Token Classification
Transformers
Safetensors
English
bert
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-MultiMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 695 Bytes
ab074a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"cls_token": {
"content": "[CLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"mask_token": {
"content": "[MASK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"sep_token": {
"content": "[SEP]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "[UNK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
|