Feature Extraction
Transformers
Safetensors
Italian
radgraph_it
radiology
information-extraction
named-entity-recognition
relation-extraction
medical
radgraph
custom_code
Instructions to use radgraphIT/Radgraph-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radgraphIT/Radgraph-IT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="radgraphIT/Radgraph-IT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("radgraphIT/Radgraph-IT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,122 Bytes
11f691c | 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 | {
"description": "Diagnostic metrics measured on the full training set at the final epoch; these are not held-out evaluation results.",
"epochs_completed": 13,
"final_epoch_index": 12,
"training_loss": 37.17397039800772,
"training_ner": {
"precision": 0.9417513858489988,
"recall": 0.9489333635632871,
"micro_f1": 0.9453287339328981,
"macro_f1": 0.6892368337795368,
"per_class_f1": {
"Anatomy::definitely present": 0.9673097061532445,
"Observation::definitely present": 0.9281885564742222,
"Observation::definitely absent": 0.9447680909367706,
"Observation::uncertain": 0.8428728514381619,
"Anatomy::definitely absent": 0.4424778761061947,
"Anatomy::uncertain": 0.00980392156862745
}
},
"training_relations": {
"precision": 0.9079743979570305,
"recall": 0.8990052123231533,
"micro_f1": 0.9034675452622059,
"macro_f1": 0.8948061133959642,
"per_class_f1": {
"modify": 0.9055610089198072,
"located_at": 0.9025979301044696,
"suggestive_of": 0.8762594011636158
}
},
"training_joint_f1": 0.924398139597552
}
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