---
language: en
license: mit
tags:
  - summarization
  - t5
  - text-to-text
  - model
  - fine-tuned
library: transformers
task:
  - text-generation
  - summarization
---


# 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("kawinduwijewardhane/BriefT5")
tokenizer = T5Tokenizer.from_pretrained("kawinduwijewardhane/BriefT5")

# 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)

```

### Explanation of the YAML metadata:
- **`language`**: Specifies the language the model supports, in this case, English (`en`).
- **`license`**: Describes the licensing information for your model, here it is set to MIT (you can change it depending on your license).
- **`tags`**: These tags help categorize your model on Hugging Face and make it easier for others to discover. I've added tags like `summarization`, `t5`, `text-to-text`, and `fine-tuned`.

This will help you resolve the warning and provide the necessary metadata for your model card!