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-PubMed-v2-109M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b48436c7b5ea15511284fca255ac79141f12516b922f08b93693faf7f08ce551
- Size of remote file:
- 218 MB
- SHA256:
- 0438235c4bd4cbba35ff4ff09b321e3d60b8635263eb30abf142381ce15940d1
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