# Copyright 2023 Together Computer # # 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. # Lint as: python3 """RedPajama V2: Quality annotated Web Text Documents.""" import json import datasets import traceback import os import gzip logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ RedPajama V2 is a Data Foundation of Web Text Documents with Quality Annotations. """ _URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0' _LANGUAGES = ("en", "de", "fr", "es", "it") _SAMPLE_SNAPSHOT_ID = "2023-06" _LISTINGS_PATTERN = "listings/{language}-{snapshot}-{partition}.txt" _CC_SNAPSHOT_IDS = ( "2014-15", "2014-23", "2014-35", "2014-41", "2014-42", "2014-49", "2014-52", "2015-14", "2015-22", "2015-27", "2015-32", "2015-35", "2015-40", "2015-48", "2016-07", "2016-18", "2016-22", "2016-26", "2016-30", "2016-36", "2016-40", "2016-44", "2016-50", "2017-04", "2017-09", "2017-17", "2017-22", "2017-26", "2017-30", "2017-34", "2017-39", "2017-43", "2017-47", "2017-51", "2018-05", "2018-09", "2018-13", "2018-17", "2018-22", "2018-26", "2018-30", "2018-34", "2018-39", "2018-43", "2018-47", "2018-51", "2019-04", "2019-09", "2019-13", "2019-18", "2019-22", "2019-26", "2019-30", "2019-35", "2019-39", "2019-43", "2019-47", "2019-51", "2020-05", "2020-10", "2020-16", "2020-24", "2020-29", "2020-34", "2020-40", "2020-45", "2020-50", "2021-04", "2021-10", "2021-17", "2021-21", "2021-25", "2021-31", "2021-39", "2021-43", "2021-49", "2022-05", "2022-21", "2022-27", "2022-33", "2022-40", "2022-49", "2023-06", "2023-14" ) class RedPajamaDataV2Config(datasets.BuilderConfig): """BuilderConfig for RedPajama.""" def __init__(self, *args, language, partition, snapshots, **kwargs): """BuilderConfig for RedPajama. Args: **kwargs: keyword arguments forwarded to super. """ super(RedPajamaDataV2Config, self).__init__(**kwargs) self.partition = partition self.snapshots = snapshots self.language = language _BUILDER_CONFIGS = [ RedPajamaDataV2Config( name=f'_sample', partition='sample', snapshots=None, language=None, version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 Sample", ), # this one is just an alias for the sample RedPajamaDataV2Config( name=f'sample', partition='sample', snapshots=None, language=None, version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 Sample", ) ] for lang in _LANGUAGES: _BUILDER_CONFIGS.extend( [ # single snapshot RedPajamaDataV2Config( name=f'{lang}-head-middle-{snapshot}', partition='head_middle', snapshots=[snapshot], language=lang, version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 head-middle {lang}-{snapshot}", ) for snapshot in _CC_SNAPSHOT_IDS ] + [ # all snapshots RedPajamaDataV2Config( name=f'{lang}-head-middle-all', partition='head_middle', snapshots=_CC_SNAPSHOT_IDS, language=lang, version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 head-middle {lang}" ) ] ) _BUILDER_CONFIGS.extend( [ # single snapshot RedPajamaDataV2Config( name=f'{lang}-tail-{snapshot}', partition='tail', snapshots=[snapshot], language=lang, version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 tail {lang}-{snapshot}", ) for snapshot in _CC_SNAPSHOT_IDS ] + [ # all snapshots RedPajamaDataV2Config( name=f'{lang}-tail-all', partition='tail', snapshots=_CC_SNAPSHOT_IDS, language=lang, version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 tail {lang}" ) ] ) class RedPajamaV2(datasets.GeneratorBasedBuilder): """ RedPajama V2: Quality annotated Web Text Documents. """ BUILDER_CONFIGS = _BUILDER_CONFIGS def _info(self): if self.config.partition == "tail": return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "raw_content": datasets.Value("string"), "doc_id": datasets.Value("string"), "meta": datasets.Value("string"), } ), supervised_keys=None, ) else: