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license: apache-2.0 |
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language: en |
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--- |
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# LongT5 (transient-global attention, base-sized model) |
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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). |
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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. |
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## Model description |
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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. |
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LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens). |
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## Intended uses & limitations |
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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. |
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### How to use |
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```python |
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from transformers import AutoTokenizer, LongT5Model |
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tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base") |
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model = LongT5Model.from_pretrained("google/long-t5-tglobal-base") |
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inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") |
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outputs = model(**inputs) |
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last_hidden_states = outputs.last_hidden_state |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@article{guo2021longt5, |
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title={LongT5: Efficient Text-To-Text Transformer for Long Sequences}, |
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author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei}, |
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journal={arXiv preprint arXiv:2112.07916}, |
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year={2021} |
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} |
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``` |
