XLM-RoBERTa-XL
| Property | Value |
|---|---|
| Parameter Count | 3.48B |
| Training Data | 2.5TB CommonCrawl |
| Languages | 100 languages |
| License | MIT |
| Author | |
| Paper | Larger-Scale Transformers for Multilingual Masked Language Modeling |
What is xlm-roberta-xl?
XLM-RoBERTa-XL is an extra-large multilingual language model that represents a significant advancement in multilingual NLP. Built upon the RoBERTa architecture, this model has been pre-trained on an impressive 2.5TB of filtered CommonCrawl data, encompassing 100 different languages. It utilizes the Masked Language Modeling (MLM) objective, where it learns to predict masked tokens in input sequences, enabling robust bidirectional representations.
Implementation Details
The model employs a transformer-based architecture with 3.48 billion parameters, making it particularly suitable for complex multilingual tasks. It uses both I64 and F32 tensor types and supports integration with PyTorch and Hugging Face's transformers library.
- Pre-trained using self-supervised learning on raw text data
- Implements masked language modeling with 15% masking rate
- Supports 94 languages for various NLP tasks
- Available through Hugging Face's model hub with Inference Endpoints support
Core Capabilities
- Multilingual masked language modeling
- Feature extraction for downstream tasks
- Sequence classification
- Token classification
- Question answering
Frequently Asked Questions
Q: What makes this model unique?
The model's massive scale (3.48B parameters), extensive language coverage (100 languages), and the volume of training data (2.5TB) make it particularly powerful for multilingual applications. It's designed to capture deep linguistic patterns across diverse languages simultaneously.
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.
