FremyCompany/BioLORD-2023

sentence-similarity model by FremyCompany

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This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B). This model is based on sentence-transformers/all-mpnet-base-v2 and was further finetuned on the BioLORD-Dataset and LLM-generated definitions from the Automatic Glossary of Clinical Terminology (AGCT). This model is accompanied…

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Model overview

FremyCompany/BioLORD-2023 is a sentence-similarity model repository published by FremyCompany on Hugging Face. The source reports the sentence-transformers library.

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Model facts

Task: sentence-similarity
Library: sentence-transformers
Recent downloads (30 days): 408,195
Cumulative likes: 58
Hugging Face trending score: 0
Reported safetensors parameters: 109,486,978
Architecture: MPNetModel
License: other
Access: Not gated by Hugging Face
Created: 2023-11-27T18:43:03.000Z
Last modified: 2025-01-09T19:25:36.000Z

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Reported languages: en

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Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:31.802Z. Daily imports are snapshots, not real-time monitoring.

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