Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7593beb7b57eae2af41be6825e414f938f2da8afbd19c3173461570333e5ad77
- Size of remote file:
- 130 MB
- SHA256:
- 87116d6b0cd5f5e968a47a606f3e7480fe54963c487dbf680497d997edf0a187
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