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fill-mask | transformers |
# ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-base-v1 | null | [
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| ALBERT Base v1
==============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
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fill-mask | transformers |
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-base-v2 | null | [
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| ALBERT Base v2
==============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
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fill-mask | transformers |
# ALBERT Large v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-large-v1 | null | [
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| ALBERT Large v1
===============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team ... | [
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fill-mask | transformers |
# ALBERT Large v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-large-v2 | null | [
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"1909.11942"
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| ALBERT Large v2
===============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
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fill-mask | transformers |
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xlarge-v1 | null | [
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| ALBERT XLarge v1
================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
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fill-mask | transformers |
# ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xlarge-v2 | null | [
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"region:us"
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#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT XLarge v2
================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
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fill-mask | transformers |
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xxlarge-v1 | null | [
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| ALBERT XXLarge v1
=================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
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fill-mask | transformers |
# ALBERT XXLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xxlarge-v2 | null | [
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| ALBERT XXLarge v2
=================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
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fill-mask | transformers |
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference bet... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-base-cased | null | [
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| BERT base model (cased)
=======================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team releasin... | [
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fill-mask | transformers |
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
### Model Descri... | {"language": "zh"} | google-bert/bert-base-chinese | null | [
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|
# Bert-base-chinese
## Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- How to Get Started With the Model
## Model Details
### Model Description
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fill-mask | transformers |
<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
<img width="300px" src="https://huggingface.co/proxy/cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT
## Overview
**Language model:** bert-base-cased
**L... | {"language": "de", "license": "mit", "tags": ["exbert"], "thumbnail": "https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466/german_bert.png"} | google-bert/bert-base-german-cased | null | [
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|
<a href="URL
<img width="300px" src="URL
</a>
# German BERT
!bert_image
## Overview
Language model: bert-base-cased
Language: German
Training data: Wiki, OpenLegalData, News (~ 12GB)
Eval data: Conll03 (NER), GermEval14 (NER), GermEval18 (Classification), GNAD (Classification)
Infrastructure: 1x TPU v2
Pu... | [
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fill-mask | transformers |
This model is the same as [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-cased) for details on the model. | {"language": "de", "license": "mit"} | google-bert/bert-base-german-dbmdz-cased | null | [
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This model is the same as dbmdz/bert-base-german-cased. See the dbmdz/bert-base-german-cased model card for details on the model. | [] | [
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fill-mask | transformers |
This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.
| {"language": "de", "license": "mit"} | google-bert/bert-base-german-dbmdz-uncased | null | [
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This model is the same as dbmdz/bert-base-german-uncased. See the dbmdz/bert-base-german-cased model card for details on the model.
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fill-mask | transformers |
# BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model... | {"language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "he", "hi", "hu", "is", "io", "id", "ga", "it", "ja", "jv", "kn", "kk"... | google-bert/bert-base-multilingual-cased | null | [
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# BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is case sensitive: it makes a difference
between english and English.
Disclai... | [
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fill-mask | transformers |
# BERT multilingual base model (uncased)
Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This mod... | {"language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "he", "hi", "hu", "is", "io", "id", "ga", "it", "ja", "jv", "kn", "kk"... | google-bert/bert-base-multilingual-uncased | null | [
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# BERT multilingual base model (uncased)
Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disc... | [
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fill-mask | transformers |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-base-uncased | null | [
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| BERT base model (uncased)
=========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team rel... | [
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question-answering | transformers |
# BERT large model (cased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is ca... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-large-cased-whole-word-masking-finetuned-squad | null | [
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# BERT large model (cased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it makes a difference between english and English.
Differently to other B... | [
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fill-mask | transformers |
# BERT large model (cased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it makes a dif... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-large-cased-whole-word-masking | null | [
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| BERT large model (cased) whole word masking
===========================================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it makes a difference between english and English.
... | [
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fill-mask | transformers |
# BERT large model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it makes a difference
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| BERT large model (cased)
========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it makes a difference
between english and English.
