beto-sentiment-analysis
| Property | Value |
|---|---|
| Author | finiteautomata |
| Downloads | 565,256 |
| Paper | arXiv:2106.09462 |
| License | Non-commercial use only |
What is beto-sentiment-analysis?
beto-sentiment-analysis is a specialized sentiment analysis model designed for Spanish language text processing. Built on top of BETO (a Spanish BERT model), it's specifically trained on the TASS 2020 corpus containing approximately 5,000 tweets from various Spanish dialects. The model classifies text into three sentiment categories: Positive (POS), Negative (NEG), and Neutral (NEU).
Implementation Details
The model leverages the BETO architecture, which is a BERT-based model pre-trained specifically for Spanish language understanding. It implements transformer-based architecture using PyTorch and supports JAX for inference.
- Trained on TASS 2020 dataset with ~5k tweets
- Built on BETO, a Spanish-specific BERT implementation
- Supports three-way classification (POS/NEG/NEU)
- Implements PyTorch and JAX compatibility
Core Capabilities
- Spanish-specific sentiment analysis
- Multi-dialect support for Spanish variations
- Three-class sentiment classification
- Production-ready with Inference Endpoints support
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Spanish language sentiment analysis, utilizing the BETO architecture that's pre-trained on Spanish text. Its training on the TASS 2020 corpus ensures effectiveness across various Spanish dialects.
Q: What are the recommended use cases?
The model is ideal for Spanish social media analysis, customer feedback processing, and general Spanish text sentiment classification. However, it's important to note that it's licensed for non-commercial use and scientific research purposes only.
