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: 937 Bytes
5d2086c | 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 | {
"encoder": {
"model_name": "IVN-RIN/medBIT-r3-plus",
"max_length": 512,
"train_parameters": true
},
"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_data_path": "data/full/train.jsonl",
"validation_data_path": "data/full/dev.jsonl",
"test_data_path": "data/full/test.jsonl",
"trainer": {
"num_epochs": 20,
"patience": 5,
"grad_norm": 5.0,
"validation_metric": "+MEAN__relation_f1",
"cuda_device": 0,
"num_serialized_models_to_keep": 3,
"optimizer": {
"lr": 0.001,
"weight_decay": 0.0,
"embedder_lr": 5e-05,
"embedder_weight_decay": 0.01
}
},
"data_loader": {
"batch_size": 1
},
"numpy_seed": 1337,
"pytorch_seed": 133,
"random_seed": 13370
} |