Upload generate_examples.py with huggingface_hub

generate_examples.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#   "datasets>=3.0,<4",
+#   "transformers>=4.51,<5",
+#   "pyarrow>=17.0,<19",
+# ]
+# ///
+"""
+Load the prefill dataset and generate example prompts for each task type.
+
+Usage:
+    uv run generate_examples.py                          # from local files
+    uv run generate_examples.py --repo di2ox3/prefill-dataset  # from HuggingFace
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import textwrap
+from pathlib import Path
+
+import pyarrow.parquet as pq
+from transformers import AutoTokenizer
+
+
+def load_tables(repo: str | None):
+    if repo:
+        from datasets import load_dataset
+        docs = load_dataset(repo, data_files="data/documents.parquet", split="train")
+        tasks = load_dataset(repo, data_files="data/tasks.parquet", split="train")
+        trans = load_dataset(repo, data_files="data/translations.parquet", split="train")
+        return docs.to_pandas(), tasks.to_pandas(), trans.to_pandas()
+    else:
+        base = Path(__file__).parent / "output"
+        docs = pq.read_table(base / "documents.parquet").to_pandas()
+        tasks = pq.read_table(base / "tasks.parquet").to_pandas()
+        trans = pq.read_table(base / "translations.parquet").to_pandas()
+        return docs, tasks, trans
+
+
+def show_summary(docs, tasks, trans):
+    print("=" * 60)
+    print("DATASET SUMMARY")
+    print("=" * 60)
+    total_tokens = docs["token_count"].sum()
+    print(f"Documents: {len(docs)}")
+    print(f"Total tokens: {total_tokens:,} ({total_tokens / 1e6:.1f}M)")
+    print(f"Tasks: {len(tasks)}")
+    print(f"Translations: {len(trans)}")
+    print()
+    print("Documents by source:")
+    for src, group in docs.groupby("source"):
+        print(f"  {src}: {len(group)} docs, {group['token_count'].sum():,} tokens")
+    print()
+    print("Tasks by type:")
+    for tt, group in tasks.groupby("task_type"):
+        print(f"  {tt}: {len(group)}")
+    print()
+    print("Translations by language:")
+    for lang, group in trans.groupby("target_language"):
+        print(f"  {lang}: {len(group)}")
+
+
+def show_qa_example(docs, tasks):
+    qa_tasks = tasks[tasks.task_type == "qa"]
+    if qa_tasks.empty:
+        print("No QA tasks found.")
+        return
+    task = qa_tasks.iloc[0]
+    doc = docs[docs.doc_id == task.doc_id].iloc[0]
+    answers = json.loads(task.answer)
+
+    print()
+    print("-" * 60)
+    print("EXAMPLE: Question Answering")
+    print("-" * 60)
+    print(f"Document: {doc.title} ({doc.token_count:,} tokens)")
+    print(f"Question: {task.question}")
+    print(f"Answers: {answers}")
+    print(f"Context (first 300 chars): {doc.text[:300]}...")
+
+
+def show_translation_example(docs, tasks, trans):
+    tr_tasks = tasks[tasks.task_type == "translation"]
+    if tr_tasks.empty:
+        print("No translation tasks found.")
+        return
+    task = tr_tasks.iloc[0]
+    doc = docs[docs.doc_id == task.doc_id].iloc[0]
+    meta = json.loads(task.metadata)
+    tr_row = trans[trans.doc_id == task.doc_id]
+
+    print()
+    print("-" * 60)
+    print("EXAMPLE: Translation")
+    print("-" * 60)
+    print(f"Document: {doc.title} ({doc.token_count:,} tokens)")
+    print(f"Target language: {meta.get('target_language', '?')}")
+    print(f"Passage tokens: {meta.get('passage_token_start')}-{meta.get('passage_token_end')}")
+    print(f"Question (truncated):")
+    print(textwrap.shorten(task.question, width=300, placeholder="..."))
+    if not tr_row.empty:
+        tr = tr_row.iloc[0]
+        print(f"Translation text (first 300 chars): {tr.target_text[:300]}...")
+
+
+def show_retrieval_example(docs, tasks):
+    ret_tasks = tasks[tasks.task_type == "retrieval"]
+    if ret_tasks.empty:
+        print("No retrieval tasks found.")
+        return
+    task = ret_tasks.iloc[0]
+    doc = docs[docs.doc_id == task.doc_id].iloc[0]
+    meta = json.loads(task.metadata)
+    answer_text = json.loads(task.answer)
+
+    print()
+    print("-" * 60)
+    print("EXAMPLE: Retrieval")
+    print("-" * 60)
+    print(f"Document: {doc.title} ({doc.token_count:,} tokens)")
+    print(f"Token range: {meta.get('passage_token_start')}-{meta.get('passage_token_end')}")
+    print(f"Question: {textwrap.shorten(task.question, width=200, placeholder='...')}")
+    print(f"Answer (first 200 chars): {answer_text[:200]}...")
+
+
+def verify_offsets(docs):
+    """Spot-check that char_offsets correctly map tokens back to text."""
+    print()
+    print("-" * 60)
+    print("OFFSET VERIFICATION (first 3 docs, first 10 tokens each)")
+    print("-" * 60)
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
+
+    for i in range(min(3, len(docs))):
+        doc = docs.iloc[i]
+        text = doc.text
+        offsets = doc.char_offsets
+        token_ids = doc.token_ids
+        print(f"\n  Doc: {doc.doc_id} ({doc.token_count:,} tokens)")
+        for j in range(min(10, len(offsets))):
+            start = offsets[j]
+            end = offsets[j + 1] if j + 1 < len(offsets) else len(text)
+            span = text[start:end]
+            decoded = tokenizer.decode([token_ids[j]])
+            match = "OK" if span.strip() == decoded.strip() else "~"
+            print(f"    [{j:3d}] offset={start:6d} span={span!r:30s} decoded={decoded!r:30s} {match}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Explore the prefill dataset")
+    parser.add_argument("--repo", type=str, default=None,
+                        help="HuggingFace repo ID (e.g. di2ox3/prefill-dataset)")
+    parser.add_argument("--verify", action="store_true",
+                        help="Run offset verification (loads tokenizer)")
+    args = parser.parse_args()
+
+    docs, tasks, trans = load_tables(args.repo)
+
+    show_summary(docs, tasks, trans)
+    show_qa_example(docs, tasks)
+    show_translation_example(docs, tasks, trans)
+    show_retrieval_example(docs, tasks)
+
+    if args.verify:
+        verify_offsets(docs)
+
+    print()
+    print("=" * 60)
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()