EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text
Paper • 2304.07805 • Published
How to use aitslab/biobert_huner_cell_v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="aitslab/biobert_huner_cell_v1") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("aitslab/biobert_huner_cell_v1")
model = AutoModelForTokenClassification.from_pretrained("aitslab/biobert_huner_cell_v1")More information can be found in our github repo and paper. Please cite the paper, if you use the model.
https://github.com/Aitslab/EasyNER
@article{ahmed2023easyner, title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text}, author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits}, year={2023}, eprint={2304.07805}, archivePrefix={arXiv}, primaryClass={q-bio.QM} }