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README.md
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---
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license: cc-by-nc-sa-4.0
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---
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---
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license: cc-by-nc-sa-4.0
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task_categories:
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- text-classification
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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: medical-bios
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size_categories:
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- 1K<n<10K
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---
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# Dataset Description
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The dataset comprises English biographies labeled with occupations and binary genders.
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This is an occupation classification task, where bias with respect to gender can be studied.
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It includes a subset of 10,000 biographies (8k train/1k dev/1k test) targeting 5 medical occupations (psychologist, surgeon, nurse, dentist, physician).
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We collect and release human rationale annotations for a subset of 100 biographies in two different settings: non-contrastive and contrastive.
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In the former, the annotators were asked to find the rationale for the question: "Why is the person in the following short bio described as a L?", where L is the gold label occupation, e.g., nurse.
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In the latter, the question was "Why is the person in the following short bio described as a L rather than a F", where F (foil) is another medical occupation, e.g., physician.
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# Dataset Structure
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We provide the `standard` version of the dataset, where examples look as follows.
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```json
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{
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"text": "He has been a practicing Dentist for 20 years. He has done BDS . He is currently associated with Sree Sai Dental Clinic in Sowkhya Ayurveda Speciality Clinic, Chennai. ... ",
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"label": 3,
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}
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```
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and the newly curated subset of examples including human rationales, dubbed `rationales', where examples look as follows.
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```json
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{
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"text": "'She is currently practising at Dr Ravindra Ratolikar Dental Clinic in Narayanguda, Hyderabad.",
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"label": 3,
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"foil": 2,
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"words": ['She', 'is', 'currently', 'practising', 'at', 'Dr', 'Ravindra', 'Ratolikar', 'Dental', 'Clinic', 'in', 'Narayanguda', ',', 'Hyderabad', '.']
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"rationale": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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"contrastive_rationale": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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}
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```
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# Use
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To load the `standard` version of the dataset:
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```python
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from datasets import load_dataset
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dataset = load_dataset("coastalcph/medical-bios", "standard")
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```
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To load the newly curated subset of examples with human rationales:
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```python
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from datasets import load_dataset
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dataset = load_dataset("coastalcph/medical-bios", "rationales")
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```
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# Citation
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[*Oliver Eberle\*, Ilias Chalkidis\*, Laura Cabello, Stephanie Brandl. Rather a Nurse than a Physician - Contrastive Explanations under Investigation. 2023. In the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore.*](xxx)
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```
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@inproceedings{eberle-etal-2023-contrast-bios,
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author={Oliver Eberle, Ilias Chalkidis, Laura Cabello, Stephanie Brandl},
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title={Rather a Nurse than a Physician - Contrastive Explanations under Investigation},
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booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
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year={2023},
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address={Singapore, Singapore}
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}
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```
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