📄 Wikitq¶

This WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables… See the full description on the dataset page: https://huggingface.co/datasets/wikitablequestions

Tags: annotations_creators:crowdsourced, arxiv:1508.00305, flags:['table-question-answering'], language:en, language_creators:found, license:cc-by-4.0, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:original, task_categories:question-answering, category:dataset

cards.wikitq

TaskCard(
    loader=LoadCSV(
        files={
            "train": "https://raw.githubusercontent.com/ppasupat/WikiTableQuestions/master/data/random-split-1-train.tsv",
            "validation": "https://raw.githubusercontent.com/ppasupat/WikiTableQuestions/master/data/random-split-1-dev.tsv",
            "test": "https://raw.githubusercontent.com/ppasupat/WikiTableQuestions/master/data/pristine-unseen-tables.tsv",
        },
        sep="   ",
        data_classification_policy=[
            "public",
        ],
    ),
    preprocess_steps=[
        Rename(
            field="utterance",
            to_field="question",
        ),
        Split(
            field="targetValue",
            to_field="answers",
            by="|",
        ),
        Set(
            fields={
                "context_type": "table",
            },
        ),
        Replace(
            field="context",
            old=".csv",
            new=".tsv",
        ),
        FormatText(
            text="https://raw.githubusercontent.com/ppasupat/WikiTableQuestions/refs/heads/master/{context}",
            to_field="table_url",
        ),
        ReadFile(
            field="table_url",
            to_field="table_content",
        ),
        ParseCSV(
            field="table_content",
            to_field="table",
            separator=" ",
            dtype="str",
            strip_cells=True,
        ),
        GetNumOfTableCells(
            field="table",
            to_field="table_cell_size",
        ),
        FilterByCondition(
            values={
                "table_cell_size": 200,
            },
            condition="le",
        ),
        Copy(
            field="table",
            to_field="context",
        ),
    ],
    task="tasks.qa.extractive[metrics=[metrics.f1_strings, metrics.unsorted_list_exact_match]]",
    templates=[
        MultiReferenceTemplate(
            instruction="Answer the question based on the provided table. Extract and output only the final answer—the exact phrase or data from the table that directly answers the question. Do not include any alterations, explanations, or introductory text.
Here are some input-output examples. Read the examples carefully to figure out the mapping. The output of the last example is not given, and your job is to figure out what it is.",
            input_format="
Question: {question}
Table: {context}
Answer: ",
            references_field="answers",
            postprocessors=[
                "processors.take_first_non_empty_line",
                "processors.to_list_by_comma_space",
                "processors.str_to_float_format",
            ],
        ),
    ],
)

[source]

from unitxt.loaders import LoadCSV
from unitxt.operators import Copy, FilterByCondition, ReadFile, Rename, Set
from unitxt.string_operators import FormatText, Replace, Split
from unitxt.struct_data_operators import GetNumOfTableCells, ParseCSV
from unitxt.templates import MultiReferenceTemplate

Explanation about TaskCard¶

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

Args:

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 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 ReadFile¶

Reads file content from local path or URL.

This operator can read files from local filesystem paths or remote URLs. The content is returned as a string.
Args:
encoding (str): Text encoding to use when reading the file. Defaults to ‘utf-8’.

Example:
Reading a local file
ReadFile(field="file_path", to_field="content")
Reading from URL
ReadFile(field="url", to_field="content")

Explanation about LoadCSV¶

Loads data from CSV files.

Supports streaming and can handle large files by loading them in chunks.
Args:
files (Dict[str, str]): A dictionary mapping names to file paths. chunksize : Size of the chunks to load at a time. loader_limit: Optional integer to specify a limit on the number of records to load. streaming: Bool indicating if streaming should be used. sep: String specifying the separator used in the CSV files. indirect_read: Bool indicating if to open a remote file with urllib first column_names: Optional list of column names to use instead of header row.

