use-cmlm-multilingual
| Property | Value |
|---|---|
| Parameter Count | 472M |
| License | Apache 2.0 |
| Framework | PyTorch (Sentence-Transformers) |
| Languages Supported | 109 languages |
What is use-cmlm-multilingual?
use-cmlm-multilingual is a PyTorch implementation of the universal-sentence-encoder-cmlm multilingual model, designed for generating multilingual sentence embeddings. Based on the LaBSE architecture, it excels at mapping sentences from 109 different languages into a shared vector space, making it particularly valuable for cross-lingual applications.
Implementation Details
The model utilizes a BERT-based architecture with 472M parameters and includes a three-component pipeline: a transformer encoder, a pooling layer, and a normalization layer. It processes sequences up to 256 tokens and employs mean pooling for generating sentence embeddings.
- Transformer-based architecture with modified BERT base
- Mean pooling strategy for sentence representation
- Normalized output embeddings
- Maximum sequence length of 256 tokens
Core Capabilities
- Multilingual sentence embedding generation
- Cross-lingual similarity comparison
- Language-agnostic text representation
- Efficient vector space mapping
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 109 languages while maintaining comparable performance to LaBSE makes it particularly valuable for multilingual applications. Its PyTorch implementation ensures easy integration with modern deep learning workflows.
Q: What are the recommended use cases?
The model is ideal for cross-lingual information retrieval, multilingual semantic similarity comparison, and document classification across different languages. It's particularly useful in applications requiring language-agnostic text representation.