Disclaimer: The team releasing BERT ... | [
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question-answering | transformers |
# BERT large model (uncased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is ... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-large-uncased-whole-word-masking-finetuned-squad | null | [
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|
# BERT large model (uncased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Differentl... | [
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fill-mask | transformers |
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does no... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-large-uncased-whole-word-masking | null | [
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| BERT large model (uncased) whole word masking
=============================================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english... | [
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fill-mask | transformers |
# BERT large model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-large-uncased | null | [
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| BERT large model (uncased)
==========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team r... | [
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fill-mask | transformers |
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretrain... | {"language": "fr", "license": "mit", "datasets": ["oscar"]} | almanach/camembert-base | null | [
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| CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining dat... | [
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text-generation | transformers |
# ctrl
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8. [Citation](... | {"language": "en", "license": "bsd-3-clause", "pipeline_tag": "text-generation"} | Salesforce/ctrl | null | [
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|
# ctrl
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
## Model Description
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question-answering | transformers |
# DistilBERT base cased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental... | {"language": "en", "license": "apache-2.0", "datasets": ["squad"], "metrics": ["squad"], "model-index": [{"name": "distilbert-base-cased-distilled-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "va... | distilbert/distilbert-base-cased-distilled-squad | null | [
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|
# DistilBERT base cased distilled SQuAD
## Table of Contents
- Model Details
- How To Get Started With the Model
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Environmental Impact
- Technical Specifications
- Citation Information
- Model Card Authors
## Model Details
Model Description: The Distil... | [
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fill-mask | transformers |
# Model Card for DistilBERT base model (cased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased).
It was introduced in [this paper](https://arxiv.org/abs/1910.01108).
The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | distilbert/distilbert-base-cased | null | [
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| Model Card for DistilBERT base model (cased)
============================================
This model is a distilled version of the BERT base model.
It was introduced in this paper.
The code for the distillation process can be found
here.
This model is cased: it does make a difference between english and English.
Al... | [
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fill-mask | transformers | ## distilbert-base-german-cased
| {"language": "de", "license": "apache-2.0"} | distilbert/distilbert-base-german-cased | null | [
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fill-mask | transformers |
# Model Card for DistilBERT base multilingual (cased)
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
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===================================================
Table of Contents
=================
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. How To Get Started With the Model
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question-answering | transformers |
# DistilBERT base uncased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environment... | {"language": "en", "license": "apache-2.0", "datasets": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; Frenc... | distilbert/distilbert-base-uncased-distilled-squad | null | [
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|
# DistilBERT base uncased distilled SQuAD
## Table of Contents
- Model Details
- How To Get Started With the Model
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Environmental Impact
- Technical Specifications
- Citation Information
- Model Card Authors
## Model Details
Model Description: The Dist... | [
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text-classification | transformers |
# DistilBERT base uncased finetuned SST-2
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
## Model Details
**Model Description:** T... | {"language": "en", "license": "apache-2.0", "datasets": ["sst2", "glue"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst-2-english", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "sst2", "split": "validation"},... | distilbert/distilbert-base-uncased-finetuned-sst-2-english | null | [
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|
# DistilBERT base uncased finetuned SST-2
## Table of Contents
- Model Details
- How to Get Started With the Model
- Uses
- Risks, Limitations and Biases
- Training
## Model Details
Model Description: This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.
This model reaches an accuracy... | [
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fill-mask | transformers |
# DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/main/e... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | distilbert/distilbert-base-uncased | null | [
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| DistilBERT base model (uncased)
===============================
This model is a distilled version of the BERT base model. It was
introduced in this paper. The code for the distillation process can be found
here. This model is uncased: it does
not make a difference between english and English.
