XLMRoberta-Alexa-Intents-Classification
| Property | Value |
|---|---|
| Author | qanastek |
| License | cc-by-4.0 |
| Languages Supported | 51 languages |
| Training Dataset | MASSIVE |
What is XLMRoberta-Alexa-Intents-Classification?
This is a powerful multilingual intent classification model built on the XLM-RoBERTa architecture, designed to understand and classify user commands across 51 different languages. The model specializes in processing voice assistant-like commands similar to Alexa, covering 60 distinct intents ranging from setting alarms to controlling IoT devices.
Implementation Details
The model is implemented using the Transformers library and can be easily integrated into existing pipelines. It achieves impressive performance metrics, with an overall accuracy of 86.17% across all intent categories. The model demonstrates particularly strong performance in common tasks like alarm management (93.38% F1-score for alarm queries) and weather queries (94.39% F1-score).
- Built on XLM-RoBERTa architecture for robust multilingual support
- Trained on the MASSIVE dataset with over 1M utterances
- Supports 60 different intents and 55 slot types
- Easy integration with HuggingFace Transformers pipeline
Core Capabilities
- Multilingual intent classification across 51 languages
- Smart home control (IoT devices, lighting, etc.)
- Media playback commands (music, audiobooks, podcasts)
- Utility functions (alarms, calendar, weather, etc.)
- Question answering (math, currency, definitions)
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 51 different languages while maintaining high accuracy across a wide range of intents makes it particularly valuable for building multilingual voice assistants. Its comprehensive coverage of practical intents and strong performance metrics make it suitable for production environments.
Q: What are the recommended use cases?
The model is ideal for building multilingual virtual assistants, smart home control systems, and voice-controlled applications. It's particularly well-suited for applications requiring natural language understanding in multiple languages, such as international smart home platforms or global virtual assistant services.
