readme: ---
  license: apache-2.0
  language: en
  ---
  
  # LongT5 (transient-global attention, XL-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](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/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](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), 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).
  
  
  ## Intended uses & limitations
  
  The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  ```python
  from transformers import AutoTokenizer, LongT5Model
  
  tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-xl")
  model = LongT5Model.from_pretrained("google/long-t5-tglobal-xl")
  
  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
  
  ```bibtex
  @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}
  }
  ```