Model description
----... | [
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text-generation | transformers |
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["openwebtext"], "co2_eq_emissions": 149200, "model-index": [{"name": "distilgpt2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "WikiText-103", "type": "wikitext"}, "metrics": [{"type": "perp... | distilbert/distilgpt2 | null | [
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# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... | [
"# DistilGPT2\n\nDistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the desig... | [
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fill-mask | transformers |
# Model Card for DistilRoBERTa base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8.... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["openwebtext"]} | distilbert/distilroberta-base | null | [
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| Model Card for DistilRoBERTa base
=================================
Table of Contents
=================
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. How To Get Started With the Model
Model Details
=============
Model Descriptio... | [] | [
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] |
text-generation | transformers |
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-im... | {"language": "en", "license": "mit"} | openai-community/gpt2-large | null | [
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| GPT-2 Large
===========
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Descripti... | [
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text-generation | transformers |
# GPT-2 Medium
## Model Details
**Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
- **Developed by:** OpenAI, see [a... | {"language": "en", "license": "mit"} | openai-community/gpt2-medium | null | [
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| GPT-2 Medium
============
Model Details
-------------
Model Description: GPT-2 Medium is the 355M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
* Developed by: Ope... | [
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text-generation | transformers |
# GPT-2 XL
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impac... | {"language": "en", "license": "mit"} | openai-community/gpt2-xl | null | [
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| GPT-2 XL
========
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Description: GP... | [
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text-generation | transformers |
# GPT-2
Test the whole generation capabilities here: https://huggingface.co/proxy/transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... | {"language": "en", "license": "mit", "tags": ["exbert"]} | openai-community/gpt2 | null | [
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| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
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text-generation | transformers |
# OpenAI GPT 1
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-i... | {"language": "en", "license": "mit"} | openai-community/openai-gpt | null | [
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| OpenAI GPT 1
============
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Descrip... | [
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text-classification | transformers |
# RoBERTa Base OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specificati... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | openai-community/roberta-base-openai-detector | null | [
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|
# RoBERTa Base OpenAI Detector
## Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Environmental Impact
- Technical Specifications
- Citation Information
- Model Card Authors
- How To Get Started With the Model
## Model Details
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fill-mask | transformers |
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
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| RoBERTa base model
==================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERTa ... | [
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text-classification | transformers |
# roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impact](#e... | {"language": ["en"], "license": "mit", "tags": ["autogenerated-modelcard"], "datasets": ["multi_nli", "wikipedia", "bookcorpus"]} | FacebookAI/roberta-large-mnli | null | [
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==================
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
M... | [
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text-classification | transformers |
# RoBERTa Large OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specificat... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | openai-community/roberta-large-openai-detector | null | [
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# RoBERTa Large OpenAI Detector
## Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Environmental Impact
- Technical Specifications
- Citation Information
- Model Card Authors
- How To Get Started With the Model
## Model Details
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fill-mask | transformers |
# RoBERTa large model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: ... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | FacebookAI/roberta-large | null | [
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| RoBERTa large model
===================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERT... | [
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translation | transformers |
# Model Card for T5 11B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"], "inference": false} | google-t5/t5-11b | null | [
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# Model Card for T5 11B
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# Table of Contents
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2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
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translation | transformers |
# Model Card for T5-3B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [B... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | google-t5/t5-3b | null | [
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# Model Card for T5-3B
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# Table of Contents
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2. Uses
3. Bias, Risks, and Limitations
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5. Evaluation
6. Environmental Impact
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translation | transformers |
# Model Card for T5 Base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. ... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"], "pipeline_tag": "translation"} | google-t5/t5-base | null | [
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# Model Card for T5 Base
