Update README.md

README.md

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----
-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-*
----
+---
+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
+
+---