MrBERT-biomed Model Card

MrBERT-biomed is a new foundational multilingual biomedical model built on the ModernBERT architecture. The model is obtained via domain adaptation from MrBERT, initializing all weights from MrBERT-es and further training on a domain-specific biomedical corpus comprising 24.13B tokens for 2 epochs. The training data is predominantly English (84.7%), followed by Spanish (14.8%), with smaller portions of German (0.18%), Italian (0.11%), French (0.11%), and minor traces of Portuguese and Russian.

Technical Description

Technical details of the MrBERT-biomed model.

Description	Value
Model Parameters	308M
Tokenizer Type	SPM
Vocabulary size	25600
Precision	bfloat16
Context length	8192

Training Hyperparemeters

Hyperparameter	Value
Pretraining Objective	Masked Language Modeling
Learning Rate	2E-03
Learning Rate Scheduler	Cosine
Warmup	2,400,000,000 tokens
Optimizer	decoupled_stableadamw
Optimizer Hyperparameters	AdamW (β1=0.9,β2=0.98,ε =1e-06 )
Weight Decay	1E-05
Global Batch Size	512
Dropout	1E-01
Activation Function	GeLU

How to use

>>> from transformers import pipeline
>>> from pprint import pprint

>>> unmasker = pipeline('fill-mask', model='BSC-LT/MrBERT-biomed')

>>> pprint(unmasker("El uso prolongado de<mask>puede causar toxicidad hepática.",top_k=3))
[{'score': 0.19885338842868805,
  'sequence': 'El uso prolongado de esteroides puede causar toxicidad '
              'hepática.',
  'token': 215060,
  'token_str': 'esteroides'},
 {'score': 0.03336358070373535,
  'sequence': 'El uso prolongado de insulina puede causar toxicidad hepática.',
  'token': 131044,
  'token_str': 'insulina'},
 {'score': 0.022234393283724785,
  'sequence': 'El uso prolongado de drogas puede causar toxicidad hepática.',
  'token': 99191,
  'token_str': 'drogas'}]
>>> pprint(unmasker("Prolonged use of<mask>can cause hepatotoxicity.", top_k=3))
[{'score': 0.10918917506933212,
  'sequence': 'Prolonged use of steroids can cause hepatotoxicity.',
  'token': 232800,
  'token_str': 'steroids'},
 {'score': 0.08295559883117676,
  'sequence': 'Prolonged use of drugs can cause hepatotoxicity.',
  'token': 101507,
  'token_str': 'drugs'},
 {'score': 0.049813270568847656,
  'sequence': 'Prolonged use of alcohol can cause hepatotoxicity.',
  'token': 52167,
  'token_str': 'alcohol'}]

EVALUATION: Retrieval

In addition to the MrBERT family, the following base foundation models were considered:

Multilingual Foundational Model	Number of Parameters	Vocab Size	Description
mmBERT	308M	250K	Multilingual ModernBERT pre-trained with staged language learning.
mGTE	306M	250K	Multilingual encoder also adapted for retrieval tasks.
Clinical ModernBERT	137M	50K	Pre-trained model on biomedical data using ModernBERT architecture
BioClinical-ModernBERT	150M	50K	Domain adaptation from ModernBERT to bioclinical data

The benchmarks used for comparison are:

MTEB: We select a subset of MTEB that evaluates legal tasks in English.
Absatnitas: An internally designed task for evaluating Spanish-language retrieval performance on biomedical abstracts.
NER: 3 Spanish Named-Entity Recognition datasets.

Task Name	Task Type	mmBERT (308M)	MrBERT (308M)	MrBERT-es (150M)	BioClinical-MdnBERT (150M)	Clinical MdnBERT (137M)	MrBERT-biomed (308M)
bsc-bio-distemist-ner (ES)	NER	78.00	77.84	78.07	75.45	70.22	77.93
cantemist (ES)	NER	78.03	68.73	73.40	66.68	30.91	70.78
pharmaconer (ES)	NER	89.66	88.58	88.97	87.66	81.69	89.92
AbSanitas (ES)	Retrieval	34.68	34.16	53.49	30.41	18.08	51.01
r2med (EN)	Retrieval	10.87	10.15	8.65	9.97	5.91	9.76
SciDocs (EN)	Retrieval	10.00	9.75	9.90	9.33	3.64	10.05
SciFact (EN)	Retrieval	32.35	31.08	31.46	32.07	20.34	30.25
TREC-COVID (EN)	Retrieval	30.77	49.53	37.51	46.08	23.88	48.76
Average (EN)	All Tasks	21.00	25.13	21.88	24.36	13.44	24.71
Average (EN + ES)	All Tasks	45.55	46.23	47.68	44.71	31.83	48.56

Additional information

Author

The Language Technologies Lab from Barcelona Supercomputing Center.

Contact

For further information, please send an email to langtech@bsc.es.

Copyright

Funding

This work has been supported and funded by the Ministerio para la Transformación Digital y de la Función Pública and the Plan de Recuperación, Transformación y Resiliencia – funded by the EU through NextGenerationEU, within the framework of the Modelos del Lenguaje project, as well as by the European Union – NextGenerationEU. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the European Commission can be held responsible for them.

Acknowledgements

This project has benefited from the contributions of numerous teams and institutions through data contributions.

In Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà.

At national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, Fundación Elcano, the "Instituto de Ingenieria del Conocimiento" and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria.

At the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration.

Their valuable efforts have been instrumental in the development of this work.

Disclaimer

Be aware that the model may contain biases or other unintended distortions. When third parties deploy systems or provide services based on this model, or use the model themselves, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence.

The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.

Citation

@article{tamayo2026mrbert,
  title={MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation},
  author={Tamayo, Daniel and Lacunza, I{\~n}aki and Rivera-Hidalgo, Paula and Da Dalt, Severino and Aula-Blasco, Javier and Gonzalez-Agirre, Aitor and Villegas, Marta},
  journal={arXiv preprint arXiv:2602.21379},
  year={2026}
}