---
license: apache-2.0
library_name: transformers
tags:
  - medical
  - radiology
  - chest-x-ray
  - text-generation
  - t5
  - fact-extraction
base_model: t5-small
pipeline_tag: text-generation
---

# T5FactExtractor — Radiology Fact Extractor

T5FactExtractor is a **T5-small** sequence-to-sequence model that extracts factual statements from chest X-ray radiology report sentences. Given a sentence, it generates a JSON-like list of short clinical facts that can be embedded, compared, or used in metrics such as CXRFEScore.

It is stage 1 of the *Extracting and Encoding* framework from Findings of ACL 2024:

1. **Fact extraction** — this model (`pamessina/T5FactExtractor`)
2. **Fact encoding** — [`pamessina/CXRFE`](https://huggingface.co/pamessina/CXRFE)

Paper: [*Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation*](https://aclanthology.org/2024.findings-acl.236/)

## Model details

| | |
|---|---|
| **Architecture** | `T5ForConditionalGeneration` |
| **Base model** | [`t5-small`](https://huggingface.co/t5-small) |
| **Task** | Sentence → list of radiology facts |
| **Typical use** | Preprocess report sentences before encoding with CXRFE |
| **License** | Apache 2.0 |

## Output format

The model generates a string containing a JSON array of fact strings, for example:

```text
["small right pleural effusion", "normal heart size"]
```

Downstream code (including [`cxrfescore`](https://pypi.org/project/cxrfescore/)) parses that array, deduplicates facts, and lightly cleans repeated words.

## How to use

### Standalone (Transformers)

```python
import re
import json
import torch
from transformers import T5ForConditionalGeneration, T5TokenizerFast

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "pamessina/T5FactExtractor"

tokenizer = T5TokenizerFast.from_pretrained(model_id)
model = T5ForConditionalGeneration.from_pretrained(model_id).to(device)
model.eval()

sentence = "There is a small right pleural effusion. The heart size is normal."
# Prefer one sentence at a time (reports are usually sentence-split first).
inputs = tokenizer(sentence, padding="longest", return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs["attention_mask"].to(device)

with torch.no_grad():
    output_ids = model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        max_new_tokens=input_ids.shape[1] * 4,
        num_beams=1,
    )

raw = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
print("raw:", raw)

# Minimal parse (same idea as cxrfescore.text_utils.parse_facts)
match = re.search(r"\[.*", raw)
if match:
    facts_str = match.group()
    if not facts_str.endswith("]"):
        facts_str += "]"
    facts = json.loads(facts_str)
    print("facts:", facts)
```

### Easiest path: CXRFEScore

For full reports, the package sentence-splits, runs this extractor, aggregates unique facts, and (optionally) embeds them with CXRFE:

```bash
pip install cxrfescore
```

```python
from cxrfescore import CXRFEScore

metric = CXRFEScore(device="cuda")
reports = [
    "There is a small right pleural effusion. The heart size is normal.",
]
facts_per_report = metric.extract_facts(reports)
print(facts_per_report[0])
```

Demo notebook: [CXR-Fact-Encoder / notebooks/cxrfescore_demo.ipynb](https://github.com/PabloMessina/CXR-Fact-Encoder/blob/main/notebooks/cxrfescore_demo.ipynb)

## Related resources

- Paper hub: https://github.com/PabloMessina/CXR-Fact-Encoder
- Metric package: https://github.com/PabloMessina/CXRFEScore · [PyPI](https://pypi.org/project/cxrfescore/)
- Companion fact encoder: https://huggingface.co/pamessina/CXRFE
- ACL Anthology: https://aclanthology.org/2024.findings-acl.236/
- arXiv: https://arxiv.org/abs/2407.01948

## Citation

If you use T5FactExtractor, please cite:

```bibtex
@inproceedings{messina-etal-2024-extracting,
    title = "Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation",
    author = "Messina, Pablo  and
      Vidal, Rene  and
      Parra, Denis  and
      Soto, Alvaro  and
      Araujo, Vladimir",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.236/",
    doi = "10.18653/v1/2024.findings-acl.236",
    pages = "3955--3986"
}
```