pubmed_qa

References:

• Code
• Huggingface

pqa_labeled

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:pubmed_qa/pqa_labeled')

• Description:

PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative
statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances.
Each PubMedQA instance is composed of (1) a question which is either an existing research article
title or derived from one, (2) a context which is the corresponding abstract without its conclusion,
(3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question,
and (4) a yes/no/maybe answer which summarizes the conclusion.
PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their
quantitative contents, is required to answer the questions.

• License: MIT License
• Version: 1.0.0
• Splits:

• Features:

{
    "pubid": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "feature": {
            "contexts": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "labels": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "meshes": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "reasoning_required_pred": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "reasoning_free_pred": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "long_answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "final_decision": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

pqa_unlabeled

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:pubmed_qa/pqa_unlabeled')

• Description:

PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative
statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances.
Each PubMedQA instance is composed of (1) a question which is either an existing research article
title or derived from one, (2) a context which is the corresponding abstract without its conclusion,
(3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question,
and (4) a yes/no/maybe answer which summarizes the conclusion.
PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their
quantitative contents, is required to answer the questions.

• License: MIT License
• Version: 1.0.0
• Splits:

• Features:

{
    "pubid": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "feature": {
            "contexts": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "labels": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "meshes": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "long_answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

pqa_artificial

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:pubmed_qa/pqa_artificial')

• Description:

PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative
statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances.
Each PubMedQA instance is composed of (1) a question which is either an existing research article
title or derived from one, (2) a context which is the corresponding abstract without its conclusion,
(3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question,
and (4) a yes/no/maybe answer which summarizes the conclusion.
PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their
quantitative contents, is required to answer the questions.

• License: MIT License
• Version: 1.0.0
• Splits:

• Features:

{
    "pubid": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "feature": {
            "contexts": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "labels": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "meshes": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "long_answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "final_decision": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}