title large_stringlengths 15 100 | video_id large_stringlengths 11 11 | transcript large_stringlengths 133 1.9M ⌀ |
|---|---|---|
PostgreSQL Tutorial for Beginners | SpfIwlAYaKk | null |
Bun Tutorial – JavaScript Runtime (Node.js Alternative) [Full Course] | eTB0UCDnMQo | welcome to this bun crash course bun is a cuttingedge toolkit designed to supercharge your JavaScript and typescript applications with its Lightning Fast startup times built in support for typescript and jsx and a range of powerful features designed to seamlessly replace node.js bun promises to revolutionize your devel... |
Build and Deploy Notion Clone – Full Stack Tutorial (NextJS 13, DALL•E, DrizzleORM, OpenAI, Vercel) | qDunJ0wVIec | learn how to build and deploy a full stack notion clone using nexs 13 Dolly and versel you'll style the app using Shad Cen and Tailwind CSS you'll also learn how to interact with databases with the efficiency of orms Elliot Chong created this course he specializes in creating comprehensive tutorials on how to build AI ... |
Prompt Engineering for Web Devs - ChatGPT and Bard Tutorial | ScKCy2udln8 | "not quite getting the results you want from chat GPT wondering how you can use AI language models t(...TRUNCATED) |
The Ethics of AI & Machine Learning [Full Course] | qpp1G0iEL_c | "welcome to this exploration in the ethics as it relates to artificial intelligence and machine lear(...TRUNCATED) |
Next.js 13 E-Commerce Tutorial (App Router, TypeScript, Deployment, TailwindCSS, Prisma, DaisyUI) | K4ziF0MhbLc | "this comprehensive course guides you through crafting a robust e-commerce website similar to amazon(...TRUNCATED) |
VS Code Tutorial – Become More Productive | heXQnM99oAI | "have you ever watched a tutorial that uses vs code and thought how do they do it so effortlessly th(...TRUNCATED) |
SvelteKit & TailwindCSS Tutorial – Build & Deploy a Web Portfolio | -2UjwQzxvBQ | "learn how to build a beautiful and responsive web portfolio with spel kit and Tailwind CSS spel kit(...TRUNCATED) |
API Security for PCI Compliance (Data Security Standard) | dlK7jec2rXo | "Learn about API security with this course\nthat is tailored to address the pivotal PCI DSS 4.0 requ(...TRUNCATED) |
Astro Web Framework Crash Course | e-hTm5VmofI | "Astro is an all-in-one web framework for building fast content-focused websites like landing pages (...TRUNCATED) |
End of preview. Expand in Data Studio
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.
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
from datasets import load_dataset
dataset = load_dataset("nuhmanpk/freecodecamp-transcripts")
print(dataset)
print(dataset["train"][0])
Example Record
{
"title": "PostgreSQL Tutorial for Beginners",
"video_id": "SpfIwlAYaKk",
"transcript": "Welcome to this PostgreSQL tutorial..."
}
Use Cases
1. Text Summarization
from transformers import pipeline
summarizer = pipeline("summarization")
text = dataset["train"][0]["transcript"]
summary = summarizer(text[:2000])
print(summary)
2. Question Answering
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
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
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(dataset["train"]["transcript"][:100])
Preprocessing Tips
dataset = dataset.filter(lambda x: x["transcript"] != "")
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
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