Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-BigMed-278M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-BigMed-278M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-BigMed-278M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-BigMed-278M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-BigMed-278M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c091ee6575509750c72ff2a2f9a41f2094c0881b46f4fd277ad46699a4ec881
- Size of remote file:
- 555 MB
- SHA256:
- 892b4138695156d1ca157e33a3abe9dc6cfdd29217332249b13f773e47663628
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