Datasets:

housing_qa / housing_qa.py
nguha's picture
Changed data
d59c804
import csv
import datasets
from datasets import Dataset, DatasetInfo, Features, ClassLabel, Value, Sequence, DatasetDict
import json
from io import StringIO
from pathlib import Path
import pandas as pd
_CITATION = """"""
_DESCRIPTION = """"""
_HOMEPAGE = ""
_URLS = {
"questions": "data/questions.json.zip",
"questions_aux": "data/questions_aux.json.zip",
"statutes": "data/statutes.tsv.zip",
}
_CONFIGS = {}
_CONFIGS["questions"] = {
"description": "Questions about housing law.",
"features" : Features({
'idx': Value('int32'),
'state': Value('string'),
'question': Value('string'),
'answer': Value('string'),
'question_group': Value('int32'),
'statutes': [{
'statute_idx': Value('int32'),
'citation': Value('string'),
'excerpt': Value('string'),
}],
'original_question': Value('string'),
'caveats': Sequence(Value('string')),
}),
"license": None,
}
_CONFIGS["questions_aux"] = {
"description": "An auxilliary set of larger questions about housing law, without statutory annotations.",
"features" : Features({
'idx': Value('int32'),
'state': Value('string'),
'question': Value('string'),
'answer': Value('string'),
'question_group': Value('int32'),
'statutes': Sequence({
'citation': Value('string'),
'excerpt': Value('string'),
}),
'original_question': Value('string'),
'caveats': Sequence(Value('string')),
}),
"license": None,
}
_CONFIGS["statutes"] = {
"description": "Corpus of statutes",
"features": Features({
"citation": datasets.Value("string"),
"path": datasets.Value("string"),
"state": datasets.Value("string"),
"text": datasets.Value("string"),
"idx": datasets.Value("int32"),
}),
"license": None,
}
class HousingQA(datasets.GeneratorBasedBuilder):
"""TODO"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=task, version=datasets.Version("1.0.0"), description=task,
)
for task in _CONFIGS
]
def _info(self):
features = _CONFIGS[self.config.name]["features"]
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_CONFIGS[self.config.name]["license"],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file_dir = Path(dl_manager.download_and_extract(_URLS[self.config.name]))
return [
datasets.SplitGenerator(
name="corpus" if self.config.name == "statutes" else "test",
gen_kwargs={
"downloaded_file_dir": downloaded_file_dir,
"name": self.config.name,
},
),
]
def _generate_examples(self, downloaded_file_dir, name):
"""Yields examples as (key, example) tuples."""
if name in ["questions", "questions_aux"]:
fpath = downloaded_file_dir / f"{name}.json"
data = json.loads(fpath.read_text())
for id_line, data in enumerate(data):
yield id_line, data
if name in ["statutes"]:
fpath = downloaded_file_dir / f"{name}.tsv"
data = pd.read_csv(fpath, sep="\t", dtype={'index': 'int32'})
data = data.to_dict(orient="records")
for id_line, data in enumerate(data):
yield id_line, data