Mex_Rbta_Opinion_Polarity
| Property | Value |
|---|---|
| Base Model | PlanTL-GOB-ES/roberta-base-bne |
| Training Precision | mixed_float16 |
| Framework | TensorFlow 2.6.0 |
| Transformers Version | 4.17.0 |
What is Mex_Rbta_Opinion_Polarity?
Mex_Rbta_Opinion_Polarity is a specialized fine-tuned version of the RoBERTa architecture, specifically adapted from the PlanTL-GOB-ES/roberta-base-bne model. This model has been optimized for analyzing opinion polarity in Mexican Spanish text, achieving notable training and validation metrics with a final training loss of 0.4033.
Implementation Details
The model employs the AdamWeightDecay optimizer with carefully tuned hyperparameters, including an initial learning rate of 2e-05 and polynomial decay over 5,986 steps. Training was conducted using mixed precision floating-point arithmetic (mixed_float16) to optimize performance and memory usage.
- Optimizer: AdamWeightDecay with beta_1=0.9, beta_2=0.999
- Learning rate: Polynomial decay from 2e-05 to 0
- Weight decay rate: 0.01
Core Capabilities
- Opinion polarity analysis for Mexican Spanish text
- Efficient training with mixed precision support
- Optimized performance with validation loss of 0.5572
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Mexican Spanish opinion analysis, leveraging the robust RoBERTa architecture with custom fine-tuning and mixed precision training for optimal performance.
Q: What are the recommended use cases?
While specific use cases are not detailed in the model documentation, the architecture suggests it's suitable for sentiment analysis and opinion mining tasks specifically focused on Mexican Spanish content.
