📺 T5 YouTube Summarizer
This is a fine-tuned 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 - 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
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)
📊 Evaluation
| Metric | Value |
|---|---|
| ROUGE-1 | ~0.60 |
| ROUGE-2 | ~0.25 |
| ROUGE-L | ~0.47 |
| Gen Len | ~187 tokens |
📌 Citation
If you use this model in your work, consider citing:
@misc{t5ytsummarizer2025,
title={T5 YouTube Transcript Summarizer},
author={Muhammad Bilal Yousaf},
year={2025},
howpublished={\url{https://huggingface.co/bilal521/t5-youtube-summarizer}},
}
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