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This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
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example_title: "Example 1"
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# mT5-base fine-tuned for News article Summarisation ✏️🧾
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[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [DaNewsroom] (https://aclanthology.org/2020.lrec-1.831/) dataset for **summarisation** downstream task.
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This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
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