
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDi... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images... |
image-classification | transformers |
# Swin Transformer (base-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.co... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swin-base-patch4-window12-384 | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer (base-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer did ... | [
"# Swin Transformer (base-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transfor... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer (base-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 384x384. It was introd... |
image-classification | transformers |
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first relea... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | microsoft/swin-base-patch4-window7-224-in22k | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"swin",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer... | [
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDi... | [
"TAGS\n#transformers #pytorch #tf #safetensors #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 m... |
image-classification | transformers |
# Swin Transformer (base-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.co... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swin-base-patch4-window7-224 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"swin",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (base-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer did ... | [
"# Swin Transformer (base-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transfor... | [
"TAGS\n#transformers #pytorch #tf #safetensors #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (base-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolutio... |
image-classification | transformers |
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first relea... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | microsoft/swin-large-patch4-window12-384-in22k | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer... | [
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDi... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images... |
image-classification | transformers |
# Swin Transformer (large-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.c... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swin-large-patch4-window12-384 | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer (large-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer did... | [
"# Swin Transformer (large-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transfo... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer (large-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 384x384. It was intro... |
image-classification | transformers |
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first relea... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | microsoft/swin-large-patch4-window7-224-in22k | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer... | [
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDi... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-21k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (large-sized model) \n\nSwin Transformer model pre-trained on ImageNet-21k (14 million images... |
image-classification | transformers |
# Swin Transformer (large-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.c... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swin-large-patch4-window7-224 | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (large-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer did... | [
"# Swin Transformer (large-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transfo... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (large-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. I... |
image-classification | transformers |
# Swin Transformer (small-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.c... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swin-small-patch4-window7-224 | null | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (small-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer did... | [
"# Swin Transformer (small-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transfo... | [
"TAGS\n#transformers #pytorch #tf #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (small-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. I... |
image-classification | transformers |
# Swin Transformer (tiny-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.co... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swin-tiny-patch4-window7-224 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"swin",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer (tiny-sized model)
Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer did ... | [
"# Swin Transformer (tiny-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transfor... | [
"TAGS\n#transformers #pytorch #tf #safetensors #swin #image-classification #vision #dataset-imagenet-1k #arxiv-2103.14030 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer (tiny-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolutio... |
text-classification | transformers |
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraini... | {"language": "en", "license": "mit", "tags": ["tapex"], "datasets": ["tab_fact"]} | microsoft/tapex-base-finetuned-tabfact | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# TAPEX (base-sized model)
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
## Model description
TAPEX (Table Pre-training via Execution) is a conceptually... | [
"# TAPEX (base-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.",
"## Model description\n\nTAPEX (Table Pre-training via Execution) is a ... | [
"TAGS\n#transformers #pytorch #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# TAPEX (base-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu... |
table-question-answering | transformers |
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraini... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"], "datasets": ["wikisql"]} | microsoft/tapex-base-finetuned-wikisql | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
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| TAPEX (base-sized model)
========================
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
Model description
-----------------
TAPEX (Table Pre-tr... | [
"### How to Use\n\n\nHere is how to use this model in transformers:",
"### How to Eval\n\n\nPlease find the eval script here.",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to Use\n\n\nHere is how to use this model in transformers:",
"### How to Eval\n\n\nPlease... |
table-question-answering | transformers |
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraini... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"]} | microsoft/tapex-base | null | [
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"safetensors",
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# TAPEX (base-sized model)
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
## Model description
TAPEX (Table Pre-training via Execution) is a conceptually... | [
"# TAPEX (base-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.",
"## Model description\n\nTAPEX (Table Pre-training via Execution) is a ... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# TAPEX (base-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQ... |
table-question-answering | transformers |
