README.md

---
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](https://github.com/therabo/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`](https://huggingface.co/IVN-RIN/medBIT-r3-plus).

## Authors

Daniel Rabottini, Edoardo Avenia, and Rocco Angelella.

## Usage

```python
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](https://github.com/therabo/RadGraph-IT) 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:

```bibtex
@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",
}
```