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"""
Parse all paragraphs from all *.fb2 files in the input directory, create a Huggingface Dataset and push it
to the Hub as `vldsavelyev/murakami`.
"""
import os
from pathlib import Path
from lxml import etree
import datasets
datasets.logging.set_verbosity_info()
_DESCRIPTION = """\
Russian translations of Murakami novels, to fine-tune a generative language model. Source is FB2 files
from http://flibusta.is/a/8570.
"""
class Builder(datasets.GeneratorBasedBuilder):
"""Murakami novels, translated to Russian."""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features({"text": datasets.Value("string")}),
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
data_dir = Path(self.base_path) / "data"
fb2_paths = list(data_dir.glob("*.fb2"))
if len(fb2_paths) > 0:
print(f"Found {len(fb2_paths)} fb2 files in {data_dir}")
else:
raise ValueError(f"No fb2 files found in {data_dir}")
smallest_path = min(fb2_paths, key=os.path.getsize)
print(f"Using smallest title as a training example: {smallest_path}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepaths": [p for p in fb2_paths if p != smallest_path],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepaths": [smallest_path],
},
),
]
def _generate_examples(self, filepaths):
for fileidx, filepath in enumerate(filepaths):
print(fileidx, filepath)
title, text = self._extract_text_from_fb2(filepath, fileidx)
yield title, {"text": text}
@staticmethod
def _extract_text_from_fb2(filepath: Path, fileidx: int) -> tuple[str, str]:
"""
Parse FB2 file and return the concatenation of its paragraphs, along with the title.
"""
# Load the FB2 format file
with filepath.open("rb") as file:
fb2_data = file.read()
# Print structure of the FB2 format file
# print(etree.tostring(etree.fromstring(fb2_data), pretty_print=True))
# Parse the FB2 format file using lxml
root = etree.fromstring(fb2_data)
# Get the title of the book
title = root.xpath(
"//fb:title-info/fb:book-title",
namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
)[0].text
print(title)
# Get all book paragraphs
paragraphs = root.xpath(
"//fb:p",
namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
)
# UNCOMMENT THE LINE BELOW TO BUILD `START_PARAGRAPHS`:
# self.helper_to_find_first_paragraphs(paragraphs, title, bi)
found_paragraphs = []
skipping = True
for pi, p in enumerate(paragraphs):
if p.text is None:
continue
if (
fileidx in Builder.START_PARAGRAPHS
and pi >= Builder.START_PARAGRAPHS[fileidx]
):
skipping = False
if skipping and p.text.lower() == title.lower():
skipping = False
if not skipping:
found_paragraphs.append(p)
print(f"Found {len(found_paragraphs)} paragraphs")
text = ""
for p in found_paragraphs:
text += p.text.replace(" ", " ") + "\n"
text += "\n"
return title, text
# Number of initial <p> element to take from each fb2, by number. This allows to skip
# intros and other junk in the beginning of an fb2. This is built semi-manually using
# the `self.helper_to_find_first_paragraphs` function.
START_PARAGRAPHS = {
3: 5,
6: 27,
7: 3,
9: 4,
10: 3,
12: 11,
18: 5,
20: 3,
21: 5,
}
@staticmethod
def helper_to_find_first_paragraphs(paragraphs, title, book_number, n=30):
"""
Helps to eyeball first few paragraphs of a book to skip junk paragraphs
in the beginning and manually construct the `tart_paragraphs` dict.
"""
found_paragraphs = []
skipping = True
for i, p in enumerate(list(paragraphs)[:n]):
if p.text is None:
continue
if (
book_number in Builder.START_PARAGRAPHS
and i >= Builder.START_PARAGRAPHS[book_number]
):
skipping = False
if skipping and p.text.lower() == title.lower():
skipping = False
if not skipping:
found_paragraphs.append(f" {i} {p.text}")
if found_paragraphs:
print("✅")
print("\n".join(found_paragraphs))
else:
print("❌")
for i, p in enumerate(list(paragraphs)[:30]):
print(f" {i} {p.text}")
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