File size: 4,764 Bytes
4649fcf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | import json
import datasets
import traceback
import os
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
"""
# Define a list of programming languages for the subsets
_LANGUAGE_SUBSETS = ["csharp", "cplusplus", "c", "go", "html", "haskell",
"java", "javascript", "jupyter", "kotlin", "php", "perl",
"python", "ruby", "rust", "shell", "swift", "typescript", "v"]
# Generate URLs for the subsets
_URL_LISTS = {language: f"https://huggingface.co/datasets/AtlasUnified/atlas-preprocessed-code/blob/main/{language}.jsonl"
for language in _LANGUAGE_SUBSETS}
_URL_BASE = 'https://data.together.xyz/redpajama-data-1T/v1.0.0'
_DATA_DIR = os.environ.get('RED_PAJAMA_DATA_DIR', None)
class RedPajama1TConfig(datasets.BuilderConfig):
"""BuilderConfig for RedPajama sample."""
def __init__(self, *args, subsets, **kwargs):
"""BuilderConfig for RedPajama.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(RedPajama1TConfig, self).__init__(**kwargs)
self.subsets = subsets
class RedPajama1T(datasets.GeneratorBasedBuilder):
"""RedPajama: Reproducing the LLaMA training dataset of over 1.2 trillion tokens. Version 1.0.0."""
# Generate builder configs for each language subset
BUILDER_CONFIGS = [RedPajama1TConfig(name=language,
subsets=[language],
version=datasets.Version("1.0.0", ""),
description=f"RedPajama1T {language} subset")
for language in _LANGUAGE_SUBSETS]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"meta": datasets.Value("string"),
"red_pajama_subset": datasets.Value("string"),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
url_lists = dl_manager.download_and_extract({
subset: _URL_LISTS[subset] for subset in self.config.subsets
})
urls = {}
for subset, url_list in url_lists.items():
with open(url_list, encoding="utf-8") as f:
urls[subset] = [line.strip() for line in f]
if _DATA_DIR is not None:
print(f'Reading data from {_DATA_DIR}')
url_prefix_slashes = len(_URL_BASE.split('/'))
downloaded_files = {
subset: [
os.path.join(_DATA_DIR, *url.split('/')[url_prefix_slashes:])
for url in url_list
]
for subset, url_list in urls.items()
}
else:
downloaded_files = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs = {
"files": {
subset: downloaded_files[subset]
for subset in self.config.subsets
}
}
)
]
def _generate_examples(self, files):
"""This function returns the examples in the raw (text) form."""
key = 0
for subset in files:
for path in files[subset]:
with open(path, encoding="utf-8") as f:
for i, row in enumerate(f):
try:
data = json.loads(row)
if "meta" not in data:
text = data["text"]
del data["text"]
yield key, {
"text": text,
"meta": json.dumps(data),
"red_pajama_subset": subset,
}
else:
yield key, {
"text": data["text"],
"meta": data["meta"],
"red_pajama_subset": subset,
}
key += 1
except Exception as e:
print(f'Subset: {subset}')
print(f'Path: {path}')
print(f'Row: {row}')
traceback.print_exc()
raise e
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