# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("GanjinZero/coder_all")
model = AutoModel.from_pretrained("GanjinZero/coder_all")Quick Links
CODER: Knowledge infused cross-lingual medical term embedding for term normalization.
Multi lingual Version.
Github Link: https://github.com/GanjinZero/CODER
@article{YUAN2022103983,
title = {CODER: Knowledge-infused cross-lingual medical term embedding for term normalization},
journal = {Journal of Biomedical Informatics},
pages = {103983},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2021.103983},
url = {https://www.sciencedirect.com/science/article/pii/S1532046421003129},
author = {Zheng Yuan and Zhengyun Zhao and Haixia Sun and Jiao Li and Fei Wang and Sheng Yu},
keywords = {medical term normalization, cross-lingual, medical term representation, knowledge graph embedding, contrastive learning}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GanjinZero/coder_all")