metadata

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:

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)