Metadata-Version: 1.2
Name: pubmed_embedding
Version: 1.0.14
Summary: A tool to work with pre-computed large pubmed embedding.
Home-page: https://github.com/LucaCappelletti94/pubmed_graph
Author: Luca Cappelletti
License: BSD3
Description: PubMed embedding
        ===================================
        |pip| |downloads| |paper|
        
        Building PubMed embedding, automatically.
        
        
        Install the package
        ----------------------------------
        As usual, just install from Pypi:
        
        .. code:: shell
        
            pip install pubmed_embedding
        
        
        Usage examples
        ----------------------------------
        You can retrieve embedding for PubMed IDs of interest as such:
        
        BERT
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            from pubmed_embedding import get_pubmed_embedding_from_curies
        
            pubmed_ids = ["PMID:24774509", "PMID:15170967", "PMID:7850793"]
        
            bert_features = get_pubmed_embedding_from_curies(
                curies=pubmed_ids,
                version="pubmed_bert_30_11_2022"
            )
        
        And the result is:
        
        |BERT|
        
        
        SciBERT
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            scibert_features = get_pubmed_embedding_from_curies(
                curies=pubmed_ids,
                version="pubmed_scibert_30_11_2022"
            )
           
        And the result is:
        
        |SciBERT|
        
        Specter
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            spected_features = get_pubmed_embedding_from_curies(
                curies=pubmed_ids,
                version="pubmed_specter_30_11_2022"
            )
        
        And the result is:
        
        |Specter|
        
        Citing this work
        -----------------------------
        If you have found these datasets useful, please do cite:
        
        .. code:: bib
        
            @software{cappellettiPubMed2022,
                author = {Cappelletti, Luca and Fontana, Tommaso and Reese, Justin},
                month = {12},
                title = {{BM25-weighted BERT-based embedding of PubMed}},
                url = {https://github.com/LucaCappelletti94/pubmed_embedding},
                version = {1.0.14},
                year = {2022}
            }
        
        
        
        .. |BERT| image:: https://github.com/LucaCappelletti94/pubmed_embedding/blob/main/bert.png?raw=true
        .. |SciBERT| image:: https://github.com/LucaCappelletti94/pubmed_embedding/blob/main/scibert.png?raw=true
        .. |Specter| image:: https://github.com/LucaCappelletti94/pubmed_embedding/blob/main/specter.png?raw=true
        
        .. |pip| image:: https://badge.fury.io/py/pubmed-embedding.svg
            :target: https://badge.fury.io/py/pubmed-embedding
            :alt: Pypi project
        
        .. |downloads| image:: https://pepy.tech/badge/pubmed-embedding
            :target: https://pepy.tech/badge/pubmed-embedding
            :alt: Pypi total project downloads 
        
        .. |paper| image:: https://img.shields.io/badge/DOI-10.48550/arXiv.2110.06196-blue.svg
            :target: https://github.com/LucaCappelletti94/pubmed_embedding/blob/main/BM25_weighted_BERT_based_embedding_of_PubMed.pdf
            :alt: Paper
        
Keywords: SciBERT,PubMed,BM25
Platform: UNKNOWN
Requires-Python: >=3.6.0
