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nuhmanpk/freecodecamp-transcripts

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 Dataset Structure Column Type Description title string Title of the YouTube video video_id string Unique YouTube video… See the full description on the dataset page: https://huggingface.co/datasets/nuhmanpk/freecodecamp-transcripts.

sourceHugging Facemitupdated 7mo agoView on Hugging Face
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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

Dataset Structure

ColumnTypeDescription
titlestringTitle of the YouTube video
video_idstringUnique YouTube video identifier
transcriptstringFull 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