onnx-community/Text-Summarizer-t5-small-ONNX

Text-Summarizer-t5-small (ONNX)

This is an ONNX version of UX4567/Text-Summarizer-t5-small. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for summarization: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.SummarizationPipeline

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Text Summarizer (T5 Fine-Tuned)

This model is a fine-tuned version of T5 designed for abstractive text summarization. It condenses long articles, documents, or paragraphs into short, accurate, and context-aware summaries.

Model Details

Model Description

• Developed by: Kartik Sharma
• Model type: Sequence-to-Sequence (Encoder-Decoder)
• Language(s): English
• Base Model: T5 Architecture
• Task: Text Summarization

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How to Get Started with the Model

You can load and test the model using the Hugging Face transformers pipeline or direct model classes:

Using Transformers Pipeline

from transformers import pipeline

summarizer = pipeline("text-generation", model="UX4567/Text-Summarizer-t5-small")

text = """
Artificial Intelligence (AI) is transforming industries across the globe. From healthcare to finance, 
machine learning models are enabling automation, improving efficiency, and driving innovation. 
As AI technology continues to evolve, ethical considerations and proper implementation become 
critical for sustainable integration.
"""

summary = summarizer(text, max_length=60, min_length=25, do_sample=False)
print(summary[0]['summary_text'])