File size: 1,504 Bytes
3312e73 |
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 |
import re
from pathlib import Path
from datasets import Dataset
import pandas as pd
def generate_chunks(text: str, chunk_size: int = 128) -> list[str]:
sentences = re.split("[?.!]", text)
chunks = []
current_chunk_tokens = []
for sentence in sentences:
tokens = sentence.split()
if (len(current_chunk_tokens) + len(tokens)) <= 128:
current_chunk_tokens.extend(tokens)
else:
chunks.append(" ".join(current_chunk_tokens))
current_chunk_tokens = [*tokens]
return chunks
textfiles = Path("Corpus-v1.1/texts").glob("*.txt")
entries = []
for file in textfiles:
year, author, work, *_ = file.stem.split("_")
with file.open() as in_file:
text = in_file.read()
entries.append(dict(year=year, author=author, work=work, text=text))
data = pd.DataFrame.from_records(entries)
data["full_title"] = data["author"] + " - " + data["work"]
data["text"] = data["text"].map(generate_chunks)
data = data.explode("text")
seed = 42
n_works = 64
n_chunks_per_work = 32
sampled_titles = pd.Series(data["full_title"].unique()).sample(
n_works, random_state=seed
)
sampled_data = data[data["full_title"].isin(sampled_titles)]
sampled_data = sampled_data.groupby(["full_title"]).sample(
n_chunks_per_work, random_state=seed
)
ds = Dataset.from_pandas(
sampled_data[["year", "author", "work", "text", "full_title"]].reset_index()
).shuffle(seed=seed)
ds.push_to_hub("kardosdrur/historical-danish-clustering")
|