LongT5 (transient-global attention, large-sized model)

LongT5 model pre-trained on English language. The model 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.

Model description

LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ( Pegasus-like generation pre-training ). LongT5 model is an extension of T5 model , and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.

LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens).

Results of LongT5 (transient-global attention, large-sized model) fine-tuned on multiple (summarization, QA) tasks.

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.

How to use

from transformers import AutoTokenizer, LongT5Model

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