Example:
Loading csv
load_csv = LoadCSV(files={'train': 'path/to/train.csv'}, chunksize=100)
Loading TSV with custom column names
load_csv = LoadCSV(
    files={'train': 'path/to/train.tsv'},
    sep='\t',
    column_names=['id', 'question', 'table_name', 'answer']
)

Explanation about Rename¶

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:
Rename(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}]

Rename(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}}]

Rename(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}}]

Rename(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}}]

Explanation about ParseCSV¶

Parse CSV/TSV text content into table format.

This operator converts CSV or TSV text content into the standard table format used by Unitxt with header and rows fields.
Args:
separator (str): Field separator character. Defaults to ‘,’. has_header (bool): Whether the first row contains column headers. Defaults to True. skip_header (bool): Whether to skip the first row entirely. Defaults to False.

Example:
Parsing CSV content
ParseCSV(field="csv_content", to_field="table", separator=",")
Parsing TSV content
ParseCSV(field="tsv_content", to_field="table", separator="\t")

Explanation about GetNumOfTableCells¶

Get the number of cells in the given table.

Explanation about FilterByCondition¶

Filters a stream, yielding only instances in which the values in required fields follow the required condition operator.

Raises an error if a required field name is missing from the input instance.

Args:
values (Dict[str, Any]): Field names and respective Values that instances must match according the condition, to be included in the output.

condition: the name of the desired condition operator between the specified (sub) field’s value and the provided constant value. Supported conditions are (“gt”, “ge”, “lt”, “le”, “ne”, “eq”, “in”,”not in”)

error_on_filtered_all (bool, optional): If True, raises an error if all instances are filtered out. Defaults to True.

Examples:

FilterByCondition(values = {"a":4}, condition = "gt") will yield only instances where field "a" contains a value > 4

FilterByCondition(values = {"a":4}, condition = "le") will yield only instances where "a"<=4

FilterByCondition(values = {"a":[4,8]}, condition = "in") will yield only instances where "a" is 4 or 8

FilterByCondition(values = {"a":[4,8]}, condition = "not in") will yield only instances where "a" is different from 4 or 8

FilterByCondition(values = {"a/b":[4,8]}, condition = "not in") will yield only instances where "a" is a dict in which key "b" is mapped to a value that is neither 4 nor 8

FilterByCondition(values = {"a[2]":4}, condition = "le") will yield only instances where “a” is a list whose 3-rd element is <= 4

FilterByCondition(values = {"a":False}, condition = "exists") will yield only instances which do not contain a field named "a"

FilterByCondition(values = {"a/b":True}, condition = "exists") will yield only instances which contain a field named "a" whose value is a dict containing, in turn, a field named "b"

Explanation about Copy¶

Copies values from specified fields to specified fields.

Args (of parent class):
field_to_field (Union[List[List], Dict[str, str]]): A list of lists, where each sublist contains the source field and the destination field, or a dictionary mapping source fields to destination fields.

Examples:
An input instance {“a”: 2, “b”: 3}, when processed by Copy(field_to_field={"a": "b"}) would yield {“a”: 2, “b”: 2}, and when processed by Copy(field_to_field={"a": "c"}) would yield {“a”: 2, “b”: 3, “c”: 2}

with field names containing / , we can also copy inside the field: Copy(field="a/0",to_field="a") would process instance {“a”: [1, 3]} into {“a”: 1}

Explanation about Set¶

Sets specified fields in each instance, in a given stream or all streams (default), with specified values. If fields exist, updates them, if do not exist – adds 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:
# Set a value of a list consisting of “positive” and “negative” do field “classes” to each and every instance of all streams Set(fields={"classes": ["positive","negatives"]})

# In each and every instance of all streams, field “span” is to become a dictionary containing a field “start”, in which the value 0 is to be set Set(fields={"span/start": 0}

# In all instances of stream “train” only, Set field “classes” to have the value of a list consisting of “positive” and “negative” Set(fields={"classes": ["positive","negatives"], apply_to_stream=["train"]})

# Set field “classes” to have the value of a given list, preventing modification of original list from changing the instance. Set(fields={"classes": alist}), use_deepcopy=True) if now alist is modified, still the instances remain intact.

References: processors.take_first_non_empty_line, metrics.unsorted_list_exact_match, processors.to_list_by_comma_space, processors.str_to_float_format, tasks.qa.extractive, metrics.f1_strings