XLM-RoBERTa Base Model
| Property | Value |
|---|---|
| Parameter Count | 279M |
| License | MIT |
| Author | FacebookAI |
| Paper | View Paper |
| Languages Supported | 94 languages |
What is xlm-roberta-base?
XLM-RoBERTa is a powerful multilingual transformer model that represents a significant advancement in cross-lingual NLP. Trained on 2.5TB of filtered CommonCrawl data across 94 languages, it serves as a base model for various downstream tasks. This model is particularly notable for its masked language modeling capabilities and ability to understand context across multiple languages.
Implementation Details
The model implements a transformer architecture with 279M parameters, utilizing self-supervised learning through masked language modeling (MLM). During pre-training, it randomly masks 15% of input tokens and learns to predict them, enabling robust bidirectional representations.
- Pre-trained on 2.5TB of filtered CommonCrawl data
- Supports 94 different languages including major and low-resource languages
- Implements bidirectional context understanding
- Uses advanced tokenization for multiple languages
Core Capabilities
- Masked language modeling across 94 languages
- Feature extraction for downstream tasks
- Cross-lingual transfer learning
- Sequence classification
- Token classification
- Question answering tasks
Frequently Asked Questions
Q: What makes this model unique?
XLM-RoBERTa's uniqueness lies in its massive multilingual training data (2.5TB) and ability to understand 94 languages simultaneously, making it particularly valuable for cross-lingual tasks and low-resource languages.
Q: What are the recommended use cases?
The model is best suited for tasks that require whole sentence understanding, including sequence classification, token classification, and question answering. It's not recommended for text generation tasks, where models like GPT-2 would be more appropriate.
