wav2vec2-base-finetuned-sentiment-mesd
| Property | Value |
|---|---|
| Base Model | facebook/wav2vec2-base |
| Task | Spanish Speech Sentiment Analysis |
| Best Accuracy | 83.08% |
| Training Epochs | 20 |
| Model Hub | Hugging Face |
What is wav2vec2-base-finetuned-sentiment-mesd?
This model is a specialized adaptation of Facebook's wav2vec2-base architecture, fine-tuned specifically for sentiment analysis of Spanish speech audio. It represents a significant advancement in Spanish language audio processing, achieving an impressive accuracy of 83.08% on the MESD dataset.
Implementation Details
The model was trained using a carefully optimized configuration with Adam optimizer (betas=0.9,0.999, epsilon=1e-08) and a linear learning rate scheduler with warmup ratio of 0.1. The training process utilized a batch size of 32 with gradient accumulation steps of 4, resulting in an effective batch size of 128.
- Learning Rate: 1.25e-05
- Training Duration: 20 epochs
- Validation Loss: 0.5729
- Gradient Accumulation: 4 steps
Core Capabilities
- Spanish speech sentiment classification
- Audio processing using wav2vec2 architecture
- Robust performance with 83.08% accuracy
- Optimized for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Spanish speech sentiment analysis, utilizing the powerful wav2vec2 architecture. Its high accuracy of 83.08% makes it particularly suitable for real-world applications in Spanish language audio processing.
Q: What are the recommended use cases?
The model is ideal for applications requiring sentiment analysis of Spanish speech, such as customer service analytics, social media audio content analysis, and automated emotion detection in Spanish language audio content.
