---
language: de
tags:
- negation
- speculation
- cross-lingual
- bert
- clinical/medical
- text-classification
extra_gated_prompt: >-
  You agree to not use the model to conduct experiments that cause harm to human
  subjects, i.e. attempting to misuse clinical data or re-identify any sensible
  data.
extra_gated_fields:
  Company: text
  Country: text
  Name: text
  Email: text
  I agree to use this model for non-commercial use ONLY: checkbox
pipeline_tag: text-classification
---

# FactualMedBERT-DE: Clinical Factuality Detection BERT model for German language

## Model description

FactualMedBERT-DE is the first pre-trained language model to address factuality/assertion detection problem in German clinical texts (primarily discharge summaries).
It is introduced in the paper [Factuality Detection using Machine Translation - a Use Case for German Clinical Text](https://arxiv.org/abs/2308.08827). The model classifies tagged medical conditions based
on their factuality value. It can support label classification of `Affirmed`, `Negated` and `Possible`.

It was intialized from [smanjil/German-MedBERT](https://huggingface.co/smanjil/German-MedBERT) German language model and
was trained on a translated subset data of [the 2010 i2b2/VA assertion challenege](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).

## How to use the model

- You might need to authenticate and login before being able to download the model (see more [here](https://huggingface.co/docs/huggingface_hub/quick-start))
- Get the model using the transformers library

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("binsumait/factual-med-bert-de")
model = AutoModelForSequenceClassification.from_pretrained("binsumait/factual-med-bert-de")
```

- Predict an instance by pre-tagging the factuality target (ideally a medical condition) with `[unused1]` special token:

```python
from transformers import TextClassificationPipeline
instance = "Der Patient hat vielleicht [unused1] Fieber [unused1]"

factuality_pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)

print(factuality_pipeline(instance))
```

which should output:
`[{'label': 'possible', 'score': 0.9744388461112976}]`

## Cite

If you use our model, please cite your paper as follows:

```bibtex
@inproceedings{bin_sumait_2023,
  title={Factuality Detection using Machine Translation - a Use Case for German Clinical Text},
  author={Bin Sumait, Mohammed and Gabryszak, Aleksandra and Hennig, Leonhard and Roller, Roland},
  booktitle={Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)},
  year={2023}
}
```