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+# 📺 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)