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3. Bias, Risks, and Limitations
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translation | transformers |
# Model Card for T5 Large
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3.... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | google-t5/t5-large | null | [
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# Model Card for T5 Large
!model image
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. Model Card Authors
9. How To Get Started With the Model
# Model Details
## Model Description
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translation | transformers |
# Model Card for T5 Small
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3.... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | google-t5/t5-small | null | [
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# Model Card for T5 Small
!model image
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. Model Card Authors
9. How To Get Started With the Model
# Model Details
## Model Description
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text-generation | transformers |
# Transfo-xl-wt103
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Started With the Model](#how-to-get-started-with... | {"language": "en", "tags": ["text-generation"], "datasets": ["wikitext-103"], "task": {"name": "Text Generation", "type": "text-generation"}, "model-index": [{"name": "transfo-xl-wt103", "results": []}]} | transfo-xl/transfo-xl-wt103 | null | [
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| Transfo-xl-wt103
================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Citation Information
* How to Get Started With the Model
Model Details
-------------
Model Description:
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fill-mask | transformers |
# xlm-clm-ende-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... | {"language": ["multilingual", "en", "de"]} | FacebookAI/xlm-clm-ende-1024 | null | [
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# xlm-clm-ende-1024
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
The XLM model was proposed in Cross-lingual Langua... | [
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fill-mask | transformers |
# xlm-clm-enfr-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... | {"language": ["multilingual", "en", "fr"]} | FacebookAI/xlm-clm-enfr-1024 | null | [
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|
# xlm-clm-enfr-1024
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
The XLM model was proposed in Cross-lingual Langua... | [
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fill-mask | transformers |
# xlm-mlm-100-1280
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
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================
Table of Contents
=================
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
Model Details
=============
xlm-mlm... | [] | [
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fill-mask | transformers |
# xlm-mlm-17-1280
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8. ... | {"language": ["multilingual", "en", "fr", "es", "de", "it", "pt", "nl", "sv", "pl", "ru", "ar", "tr", "zh", "ja", "ko", "hi", "vi"], "license": "cc-by-nc-4.0"} | FacebookAI/xlm-mlm-17-1280 | null | [
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| xlm-mlm-17-1280
===============
Table of Contents
=================
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
Model Details
=============
xlm-mlm-1... | [] | [
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fill-mask | transformers |
# xlm-mlm-en-2048
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8. [Model Card Authors](#model-card... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["exbert"]} | FacebookAI/xlm-mlm-en-2048 | null | [
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"autotrain_compatible",
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|
# xlm-mlm-en-2048
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Citation
8. Model Card Authors
9. How To Get Started With the Model
# Model Details
The XLM model was proposed in Cross-lingual Language Model Pretraining by Guillau... | [
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# xlm-mlm-ende-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... | {"language": ["multilingual", "en", "de"], "license": "cc-by-nc-4.0"} | FacebookAI/xlm-mlm-ende-1024 | null | [
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|
# xlm-mlm-ende-1024
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
The XLM model was proposed in Cross-lingual Langua... | [
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fill-mask | transformers |
# xlm-mlm-enfr-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... | {"language": ["multilingual", "en", "fr"], "license": "cc-by-nc-4.0"} | FacebookAI/xlm-mlm-enfr-1024 | null | [
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|
# xlm-mlm-enfr-1024
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
The XLM model was proposed in Cross-lingual Langua... | [
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fill-mask | transformers |
# xlm-mlm-enro-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical-specifications)
8... | {"language": ["multilingual", "en", "ro"], "license": "cc-by-nc-4.0"} | FacebookAI/xlm-mlm-enro-1024 | null | [
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|
# xlm-mlm-enro-1024
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
The XLM model was proposed in Cross-lingual Langua... | [
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fill-mask | transformers |
# xlm-mlm-tlm-xnli15-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#techn... | {"language": ["multilingual", "en", "fr", "es", "de", "el", "bg", "ru", "tr", "ar", "vi", "th", "zh", "hi", "sw", "ur"], "license": "cc-by-nc-4.0"} | FacebookAI/xlm-mlm-tlm-xnli15-1024 | null | [
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| xlm-mlm-tlm-xnli15-1024
=======================
Table of Contents
=================
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
Model Details
=... | [
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fill-mask | transformers |
# xlm-mlm-xnli15-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#technical... | {"language": ["multilingual", "en", "fr", "es", "de", "el", "bg", "ru", "tr", "ar", "vi", "th", "zh", "hi", "sw", "ur"], "license": "cc-by-nc-4.0"} | FacebookAI/xlm-mlm-xnli15-1024 | null | [
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| xlm-mlm-xnli15-1024
===================
Table of Contents
=================
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
Model Details
=========... | [
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fill-mask | transformers |
# XLM-RoBERTa (base-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | FacebookAI/xlm-roberta-base | null | [
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# XLM-RoBERTa (base-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.
