With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.

T5-Base is the checkpoint with 220 million parameters.

Developed by: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See associated paper and GitHub repo
Model type: Language model
Language(s) (NLP): English, French, Romanian, German
License: Apache 2.0
Related Models: All T5 Checkpoints
Resources for more information:

Uses

Direct Use and Downstream Use

The developers write in a blog post that the model:

Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.

See the blog post and research paper for further details.

Out-of-Scope Use

More information needed.

Bias, Risks, and Limitations

More information needed.

Recommendations

More information needed.

Training Details

Training Data

The model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.

The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.). Thereby, the following datasets were being used for (1.) and (2.):

Datasets used for Unsupervised denoising objective:

C4
Wiki-DPR

Datasets used for Supervised text-to-text language modeling objective

Sentence acceptability judgment
- CoLA Warstadt et al., 2018
Sentiment analysis
- SST-2 Socher et al., 2013
Paraphrasing/sentence similarity
- MRPC Dolan and Brockett, 2005
- STS-B Ceret al., 2017
- QQP Iyer et al., 2017
Natural language inference
- MNLI Williams et al., 2017
- QNLI Rajpurkar et al.,2016
- RTE Dagan et al., 2005
- CB De Marneff et al., 2019
Sentence completion
- COPA Roemmele et al., 2011
Word sense disambiguation
- WIC Pilehvar and Camacho-Collados, 2018
Question answering
- MultiRC Khashabi et al., 2018
- ReCoRD Zhang et al., 2018
- BoolQ Clark et al., 2019

Training Procedure

In their abstract, the model developers write:

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.

The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the research paper for further details.

Evaluation

Testing Data, Factors & Metrics

The developers evaluated the model on 24 tasks, see the research paper for full details.

Results

For full results for T5-Base, see the research paper, Table 14.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: Google Cloud TPU Pods
Hours used: More information needed
Cloud Provider: GCP
Compute Region: More information needed
Carbon Emitted: More information needed

Citation

BibTeX:

bibtex

@article{2020t5,
  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {140},
  pages   = {1-67},
  url     = {http://jmlr.org/papers/v21/20-074.html}
}

APA:

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.

Model Card Authors

This model card was written by the team at Hugging Face.

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

python

from transformers import T5Tokenizer, T5Model

tokenizer = T5Tokenizer.from_pretrained("t5-base")
model = T5Model.from_pretrained("t5-base")

input_ids = tokenizer(
    "Studies have been shown that owning a dog is good for you", return_tensors="pt"
).input_ids  # Batch size 1
decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1

# forward pass
outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
last_hidden_states = outputs.last_hidden_state

See the Hugging Face T5 docs and a Colab Notebook created by the model developers for more examples.

google-t5/t5-base

Model Card for T5 Base

Table of Contents

Model Details

Model Description

Uses

Direct Use and Downstream Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

Training Details

Training Data

Training Procedure

Evaluation

Testing Data, Factors & Metrics

Results

Environmental Impact

Citation

Model Card Authors

How to Get Started with the Model

google-t5/
t5-base