README.md

---
license: mit
dataset_info:
  features:
  - name: title
    dtype: large_string
  - name: video_id
    dtype: large_string
  - name: transcript
    dtype: large_string
  splits:
  - name: train
    num_bytes: 130792887
    num_examples: 1192
  download_size: 61288449
  dataset_size: 130792887
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- code
pretty_name: Free Code Camp Transcripts
size_categories:
- 1K<n<10K
---


# Free Code Camp Transcripts

## Overview

This dataset contains transcripts of programming tutorials from FreeCodeCamp videos. Each entry includes the video title, YouTube video ID, and the full transcript, making it suitable for training and evaluating NLP and LLM systems focused on developer education.

[DataSource](https://www.kaggle.com/datasets/nuhmanpk/all-programming-tutorial-from-free-code-camp)

---

## Dataset Structure

| Column     | Type   | Description                     |
| ---------- | ------ | ------------------------------- |
| title      | string | Title of the YouTube video      |
| video_id   | string | Unique YouTube video identifier |
| transcript | string | Full transcript of the video    |

---

## Dataset Details

* **Total Samples:** 1,192
* **Language:** English
* **Format:** Parquet (auto-converted by Hugging Face)
* **Domain:** Programming / Software Development

---

## How to Load the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("nuhmanpk/freecodecamp-transcripts")
print(dataset)
```

```python
print(dataset["train"][0])
```

---

## Example Record

```python
{
  "title": "PostgreSQL Tutorial for Beginners",
  "video_id": "SpfIwlAYaKk",
  "transcript": "Welcome to this PostgreSQL tutorial..."
}
```

---

## Use Cases

### 1. Text Summarization

```python
from transformers import pipeline

summarizer = pipeline("summarization")

text = dataset["train"][0]["transcript"]
summary = summarizer(text[:2000])

print(summary)
```

---

### 2. Question Answering

```python
from transformers import pipeline

qa = pipeline("question-answering")

context = dataset["train"][0]["transcript"]
question = "What is PostgreSQL?"

result = qa(question=question, context=context)
print(result)
```

---

### 3. Instruction Dataset

```python
def to_instruction(example):
    return {
        "prompt": f"Explain this tutorial: {example['title']}",
        "response": example["transcript"][:1000]
    }

instruction_ds = dataset["train"].map(to_instruction)
```

---

### 4. Embeddings

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

embeddings = model.encode(dataset["train"]["transcript"][:100])
```

---

## Preprocessing Tips

```python
dataset = dataset.filter(lambda x: x["transcript"] != "")
```

```python
def chunk_text(text, size=1000):
    return [text[i:i+size] for i in range(0, len(text), size)]
```

---

## Limitations

* Transcripts may contain noise
* No timestamps
* Limited to programming tutorials

---

## License

MIT License

---

## Future Improvements

* Add topic tags
* Generate QA pairs
* Instruction tuning

---