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Error code: FeaturesError Exception: ValueError Message: Not able to read records in the JSON file at hf://datasets/HiTZ/casimedicos-squad@0fb78ce93a5dfdae7309fe64a9fd2bee9688104e/data/es/es_train_casimedicos_squad.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['version', 'data']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__ yield from islice(self.ex_iterable, self.n) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__ for key, pa_table in self.generate_tables_fn(**self.kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables raise ValueError( ValueError: Not able to read records in the JSON file at hf://datasets/HiTZ/casimedicos-squad@0fb78ce93a5dfdae7309fe64a9fd2bee9688104e/data/es/es_train_casimedicos_squad.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['version', 'data']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
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Antidote CasiMedicos in SQuAD Format for Explanatory Argument Extraction
We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments for the correct answer but also arguments to explain why the remaining possible answers are incorrect.
Furthermore, this dataset allows us to setup a novel extractive task which consists of identifying the explanation of the correct answer written by medical doctors. In order to do so we leverage the SQuAD extractive QA paradigm to automatically evaluate performance of language models to identify the explanation of the correct answer in medical exams without relying on costly manual evaluation by medical experts.
The data source consists of Resident Medical Intern or Médico Interno Residente (MIR) exams, originally created by CasiMedicos, 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.
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).
casimedicos-squad splits | |
---|---|
train | 404 |
validation | 56 |
test | 119 |
- 📖 Paper:Explanatory Argument Extraction of Correct Answers in Resident Medical Exams
- 💻 Github Repo (Data and Code): https://github.com/ixa-ehu/antidote-casimedicos
- 🌐 Project Website: 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
Example
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:
@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 and Rodrigo Agerri HiTZ Center - Ixa, University of the Basque Country UPV/EHU
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