Disclaimer: The team releasing XLM-RoBER... | [
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fill-mask | transformers |
# xlm-roberta-large-finetuned-conll02-dutch
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#tec... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | FacebookAI/xlm-roberta-large-finetuned-conll02-dutch | null | [
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# xlm-roberta-large-finetuned-conll02-dutch
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
## Model Description
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fill-mask | transformers |
# xlm-roberta-large-finetuned-conll02-spanish
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
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# xlm-roberta-large-finetuned-conll02-spanish
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
## Model Description
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token-classification | transformers |
# xlm-roberta-large-finetuned-conll03-english
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
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# xlm-roberta-large-finetuned-conll03-english
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
# Model Details
## Model Description
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token-classification | transformers |
# xlm-roberta-large-finetuned-conll03-german
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
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# xlm-roberta-large-finetuned-conll03-german
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training
5. Evaluation
6. Environmental Impact
7. Technical Specifications
8. Citation
9. Model Card Authors
10. How To Get Started With the Model
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## Model Description
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fill-mask | transformers |
# XLM-RoBERTa (large-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](http... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | FacebookAI/xlm-roberta-large | null | [
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# XLM-RoBERTa (large-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.
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text-generation | transformers |
# XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
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# XLNet (base-sized model)
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text-generation | transformers |
# XLNet (large-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
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# XLNet (large-sized model)
XLNet model pre-trained on English language. It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al. and first released in this repository.
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | 09panesara/distilbert-base-uncased-finetuned-cola | null | [
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7580
* Matthews Correlation: 0.5406
Model description
-----------------
More informa... | [
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text2text-generation | transformers | ## keyT5. Base (small) version
[](https://github.com/0x7o/text2keywords "Go to GitHub repo")
[](https://github.com... | {"language": ["ru"], "license": "mit", "inference": {"parameters": {"top_p": 0.9}}, "widget": [{"text": "\u0412 \u0420\u043e\u0441\u0441\u0438\u0438 \u043c\u043e\u0436\u0435\u0442 \u043f\u043e\u044f\u0432\u0438\u0442\u044c\u0441\u044f \u043d\u043e\u0432\u044b\u0439 \u0448\u0442\u0430\u043c\u043c \u043a\u043e\u0440\u043... | 0x7o/keyt5-base | null | [
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| ## keyT5. Base (small) version
![stars - text2keywords](URL
![forks - text2keywords](URL
Supported languages: ru
Github - text2keywords
Pretraining Large version
|
Pretraining Base version
# Usage
Example usage (the code returns a list with keywords. duplicates are ... | [
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| ## keyT5. Large version
![stars - text2keywords](URL
![forks - text2keywords](URL
Supported languages: ru
Github - text2keywords
Pretraining Large version
|
Pretraining Base version
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text-generation | transformers |
# Rick n Morty DialoGPT Model | {"tags": ["conversational"]} | 0xDEADBEA7/DialoGPT-small-rick | null | [
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"gpt2",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | 123abhiALFLKFO/distilbert-base-uncased-finetuned-cola | null | [
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8628
* Matthews Correlation: 0.5331
Model description
-----------------
More informa... | [
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text-generation | transformers |
#Jake Peralta DialoGPT Model | {"tags": ["conversational"]} | 1Basco/DialoGPT-small-jake | null | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | 202015004/wav2vec2-base-timit-demo-colab | null | [
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| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6259
* Wer: 0.3544
Model description
-----------------
More information needed
Intended uses & limi... | [
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text-generation | transformers |
# Deadpool DialoGPT Model | {"tags": ["conversational"]} | 2early4coffee/DialoGPT-medium-deadpool | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
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"region:us"
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text-generation | transformers |
# Deadpool DialoGPT Model | {"tags": ["conversational"]} | 2early4coffee/DialoGPT-small-deadpool | null | [
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"text-generation",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | 2umm3r/distilbert-base-uncased-finetuned-cola | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7816
* Matthews Correlation: 0.5156
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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feature-extraction | transformers | this is a fine tuned GPT2 text generation model on a Hunter x Hunter TV anime series dataset.\
you can find a link to the used dataset here : https://www.kaggle.com/bkoozy/hunter-x-hunter-subtitles
you can find a colab notebook for fine-tuning the gpt2 model here : https://github.com/3koozy/fine-tune-gpt2-HxH/ | {} | 3koozy/gpt2-HxH | null | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| this is a fine tuned GPT2 text generation model on a Hunter x Hunter TV anime series dataset.\
you can find a link to the used dataset here : URL
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token-classification | transformers |
## Model description
This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding n... | {"language": "zh", "license": "apache-2.0", "tags": ["Token Classification"], "metrics": ["precision", "recall", "f1", "accuracy"]} | 9pinus/macbert-base-chinese-medical-collation | null | [
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"bert",
"token-classification",
"Token Classification",
"zh",
"license:apache-2.0",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
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#transformers #pytorch #bert #token-classification #Token Classification #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding n... | [
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token-classification | transformers |
## Model description
This model is a fine-tuned version of bert-base-chinese for the purpose of medicine name recognition. We fine-tuned bert-base-chinese on a 500M dataset including 100K+ authorized medical articles on which we labeled all the medicine names. The model achieves 92% accuracy on our test dataset.
