README.md

---
license: apache-2.0
---

---
license: cc-by-4.0
language:
- es
tags:
- casimedicos
- explainability
- medical exams
- medical question answering
- extractive question answering
- squad
- multilinguality
- LLMs
- LLM
pretty_name: mdeberta-expl-extraction-multi
task_categories:
- question-answering
size_categories:
- 1K<n<10K
---

<p align="center">
    <br>
    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;">
    <br>

# mdeberta-v3-base finetuned for Explanatory Argument Extraction

We finetuned mdeberta-v3-base on a **novel extractive task** which consists of **identifying the explanation of the correct answer written by
medical doctors in medical exams**.

The training data is based on [Antidote CasiMedicos](https://huggingface.co/datasets/HiTZ/casimedicos-squad) for EN,ES,FR,IT languages.

The data source consists of Resident Medical Intern or Médico Interno Residente (MIR) exams, originally
created by [CasiMedicos](https://www.casimedicos.com), a Spanish community of medical professionals who collaboratively, voluntarily, 
and free of charge, publishes written explanations about the possible answers included in the MIR exams. The aim is to generate a resource that
helps future medical doctors to study towards the MIR examinations. The commented MIR exams, including the explanations, are published in the [CasiMedicos 
Project MIR 2.0 website](https://www.casimedicos.com/mir-2-0/).

We have extracted, clean, structure and annotated the available data so that each document in **casimedicos-squad** includes the clinical case, the correct answer, 
the multiple-choice questions and the commented exam written by native Spanish medical doctors. The comments have been annotated with the span in the text that
corresponds to the explanation of the correct answer (see example below).

<table style="width:33%">
    <tr>
         <th>casimedicos-squad splits</th>
     <tr>
         <td>train</td>
         <td>404</td>
     </tr>
     <tr>
         <td>validation</td>
         <td>56</td>
     </tr>
     <tr>
         <td>test</td>
         <td>119</td>
     </tr>
 </table>




## Example

<p align="center">
<img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;">
</p>

The example above shows a document in CasiMedicos containing the textual content, including Clinical Case (C), Question (Q), Possible Answers (P), 
and Explanation (E). Furthermore, for **casimedicos-squad** we annotated the span in the explanation (E) that corresponds to the correct answer (A).

 The process of manually annotating the corpus consisted of specifying where the explanations of the correct answers begin and end. 
 In order to obtain grammatically complete correct answer explanations, annotating full sentences or subordinate clauses was preferred over
 shorter spans. 

## Data Explanation

The dataset is structured as a list of documents ("paragraphs") where each of them include:

- **context**: the explanation (E) in the document
- **qas**: list of possible answers and questions. This element contains:
    - **answers**: an answer which corresponds to the explanation of the correct answer (A)
    - **question**: the clinical case (C) and question (Q)
    - **id**: unique identifier for the document

## Citation


If you use this data please **cite the following paper**:

```bibtex
@misc{goenaga2023explanatory,
      title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams}, 
      author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri},
      year={2023},
      eprint={2312.00567},
      archivePrefix={arXiv}
}
```

**Contact**: [Iakes Goenaga](http://www.hitz.eus/es/node/65) and [Rodrigo Agerri](https://ragerri.github.io/)
HiTZ Center - Ixa, University of the Basque Country UPV/EHU




### Model Description


- 📖 **Paper**:[Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://arxiv.org/abs/2312.00567)
- 💻 **Github Repo** (Data and Code): [https://github.com/ixa-ehu/antidote-casimedicos](https://github.com/ixa-ehu/antidote-casimedicos)
- 🌐 **Project Website**: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
- **Funding**: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
- **Language(s) (NLP):** EN,ES,FR,IT
- **License:** Apache License 2
- **Finetuned from model:** microsoft/mdeberta-v3-base

## Uses

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### Direct Use

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### Recommendations

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## How to Get Started with the Model

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## Training Details

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

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