Long T5 Local Large

🚀 LongT5 (local attention, large-sized model)

The LongT5 model is pre-trained on English. It efficiently handles long input sequences for text generation tasks.

🚀 Quick Start

The LongT5 model is pre - trained on the English language. It was introduced in the paper LongT5: Efficient Text - To - Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in Flaxformer repository which uses another Google research project repository T5x.

Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team.

✨ Features

• The LongT5 model is an encoder - decoder transformer pre - trained in a text - to - text denoising generative setting (Pegasus - like generation pre - training).
• It is an extension of T5 model, enabling the use of one of two different efficient attention mechanisms: (1) Local attention, or (2) Transient - Global attention.
• The use of attention sparsity patterns allows the model to efficiently handle input sequences.
• It is particularly effective when fine - tuned for text generation (summarization, question answering) that requires handling long input sequences (up to 16,384 tokens).

📚 Documentation

Intended uses & limitations

The model is mostly meant to be fine - tuned on a supervised dataset. See the model hub to look for fine - tuned versions on a task that interests you.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, LongT5Model

tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-large")
model = LongT5Model.from_pretrained("google/long-t5-local-large")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state

BibTeX entry and citation info

@article{guo2021longt5,
  title={LongT5: Efficient Text-To-Text Transformer for Long Sequences},
  author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei},
  journal={arXiv preprint arXiv:2112.07916},
  year={2021}
}

📄 License

The model is licensed under the Apache - 2.0 license.