text2vec-base-multilingual
| Property | Value |
|---|---|
| Base Model | paraphrase-multilingual-MiniLM-L12-v2 |
| Embedding Dimension | 384 |
| Max Sequence Length | 256 |
| Supported Languages | de, en, es, fr, it, nl, pl, pt, ru, zh |
| Author | shibing624 |
What is text2vec-base-multilingual?
text2vec-base-multilingual is a powerful multilingual sentence embedding model that utilizes the CoSENT (Cosine Sentence) architecture to map sentences into a 384-dimensional vector space. Built upon the paraphrase-multilingual-MiniLM-L12-v2 base model, it has been fine-tuned using carefully curated multilingual STS datasets to enhance its semantic understanding capabilities across multiple languages.
Implementation Details
The model employs a sophisticated architecture combining a transformer-based encoder with mean pooling operations. It processes input text up to 256 tokens and produces fixed-size embeddings that capture semantic meaning across different languages. The training procedure involves a contrastive objective using cosine similarity between sentence pairs and specialized rank loss optimization.
- Transformer-based architecture with BERT-style encoding
- Mean pooling strategy for sentence representation
- Contrastive learning with cosine similarity optimization
- Multilingual capability covering 10 major languages
Core Capabilities
- Semantic search across multiple languages
- Text similarity assessment
- Cross-lingual sentence matching
- Document clustering and classification
- Information retrieval tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's strength lies in its ability to generate comparable embeddings across 10 different languages while maintaining high performance in semantic matching tasks. It shows significant improvements over the base model in various NLI benchmarks, particularly in multilingual contexts.
Q: What are the recommended use cases?
The model is particularly well-suited for cross-lingual information retrieval, semantic search applications, and text similarity tasks. It's ideal for applications requiring multilingual understanding and comparison of text segments up to 256 tokens in length.