... | {"language": ["zh"], "license": "apache-2.0", "tags": ["Token Classification"]} | 9pinus/macbert-base-chinese-medicine-recognition | null | [
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"bert",
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"zh",
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|
## Model description
This model is a fine-tuned version of bert-base-chinese for the purpose of medicine name recognition. We fine-tuned bert-base-chinese on a 500M dataset including 100K+ authorized medical articles on which we labeled all the medicine names. The model achieves 92% accuracy on our test dataset.
... | [
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text-classification | transformers |
bert-base-cased model trained on quora question pair dataset. The task requires to predict whether the two given sentences (or questions) are `not_duplicate` (label 0) or `duplicate` (label 1). The model achieves 89% evaluation accuracy
| {"datasets": ["qqp"], "inference": false} | A-bhimany-u08/bert-base-cased-qqp | null | [
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|
bert-base-cased model trained on quora question pair dataset. The task requires to predict whether the two given sentences (or questions) are 'not_duplicate' (label 0) or 'duplicate' (label 1). The model achieves 89% evaluation accuracy
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text-generation | transformers |
@Harry Potter DialoGPT model | {"tags": ["conversational"]} | ABBHISHEK/DialoGPT-small-harrypotter | null | [
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@Harry Potter DialoGPT model | [] | [
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feature-extraction | transformers | Pre trained on clus_ chapter only. | {} | AG/pretraining | null | [
"transformers",
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"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| Pre trained on clus_ chapter only. | [] | [
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sentence-similarity | sentence-transformers |
# PatentSBERTa
## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
### Aalborg University Business School, AI: Growth-Lab
https://arxiv.org/abs/2103.11933
https://github.com/AI-Growth-Lab/PatentSBERTa
This is a [sentence-transformers](https://www.SBERT.ne... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | AI-Growth-Lab/PatentSBERTa | null | [
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"mpnet",
"feature-extraction",
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"arxiv:2103.11933",
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"region:us"
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|
# PatentSBERTa
## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
### Aalborg University Business School, AI: Growth-Lab
URL
URL
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 474612462
- CO2 Emissions (in grams): 133.0219882109991
## Validation Metrics
- Loss: 1.336498737335205
- Rouge1: 52.5404
- Rouge2: 31.6639
- RougeL: 50.1696
- RougeLsum: 50.3398
- Gen Len: 39.046
## Usage
You can use cURL to access thi... | {"language": "unk", "tags": "autonlp", "datasets": ["Eric Peter/autonlp-data-EN-LUG"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 133.0219882109991} | AI-Lab-Makerere/en_lg | null | [
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# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 474612462
- CO2 Emissions (in grams): 133.0219882109991
## Validation Metrics
- Loss: 1.336498737335205
- Rouge1: 52.5404
- Rouge2: 31.6639
- RougeL: 50.1696
- RougeLsum: 50.3398
- Gen Len: 39.046
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 475112539
- CO2 Emissions (in grams): 126.34446293851818
## Validation Metrics
- Loss: 1.5376628637313843
- Rouge1: 62.4613
- Rouge2: 39.4759
- RougeL: 58.183
- RougeLsum: 58.226
- Gen Len: 26.5644
## Usage
You can use cURL to access th... | {"language": "unk", "tags": "autonlp", "datasets": ["EricPeter/autonlp-data-MarianMT_lg_en"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 126.34446293851818} | AI-Lab-Makerere/lg_en | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 475112539
- CO2 Emissions (in grams): 126.34446293851818
## Validation Metrics
- Loss: 1.5376628637313843
- Rouge1: 62.4613
- Rouge2: 39.4759
- RougeL: 58.183
- RougeLsum: 58.226
- Gen Len: 26.5644
## Usage
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fill-mask | transformers |
# A Swedish Bert model
## Model description
This model follows the Bert Large model architecture as implemented in [Megatron-LM framework](https://github.com/NVIDIA/Megatron-LM). It was trained with a batch size of 512 in 600k steps. The model contains following parameters:
<figure>
| Hyperparameter | Value ... | {"language": "sv"} | AI-Nordics/bert-large-swedish-cased | null | [
