README.md

---
language: en
tags:
  - summarization
  - transformers
  - t5
  - youtube
license: apache-2.0
datasets:
  - custom
model-index:
  - name: T5 YouTube Summarizer
    results: []
---




# 📺 T5 YouTube Summarizer

This is a fine-tuned [`t5-base`](https://huggingface.co/t5-base) model for abstractive summarization of YouTube video transcripts. The model is trained on a custom dataset of video transcriptions and their manually written summaries.

---

## ✨ Model Details

- **Base Model**: [`t5-base`](https://huggingface.co/t5-base)
- **Task**: Abstractive Summarization
- **Training Data**: YouTube video transcripts and human-written summaries
- **Max Input Length**: 512 tokens
- **Max Output Length**: 256 tokens
- **Fine-tuning Epochs**: 10
- **Tokenizer**: `T5Tokenizer` (pretrained)

---

## 🧠 Intended Use

This model is designed to generate short, informative summaries from long transcripts of educational or conceptual YouTube videos. It can be used for:

- Quick understanding of long videos
- Automated content summaries for blogs, platforms, or note-taking tools
- Enhancing accessibility for long-form spoken content

---

## 🚀 How to Use

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the model
model = T5ForConditionalGeneration.from_pretrained("your-username/t5-youtube-summarizer")
tokenizer = T5Tokenizer.from_pretrained("your-username/t5-youtube-summarizer")

# Define input text
text = "The video talks about coordinate covalent bonds, giving examples from..."

# Preprocess and summarize
inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)

summary_ids = model.generate(
    inputs,
    max_length=256,
    min_length=80,
    num_beams=5,
    length_penalty=2.0,
    no_repeat_ngram_size=3,
    early_stopping=True
)

summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)