Bi-encoder models for Dense Retrieval (Entity Linking)
AI & ML interests
The NLP4BIA team focuses on the creation of publicly accessible high quality biomedical NLP resources to unlock key information and improve analysis of a variety of unstructured data sources, including clinical reports, biomedical literature, clinical trials, patents, or social media content written in different languages, mainly Spanish and English (but also other languages like Catalan).
Recent Activity
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models
59

BSC-NLP4BIA/General-SapBERT-15-Parents
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BSC-NLP4BIA/Symptemist-CE-Reranker
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11

BSC-NLP4BIA/Symptemist-Biencoder
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7

BSC-NLP4BIA/Distemist-CE-Reranker
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23

BSC-NLP4BIA/Distemist-Biencoder
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0.1B
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7

BSC-NLP4BIA/Medprocner-Biencoder
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12

BSC-NLP4BIA/Medprocner-CE-Reranker
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0.1B
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42

BSC-NLP4BIA/age-classification
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3

BSC-NLP4BIA/gender-classification
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21

BSC-NLP4BIA/biomat-MONER-mtl
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datasets
6
BSC-NLP4BIA/swedish_medical_ner
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795k
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44
BSC-NLP4BIA/swedish_medical_exams_qa
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5.69k
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31
BSC-NLP4BIA/swedish_openchart_se
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51
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41
BSC-NLP4BIA/swedish_cardiology_journals
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5.69k
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40
BSC-NLP4BIA/swedish_medical_qa
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3.18k
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29
BSC-NLP4BIA/bsc-bio-distemist-ner
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48.7k
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37