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--- |
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license: apache-2.0 |
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--- |
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license: cc-by-4.0 |
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language: |
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- es |
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tags: |
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- casimedicos |
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- explainability |
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- medical exams |
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- medical question answering |
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- extractive question answering |
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- squad |
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- multilinguality |
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- LLMs |
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- LLM |
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pretty_name: mdeberta-expl-extraction-multi |
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task_categories: |
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- question-answering |
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size_categories: |
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- 1K<n<10K |
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--- |
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<p align="center"> |
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<br> |
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<img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;"> |
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<br> |
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# mdeberta-v3-base finetuned for Explanatory Argument Extraction |
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We finetuned mdeberta-v3-base on a **novel extractive task** which consists of **identifying the explanation of the correct answer written by |
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medical doctors in medical exams**. |
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The training data is based on [Antidote CasiMedicos](https://huggingface.co/datasets/HiTZ/casimedicos-squad) for EN,ES,FR,IT languages. |
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The data source consists of Resident Medical Intern or M茅dico Interno Residente (MIR) exams, originally |
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created by [CasiMedicos](https://www.casimedicos.com), a Spanish community of medical professionals who collaboratively, voluntarily, |
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and free of charge, publishes written explanations about the possible answers included in the MIR exams. The aim is to generate a resource that |
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helps future medical doctors to study towards the MIR examinations. The commented MIR exams, including the explanations, are published in the [CasiMedicos |
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Project MIR 2.0 website](https://www.casimedicos.com/mir-2-0/). |
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We have extracted, clean, structure and annotated the available data so that each document in **casimedicos-squad** includes the clinical case, the correct answer, |
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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 |
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corresponds to the explanation of the correct answer (see example below). |
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<table style="width:33%"> |
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<tr> |
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<th>casimedicos-squad splits</th> |
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<tr> |
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<td>train</td> |
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<td>404</td> |
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</tr> |
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<tr> |
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<td>validation</td> |
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<td>56</td> |
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</tr> |
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<tr> |
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<td>test</td> |
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<td>119</td> |
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</tr> |
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</table> |
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## Example |
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<p align="center"> |
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<img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;"> |
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</p> |
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The example above shows a document in CasiMedicos containing the textual content, including Clinical Case (C), Question (Q), Possible Answers (P), |
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and Explanation (E). Furthermore, for **casimedicos-squad** we annotated the span in the explanation (E) that corresponds to the correct answer (A). |
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The process of manually annotating the corpus consisted of specifying where the explanations of the correct answers begin and end. |
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In order to obtain grammatically complete correct answer explanations, annotating full sentences or subordinate clauses was preferred over |
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shorter spans. |
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## Data Explanation |
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The dataset is structured as a list of documents ("paragraphs") where each of them include: |
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- **context**: the explanation (E) in the document |
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- **qas**: list of possible answers and questions. This element contains: |
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- **answers**: an answer which corresponds to the explanation of the correct answer (A) |
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- **question**: the clinical case (C) and question (Q) |
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- **id**: unique identifier for the document |
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## Citation |
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If you use this data please **cite the following paper**: |
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```bibtex |
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@misc{goenaga2023explanatory, |
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title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams}, |
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author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri}, |
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year={2023}, |
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eprint={2312.00567}, |
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archivePrefix={arXiv} |
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} |
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``` |
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**Contact**: [Iakes Goenaga](http://www.hitz.eus/es/node/65) and [Rodrigo Agerri](https://ragerri.github.io/) |
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HiTZ Center - Ixa, University of the Basque Country UPV/EHU |
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### Model Description |
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- 馃摉 **Paper**:[Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://arxiv.org/abs/2312.00567) |
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- 馃捇 **Github Repo** (Data and Code): [https://github.com/ixa-ehu/antidote-casimedicos](https://github.com/ixa-ehu/antidote-casimedicos) |
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- 馃寪 **Project Website**: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) |
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- **Funding**: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR |
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- **Language(s) (NLP):** EN,ES,FR,IT |
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- **License:** Apache License 2 |
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- **Finetuned from model:** microsoft/mdeberta-v3-base |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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[More Information Needed] |
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### Downstream Use [optional] |
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[More Information Needed] |
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### Out-of-Scope Use |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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[More Information Needed] |
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### Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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[More Information Needed] |
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### Training Procedure |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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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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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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## Citation [optional] |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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## Model Card Authors [optional] |
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## Model Card Contact |
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