处理预计算大型PubMed嵌入的工具。
项目描述
自动构建PubMed嵌入。
安装包
像往常一样，只需从Pypi安装
pip install pubmed_embedding用法示例
您可以通过以下方式检索感兴趣PubMed ID的嵌入
BERT
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"
)结果是
SciBERT
scibert_features = get_pubmed_embedding_from_curies(
curies=pubmed_ids,
version="pubmed_scibert_30_11_2022"
)结果是
Specter
spected_features = get_pubmed_embedding_from_curies(
curies=pubmed_ids,
version="pubmed_specter_30_11_2022"
)结果是
引用这项工作
如果您发现这些数据集有用，请引用
@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}
}
关闭
pubmed_embedding-1.0.14.tar.gz的哈希值
| 算法 | 哈希摘要 | |
|---|---|---|
| SHA256 | 4a7b16fe671c00f7725d4bfa5ab1bbeb1afea7e84e2d19f10e7fd21483317f65 |
|
| MD5 | da227d8455868c13b8ae3b60d022b850 |
|
| BLAKE2b-256 | a41204dc1c9ee422036cf60786e0e7c2cd738fc15af3e39229b056274da80f38 |
