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)# 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: 2,930 Bytes
c41b501 8839113 c41b501 8839113 c41b501 8839113 c41b501 | 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 46 47 48 49 50 51 | {
"training_ner_perclass/Anatomy::definitely present": 0.9426991571539427,
"training_ner_perclass/Observation::definitely present": 0.8817818462303422,
"training_ner_perclass/Observation::definitely absent": 0.9103946033917495,
"training_ner_perclass/Observation::uncertain": 0.7692726777444838,
"training_ner_perclass/Anatomy::definitely absent": 0.23170731707317074,
"training_ner_perclass/Anatomy::uncertain": 0.0,
"training_ner_macro_f1": 0.6226426002656148,
"training_radgraph-it__ner_precision": 0.9064632963857254,
"training_radgraph-it__ner_recall": 0.9133660284102338,
"training_radgraph-it__ner_f1": 0.9099015711402576,
"training_MEAN__ner_precision": 0.9064632963857254,
"training_MEAN__ner_recall": 0.9133660284102338,
"training_MEAN__ner_f1": 0.9099015711402576,
"training_relation_perclass/modify": 0.8131500323411945,
"training_relation_perclass/located_at": 0.8162794753056518,
"training_relation_perclass/suggestive_of": 0.7143992230952592,
"training_relation_macro_f1": 0.7812762435807018,
"training_radgraph-it__relation_precision": 0.8429081423413853,
"training_radgraph-it__relation_recall": 0.780637087032863,
"training_radgraph-it__relation_f1": 0.8105784119451618,
"training_MEAN__relation_precision": 0.8429081423413853,
"training_MEAN__relation_recall": 0.780637087032863,
"training_MEAN__relation_f1": 0.8105784119451618,
"training_loss": 71.19069803560308,
"validation_ner_perclass/Anatomy::definitely present": 0.8997013116315311,
"validation_ner_perclass/Observation::definitely present": 0.8152192654750272,
"validation_ner_perclass/Observation::definitely absent": 0.8775681341719078,
"validation_ner_perclass/Observation::uncertain": 0.6886446886446886,
"validation_ner_perclass/Anatomy::definitely absent": 0.21052631578947367,
"validation_ner_perclass/Anatomy::uncertain": 0.0,
"validation_ner_macro_f1": 0.5819432859521048,
"validation_radgraph-it__ner_precision": 0.848412096706259,
"validation_radgraph-it__ner_recall": 0.868157917420912,
"validation_radgraph-it__ner_f1": 0.8581714383094751,
"validation_MEAN__ner_precision": 0.848412096706259,
"validation_MEAN__ner_recall": 0.868157917420912,
"validation_MEAN__ner_f1": 0.8581714383094751,
"validation_relation_perclass/modify": 0.6938028414538483,
"validation_relation_perclass/located_at": 0.7035013880182681,
"validation_relation_perclass/suggestive_of": 0.5048543689320388,
"validation_relation_macro_f1": 0.6340528661347183,
"validation_radgraph-it__relation_precision": 0.6998282770463652,
"validation_radgraph-it__relation_recall": 0.6799021243465688,
"validation_radgraph-it__relation_f1": 0.6897213133250593,
"validation_MEAN__relation_precision": 0.6998282770463652,
"validation_MEAN__relation_recall": 0.6799021243465688,
"validation_MEAN__relation_f1": 0.6897213133250593,
"validation_loss": 153.25496798013253,
"best_epoch": 8
} |