Amazon Review Summarizer Bart

🚀 BART (Large-sized Model), Fine-tuned on Amazon Reviews (English Language)

This project fine-tunes the BART large-sized model on Amazon reviews in English to build a pipeline for summarizing user reviews on Amazon.com.

🚀 Quick Start

The BART model was initially pre-trained on the CNN - DailyMail dataset. Subsequently, it was re - trained on English - language Amazon website purchase reviews. The aim is to create a pipeline for summarizing Amazon user reviews.

✨ Features

According to Hugging Face, BART is a transformer encoder - encoder (seq2seq) model. It consists of a bidirectional (BERT - like) encoder and an autoregressive (GPT - like) decoder. BART is pre - trained by (1) corrupting text with an arbitrary noising function and (2) learning a model to reconstruct the original text.

📦 Installation

No installation steps are provided in the original README, so this section is skipped.

💻 Usage Examples

Basic Usage

Here is how to use this model with the pipeline API:

from transformers import pipeline
summarizer = pipeline("summarization", model="mabrouk/amazon-review-summarizer-bart")
review = """ I really like this book. It takes a step-by-step approach to introduce the reader to the IBM Q Experience, to the basics underlying quantum computing, and to the reality of the noise involved in the current machines. This introduction is technical and shows the user how to use the IBM system either directly through the GUI on their website or by running Python code on one's own machine. The text provides examples of small exercises to try and stimulates ideas of new things to try. The IBM Q Exp Qiskit software modules are identified and introduced - Terra, Aer, Ignis, and Aqua, as well as the backends that one can choose to do the computing. The book ends with two great chapters on quantum algorithms.
"""
print(summarizer(review, min_length = 60))
>>> [{'summary': 'This book is a great resource, and a great read, to learn about quantum and start writing your first programs, or to brush up on your programming skills. I loved that there is a quiz at the end of every chapter so you can check and see how...'}]

📚 Documentation

Datasets

Link: Amazon Reviews Corpus

Intended uses & limitations

This model is intended to be used for summarizing user reviews on websites.

Reference

Pre - trained Model: [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81 - storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man - made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free - standing+structure+in+France+after+the+Millau+Viaduct.) Re - trained Dataset: Amazon Reviews Corpus

🔧 Technical Details

No technical details meeting the requirement (>50 words) are provided in the original README, so this section is skipped.

📄 License

No license information is provided in the original README, so this section is skipped.