| 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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|
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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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|
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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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|
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| Args:
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| dl_manager (DownloadManager): The Hugging Face datasets download manager.
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|
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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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|
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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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|
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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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|
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| Args:
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| filepath (str): Path to the JSON file containing dataset examples.
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|
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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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|
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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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|
|
