# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this file is here for backward compatibility (e.g. for lm-evaluation-harness), when this dataset used to be named "hendrycks_test" import json import datasets _CITATION = """ """ _DESCRIPTION = """ """ _HOMEPAGE = """ """ _URL = """ """ _LICENSE = """ """ _DATASETS = [ 'ConvFinQA', 'FinanceBench', 'FinDER', 'FinQA', 'FinQABench', 'MultiHeirtt', 'TATQA', ] _SPLITS = ["corpus", "queries"] _URLs = {dataset: f"{dataset}/" for dataset in _DATASETS for subset in _SPLITS} class FinanceRAG(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( name=f"{dataset}", version=datasets.Version("1.0.0"), description=f"{dataset} dataset." ) for dataset in _DATASETS ] def _info(self): features = datasets.Features( { "_id": datasets.Value("string"), "title": datasets.Value("string"), "text": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE, supervised_keys=None, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir + f"{split}.jsonl.gz"} ) for split in _SPLITS ] # Copied from BeIR. def _generate_examples(self, filepath): with open(filepath, encoding="utf-8") as f: texts = f.readlines() for i, text in enumerate(texts): text = json.loads(text) if 'metadata' in text: del text['metadata'] if "title" not in text: text["title"] = "" yield i, text