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import datasets
from typing import List
_DESCRIPTION = """\
Dataset for the shared baby language modeling task.
The goal is to train a language model from scratch on this data which represents
roughly the amount of text and speech data a young child observes.
"""
_HOMEPAGE = "https://babylm.github.io"
filenames = [
"aochildes.txt",
"bnc_spoken.txt",
"cbt.txt",
"children_stories.txt",
"gutenberg.txt",
"open_subtitles.txt",
"qed.txt",
"simple_wikipedia.txt",
"switchboard.txt",
"wikipedia.txt"
]
class BabyLM(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="strict_small",
description="Small version of the dataset with 10M words",
version="1.0.0",
),
datasets.BuilderConfig(
name="strict",
description="Full version of the dataset with 100M words",
version="1.0.0",
),
datasets.BuilderConfig(
name="strict_small_gold",
description="Small version of the dataset with 10M words and gold POS tags",
version="1.0.0",
),
datasets.BuilderConfig(
name="strict_gold",
description="Full version of the dataset with 100M words and gold POS tags",
version="1.0.0",
)
]
DEFAULT_CONFIG_NAME = "strict_small"
def _info(self):
features = datasets.Features(
{
"text": datasets.Value("string"),
"tagged_text": datasets.Value("string"),
"filename": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=features, # Here we define them above because they are different between the two configurations
homepage=_HOMEPAGE,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""
Returns data for different splits
"""
if self.config.name == "strict_small":
train_data_dir = "10M"
else:
train_data_dir = "100M"
if 'gold' in self.config.name:
folder = 'tagged_gold'
else:
folder = 'tagged'
urls_to_download = {
"train": [f"{folder}/{train_data_dir}/{fn}" for fn in filenames],
"dev": [f"{folder}/dev/{fn}" for fn in filenames],
"test": [f"{folder}/test/{fn}" for fn in filenames]
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split": "train",
"filepaths": downloaded_files["train"]}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"split": "dev",
"filepaths": downloaded_files["dev"]}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"split": "test",
"filepaths": downloaded_files["test"]
}
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, split, filepaths):
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
# the filepaths should be a list of filepaths
if isinstance(filepaths, str):
filepaths = [filepaths]
global_idx = 0
for filepath in filepaths:
with open(filepath, encoding="utf-8") as f:
filename = str(filepath.split("/")[-1])
is_tags = False
text = ""
# Every other row contains POS tags
for row in f:
if is_tags:
yield global_idx, {"text": text, "tagged_text": row, "filename": filename}
global_idx += 1
is_tags = False
else:
text = row
is_tags = True
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