| | --- |
| | license: mit |
| | language: |
| | - en |
| | tags: |
| | - life-sciences |
| | - clinical |
| | - biomedical |
| | - bio |
| | - medical |
| | - biology |
| | - synthetic |
| | pretty_name: TransCorpus-bio |
| | size_categories: |
| | - 10M<n<100M |
| | --- |
| | # TransCorpus-bio |
| | **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. |
| |
|
| | # Currently Translated with [TransCorpus Toolkit](https://github.com/jknafou/TransCorpus) |
| | - In French : [TransCorpus-bio-fr 🤗](https://huggingface.co/datasets/jknafou/TransCorpus-bio-fr) |
| | - In Spanish : [TransCorpus-bio-es 🤗](https://huggingface.co/datasets/jknafou/TransCorpus-bio-es) |
| |
|
| | # Dataset Details |
| | - **Source**: PubMed abstracts (English) |
| | - **Size**: 22 million abstracts, 30.2GB of text |
| | - **Domain**: Biomedical, clinical, life sciences |
| | - **Format**: one abstract per line |
| |
|
| | # Motivation |
| | 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. |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | dataset = load_dataset("jknafou/TransCorpus-bio", split="train") |
| | |
| | print(dataset) |
| | # Output: |
| | # Dataset({ |
| | # features: ['text'], |
| | # num_rows: 21567136 |
| | # }) |
| | |
| | print(dataset[0]) |
| | ``` |
| |
|
| | # Benchmark Results in our French Experiment |
| | [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. |
| |
|
| | # Why Synthetic Translation? |
| | - **Scalable**: Enables creation of large-scale corpora for any language with a strong MT system. |
| | - **Effective**: Supports state-of-the-art performance in downstream tasks. |
| | - **Accessible**: Makes domain-specific NLP feasible for any languages. |
| |
|
| | # Citation |
| | If you use this corpus, please cite: |
| | ```text |
| | @inproceedings{knafou-etal-2025-transbert, |
| | title = "{T}rans{BERT}: A Framework for Synthetic Translation in Domain-Specific Language Modeling", |
| | author = {Knafou, Julien and |
| | Mottin, Luc and |
| | Mottaz, Ana{\"i}s and |
| | Flament, Alexandre and |
| | Ruch, Patrick}, |
| | editor = "Christodoulopoulos, Christos and |
| | Chakraborty, Tanmoy and |
| | Rose, Carolyn and |
| | Peng, Violet", |
| | booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025", |
| | month = nov, |
| | year = "2025", |
| | address = "Suzhou, China", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://aclanthology.org/2025.findings-emnlp.1053/", |
| | doi = "10.18653/v1/2025.findings-emnlp.1053", |
| | pages = "19338--19354", |
| | ISBN = "979-8-89176-335-7", |
| | 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." |
| | } |
| | ``` |
| |
|
| |
|
