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
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |