robertuito-sentiment-analysis
| Property | Value |
|---|---|
| Parameter Count | 109M |
| Language | Spanish |
| Framework | PyTorch |
| Training Data | TASS 2020 corpus (~5k tweets) |
| Author | pysentimiento |
What is robertuito-sentiment-analysis?
robertuito-sentiment-analysis is a specialized Spanish language model designed for sentiment analysis of social media content, particularly Twitter data. Built on the RoBERTuito architecture, which is a RoBERTa-based model specifically trained on Spanish tweets, it provides accurate sentiment classification using three labels: POS (positive), NEG (negative), and NEU (neutral).
Implementation Details
The model is trained on the TASS 2020 corpus, comprising approximately 5,000 tweets from various Spanish dialects. It achieves a impressive Macro F1 score of 0.705 ±0.003 for sentiment analysis tasks, outperforming other Spanish language models like BETO and mBERT.
- Built on RoBERTuito base model trained on 500M+ Spanish tweets
- Implements PyTorch framework with Safetensors support
- Easy integration through pysentimiento library
- Specialized for Spanish social media text analysis
Core Capabilities
- Three-way sentiment classification (Positive, Negative, Neutral)
- Handles Spanish social media linguistics and dialects
- Provides probability scores for each sentiment category
- Cross-lingual capabilities for English-Spanish code-switching scenarios
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Spanish social media text, trained on a massive dataset of tweets, making it particularly effective for analyzing informal language, dialectal variations, and social media expressions in Spanish.
Q: What are the recommended use cases?
The model is ideal for social media monitoring, brand sentiment analysis, customer feedback analysis, and any application requiring sentiment analysis of Spanish text, particularly from social media sources. It's especially effective for analyzing tweets and informal communication.
