SemTec at ZB MED
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mlentory-etl-pipeline
mlentory-etl-pipeline PublicThis repository aims at exploring APIs from different ML-related platforms so we can harvest and harmonize data from them
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comet-metadata-extraction
comet-metadata-extraction PublicA tool for transforming GitHub repositories into FAIR (Findable, Accessible, Interoperable, and Reusable) and machine-actionable metadata, enabling better data discovery and automation.
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bheu24-cm4mlds
bheu24-cm4mlds PublicBioHackathon Europe 2024 repository to explore Croissant ML metadata extraction from HuggingFace
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BioHackOutcomes
BioHackOutcomes PublicBioHackathon project to define and follow up BioHackathon projects
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Repositories
- comet-metadata-extraction Public
A tool for transforming GitHub repositories into FAIR (Findable, Accessible, Interoperable, and Reusable) and machine-actionable metadata, enabling better data discovery and automation.
- fasttext2doc2vec-doc-relevance Public
An approach exploring and assessing literature-based doc-2-doc recommendations using fastText and applying to the RELISH dataset.
- doc2vec-doc-relevance-training Public
An approach exploring and assessing literature-based doc-2-doc recommendations using doc2vec and applying it to the RELISH dataset
- word2doc2vec-doc-relevance-training Public
An approach exploring and assessing literature-based doc-2-doc recommendations using word2vec and applying it to the RELISH dataset
- hybrid-doc-relevance-training Public
A collection of hybrid approaches combining Word2Vec, Doc2Vec, FastText, and ontology-based background knowledge to enhance document similarity, relevance, and recommendation.
- wmd-word2vec-training Public
An approach utilizing a word mover's distance solution to generate word embeddings from the RELISH dataset as part to explore document-to-document similarity
- fasttext2doc2vec-doc-relevance-training Public
An approach exploring and assessing literature-based doc-2-doc recommendations using fastText and applying it to the RELISH dataset
- bert-embeddings-doc-relevance Public
An approach exploring and assessing literature-based doc-2-doc recommendations using BERT embeddings, and applying it to TREC and RELISH datasets
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