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| A Swedish Bert model
====================
Model description
-----------------
This model follows the Bert Large model architecture as implemented in Megatron-LM framework. It was trained with a batch size of 512 in 600k steps. The model contains following parameters:
Training data
-------------
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | AIDA-UPM/MSTSb_paraphrase-multilingual-MiniLM-L12-v2 | null | [
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|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
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sentence-similarity | sentence-transformers |
# AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1 | null | [
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|
# AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | AIDA-UPM/MSTSb_stsb-xlm-r-multilingual | null | [
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# {MODEL_NAME}
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text-classification | transformers |
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels.
# Multilabels
label2id={
"misogynous": 0,
"shaming": 1,
"stereotype": 2,
"objectification": 3,
"violence": 4,... | {"language": "en", "license": "apache-2.0", "tags": ["text-classification", "misogyny"], "pipeline_tag": "text-classification", "widget": [{"text": "Women wear yoga pants because men don't stare at their personality", "example_title": "Misogyny detection"}]} | AIDA-UPM/bertweet-base-multi-mami | null | [
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|
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels.
# Multilabels
label2id={
"misogynous": 0,
"shaming": 1,
"stereotype": 2,
"objectification": 3,
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sentence-similarity | transformers |
# mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences &... | {"language": "multilingual", "tags": ["feature-extraction", "sentence-similarity", "transformers", "multilingual"], "pipeline_tag": "sentence-similarity"} | AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | null | [
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| mstsb-paraphrase-multilingual-mpnet-base-v2
===========================================
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text-classification | transformers |
This is a finetuned XLM-RoBERTA model for natural language inference. It has been trained with a massive ammount of data following the ANLI pipeline training. We include data from:
- [mnli](https://cims.nyu.edu/~sbowman/multinli/) {train, dev and test}
- [snli](https://nlp.stanford.edu/projects/snli/) {train, dev and ... | {"language": "en", "license": "apache-2.0", "tags": ["natural-language-inference", "misogyny"], "pipeline_tag": "text-classification", "widget": [{"text": "Las mascarillas causan hipoxia. Wearing masks is harmful to human health", "example_title": "Natural Language Inference"}]} | AIDA-UPM/xlm-roberta-large-snli_mnli_xnli_fever_r1_r2_r3 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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| This is a finetuned XLM-RoBERTA model for natural language inference. It has been trained with a massive ammount of data following the ANLI pipeline training. We include data from:
* mnli {train, dev and test}
* snli {train, dev and test}
* xnli {train, dev and test}
* fever {train, dev and test}
* anli {train}
The... | [] | [
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] |
text-generation | transformers |
# tests | {"tags": ["conversational"]} | AIDynamics/DialoGPT-medium-MentorDealerGuy | null | [
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"gpt2",
"text-generation",
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"text-generation-inference",
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text-generation | transformers |
# Uses DialoGPT | {"tags": ["conversational"]} | AJ/DialoGPT-small-ricksanchez | null | [
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text-generation | transformers |
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text-generation | transformers |
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | AK270802/DialoGPT-small-harrypotter | null | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-epochs10
This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs5](https://huggingface.co/AK... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs10", "results": []}]} | AKulk/wav2vec2-base-timit-epochs10 | null | [
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# wav2vec2-base-timit-epochs10
This model is a fine-tuned version of AKulk/wav2vec2-base-timit-epochs5 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
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