Instructions to use pruas/BENT-PubMedBERT-NER-Cell-Component with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pruas/BENT-PubMedBERT-NER-Cell-Component with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pruas/BENT-PubMedBERT-NER-Cell-Component")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pruas/BENT-PubMedBERT-NER-Cell-Component") model = AutoModelForTokenClassification.from_pretrained("pruas/BENT-PubMedBERT-NER-Cell-Component") - Notebooks
- Google Colab
- Kaggle
Named Entity Recognition (NER) model to recognize cell component entities.
Please cite our work:
@article{NILNKER2022,
title = {NILINKER: Attention-based approach to NIL Entity Linking},
journal = {Journal of Biomedical Informatics},
volume = {132},
pages = {104137},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2022.104137},
url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526},
author = {Pedro Ruas and Francisco M. Couto},
}
PubMedBERT fine-tuned on the following datasets:
- CRAFT: entity type "GO-CC"
- MLEE: entity type "Cellular_component"
- BioNLP13CG-cc
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