README.md

---
language: en
license: mit
tags:
  - t5
  - t5-small
  - summarization
  - text-summarization
  - pytorch
  - transformers
pipeline_tag: summarization
library_name: transformers
---

# T5 Dialogue Summarizer

A fine-tuned T5-small model for text and dialogue summarization.

## Model Details

- **Base model:** T5-small
- **Task:** Text summarization
- **Framework:** PyTorch
- **Tokenizer:** T5Tokenizer (max_length: 512)
- **Decoding:** Beam search (num_beams=4, max_length=150, early_stopping=True)

## Usage

### Using Pipeline

```python
from transformers import pipeline

summarizer = pipeline("summarization", model="unnat17/t5-dialogue-summarizer")
result = summarizer("Your text here...")
print(result[0]["summary_text"])
```

### Direct Loading

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("unnat17/t5-dialogue-summarizer")
tokenizer = T5Tokenizer.from_pretrained("unnat17/t5-dialogue-summarizer")

input_text = "summarize: " + "Your text here..."
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
output = model.generate(**inputs, num_beams=4, max_length=150, early_stopping=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Web Application

A full-stack web application using this model is available at:
[github.com/unnat-git/Text-Summarizer](https://github.com/unnat-git/Text-Summarizer)