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import csv |
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import datasets |
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import json |
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from io import StringIO |
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from pathlib import Path |
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import pandas as pd |
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_CITATION = """""" |
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_DESCRIPTION = """""" |
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_HOMEPAGE = "" |
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_URLS = { |
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"knowledge_questions": "data/knowledge_questions.json.zip", |
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"rc_questions": "data/rc_questions.json.zip", |
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"statutes": "data/statutes.tsv.zip", |
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} |
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_CONFIGS = {} |
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_CONFIGS["knowledge_questions"] = { |
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"description": "Answer knowledge questions about housing law in the different states.", |
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"features": { |
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"idx": datasets.Value("int32"), |
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"state": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"question_group": datasets.Value("int32"), |
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"statutes": datasets.Sequence( |
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{ |
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"citation": datasets.Value("string"), |
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"excerpt": datasets.Value("string"), |
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} |
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), |
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"original_question": datasets.Value("string"), |
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"caveats": datasets.Sequence([datasets.Value("string")]), |
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}, |
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"license": None, |
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} |
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_CONFIGS["rc_questions"] = { |
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"description": "Answer RC questions about housing law in the different states.", |
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"features": { |
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"idx": datasets.Value("int32"), |
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"state": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"question_group": datasets.Value("int32"), |
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"statutes": datasets.Sequence( |
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{ |
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"statute_idx": datasets.Value("int32"), |
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"citation": datasets.Value("string"), |
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"excerpt": datasets.Value("string"), |
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} |
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), |
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"original_question": datasets.Value("string"), |
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"caveats": datasets.Sequence([datasets.Value("string")]), |
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}, |
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"license": None, |
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} |
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_CONFIGS["statutes"] = { |
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"description": "Corpus of statutes", |
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"features": { |
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"citation": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"state": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"idx": datasets.Value("int32"), |
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}, |
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"license": None, |
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} |
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class HousingQA(datasets.GeneratorBasedBuilder): |
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"""TODO""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=task, version=datasets.Version("1.0.0"), description=task, |
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) |
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for task in _CONFIGS |
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] |
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def _info(self): |
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features = _CONFIGS[self.config.name]["features"] |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(features), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_CONFIGS[self.config.name]["license"], |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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downloaded_file_dir = Path(dl_manager.download_and_extract(_URLS[self.config.name])) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"downloaded_file_dir": downloaded_file_dir, |
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"name": self.config.name, |
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}, |
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), |
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] |
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def _generate_examples(self, downloaded_file_dir, name): |
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"""Yields examples as (key, example) tuples.""" |
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if name in ["knowledge_questions"]: |
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fpath = downloaded_file_dir / f"{name}.json" |
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data = json.loads(fpath.read_text()) |
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for id_line, data in enumerate(data): |
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yield id_line, data |
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if name in ["rc_questions"]: |
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fpath = downloaded_file_dir / f"{name}.json" |
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data = json.loads(fpath.read_text()) |
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for id_line, data in enumerate(data): |
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yield id_line, data |
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if name in ["statutes"]: |
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fpath = downloaded_file_dir / f"{name}.tsv" |
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data = pd.read_csv(fpath, sep="\t", dtype={'index': 'int32'}) |
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data = data.to_dict(orient="records") |
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for id_line, data in enumerate(data): |
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yield id_line, data |
