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import os
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from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Features, Value
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class FreeformTableQA(GeneratorBasedBuilder):
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"""
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A simple Hugging Face dataset builder for evaluating question-answering (QA)
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over tabular data, using file paths as context (CSV, HTML, TSV).
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The dataset is loaded from a JSON file containing QA samples and context file paths.
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"""
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def _info(self):
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"""
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Returns the metadata and schema of the dataset.
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Returns:
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DatasetInfo: Contains description, features (schema), and supervised keys.
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"""
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return DatasetInfo(
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description="QA over tabular data with file paths as context",
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features=Features({
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"id": Value("string"),
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"utterance": Value("string"),
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"target_value": Value("string"),
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"context": {
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"csv": Value("string"),
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"html": Value("string"),
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"tsv": Value("string"),
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},
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}),
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supervised_keys=None,
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)
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def _split_generators(self, dl_manager):
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"""
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Downloads and defines dataset splits.
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Args:
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dl_manager (DownloadManager): The Hugging Face datasets download manager.
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Returns:
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List[SplitGenerator]: A list containing a single test split generator.
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"""
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downloaded_files = dl_manager.download({
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"test": "examples/examples-test.json",
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"train": "examples/examples-train.json",
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"dev": "examples/examples-dev.json"
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})
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return [
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SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
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SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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]
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def _generate_examples(self, filepath):
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"""
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Yields examples from the dataset JSON file.
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Each example consists of a question, target value, and paths to context files
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(CSV, HTML, TSV). The relative paths are resolved into absolute paths based
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on the JSON file's directory.
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Args:
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filepath (str): Path to the JSON file containing dataset examples.
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Yields:
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Tuple[int, dict]: A tuple of the index and the data sample dictionary.
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"""
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import json
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with open(filepath, encoding="utf-8") as f:
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data = json.load(f)
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for i, item in enumerate(data):
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yield i, {
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"id": item["id"],
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"utterance": item["utterance"],
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"target_value": item["target_value"],
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"context": {
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"csv": item["context"]["csv"],
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"html": item["context"]["html"],
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"tsv": item["context"]["tsv"],
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},
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}
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