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: 621 Bytes
6497b88 | 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 | {
"manifest_version": 1,
"package": "radgraphit",
"min_package_version": "0.1.0",
"schema": "radgraph-xl",
"backend": "dygie_v2",
"encoder": {
"model_name": "IVN-RIN/medBIT-r3-plus",
"max_length": 512
},
"revision": "v1",
"weights_file": "best.pt",
"files": {
"config.json": {
"sha256": "378f0c083f71662e204c8876b805ba8298f7a056db4b6cc64b95079a0d476d23"
},
"vocab.json": {
"sha256": "e32c6ab26dad1625dfc3f5fa2bcf91d466ba7cf231da0ab78846eae84ad20f76"
},
"best.pt": {
"sha256": "987fc75edf3ac78d1a0f05b8260f832c16ccd0cb6d6612432b2c0dbdf00a757a"
}
}
}
|