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README.md
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---
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dataset_info:
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features:
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- name: title
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dtype: string
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- name: text
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dtype: string
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- name: wiki_id
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dtype: int32
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- name: paragraph_id
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dtype: int32
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- name: topic_infer
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dtype: int64
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- name: prob
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dtype: float64
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splits:
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- name: train
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num_bytes: 565706758
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num_examples: 1139464
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download_size: 0
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dataset_size: 565706758
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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task_categories:
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- text-generation
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language:
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- en
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tags:
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- medical
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pretty_name: w
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size_categories:
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- 1M<n<10M
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license: cc
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---
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# MedWiki from ClinicalCorp
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This repo generates on-the-fly
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## Original Dataset
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https://huggingface.co/datasets/Cohere/wikipedia-22-12
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## Topic modelling
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https://huggingface.co/MaartenGr/BERTopic_Wikipedia
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#
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title={IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents},
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author={Jean-Philippe Corbeil},
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journal={arXiv preprint arXiv:2404.15488},
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year={2024}
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}
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---
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dataset_info:
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features:
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- name: title
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dtype: string
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- name: text
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dtype: string
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- name: wiki_id
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dtype: int32
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- name: paragraph_id
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dtype: int32
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- name: topic_infer
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dtype: int64
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- name: prob
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dtype: float64
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splits:
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- name: train
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num_bytes: 565706758
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num_examples: 1139464
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download_size: 0
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dataset_size: 565706758
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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task_categories:
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- text-generation
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language:
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- en
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tags:
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- medical
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pretty_name: w
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size_categories:
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- 1M<n<10M
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license: cc
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---
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# MedWiki from ClinicalCorp
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This repo generates on-the-fly a filtered version of the `Cohere/wikipedia-22-12` on medical topic articles based on `MaartenGr/BERTopic_Wikipedia`. DISCLAIMER: there is no data in this repo only scripts to build it, the `trust_remote_code=True` flag must be set.
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## Original Dataset
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https://huggingface.co/datasets/Cohere/wikipedia-22-12
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## Topic modelling
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https://huggingface.co/MaartenGr/BERTopic_Wikipedia
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Topics from this model were classified as part of the medical field by prompting `GPT3.5-turbo 0613` over word representations of topics. The original topic list can be obtained from the topic model.
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# Citation
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@article{corbeil2024iryonlp,
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title={IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents},
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author={Jean-Philippe Corbeil},
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journal={arXiv preprint arXiv:2404.15488},
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year={2024}
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}
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