Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
protein-recognition
gene-recognition
molecular-biology
genomics
dna
rna
cell_line
cell_type
protein
Instructions to use OpenMed/OpenMed-NER-DNADetect-PubMed-109M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DNADetect-PubMed-109M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DNADetect-PubMed-109M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DNADetect-PubMed-109M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DNADetect-PubMed-109M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41ac8c5ff1757440d213e661e5728620b14738be0ac3d95b32899276e8681204
- Size of remote file:
- 218 MB
- SHA256:
- 4a4ca91a40816870f8ebab6638b150b51bbe031fd7aeb3bb60ea5b522edab225
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