metadata

library_name: transformers
tags:
  - text2text-generation
  - summarization
  - product-review
  - sentiment-analysis
  - t5-small
  - huggingface

📝 Review Summariser GPT - Config1

A fine-tuned t5-small model that generates concise summaries from product reviews. This model is part of the ReviewSummariserGPT project and also pairs well with a Hugging Face sentiment analysis pipeline for classifying tone.

Model Details

Model Description

This is a sequence-to-sequence Transformer model based on t5-small, fine-tuned on a dataset of 1,000 product review → summary pairs. It is designed to take in a review (e.g., from Amazon or Yelp) and output a short, helpful summary.

Developed by: Manish Kumar Kondoju
Finetuned from model: t5-small
Language(s) (NLP): English
License: Apache 2.0
Model type: Seq2Seq Transformer (Text-to-Text Generation)
Shared by: Manish014

Model Sources

Repository: https://huggingface.co/Manish014/review-summariser-gpt-config1
Demo: Coming soon via Gradio Hugging Face Space

Uses

Direct Use

Generate summaries for user reviews to improve content digestibility.
Enhance e-commerce UX by auto-summarizing reviews.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Manish014/review-summariser-gpt-config1")
model = AutoModelForSeq2SeqLM.from_pretrained("Manish014/review-summariser-gpt-config1")

input_text = "summarize: The build quality is terrible and the support team was unhelpful."
inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
outputs = model.generate(inputs["input_ids"], max_length=60)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)