Milan Straka commited on
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Initial upload

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README.md ADDED
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+ ---
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+ language: es
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+ datasets:
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+ - mc4
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+ - wikipedia
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+ - multilexnorm
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+ tags:
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+ - lexical normalization
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+ license: apache-2.0
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+
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+ ---
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+
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+ # Fine-tuned ByT5-small for MultiLexNorm (Spanish version)
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+
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+ ![model image](https://github.com/ufal/multilexnorm2021/raw/master/img/overall.png)
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+
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+ This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages.
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+
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+ Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing).
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+
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+
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+ ## How to use
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+
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+ The model was *not* fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [**the interactive demo on Colab notebook**](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models.
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+
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+
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+ ## How to cite
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+
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+ ```bibtex
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+ @inproceedings{wnut-ufal,
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+ title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}",
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+ author = "Samuel, David and Straka, Milan",
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+ booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)",
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+ year = "2021",
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+ publisher = "Association for Computational Linguistics",
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+ address = "Punta Cana, Dominican Republic"
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+ }
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+ ```
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+
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+
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+ ## ByT5 - Small
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+
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+ ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small).
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+
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+ ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
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+
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+ ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292).
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+
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+ Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
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+
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+ Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*
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+ "vocab_size": 384
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+ }
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