Instructions to use GanjinZero/coder_eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GanjinZero/coder_eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GanjinZero/coder_eng")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("GanjinZero/coder_eng") model = AutoModel.from_pretrained("GanjinZero/coder_eng", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - bert | |
| - biomedical | |
| CODER: Knowledge infused cross-lingual medical term embedding for term normalization. | |
| English 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} | |
| } | |
| ``` |