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
| { | |
| "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 | |
| } |