merqsous/mBart

Model Name

Model Description

This multi-label classification model is a fine-tuned version of Facebook's BART model specifically tailored for analyzing mobile application user reviews. It can classify reviews into 38 distinct classes, covering various aspects like usability, functionality, pricing, and more. This model aims to assist developers and marketers in gaining insights from user feedback efficiently.

Model Details

• Architecture: The model is based on the BART (Bidirectional and Auto-Regressive Transformers) architecture, renowned for its effectiveness in natural language understanding tasks.
• Training Data: Fine-tuned on a dataset comprising user reviews from multiple mobile applications, collected from various app stores. The dataset includes over 4k user reviews categorized into 38 classes.
• Training Procedure: The model was trained using a multi-label classification approach, with a focus on maximizing the accuracy across all categories. Training involved adjusting the learning rate and using a batch size optimized for GPU capabilities.

Example Usage

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("merqsous/mBart")
tokenizer = AutoTokenizer.from_pretrained("merqsous/mBart")

inputs = tokenizer("Example input text here", return_tensors="pt")
outputs = model(**inputs)