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretrain... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"], "datasets": ["tab_fact"]} | microsoft/tapex-large-finetuned-tabfact | null | [
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"tapex",
"table-question-answering",
"en",
"dataset:tab_fact",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text-classification #tapex #table-question-answering #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# TAPEX (large-sized model)
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
## Model description
TAPEX (Table Pre-training via Execution) is a conceptuall... | [
"# TAPEX (large-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.",
"## Model description\n\nTAPEX (Table Pre-training via Execution) is a... | [
"TAGS\n#transformers #pytorch #bart #text-classification #tapex #table-question-answering #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPEX (large-sized model) \n\nTAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Exec... |
image-to-text | transformers |
# TrOCR (base-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and ... | {"tags": ["trocr", "image-to-text"], "widget": [{"src": "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg", "example_title": "Note 1"}, {"src": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU", "example_title": "Note 2"}, {"src": ... | microsoft/trocr-base-handwritten | null | [
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"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (base-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: The team releasing TrOCR did not write a model card ... | [
"# TrOCR (base-sized model, fine-tuned on IAM) \n\nTrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. \n\nDisclaimer: The team releasing TrOCR did not write a mod... | [
"TAGS\n#transformers #pytorch #safetensors #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (base-sized model, fine-tuned on IAM) \n\nTrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical ... |
image-to-text | transformers |
# TrOCR (base-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repo... | {"tags": ["trocr", "image-to-text"], "widget": [{"src": "https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X00016469612_1.jpg", "example_title": "Printed 1"}, {"src": "https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X51005255805_7.jpg", "example_title": "Printed... | microsoft/trocr-base-printed | null | [
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"vision-encoder-decoder",
"trocr",
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"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (base-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: The team releasing TrOCR did not write a model c... | [
"# TrOCR (base-sized model, fine-tuned on SROIE) \n\nTrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. \n\nDisclaimer: The team releasing TrOCR did not write a... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (base-sized model, fine-tuned on SROIE) \n\nTrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character... |
image-to-text | transformers |
# TrOCR (base-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/mas... | {"tags": ["trocr", "image-to-text"]} | microsoft/trocr-base-stage1 | null | [
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"pytorch",
"vision-encoder-decoder",
"trocr",
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"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (base-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: The team releasing TrOCR did not write a model card for this model... | [
"# TrOCR (base-sized model, pre-trained only) \n\nTrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. \n\nDisclaimer: The team releasing TrOCR did not write a model card for th... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (base-sized model, pre-trained only) \n\nTrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with ... |
image-to-text | transformers |
# TrOCR (large-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and... | {"tags": ["trocr", "image-to-text"], "widget": [{"src": "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg", "example_title": "Note 1"}, {"src": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU", "example_title": "Note 2"}, {"src": ... | microsoft/trocr-large-handwritten | null | [
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"trocr",
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"arxiv:2109.10282",
"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (large-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: The team releasing TrOCR did not write a model card... | [
"# TrOCR (large-sized model, fine-tuned on IAM) \n\nTrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. \n\nDisclaimer: The team releasing TrOCR did not write a mo... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (large-sized model, fine-tuned on IAM) \n\nTrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Re... |
image-to-text | transformers |
# TrOCR (large-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this rep... | {"tags": ["trocr", "image-to-text"], "widget": [{"src": "https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X00016469612_1.jpg", "example_title": "Printed 1"}, {"src": "https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X51005255805_7.jpg", "example_title": "Printed... | microsoft/trocr-large-printed | null | [
"transformers",
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"vision-encoder-decoder",
"trocr",
"image-to-text",
"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (large-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: The team releasing TrOCR did not write a model ... | [
"# TrOCR (large-sized model, fine-tuned on SROIE) \n\nTrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. \n\nDisclaimer: The team releasing TrOCR did not write ... | [
"TAGS\n#transformers #pytorch #safetensors #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (large-sized model, fine-tuned on SROIE) \n\nTrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Opt... |
image-to-text | transformers |
# TrOCR (large-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/ma... | {"tags": ["trocr", "image-to-text"]} | microsoft/trocr-large-stage1 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"trocr",
"image-to-text",
"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (large-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: The team releasing TrOCR did not write a model card for this mode... | [
"# TrOCR (large-sized model, pre-trained only) \n\nTrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. \n\nDisclaimer: The team releasing TrOCR did not write a model card for t... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (large-sized model, pre-trained only) \n\nTrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with... |
image-to-text | transformers |
# TrOCR (small-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and... | {"tags": ["trocr", "image-to-text"], "widget": [{"src": "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg", "example_title": "Note 1"}, {"src": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU", "example_title": "Note 2"}, {"src": ... | microsoft/trocr-small-handwritten | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"trocr",
"image-to-text",
"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (small-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
## Model description
The TrOCR model is an encoder-decoder mo... | [
