Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-BioMed-109M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-BioMed-109M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-BioMed-109M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-BioMed-109M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-BioMed-109M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29dc97873e11dcab5624ba83f963b29971e42642bf3eb8dd7c75a9a96a35e2c4
- Size of remote file:
- 218 MB
- SHA256:
- 31af33e1103c0b08791b780b5fbeb57a35d24dc20f493cb7ff8c735f897765b6
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