EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text
Paper • 2304.07805 • Published
# 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", device_map="auto")Quick Links
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} }
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aitslab/biobert_huner_cell_v1")