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import datasets
import os
from datasets import load_dataset
from .m2d2_split_names import M2D2_SPLIT_NAMES
CITATION = """
@inproceedings{reid2022m2d2,
title={ {M2D2}: A Massively Multi-Domain Language Modeling Dataset },
author={ Machel Reid and Victor Zhong and Suchin Gururangan and Luke Zettlemoyer },
booktitle={ EMNLP },
year={ 2022 }
}
"""
DESCRIPTION = """
M2D2 dataset from 'M2D2: A Massively Multi-Domain Language Modeling Dataset'
"""
FEATURES = datasets.Features({"text": datasets.Value("string")})
def _URLS(split):
return f"https://huggingface.co/datasets/machelreid/m2d2/resolve/main/data/{split}.tar.gz"
class M2D2Config(datasets.BuilderConfig):
def __init__(self, features, citation, **kwargs):
super().__init__(**kwargs)
self.features = features
self.citation = citation
class M2D2(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
M2D2Config(name=name, features=FEATURES, citation=CITATION)
for name in M2D2_SPLIT_NAMES
]
def _info(self):
return datasets.DatasetInfo(
description=DESCRIPTION, citation=CITATION, features=FEATURES
)
def _split_generators(self, dl_manager):
urls = _URLS(self.config.name)
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "train.txt"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "valid.txt"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "test.txt"),
},
),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = row.strip()
yield key, {"text": data}
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