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: 1,108 Bytes
0c48771 | 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 | {
"model_type": "radgraph_it",
"architectures": [
"RadgraphModel"
],
"auto_map": {
"AutoConfig": "configuration_radgraph.RadgraphConfig",
"AutoModel": "modeling_radgraph.RadgraphModel"
},
"encoder_name": "IVN-RIN/medBIT-r3-plus",
"max_length": 512,
"max_span_width": 12,
"feature_size": 20,
"feedforward_params": {
"hidden_dims": [
150,
150
],
"dropout": 0.4
},
"loss_weights": {
"ner": 0.2,
"relation": 1.0
},
"relation_spans_per_word": 0.5,
"train_encoder": true,
"span_pooling": false,
"transformer_params": null,
"relation_context": false,
"relation_feedforward_params": null,
"dataset": "radgraph-it",
"ner_labels": {
"": 0,
"Anatomy::definitely present": 1,
"Observation::definitely present": 2,
"Observation::definitely absent": 3,
"Observation::uncertain": 4,
"Anatomy::definitely absent": 5,
"Anatomy::uncertain": 6
},
"relation_labels": {
"": 0,
"modify": 1,
"located_at": 2,
"suggestive_of": 3
},
"torch_dtype": "float32",
"transformers_version": "5.14.1"
} |