📄 Race High¶

Dataset Card for “race” Dataset Summary RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension. Supported Tasks and Leaderboards More Information Needed Languages More… See the full description on the dataset page: https://huggingface.co/datasets/ehovy/race.

Tags: annotations_creators:expert-generated, arxiv:1704.04683, croissant:True, language:en, language_creators:found, license:other, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:original, task_categories:multiple-choice, task_ids:multiple-choice-qa

Note

ID: cards.race_high | Type: TaskCard

{
    "__description__": "Dataset Card for \"race\"\nDataset Summary\nRACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The\ndataset is collected from English examinations in China, which are designed for middle school and high school students.\nThe dataset can be served as the training and test sets for machine comprehension.\nSupported Tasks and Leaderboards\nMore Information Needed\nLanguages\nMore… See the full description on the dataset page: https://huggingface.co/datasets/ehovy/race.",
    "__tags__": {
        "annotations_creators": "expert-generated",
        "arxiv": "1704.04683",
        "croissant": true,
        "language": "en",
        "language_creators": "found",
        "license": "other",
        "multilinguality": "monolingual",
        "region": "us",
        "size_categories": "10K<n<100K",
        "source_datasets": "original",
        "task_categories": "multiple-choice",
        "task_ids": "multiple-choice-qa"
    },
    "loader": {
        "name": "high",
        "path": "race",
        "type": "load_hf"
    },
    "preprocess_steps": [
        {
            "fields": {
                "numbering": [
                    "A",
                    "B",
                    "C",
                    "D",
                    "E",
                    "F",
                    "G",
                    "H",
                    "I",
                    "J",
                    "K",
                    "L",
                    "M",
                    "N",
                    "O",
                    "P",
                    "Q",
                    "R",
                    "S",
                    "T",
                    "U",
                    "V",
                    "W",
                    "X",
                    "Y",
                    "Z"
                ]
            },
            "type": "add_fields"
        },
        {
            "index_of": "answer",
            "search_in": "numbering",
            "to_field": "answer",
            "type": "index_of"
        },
        {
            "field_to_field": {
                "article": "context",
                "options": "choices"
            },
            "type": "rename_fields"
        },
        {
            "fields": {
                "context_type": "article"
            },
            "type": "add_fields"
        }
    ],
    "task": "tasks.qa.multiple_choice.with_context",
    "templates": "templates.qa.multiple_choice.with_context.all",
    "type": "task_card"
}

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into a model-input, and specifies the metrics for evaluation of model-output.

Attributes:
loader: specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps: list of unitxt operators to process the data source into a model-input.

task: specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates: format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

Explanation about AddFields¶

Adds specified fields to each instance in a given stream or all streams (default) If fields exist, updates them.

Args:

fields (Dict[str, object]): The fields to add to each instance.
Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Add a ‘classes’ field with a value of a list “positive” and “negative” to all streams AddFields(fields={“classes”: [“positive”,”negatives”]})

# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams AddFields(fields={“span/start”: 0}

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream AddFields(fields={“classes”: [“positive”,”negatives”], apply_to_stream=[“train”]})

# Add a ‘classes’ field on a given list, prevent modification of original list # from changing the instance. AddFields(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.

Explanation about IndexOf¶

For a given instance, finds the offset of value of field ‘index_of’, within the value of field ‘search_in’.

Explanation about RenameFields¶

Renames fields.

Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.

Examples:
RenameFields(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]

RenameFields(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]

RenameFields(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]

RenameFields(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]

References: tasks.qa.multiple_choice.with_context, templates.qa.multiple_choice.with_context.all