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README.md

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+---
+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