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--- |
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license: apache-2.0 |
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language: |
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- multilingual |
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- af |
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- am |
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- ar |
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- az |
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- be |
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- bg |
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- bn |
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- ca |
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- ceb |
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- co |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fil |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gu |
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- ha |
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- haw |
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- hi |
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- hmn |
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- ht |
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- hu |
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- hy |
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- ig |
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- is |
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- it |
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- iw |
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- ja |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- ku |
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- ky |
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- la |
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- lb |
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- lo |
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- lt |
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- lv |
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- mg |
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- mi |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- mt |
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- my |
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- ne |
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- nl |
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- no |
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- ny |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sd |
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- si |
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- sk |
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- sl |
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- sm |
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- sn |
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- so |
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- sq |
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- sr |
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- st |
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- su |
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- sv |
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- sw |
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- ta |
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- te |
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- tg |
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- th |
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- tr |
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- uk |
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- und |
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- ur |
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- uz |
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- vi |
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- xh |
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- yi |
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- yo |
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- zh |
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- zu |
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datasets: |
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- mc4 |
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--- |
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# MLongT5 (transient-global attention, base-sized model) |
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MLongT5 model pre-trained on Multi-language corpus. The model was introduced in the paper [mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences](https://arxiv.org/pdf/2305.11129.pdf) by Uthus 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 MLongT5 did not write a model card for this model so this model card has been written by Ahmed Elnaggar. |
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## Model description |
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MLongT5 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)). MLongT5 model is an extension of [LongT5 model](https://arxiv.org/abs/2112.07916), 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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MLongT5 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=mlongt5) 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 T5Tokenizer, LongT5Model |
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tokenizer = T5Tokenizer.from_pretrained("agemagician/mlong-t5-tglobal-base") |
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model = LongT5Model.from_pretrained("agemagician/mlong-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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@misc{uthus2023mlongt5, |
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title={mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences}, |
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author={David Uthus and Santiago Ontañón and Joshua Ainslie and Mandy Guo}, |
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year={2023}, |
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eprint={2305.11129}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) |
