---
license: apache-2.0
language:
  - en
pretty_name: "StackPulse-QA: Instruction-Tuning Q&A Pairs from Stack Overflow"
size_categories:
  - 100K<n<1M
task_categories:
  - question-answering
  - text-generation
  - text2text-generation
tags:
  - stackoverflow
  - instruction-tuning
  - qa
  - code
  - fine-tuning
  - alpaca-format
  - llm-training
---

# 🧩 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](https://huggingface.co/datasets/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.

---

## 📊 Processing Progress

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

---

## 📁 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 |

---

## 🏗️ 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 |

---

## 🚀 Quick Start

### Load with pandas
```python
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`
```python
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)
```python
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)
```

---

## 📋 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}

---

## ⚠️ 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.

---

## 🔁 Source Dataset

Question IDs and metadata sourced from:
- [Omarrran/StackPulse_778K_QnA_Code_dataset](https://huggingface.co/datasets/Omarrran/StackPulse_778K_QnA_Code_dataset)

Answers joined from:
- `bigquery-public-data.stackoverflow.posts_answers` (Google BigQuery Public Dataset)

---

## 📋 Citation

```bibtex
@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}
}
```

---

## 👤 Author

**Omar Haq Nawaz Malik** (HuggingFace: [Omarrran](https://huggingface.co/Omarrran))
AI Engineer & NLP Researcher | BITS Pilani | Srinagar, Kashmir