Omarrran/stackpulse_qa_output

🧩 StackPulse-QA: Instruction-Tuning Q&A Pairs from Stack Overflow

Dataset Summary

Instruction-tuning Q&A dataset built from Omarrran/StackPulse_778K_QnA_Code_dataset by joining question IDs with BigQuery bigquery-public-data.stackoverflow.posts_answers on accepted_answer_id.

Each sample consists of:

• input_text_instruct — A question (title + body) prefixed with an instruction
• output_text — The accepted answer from Stack Overflow

Format mirrors the instruction-tuning dataset from DeepLearning.AI's Finetuning Large Language Models course, ready for fine-tuning PaLM, LLaMA, Mistral, Gemma, Phi, and similar models.

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📊 Processing Progress

• Runs completed : 4 / 6
• Questions processed : 400,000 / 554,196
• Remaining : 154,196

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📁 Files in This Dataset

🏋️ Training Files (80% split)

File	Format	Description
data/tune_data_stack_overflow_python_qa_run1-07:19:04:2026.jsonl	JSONL	Training split from 1
data/tune_data_stack_overflow_python_qa_run2-07:19:04:2026.jsonl	JSONL	Training split from 2
data/tune_data_stack_overflow_python_qa_run3-07:19:04:2026.jsonl	JSONL	Training split from 3
data/tune_data_stack_overflow_python_qa_run4-07:19:04:2026.jsonl	JSONL	Training split from 4
data/tune_data_stack_overflow_python_qa_run5-07:19:04:2026.jsonl	JSONL	Training split from 5

🧪 Evaluation Files (20% split)

File	Format	Description
data/tune_eval_data_stack_overflow_python_qa_run1-07:19:04:2026.jsonl	JSONL	Eval split from run 1
data/tune_eval_data_stack_overflow_python_qa_run2-07:19:04:2026.jsonl	JSONL	Eval split from run 2
data/tune_eval_data_stack_overflow_python_qa_run3-07:19:04:2026.jsonl	JSONL	Eval split from run 3
data/tune_eval_data_stack_overflow_python_qa_run4-07:19:04:2026.jsonl	JSONL	Eval split from run 4

📄 Full Metadata CSVs

File	Format	Description
data/stackpulse_qa_full_run1-07:19:04:2026.csv	CSV	Full metadata for run 1
data/stackpulse_qa_full_run2-07:19:04:2026.csv	CSV	Full metadata for run 2
data/stackpulse_qa_full_run3-07:19:04:2026.csv	CSV	Full metadata for run 3
data/stackpulse_qa_full_run4-07:19:04:2026.csv	CSV	Full metadata for run 4

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🏗️ Schema

JSONL Files (training / eval)

Exactly 2 fields per row — ready for instruction fine-tuning:

Field	Type	Description
input_text_instruct	string	Instruction prefix + question title + question body
output_text	string	Accepted answer body (HTML format)

CSV Files (full metadata)

Column	Description
question_id	Stack Overflow question ID
input_text	title + body (no instruction prefix)
output_text	accepted answer body
input_text_instruct	instruction-prefixed input (same as JSONL)
title	question title only
tags	pipe-separated tags
q_score	question upvote score
view_count	total views
answer_count	number of answers
accepted_answer_id	ID of the accepted answer
answer_id	ID of this answer (= accepted_answer_id)
a_score	answer upvote score
is_accepted	always True (we only keep accepted answers)
creation_date	question creation timestamp

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🚀 Quick Start

Load with pandas

import pandas as pd

# Training data
train = pd.read_json("data/tune_data_stack_overflow_python_qa_run1-*.jsonl", lines=True)

# Eval data
eval_  = pd.read_json("data/tune_eval_data_stack_overflow_python_qa_run1-*.jsonl", lines=True)

print(train.iloc[0]["input_text_instruct"][:300])
print(train.iloc[0]["output_text"][:300])

Load with HuggingFace datasets

from datasets import load_dataset

# Load all training shards
ds = load_dataset(
    "json",
    data_files={
        "train": "data/tune_data_stack_overflow_python_qa_run*.jsonl",
        "eval"  : "data/tune_eval_data_stack_overflow_python_qa_run*.jsonl",
    }
)
print(ds)

Use for fine-tuning (Alpaca-style)

def format_prompt(ex):
    return {
        "text": f"{ex['input_text_instruct']}\n\n### Response:\n{ex['output_text']}"
    }

train_formatted = ds["train"].map(format_prompt)

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📋 Instruction Template Used

Please answer the following Stackoverflow question on Programming. Answer it like you are a developer answering Stackoverflow questions. Stackoverflow question: {title}{body}

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⚠️ Caveats

1. HTML in answers: output_text contains raw HTML tags (<p>, <pre>, <code>). Strip or preserve depending on your use case.
2. Accepted answers only: We filter q.accepted_answer_id = a.id — other community answers are skipped.
3. ~60% match rate: Of each 100K question IDs queried, ~60K have accepted answers in BigQuery. The rest are self-answered, deleted, or lack acceptance.
4. 80/20 split: Each run uses random_state=42 for reproducible train/eval splits.
5. Mirrors L2_data.ipynb: Format exactly matches DeepLearning.AI's Finetuning Large Language Models course notebook structure.

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🔁 Source Dataset

Question IDs and metadata sourced from:

• Omarrran/StackPulse_778K_QnA_Code_dataset

Answers joined from:

• bigquery-public-data.stackoverflow.posts_answers (Google BigQuery Public Dataset)

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📋 Citation

@dataset{malik2026stackpulseqa,
  author    = {Malik, Omar Haq Nawaz},
  title     = {StackPulse-QA: Instruction-Tuning Q&A Pairs from Stack Overflow},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/Omarrran/stackpulse_qa_output},
  license   = {Apache-2.0}
}

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👤 Author

Omar Haq Nawaz Malik (HuggingFace: Omarrran) AI Engineer & NLP Researcher | BITS Pilani | Srinagar, Kashmir