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
Daniel Rabottini commited on
Commit ·
6497b88
1
Parent(s): 0c48771
Add model_manifest.json for the radgraphit package
Browse filesRecords package/schema/backend compatibility, the medBIT encoder, and
SHA-256 checksums for config.json, vocab.json and best.pt. Enables
integrity-verified loading via RadGraphIT.from_pretrained().
- model/model_manifest.json +24 -0
model/model_manifest.json
ADDED
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{
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"manifest_version": 1,
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"package": "radgraphit",
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"min_package_version": "0.1.0",
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"schema": "radgraph-xl",
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"backend": "dygie_v2",
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"encoder": {
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"model_name": "IVN-RIN/medBIT-r3-plus",
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"max_length": 512
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},
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"revision": "v1",
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"weights_file": "best.pt",
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"files": {
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"config.json": {
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"sha256": "378f0c083f71662e204c8876b805ba8298f7a056db4b6cc64b95079a0d476d23"
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},
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"vocab.json": {
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"sha256": "e32c6ab26dad1625dfc3f5fa2bcf91d466ba7cf231da0ab78846eae84ad20f76"
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},
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"best.pt": {
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"sha256": "987fc75edf3ac78d1a0f05b8260f832c16ccd0cb6d6612432b2c0dbdf00a757a"
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}
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}
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}
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