return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "raw_content": datasets.Value("string"), "doc_id": datasets.Value("string"), "meta": datasets.Value("string"), "quality_signals": datasets.Value("string") } ), supervised_keys=None, ) def _split_generators_sample(self, dl_manager): # fetch documents sample_listings = dl_manager.download_and_extract( "sample/sample_listings.txt" ) with open(sample_listings, "r") as fd: listings = [line.strip() for line in fd] # fetch documents docs_files = dl_manager.download({ _SAMPLE_SNAPSHOT_ID: [ f"sample/documents/{lst}.json.gz" for lst in listings ] }) # fetch quality signals signals_files = dl_manager.download({ _SAMPLE_SNAPSHOT_ID: [ f"sample/quality_signals/{lst}.signals.json.gz" for lst in listings ] }) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "documents_files": { _SAMPLE_SNAPSHOT_ID: docs_files[_SAMPLE_SNAPSHOT_ID] }, "quality_signals_files": { _SAMPLE_SNAPSHOT_ID: signals_files[_SAMPLE_SNAPSHOT_ID] } } ) ] def _split_generators_full(self, dl_manager): url_lists = dl_manager.download_and_extract({ snapshot_id: _LISTINGS_PATTERN.format( language=self.config.language, snapshot=snapshot_id, partition=self.config.partition, ) for snapshot_id in self.config.snapshots }) listings_ids = {} for snapshot_id, listings_file in url_lists.items(): with open(listings_file, encoding="utf-8") as f: listings_ids[snapshot_id] = [line.strip() for line in f] # build urls pointing to documents document_urls = { snapshot_id: [ os.path.join(_URL_BASE, f"documents/{lst_id}.json.gz") for lst_id in listings_ids[snapshot_id] ] for snapshot_id in self.config.snapshots } documents_files = dl_manager.download(document_urls) # build urls pointing to quality signals if self.config.partition == "head_middle": quality_signals_urls = { snapshot_id: [ os.path.join( _URL_BASE, f"quality_signals/{lst_id}.signals.json.gz" ) for lst_id in listings_ids[snapshot_id] ] for snapshot_id in self.config.snapshots } quality_signals_files = dl_manager.download( quality_signals_urls ) else: quality_signals_files = {} return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "documents_files": { snapshot_id: documents_files[snapshot_id] for snapshot_id in self.config.snapshots }, "quality_signals_files": { snapshot_id: quality_signals_files.get(snapshot_id) for snapshot_id in self.config.snapshots } } ) ] def _split_generators(self, dl_manager): if self.config.name.endswith("sample"): return self._split_generators_sample(dl_manager) return self._split_generators_full(dl_manager) def _generate_examples(self, documents_files, quality_signals_files): """ This function returns examples """ snapshots = list(documents_files.keys()) key = 0 for snapshot in snapshots: docs_files = documents_files[snapshot] if self.config.partition in ("head_middle", "sample"): qs_files = quality_signals_files[snapshot] else: qs_files = None assert len(docs_files) == len(qs_files) for doc_file, qs_file in zip(docs_files, qs_files): with gzip.open(doc_file, "rt", encoding="utf-8") as df: with gzip.open(qs_file, "rt", encoding="utf-8") as qf: for row, (doc, qs) in enumerate(zip(df, qf)): try: doc = json.loads(doc) qs = json.loads(qs) doc_id = qs["id"] meta = { "url": doc["url"], "language": doc["language"], "source_domain": doc["source_domain"], "date_download": doc["date_download"], "digest": doc["digest"], } if self.config.partition == "tail": yield key, { "raw_content": doc["raw_content"], "doc_id": doc_id, "meta": json.dumps(meta), } else: yield key, { "raw_content": doc["raw_content"], "doc_id": doc_id, "meta": json.dumps(meta), "quality_signals": json.dumps( qs["quality_signals"] ), } key += 1 except Exception as e: print(f'doc_file: {doc_file}') print(f'qs_file: {qs_file}') print(f'row: {row}') traceback.print_exc() raise e