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-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M: direct link, hf CLI and curl.
- Browser
- Download file 679 kB
-
https://huggingface.co/OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/OpenMed/OpenMed-NER-PharmaDetect-BioMed-335M/resolve/main/tokenizer.json
679 kB
File too large to display, you can check the raw version instead.