Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
chemistry
chem
Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-TinyMed-65M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ChemicalDetect-TinyMed-65M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-TinyMed-65M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-TinyMed-65M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-TinyMed-65M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fb52759943bec3df0b222bdc33444ae3e552b72ca1d6222ebc7fc55cae026fe
- Size of remote file:
- 130 MB
- SHA256:
- 5585ff50d2b08b7172ee92defa3306377fe8e6656c78a4a66058dc72ff665cbb
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