
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) 

Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)

Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)

Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)

Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* 


## Abstract

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Ã¢â‚¬Å“Colossal Clean Crawled CorpusÃ¢â‚¬Â, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)

## Usage
```
from tf_transformers.models import T5Model
# Any T5 model (t5-small, t5-base, t5-large etc)
model_name = 't5-small' 
model = T5Model.from_pretrained(model_name)
```
{"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]}
transformers summarization translation en fr ro de dataset:c4 arxiv:1910.10683 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us 


Google's T5 

Pretraining Dataset: C4

Other Community Checkpoints: here

Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* 


## Abstract

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Ã¢â‚¬Å“Colossal Clean Crawled CorpusÃ¢â‚¬Â, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

!model image

## Usage

## Abstract

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Ã¢â‚¬Å“Colossal Clean Crawled CorpusÃ¢â‚¬Â, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

!model image ## Usage
TAGS
#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us 
 ## Abstract

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Ã¢â‚¬Å“Colossal Clean Crawled CorpusÃ¢â‚¬Â, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

!model image ## Usage
passage: TAGS
#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us 
## Abstract

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Ã¢â‚¬Å“Colossal Clean Crawled CorpusÃ¢â‚¬Â, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

!model image## Usage