File size: 3,026 Bytes
3f6d2c1 c225120 3f6d2c1 206f36e 3f6d2c1 206f36e 3f6d2c1 c225120 3f6d2c1 c225120 3f6d2c1 c225120 3f6d2c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
import os
from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Features, Value
class FreeformTableQA(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": {
"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",
"train": "examples/examples-train.json",
"dev": "examples/examples-dev.json"
})
return [
SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
]
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"],
},
}
|