"# TrOCR (small-sized model, fine-tuned on IAM) \n\nTrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.",
"## Model description\n\nThe TrOCR model is an encoder-... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (small-sized model, fine-tuned on IAM) \n\nTrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Re... |
image-to-text | transformers |
# TrOCR (small-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this rep... | {"tags": ["trocr", "image-to-text"], "widget": [{"src": "https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X00016469612_1.jpg", "example_title": "Printed 1"}, {"src": "https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X51005255805_7.jpg", "example_title": "Printed... | microsoft/trocr-small-printed | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"trocr",
"image-to-text",
"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (small-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
## Model description
The TrOCR model is an encoder-decode... | [
"# TrOCR (small-sized model, fine-tuned on SROIE) \n\nTrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.",
"## Model description\n\nThe TrOCR model is an enco... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (small-sized model, fine-tuned on SROIE) \n\nTrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Characte... |
image-to-text | transformers |
# TrOCR (small-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/ma... | {"tags": ["trocr", "image-to-text"]} | microsoft/trocr-small-stage1 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"trocr",
"image-to-text",
"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
|
# TrOCR (small-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
## Model description
The TrOCR model is an encoder-decoder model, consistin... | [
"# TrOCR (small-sized model, pre-trained only) \n\nTrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.",
"## Model description\n\nThe TrOCR model is an encoder-decoder model,... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #trocr #image-to-text #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"# TrOCR (small-sized model, pre-trained only) \n\nTrOCR pre-trained only model. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with... |
feature-extraction | transformers |
# Unihan LM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database
## Model description
Chinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretra... | {"language": ["zh", "ja"], "license": "apache-2.0", "tags": ["crosslingual"], "datasets": ["Wikipedia"]} | microsoft/unihanlm-base | null | [
"transformers",
"pytorch",
"tf",
"xlm",
"feature-extraction",
"crosslingual",
"zh",
"ja",
"dataset:Wikipedia",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh",
"ja"
] | TAGS
#transformers #pytorch #tf #xlm #feature-extraction #crosslingual #zh #ja #dataset-Wikipedia #license-apache-2.0 #endpoints_compatible #region-us
|
# Unihan LM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database
## Model description
Chinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretra... | [
"# Unihan LM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database",
"## Model description\n\nChinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japan... | [
"TAGS\n#transformers #pytorch #tf #xlm #feature-extraction #crosslingual #zh #ja #dataset-Wikipedia #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Unihan LM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database",
"## Model description\n\nChinese and Japanese share ma... |
automatic-speech-recognition | transformers |
# UniSpeech-Large-plus Spanish
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Spanish phon... | {"language": ["es"], "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | microsoft/unispeech-1350-en-168-es-ft-1h | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"audio",
"es",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07597"
] | [
"es"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #audio #es #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us
|
# UniSpeech-Large-plus Spanish
Microsoft's UniSpeech
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Spanish phonemes.
When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonem... | [
"# UniSpeech-Large-plus Spanish\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Spanish phonemes. \nWhen using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence ... | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #audio #es #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us \n",
"# UniSpeech-Large-plus Spanish\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently ... |
automatic-speech-recognition | transformers |
# UniSpeech-Large-plus Kyrgyz
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Kyrgyz phonem... | {"language": ["ky"], "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | microsoft/unispeech-1350-en-17h-ky-ft-1h | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"audio",
"ky",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07597"
] | [
"ky"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #audio #ky #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us
|
# UniSpeech-Large-plus Kyrgyz
Microsoft's UniSpeech
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Kyrgyz phonemes.
When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes... | [
"# UniSpeech-Large-plus Kyrgyz\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Kyrgyz phonemes. \nWhen using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of... | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #audio #ky #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us \n",
"# UniSpeech-Large-plus Kyrgyz\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently f... |
automatic-speech-recognition | transformers |
# UniSpeech-Large-plus FRENCH
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of French phonem... | {"language": ["fr"], "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | microsoft/unispeech-1350-en-353-fr-ft-1h | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"audio",
"fr",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07597"
] | [
"fr"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #audio #fr #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us
|
# UniSpeech-Large-plus FRENCH
Microsoft's UniSpeech
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of French phonemes.
When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes... | [
"# UniSpeech-Large-plus FRENCH\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of French phonemes. \nWhen using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of... | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #audio #fr #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us \n",
"# UniSpeech-Large-plus FRENCH\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently f... |
automatic-speech-recognition | transformers |
# UniSpeech-Large-plus ITALIAN
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Italian phon... | {"language": ["it"], "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | microsoft/unispeech-1350-en-90-it-ft-1h | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"audio",
"it",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07597"
] | [
"it"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #audio #it #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us
|
# UniSpeech-Large-plus ITALIAN
Microsoft's UniSpeech
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Italian phonemes.
