RoBERTuito-base-uncased
| Property | Value |
|---|---|
| Author | pysentimiento |
| Model Type | RoBERTa-based Language Model |
| Training Data | 500M Spanish Tweets |
| Paper | Research Paper |
What is robertuito-base-uncased?
RoBERTuito is a specialized pre-trained language model designed specifically for Spanish social media text analysis. Trained on over 500 million tweets following RoBERTa guidelines, it represents a significant advancement in Spanish natural language processing, particularly for user-generated content.
Implementation Details
The model implements a RoBERTa-based architecture specifically optimized for Spanish social media text. It requires preprocessing through pysentimiento library and achieves state-of-the-art performance across multiple benchmark tasks.
- Outperforms other Spanish language models like BETO, BERTin, and RoBERTa-BNE
- Achieves 80.1% accuracy in hate speech detection
- 70.7% accuracy in sentiment analysis
- 73.6% accuracy in irony detection
Core Capabilities
- Hate Speech Detection
- Sentiment Analysis
- Emotion Analysis
- Irony Detection
- Masked Language Modeling for Spanish text
Frequently Asked Questions
Q: What makes this model unique?
RoBERTuito is specifically trained on Spanish social media content, making it highly effective for analyzing user-generated content. It comes in three variants (cased, uncased, and deaccented) and consistently outperforms other Spanish language models.
Q: What are the recommended use cases?
The model is ideal for analyzing Spanish social media content, particularly for tasks like hate speech detection, sentiment analysis, and irony detection. It's specifically optimized for Twitter-like content and requires proper preprocessing through the pysentimiento library.
