XLM-RoBERTa Large
| Property | Value |
|---|---|
| Parameter Count | 561M |
| Model Type | Multilingual Transformer |
| Architecture | RoBERTa-based |
| License | MIT |
| Paper | View Paper |
| Languages Supported | 94 languages |
What is xlm-roberta-large?
XLM-RoBERTa large is a powerful multilingual transformer model developed by Facebook AI, trained on an impressive 2.5TB of filtered CommonCrawl data. It represents a significant advancement in cross-lingual natural language processing, capable of understanding and processing 94 different languages within a single model architecture.
Implementation Details
The model utilizes a masked language modeling (MLM) approach, where it randomly masks 15% of input words and learns to predict them, enabling robust bidirectional understanding of text. Built on the RoBERTa architecture, it leverages self-supervised learning techniques to develop deep cross-lingual representations.
- Built on RoBERTa architecture with 561M parameters
- Trained on 2.5TB of cleaned CommonCrawl data
- Supports masked language modeling tasks
- Compatible with PyTorch, TensorFlow, and JAX
Core Capabilities
- Cross-lingual text understanding and processing
- Masked language modeling predictions
- Feature extraction for downstream tasks
- Sequence classification and token classification
- Question answering tasks
Frequently Asked Questions
Q: What makes this model unique?
XLM-RoBERTa large stands out for its extensive multilingual capabilities, covering 94 languages while maintaining high performance across them. Its large parameter count (561M) and training on 2.5TB of data make it particularly robust for cross-lingual tasks.
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 particularly valuable for multilingual applications but should not be used for text generation tasks, where models like GPT-2 would be more appropriate.
