Sentimental-Analysis
| Property | Value |
|---|---|
| Parameter Count | 67M parameters |
| Model Type | DistilBERT |
| Tensor Type | F32 |
| Downloads | 139,949 |
What is Sentimental-Analysis?
Sentimental-Analysis is a powerful text classification model built on DistilBERT architecture, designed to analyze and classify text into three sentiment categories: positive, negative, and neutral. Created by Dmyadav2001, this model has gained significant traction with nearly 140,000 downloads, demonstrating its utility in real-world applications.
Implementation Details
The model leverages DistilBERT's efficient architecture, implementing a three-class classification system using state-of-the-art transformer technology. It utilizes F32 tensor types and includes comprehensive text preprocessing capabilities for handling special characters, links, and user mentions.
- Built on DistilBERT's lightweight architecture (67M parameters)
- Includes custom tokenization and preprocessing pipeline
- Supports three-class sentiment classification
- Implements efficient training configuration systems
Core Capabilities
- Text preprocessing and cleaning
- Multi-class sentiment classification
- Efficient inference with DistilBERT architecture
- Support for custom dataset integration
- Evaluation and performance metrics generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation of sentiment analysis using DistilBERT, offering a perfect balance between performance and computational efficiency. With 67M parameters, it's significantly lighter than full BERT models while maintaining robust classification capabilities.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in customer feedback systems, social media monitoring, product review analysis, and any application requiring efficient text sentiment classification. It's particularly suitable for deployments where computational resources are limited but accurate sentiment analysis is crucial.
