TransCorpus-bio-es

TransCorpus-bio-es is a large-scale, parallel biomedical corpus consisting of Spanish synthetic translations of PubMed abstracts. This dataset was created using the TransCorpus framework and is designed to enable high-quality Spanish biomedical language modeling and downstream NLP research.

Dataset Details

• Source: PubMed abstracts (English)
• Target: Spanish (synthetic, machine-translated)
• Translation Model: M2M-100 (1.2B) using TransCorpus Toolkit
• Size: 22 million abstracts, 34.6GB of text
• Domain: Biomedical, clinical, life sciences
• Format: one abstract per line

Motivation

Spanish is a low-resource language for biomedical NLP, with limited availability of large, high-quality corpora. TransCorpus-bio-es 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.

from datasets import load_dataset

dataset = load_dataset("jknafou/TransCorpus-bio-es", split="train")

print(dataset)
# Output:
# Dataset({
#    features: ['text'],
#    num_rows: 21567136
# })

print(dataset[0])
# {'text': '[Estudios bioquímicos sobre los componentes de la camomila/III. Estudios in vitro sobre la actividad antipéptica de (--)-alfa-bisabolol (transl del autor)]. (--)-Alfa-Bisabolol tiene una acción antipéptica primaria dependiendo de la dosis, que no es causada por una alteración del valor de pH. La actividad proteolítica de la pepsina se reduce en un 50 por ciento por la adición de bisabolol en la proporción de 1/0,5. La acción antipéptica del bisabolol sólo ocurre en caso de contacto directo. En caso de contacto previo con el substrato, se pierde el efecto inhibidor. '}

Benchmark Results in our French Experiment

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:

  @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."
  }