Create produce_dataset.py

produce_dataset.py

@@ -0,0 +1,49 @@
+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")