T5 Large

🚀 Model Card for T5 Large

T5 Large is a powerful language model that reframes NLP tasks into a unified text - to - text format, enabling it to handle various tasks like machine translation and document summarization.

🚀 Quick Start

Use the code below to get started with the model.

from transformers import T5Tokenizer, T5Model

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

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

✨ Features

• Unified Text - to - Text Format: Allows using the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks.
• Multilingual Support: Supports English, French, Romanian, and German.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import T5Tokenizer, T5Model

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

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

Advanced Usage

No advanced usage examples are provided in the original document, so this part is skipped.

📚 Documentation

Model Details

Model Description

The developers of the Text - To - Text Transfer Transformer (T5) write:

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 - Large is the checkpoint with 770 million parameters.

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.

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.):

1. Datasets used for Unsupervised denoising objective:

• C4
• Wiki - DPR

2. 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 - Large, 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).

Citation

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.

🔧 Technical Details

No specific technical details that meet the requirements are provided in the original document, so this section is skipped.

📄 License

The model is licensed under the Apache 2.0 license.