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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- life-sciences |
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- clinical |
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- biomedical |
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- bio |
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- medical |
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- biology |
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- synthetic |
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pretty_name: TransCorpus-bio |
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size_categories: |
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- 10M<n<100M |
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--- |
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# TransCorpus-bio |
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**TransCorpus-bio** is a large-scale, parallel biomedical corpus consisting of PubMed abstracts. This dataset is used in the [TransCorpus Toolkit](https://github.com/jknafou/TransCorpus) and is designed to enable high-quality multi-lingual biomedical language modeling and downstream NLP research. |
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# Currently Translated with [TransCorpus Toolkit](https://github.com/jknafou/TransCorpus) |
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- In French : [TransCorpus-bio-fr 🤗](https://huggingface.co/datasets/jknafou/TransCorpus-bio-fr) |
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- In Spanish : [TransCorpus-bio-es 🤗](https://huggingface.co/datasets/jknafou/TransCorpus-bio-es) |
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# Dataset Details |
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- **Source**: PubMed abstracts (English) |
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- **Size**: 22 million abstracts, 30.2GB of text |
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- **Domain**: Biomedical, clinical, life sciences |
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- **Format**: one abstract per line |
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# Motivation |
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Non-English languages are low-resource languages for biomedical NLP, with limited availability of large, high-quality corpora. **TransCorpus-bio** bridges this gap by leveraging state-of-the-art neural machine translation to generate a massive, high-quality synthetic corpus, enabling robust pretraining and evaluation of Spanish biomedical language models. |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("jknafou/TransCorpus-bio", split="train") |
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print(dataset) |
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# Output: |
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# Dataset({ |
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# features: ['text'], |
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# num_rows: 21567136 |
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# }) |
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print(dataset[0]) |
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``` |
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# Benchmark Results in our French Experiment |
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[TransBERT-bio-fr](https://huggingface.co/jknafou/TransBERT-bio-fr) pretrained on TransCorpus-bio-fr achieve state-of-the-art results on the French biomedical benchmark DrBenchmark, outperforming both general-domain and previous domain-specific models on classification, NER, POS, and STS tasks. See TransBERT-bio-fr for details. |
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# Why Synthetic Translation? |
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- **Scalable**: Enables creation of large-scale corpora for any language with a strong MT system. |
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- **Effective**: Supports state-of-the-art performance in downstream tasks. |
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- **Accessible**: Makes domain-specific NLP feasible for any languages. |
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# Citation |
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If you use this corpus, please cite: |
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```text |
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@inproceedings{knafou-etal-2025-transbert, |
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title = "{T}rans{BERT}: A Framework for Synthetic Translation in Domain-Specific Language Modeling", |
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author = {Knafou, Julien and |
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Mottin, Luc and |
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Mottaz, Ana{\"i}s and |
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Flament, Alexandre and |
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Ruch, Patrick}, |
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editor = "Christodoulopoulos, Christos and |
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Chakraborty, Tanmoy and |
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Rose, Carolyn and |
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Peng, Violet", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025", |
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month = nov, |
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year = "2025", |
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address = "Suzhou, China", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2025.findings-emnlp.1053/", |
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doi = "10.18653/v1/2025.findings-emnlp.1053", |
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pages = "19338--19354", |
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ISBN = "979-8-89176-335-7", |
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abstract = "The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models using exclusively synthetically translated text, and introduce TransCorpus, a scalable translation toolkit. Focusing on the life sciences domain in French, our approach demonstrates that state-of-the-art performance on various downstream tasks can be achieved solely by leveraging synthetically translated data. We release the TransCorpus toolkit, the TransCorpus-bio-fr corpus (36.4GB of French life sciences text), TransBERT-bio-fr, its associated pre-trained language model and reproducible code for both pre-training and fine-tuning. Our results highlight the viability of synthetic translation in a high-resource translation direction for building high-quality NLP resources in low-resource language/domain pairs." |
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} |
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``` |
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