---
language: en
license: mit
tags:
  - summarization
  - t5
  - text-to-text
  - model
  - fine-tuned
---

# Fine-tuned T5 Model for Text Summarization

This model is a fine-tuned version of the T5 model (`t5-small`) for text summarization tasks. It has been trained on a diverse set of text data to generate concise and coherent summaries from input text.

## Model Overview

- **Model Type**: T5 (Text-to-Text Transfer Transformer)
- **Base Model**: `t5-small`
- **Task**: Text Summarization
- **Language**: English (other languages may be supported depending on the dataset used)

## Intended Use

This model is designed to summarize long documents, articles, or any form of textual content into shorter, coherent summaries. It can be used for tasks such as:

- Summarizing news articles
- Generating abstracts for academic papers
- Condensing lengthy documents
- Summarizing customer feedback or reviews

## Model Details

- **Fine-Tuned On**: A custom dataset containing text and corresponding summaries.
- **Input**: Text (e.g., news articles, papers, or long-form content)
- **Output**: A concise summary of the input text

## How to Use

To use this model for text summarization, you can follow the code example below:

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the fine-tuned model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("username/fine_tuned_t5")
tokenizer = T5Tokenizer.from_pretrained("username/fine_tuned_t5")

# Input text for summarization
input_text = "Your long input text here."

# Tokenize and summarize
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
summary_ids = model.generate(inputs["input_ids"], max_length=150, num_beams=4, early_stopping=True)

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