bert-fa-base-uncased-sentiment-snappfood
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | HooshvareLab |
| Framework | PyTorch, TensorFlow |
| Language | Persian |
What is bert-fa-base-uncased-sentiment-snappfood?
This model is a specialized version of ParsBERT v2.0, fine-tuned specifically for sentiment analysis of Persian food delivery reviews from SnappFood. It's designed to classify comments into binary sentiments (Happy/Sad) with high accuracy, achieving an impressive F1 score of 87.98%.
Implementation Details
The model is built upon the ParsBERT architecture, which is a Transformer-based model for Persian language understanding. It was trained on a balanced dataset of 70,000 SnappFood user comments, with 35,000 positive and 35,000 negative reviews. The model utilizes uncased text processing and implements modern transformer architecture for optimal performance in sentiment classification tasks.
- Built on ParsBERT v2.0 architecture
- Trained on 70,000 balanced user comments
- Binary classification capability (Positive/Negative)
- Achieves 87.98% F1 score
Core Capabilities
- Persian text sentiment analysis
- Restaurant review classification
- Customer feedback analysis
- Large-scale text processing
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Persian food delivery reviews, making it highly accurate for restaurant-related sentiment analysis. It outperforms mBERT and matches the performance of earlier ParsBERT versions.
Q: What are the recommended use cases?
The model is ideal for analyzing customer feedback in the food delivery industry, restaurant review platforms, and general Persian sentiment analysis tasks. It's particularly suited for businesses needing to process large volumes of Persian customer feedback.
