Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
cancer-genetics
oncology
gene-regulation
cancer-research
amino_acid
anatomical_system
cancer
cell
cellular_component
developing_anatomical_structure
gene_or_gene_product
immaterial_anatomical_entity
multi-tissue_structure
organ
organism
organism_subdivision
organism_substance
pathological_formation
simple_chemical
tissue
Instructions to use OpenMed/OpenMed-NER-OncologyDetect-TinyMed-66M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OncologyDetect-TinyMed-66M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-TinyMed-66M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-TinyMed-66M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-TinyMed-66M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ef556fc6db1baeb6239ad1e60551f72c6a1153b386e0e809e57c3b2473aad39
- Size of remote file:
- 133 MB
- SHA256:
- 52c6ba37d3f71feb107c24206cb6f42df7c98d7a1744f831f9753b0695eeac37
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