language:
- it
license: mit
library_name: transformers
base_model: IVN-RIN/medBIT-r3-plus
tags:
- radiology
- information-extraction
- named-entity-recognition
- relation-extraction
- medical
- radgraph
RadGraph-IT
RadGraph graph inference for Italian radiology reports, with output in the canonical RadGraph-XL format.
This repository hosts the v1 Italian DyGIE model used by the
RadGraph-IT package. It jointly extracts named
entities and relations from Italian radiology reports using a shared encoder, span extractor,
NER head, and relation head. The encoder is
IVN-RIN/medBIT-r3-plus.
Authors
Daniel Rabottini, Edoardo Avenia, and Rocco Angelella.
Usage
from transformers import AutoModel
model = AutoModel.from_pretrained("radgraphIT/Radgraph-IT", trust_remote_code=True)
model.eval()
out = model.predict(
"Non versamento pleurico. Addensamento parenchimale al lobo inferiore del polmone destro."
)
print(out["words"])
print(out["entities"]) # [start, end, label, raw_score, softmax_score] (inclusive word-index spans)
print(out["relations"]) # [start1, end1, start2, end2, label, raw_score, softmax_score]
model.predict(text) applies the word-level preprocessing used during training (regex cleanup
and nltk.wordpunct_tokenize). If you already have a list of words tokenized with that exact
scheme, use model.predict_words(words) instead.
The bundled tokenizer (AutoTokenizer.from_pretrained("radgraphIT/Radgraph-IT")) is the
encoder's subword tokenizer; it is not the word-level splitter used by predict().
The model requires transformers, torch, numpy, and nltk (for predict()). The
radgraphit package provides the supported Python
and CLI interface, including RadGraph-XL-compatible serialization.
Architecture
Word-level embeddings are produced by mean-pooling medBIT-r3-plus subwords back to one vector
per word. Documents longer than 512 tokens are split into word-aligned chunks. Span
representations concatenate start/end word embeddings with a span-width embedding. The NER head
scores candidate spans of width up to 12; the relation head prunes to the top
spans-per-word and scores each retained pair.
Entity labels are Anatomy and Observation, each with definitely present,
definitely absent, or uncertain status. Relation labels are modify, located_at, and
suggestive_of.
Training and evaluation
The model was trained on the full non-cross-validation split. The best validation epoch was 8.
| Metric | NER | Relation |
|---|---|---|
| Precision | 0.848 | 0.700 |
| Recall | 0.868 | 0.680 |
| F1 | 0.858 | 0.690 |
The full per-class breakdown is in model/metrics.json.
Limitations and responsible use
RadGraph-IT is a research software model for automatic information extraction. It is not a medical device; its output is not a diagnosis, report, or clinical opinion. Validate outputs for the intended setting and keep human clinical judgement in the loop.
Citation
Please cite both RadGraph-IT and RadGraph-XL when using this model:
@software{radgraphit,
author = "Daniel Rabottini and Edoardo Avenia and Rocco Angelella",
title = "{RadGraph-IT}: {RadGraph} graph inference for {Italian} radiology reports",
year = "2026",
version = "1.0.0",
url = "https://github.com/therabo/RadGraph-IT",
}
@inproceedings{delbrouck-etal-2024-radgraph,
title = "{R}ad{G}raph-{XL}: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports",
author = "Delbrouck, Jean-Benoit and Chambon, Pierre and Chen, Zhihong and Varma, Maya and Johnston, Andrew and Blankemeier, Louis and Van Veen, Dave and Bui, Tan and Truong, Steven and Langlotz, Curtis",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
year = "2024",
url = "https://aclanthology.org/2024.findings-acl.765",
pages = "12902--12915",
}