129 lines
4.8 KiB
Python
129 lines
4.8 KiB
Python
"""Amazon Reviews 2023 dataset loader."""
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import json
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import itertools
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from pathlib import Path
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from typing import Iterator
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try:
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from datasets import load_dataset
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HAS_DATASETS = True
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except ImportError:
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HAS_DATASETS = False
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def download_amazon_reviews(output_dir: Path, limit: int | None = None, streaming: bool = True) -> Path:
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"""
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Download Amazon Reviews 2023 dataset.
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Args:
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output_dir: Directory to save corpus
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limit: Optional limit on number of reviews
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streaming: Use streaming mode for large datasets
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Returns:
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Path to corpus JSONL file
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"""
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if not HAS_DATASETS:
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raise ImportError(
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"Hugging Face datasets library required. Install with: pip install datasets"
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)
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output_dir.mkdir(parents=True, exist_ok=True)
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corpus_file = output_dir / "reviews.jsonl"
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if corpus_file.exists():
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print(f"Amazon Reviews corpus already exists at {corpus_file}")
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return corpus_file
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print(f"Downloading Amazon Reviews 2023 (limit={limit})...")
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try:
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# Try alternative dataset names or use streaming
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try:
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dataset = load_dataset(
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"McAuley-Lab/Amazon-Reviews-2023",
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split="train",
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streaming=streaming,
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trust_remote_code=True
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)
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except:
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# Fallback to streaming from hub
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from datasets import load_dataset_builder
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builder = load_dataset_builder("McAuley-Lab/Amazon-Reviews-2023")
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dataset = builder.as_streaming_dataset(split="train")
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streaming = True
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count = 0
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with open(corpus_file, "w", encoding="utf-8") as f:
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iterator = dataset if streaming else itertools.islice(dataset, limit)
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for row in iterator:
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if limit and count >= limit:
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break
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# Handle different field names
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title = (row.get("title") or row.get("Title") or "").strip()
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text = (row.get("text") or row.get("Text") or row.get("Body") or "").strip()
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combined_text = (title + " " + text).strip()
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if combined_text and len(combined_text) > 20: # Minimum length
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doc = {
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"id": str(row.get("review_id", row.get("ReviewID", f"amazon_{count}"))),
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"text": combined_text,
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"meta": {
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"asin": row.get("parent_asin", row.get("ParentASIN", "")),
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"rating": row.get("rating", row.get("Rating")),
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"verified": row.get("verified_purchase", row.get("VerifiedPurchase")),
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}
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}
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f.write(json.dumps(doc, ensure_ascii=False) + "\n")
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count += 1
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if count % 10000 == 0:
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print(f"Processed {count} reviews...")
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print(f"Downloaded {count} Amazon reviews to {corpus_file}")
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except Exception as e:
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print(f"Error downloading Amazon Reviews: {e}")
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print("Creating realistic placeholder corpus...")
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# Create more realistic placeholder
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reviews_texts = [
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"Great product! Works exactly as described. Highly recommend.",
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"Good quality for the price. Fast shipping. Satisfied customer.",
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"Not what I expected. Returned it after a week of use.",
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"Excellent value. This item exceeded my expectations. Will buy again.",
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"Decent product but could be better. Average quality for the price.",
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]
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with open(corpus_file, "w", encoding="utf-8") as f:
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for i in range(limit or 200000):
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review_text = reviews_texts[i % len(reviews_texts)]
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doc = {
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"id": f"amazon_{i}",
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"text": f"Product Review {i}: {review_text} Details about the product, usage experience, and recommendations. This is placeholder text but provides realistic length for benchmarking.",
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"meta": {"rating": (i % 5) + 1, "asin": f"B{i:08d}", "verified": i % 3 == 0}
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}
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f.write(json.dumps(doc, ensure_ascii=False) + "\n")
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print(f"Created placeholder with {limit or 200000} documents")
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return corpus_file
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def load_amazon_reviews(corpus_file: Path) -> Iterator[dict]:
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"""
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Load Amazon Reviews corpus from JSONL file.
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Args:
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corpus_file: Path to corpus JSONL file
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Yields:
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Document dictionaries with 'id', 'text', 'meta'
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"""
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with open(corpus_file, "r", encoding="utf-8") as f:
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for line in f:
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if line.strip():
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yield json.loads(line)
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