Token Classification
Transformers
Safetensors
English
bert
biomedical
named-entity-recognition
ner
biolinkbert
biored
gene
disease
variant
gdv-scout
Eval Results (legacy)
Instructions to use nobal/gdv-scout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nobal/gdv-scout with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nobal/gdv-scout")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nobal/gdv-scout") model = AutoModelForTokenClassification.from_pretrained("nobal/gdv-scout", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 939 Bytes
55a720e | 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 39 40 41 42 43 44 45 | {
"task": "token_classification_ner",
"scheme": "BIO",
"labels": [
"O",
"B-Disease",
"I-Disease",
"B-GeneOrGeneProduct",
"I-GeneOrGeneProduct",
"B-Variant",
"I-Variant"
],
"label2id": {
"O": 0,
"B-Disease": 1,
"I-Disease": 2,
"B-GeneOrGeneProduct": 3,
"I-GeneOrGeneProduct": 4,
"B-Variant": 5,
"I-Variant": 6
},
"id2label": {
"0": "O",
"1": "B-Disease",
"2": "I-Disease",
"3": "B-GeneOrGeneProduct",
"4": "I-GeneOrGeneProduct",
"5": "B-Variant",
"6": "I-Variant"
},
"types": [
"Disease",
"GeneOrGeneProduct",
"Variant"
],
"ignore_index": -100,
"label_policy": "gold (BioRED); Chemical/Species/CellLine folded to O",
"alignment": "char spans -> subword tokens via tokenizer offset_mapping",
"source": "BioRED",
"type_map": {
"Gene": "GeneOrGeneProduct",
"Disease": "Disease",
"Variant": "Variant"
}
} |