When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonem... | [
"# UniSpeech-Large-plus ITALIAN\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Italian phonemes. \nWhen using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence ... | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #audio #it #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us \n",
"# UniSpeech-Large-plus ITALIAN\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently ... |
null | transformers |
# UniSpeech-Large
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also... | {"language": ["en"], "tags": ["speech"], "datasets": ["common_voice"]} | microsoft/unispeech-large-1500h-cv | null | [
"transformers",
"pytorch",
"unispeech",
"pretraining",
"speech",
"en",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07597"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #pretraining #speech #en #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us
|
# UniSpeech-Large
Microsoft's UniSpeech
The large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes.
Note: This model does not have a tokenizer as it was pretraine... | [
"# UniSpeech-Large\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes. \n\nNote: This model does not have a tokenizer as it was... | [
"TAGS\n#transformers #pytorch #unispeech #pretraining #speech #en #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us \n",
"# UniSpeech-Large\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your spee... |
null | transformers |
# UniSpeech-Large-Multi-Lingual
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The multi-lingual large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure th... | {"language": ["it", "en", "fr", "es"], "tags": ["speech"], "datasets": ["common_voice"]} | microsoft/unispeech-large-multi-lingual-1500h-cv | null | [
"transformers",
"pytorch",
"unispeech",
"pretraining",
"speech",
"it",
"en",
"fr",
"es",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.07597"
] | [
"it",
"en",
"fr",
"es"
] | TAGS
#transformers #pytorch #unispeech #pretraining #speech #it #en #fr #es #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us
|
# UniSpeech-Large-Multi-Lingual
Microsoft's UniSpeech
The multi-lingual large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes.
Note: This model does not have a to... | [
"# UniSpeech-Large-Multi-Lingual\n\nMicrosoft's UniSpeech\n\nThe multi-lingual large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes.\n\nNote: This model does not... | [
"TAGS\n#transformers #pytorch #unispeech #pretraining #speech #it #en #fr #es #dataset-common_voice #arxiv-2101.07597 #endpoints_compatible #region-us \n",
"# UniSpeech-Large-Multi-Lingual\n\nMicrosoft's UniSpeech\n\nThe multi-lingual large model pretrained on 16kHz sampled speech audio and phonetic labels. When ... |
automatic-speech-recognition | transformers |
# UniSpeech-SAT-Base-Finetuned-100h-Libri
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
A [unispeech-sat-base model]( ) that was fine-tuned on 100h hours of Librispeech on 16kHz sampled speech audio. Whe... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media... | microsoft/unispeech-sat-base-100h-libri-ft | null | [
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2110.05752",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2110.05752 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# UniSpeech-SAT-Base-Finetuned-100h-Libri
Microsoft's UniSpeech
A unispeech-sat-base model that was fine-tuned on 100h hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
The model was fine-tuned on:
- 100 hours of LibriSpeech
Paper: ... | [
"# UniSpeech-SAT-Base-Finetuned-100h-Libri\n\nMicrosoft's UniSpeech\n\nA unispeech-sat-base model that was fine-tuned on 100h hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nThe model was fine-tuned on:\n\n- 100 hours of LibriSp... | [
"TAGS\n#transformers #pytorch #unispeech-sat #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2110.05752 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# UniSpeech-SAT-Base-Finetuned-100h-Libri\n\nMicrosoft's UniSpeech\n\nA unispeech-sat-base model that was fine-tune... |
null | transformers |
# UniSpeech-SAT-Base for Speaker Diarization
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using t... | {"language": ["en"], "tags": ["speech"]} | microsoft/unispeech-sat-base-plus-sd | null | [
"transformers",
"pytorch",
"unispeech-sat",
"audio-frame-classification",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #audio-frame-classification #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us
|
# UniSpeech-SAT-Base for Speaker Diarization
Microsoft's UniSpeech
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of Libri-Light
- 10,0... | [
"# UniSpeech-SAT-Base for Speaker Diarization\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 60,000 hours of Libri-... | [
"TAGS\n#transformers #pytorch #unispeech-sat #audio-frame-classification #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us \n",
"# UniSpeech-SAT-Base for Speaker Diarization\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled spe... |
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