xlm-roberta-longformer-base-16384
| Property | Value |
|---|---|
| Architecture | Longformer based on XLM-RoBERTa |
| Context Window | 16384 tokens |
| Hidden Size | 768 |
| Attention Window | 256 |
| Number of Layers | 12 |
| License | MIT |
| Languages Supported | 94 |
What is xlm-roberta-longformer-base-16384?
xlm-roberta-longformer-base-16384 is a sophisticated multilingual model that combines the strengths of the Longformer architecture with XLM-RoBERTa's pre-trained weights. It's designed to handle extremely long sequences up to 16,384 tokens while maintaining the multilingual capabilities across 94 different languages. This model hasn't undergone additional pre-training, making it ready for fine-tuning on specific downstream tasks.
Implementation Details
The model is built using Transformers 4.26.0 and TensorFlow 2.11.0, implementing the Longformer architecture with specific attention mechanisms. It features a 256-token attention window and 768 hidden dimensions across 12 hidden layers, offering a balance between computational efficiency and model capacity.
- Efficient attention mechanism with 256-token window size
- 768-dimensional hidden states
- 12-layer deep architecture
- Support for 16,384 token sequences
- Compatible with 94 languages including major world languages
Core Capabilities
- Long document processing with 16K token context window
- Multilingual text understanding and processing
- Feature extraction for downstream tasks
- Efficient memory usage through specialized attention mechanism
- Cross-lingual transfer learning capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines the long-sequence processing capabilities of Longformer with the multilingual abilities of XLM-RoBERTa, supporting an extensive context window of 16,384 tokens while maintaining proficiency in 94 languages.
Q: What are the recommended use cases?
The model is particularly well-suited for: processing long documents in multiple languages, cross-lingual document classification, multilingual text analysis requiring long context windows, and fine-tuning for specific downstream tasks requiring extensive context understanding.
