README.md

metadata

language:
  - en
  - fr
license: apache-2.0
task_categories:
  - question-answering
  - translation
  - text-retrieval
tags:
  - long-context
  - prefill
  - tokenized
  - qwen3
  - kv-cache
  - benchmark
pretty_name: Prefill Dataset - Long-Context Tokenized Corpus for Qwen3-8B
size_categories:
  - 1K<n<10K

Prefill Dataset

Long-context tokenized corpus for benchmarking LLM prefill computation with Qwen3-8B. Contains ~10M tokens of copyright-free English text pre-tokenized with character offset mappings for fast position lookup.

Dataset Structure

Files

File	Description	Rows
data/documents.parquet	English documents with token IDs and char offsets	~100-500
data/tasks.parquet	QA, translation, and retrieval tasks	~1K-5K
data/translations.parquet	French translations of OPUS-Books English documents	~100-500
data/aligned_chunks.parquet	EN/FR aligned chunk pairs packed to ~1k source tokens	~1K-5K

documents.parquet Schema

Column	Type	Description
doc_id	string	Unique document ID
source	string	"narrativeqa" / "opus_books" / "pg19"
title	string	Book title
language	string	Always "en"
text	large_string	Full document text
token_ids	list<int32>	Qwen3-8B token IDs
char_offsets	list<int32>	Character start position per token
token_count	int32	Number of tokens

tasks.parquet Schema

Column	Type	Description
task_id	string	Unique task ID
doc_id	string	References documents.doc_id
task_type	string	"qa" / "translation" / "retrieval"
question	string	Task prompt
answer	string	Expected answer (JSON list for multi-answer)
metadata	string	JSON with extra fields

translations.parquet Schema

Column	Type	Description
doc_id	string	References English document
target_language	string	Always "fr"
target_text	large_string	Full translation text
target_token_ids	list<int32>	Tokenized translation
target_char_offsets	list<int32>	Char offsets for translation tokens

aligned_chunks.parquet Schema

Column	Type	Description
chunk_id	string	Unique chunk ID (doc_id + chunk index)
doc_id	string	References OPUS English document
chunk_idx	int32	Chunk index within document
segment_start_idx	int32	Start aligned segment index (inclusive)
segment_end_idx	int32	End aligned segment index (exclusive)
src_lang	string	Always "en"
tgt_lang	string	Always "fr"
src_text	large_string	English chunk text
tgt_text	large_string	French chunk text
src_char_start / src_char_end	int32	Character span in source document
tgt_char_start / tgt_char_end	int32	Character span in translation document
src_tok_start / src_tok_end	int32	Token span in source token IDs
tgt_tok_start / tgt_tok_end	int32	Token span in target token IDs
src_token_count	int32	Source tokens in chunk (target ~1000)
tgt_token_count	int32	Target tokens in chunk

Sources

Source	Purpose	Target Tokens
NarrativeQA	Gutenberg books with human Q&A pairs	~5M
OPUS-Books	Parallel EN-FR book translations	~3M
pg19	Supplementary long Gutenberg books	~2M+

Tokenizer

• Model: Qwen/Qwen3-8B (vocab size: 151,936)
• Offset mapping: char_offsets[i] is the character position where token i starts. BPE tokens with leading spaces point to the space character — this is correct: text[char_offsets[i]:char_offsets[i+1]] recovers exact token text.

Usage

import pyarrow.parquet as pq

# Load
docs = pq.read_table("data/documents.parquet").to_pandas()
tasks = pq.read_table("data/tasks.parquet").to_pandas()

# Get a document and its tasks
doc = docs.iloc[0]
doc_tasks = tasks[tasks.doc_id == doc.doc_id]

print(f"Title: {doc.title}")
print(f"Tokens: {doc.token_count:,}")
print(f"Tasks: {len(doc_tasks)}")

# Verify token-to-text mapping
offsets = doc.char_offsets
text = doc.text
for i in range(5):
    end = offsets[i + 1] if i + 1 < len(offsets) else len(text)
    print(f"  Token {i}: '{text[offsets[i]:end]}'")

See generate_examples.py for a full usage example.

Regeneration

uv run build_dataset.py

Requires Python 3.11+. Dependencies are declared inline (PEP 723) — uv run handles them automatically.

License

The dataset is released under Apache 2.0. Source texts are public domain (Project Gutenberg).