Datasets:
Merge branch 'deduplicate_flag'
Browse files- RedPajama-Data-V2.py +153 -117
- urls/missing-documents.txt +8 -0
- urls/missing-duplicates.txt +324 -0
- urls/missing-signals.txt +74 -0
RedPajama-Data-V2.py
CHANGED
@@ -19,10 +19,11 @@ import json
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import datasets
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import traceback
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import os
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import gzip
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from typing import List
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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@@ -31,7 +32,8 @@ RedPajama V2: an Open Dataset for Training Large Language Models
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_URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0'
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_LANGUAGES = ("en", "de", "fr", "es", "it")
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_CC_SNAPSHOT_IDS = (
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"2014-15",
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@@ -166,35 +168,42 @@ class RedPajamaV2(datasets.GeneratorBasedBuilder):
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)
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def _split_generators_sample(self, dl_manager):
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# fetch
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"sample/sample_listings.txt"
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)
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with open(
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# fetch documents
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documents_files = dl_manager.download({
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"
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]
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})
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# fetch quality signals
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quality_signals_files = dl_manager.download({
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})
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"
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"documents_files": documents_files,
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"quality_signals_files": quality_signals_files
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}
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)
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]
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@@ -204,66 +213,75 @@ class RedPajamaV2(datasets.GeneratorBasedBuilder):
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languages = getattr(self.config, 'languages', _LANGUAGES)
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partition = getattr(self.config, 'partition', 'all')
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# fetch
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for part, part_listings_files in listings_files.items():
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listings_ids[part] = []
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for listings_file in part_listings_files:
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with open(listings_file, encoding="utf-8") as f:
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listings_ids[part].extend([
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line.strip() for line in f
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])
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# build urls pointing to documents and quality signals
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document_urls = {}
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quality_signals_urls = {}
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quality_signals_files = dl_manager.download(quality_signals_urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"
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"documents_files": documents_files,
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"quality_signals_files": quality_signals_files
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}
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)
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]
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@@ -275,71 +293,88 @@ class RedPajamaV2(datasets.GeneratorBasedBuilder):
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return self._split_generators_full(dl_manager)
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def _generate_examples(
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self,
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):
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key = 0
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for
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-
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-
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-
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if
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for sample in self._handle_tail_partition(
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part, part_docs_files, part_listings_ids
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):
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yield key, sample
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key += 1
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continue
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for sample in self.
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):
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yield key, sample
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key += 1
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def
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with gzip.open(doc_file, "rt", encoding="utf-8") as df:
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for row, doc in enumerate(df):
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doc_id = f"{
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try:
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yield self.handle_record(
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except Exception as e:
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print(f'row: {row}')
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traceback.print_exc()
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):
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@staticmethod
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def handle_record(part, doc_id, doc, qs):
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doc = json.loads(doc)
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qs = json.loads(qs) if qs is not None else {}
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@@ -352,11 +387,12 @@ class RedPajamaV2(datasets.GeneratorBasedBuilder):
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"digest": doc["digest"],
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}
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quality_signals =
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return {
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"raw_content": doc["raw_content"],
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"doc_id": doc_id,
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"meta": json.dumps(meta),
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"quality_signals": quality_signals,
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}
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import datasets
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import traceback
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import gzip
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from typing import List
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import pyarrow.parquet as pq
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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_URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0'
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_LANGUAGES = ("en", "de", "fr", "es", "it")
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_MISSING_FILES_PATTERN = "urls/missing-{component}.txt"
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_NUM_SHARDS = 5000
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_CC_SNAPSHOT_IDS = (
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"2014-15",
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)
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def _split_generators_sample(self, dl_manager):
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# fetch list of base tags
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sample_base_tags_fp = dl_manager.download_and_extract(
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"sample/sample_listings.txt"
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)
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with open(sample_base_tags_fp, "r") as fd:
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sample_base_tags = [line.strip() for line in fd]
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# fetch documents
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logger.info(f"Downloading {len(sample_base_tags)} documents files.")
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documents_files = dl_manager.download({
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base_tag: f"sample/documents/{base_tag}.json.gz"
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for base_tag in sample_base_tags
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})
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# fetch quality signals
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logger.info(f"Downloading {len(sample_base_tags)} quality signals files.")
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quality_signals_files = dl_manager.download({
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base_tag: f"sample/quality_signals/{base_tag}.signals.json.gz"
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for base_tag in sample_base_tags
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})
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# fetch ids of duplicates
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logger.info(f"Downloading {len(sample_base_tags)} duplicates ids files.")
