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