Instructions to use Danish-summarisation/DanSumT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Danish-summarisation/DanSumT5-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Danish-summarisation/DanSumT5-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Danish-summarisation/DanSumT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("Danish-summarisation/DanSumT5-base") - Notebooks
- Google Colab
- Kaggle
adding basic model card
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README.md
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pipeline_tag: summarization
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---
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pipeline_tag: summarization
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license: apache-2.0
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language:
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- da
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---
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# mT5-base fine-tuned for News article Summarisation ✏️🧾
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[Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for **summarisation** downstream task.
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# Model summary
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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.
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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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# References
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Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283
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Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831
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