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duplicates_ids_files = dl_manager.download({
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base_tag: f"sample/duplicates/{base_tag}.duplicates.parquet"
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for base_tag in sample_base_tags
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})
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"base_tags": sample_base_tags,
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"documents_files": documents_files,
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"quality_signals_files": quality_signals_files,
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"duplicates_ids_files": duplicates_ids_files
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}
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)
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]
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languages = getattr(self.config, 'languages', _LANGUAGES)
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partition = getattr(self.config, 'partition', 'all')
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if partition == 'all':
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partitions = ['head', 'middle', 'tail']
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elif partition == 'head_middle':
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partitions = ['head', 'middle']
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elif partition == 'tail':
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partitions = [partition]
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else:
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raise ValueError(f'invalid partition: {partition}')
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# fetch list of missing files (e.g., missing duplicates or corrupted documents and
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# quality signal files)
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missing_files_paths = dl_manager.download_and_extract({
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component: _MISSING_FILES_PATTERN.format(component=component)
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for component in ("documents", "signals", "duplicates")
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})
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missing_files = {}
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for component, missing_file in missing_files_paths.items():
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with open(missing_file) as f:
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missing_files[component] = set(line.strip() for line in f)
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# build list of urls to fetch
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documents_urls = {}
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quality_signals_urls = {}
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duplicates_ids_urls = {}
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base_tags = []
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for lang in languages:
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for snapshot in snapshots:
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for part in partitions:
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for n in range(_NUM_SHARDS):
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base_tag = f"{snapshot}/{n:04d}/{lang}_{part}"
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base_tags.append(base_tag)
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# documents
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url = f"{_URL_BASE}/documents/{base_tag}.json.gz"
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if url not in missing_files["documents"]:
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documents_urls[base_tag] = url
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# quality signals
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url = f"{_URL_BASE}/quality_signals/{base_tag}.signals.json.gz"
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if url not in missing_files["signals"]:
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quality_signals_urls[base_tag] = url
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# duplicates
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url = f"{_URL_BASE}/duplicates/{base_tag}.duplicates.parquet"
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if url not in missing_files["duplicates"]:
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duplicates_ids_urls[base_tag] = url
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# download documents files
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logger.info(f"Downloading {len(documents_urls)} documents files.")
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documents_files = dl_manager.download(documents_urls)
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# download quality signals files
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logger.info(f"Downloading {len(quality_signals_urls)} quality signals files.")
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quality_signals_files = dl_manager.download(quality_signals_urls)
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# download duplicates ids files
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logger.info(f"Downloading {len(duplicates_ids_urls)} duplicates ids files.")
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duplicates_ids_files = dl_manager.download(duplicates_ids_urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"base_tags": base_tags,
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"documents_files": documents_files,
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"quality_signals_files": quality_signals_files,
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"duplicates_ids_files": duplicates_ids_files
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}
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)
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]
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return self._split_generators_full(dl_manager)
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def _generate_examples(
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self, base_tags, documents_files, quality_signals_files,
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duplicates_ids_files
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):
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key = 0
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for base_tag in base_tags:
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doc_file = documents_files.get(base_tag)
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qs_file = quality_signals_files.get(base_tag)
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dupe_file = duplicates_ids_files.get(base_tag)
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if doc_file is None:
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continue
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for sample in self.__get_generator(
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base_tag, doc_file, qs_file, dupe_file
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):
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yield key, sample
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key += 1
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def __get_generator(self, base_tag, doc_file, qs_file, dupe_file):
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if "_tail" in base_tag:
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yield from self._handle_tail(base_tag, doc_file, qs_file, dupe_file)
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else:
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yield from self._handle_head_middle(base_tag, doc_file, qs_file, dupe_file)
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def _handle_tail(self, base_tag, doc_file, qs_file, dupe_file):
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try:
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with gzip.open(doc_file, "rt", encoding="utf-8") as df:
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for row, doc in enumerate(df):
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doc_id = f"{base_tag}.json.gz/{row}"
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try:
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yield self.handle_record("tail", doc_id, doc, None, None)
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except Exception as e:
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logger.warning(f'failed handling row {row} in {doc_file}')
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traceback.print_exc()
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continue
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except gzip.BadGzipFile as e:
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# skip broken gzip files
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print(f'BadGzipFile: {doc_file, qs_file}')
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traceback.print_exc()
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return
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def _handle_head_middle(
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self, base_tag, doc_file, qs_file, dupe_file
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):
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if qs_file is None:
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yield from self._handle_tail(base_tag, doc_file, None, None)
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return
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# load duplicates
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try:
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with open(dupe_file, "rb") as df:
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duplicates = set(pq.read_table(
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df, columns=["doc_id"], use_pandas_metadata=False
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)["doc_id"].to_pylist())
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except Exception as e:
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logger.warning(f'no duplicate ids found for {base_tag}')
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duplicates = set()
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try:
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with gzip.open(doc_file, "rt", encoding="utf-8") as df:
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with gzip.open(qs_file, "rt", encoding="utf-8") as qf:
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for row, (doc, qs) in enumerate(zip(df, qf)):
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doc_id = f"{base_tag}.json.gz/{row}"
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try:
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yield self.handle_record(
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"head_middle", doc_id, doc, qs, is_duplicate=doc_id in duplicates
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)
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except Exception as e:
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logger.warning(f'failed handling row {row} in {doc_file} ({qs_file})')
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traceback.print_exc()
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continue
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except gzip.BadGzipFile as e:
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# skip broken gzip files
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print(f'BadGzipFile: {doc_file, qs_file}')
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traceback.print_exc()
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return
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@staticmethod
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def handle_record(part, doc_id, doc, qs, is_duplicate=None):
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doc = json.loads(doc)
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qs = json.loads(qs) if qs is not None else {}
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"digest": doc["digest"],
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}
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quality_signals = qs.get("quality_signals", {})
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quality_signals["is_duplicate"] = is_duplicate
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return {
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"raw_content": doc["raw_content"],
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"doc_id": doc_id,
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"meta": json.dumps(meta),
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"quality_signals": json.dumps(quality_signals),
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}
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urls/missing-documents.txt
ADDED
@@ -0,0 +1,8 @@
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https://data.together.xyz/redpajama-data-v2/v1.0.0/documents/2014-52/2811/en_head.json.gz
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https://data.together.xyz/redpajama-data-v2/v1.0.0/documents/2014-52/3215/en_middle.json.gz
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+
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urls/missing-signals.txt
ADDED
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