multilingual-e5-base-dolly-15k
| Property | Value |
|---|---|
| Parameter Count | 278M |
| Model Type | Sentence Transformer |
| Embedding Dimension | 768 |
| Downloads | 31,249 |
What is multilingual-e5-base-dolly-15k?
This is a sophisticated sentence transformer model built on the XLM-RoBERTa architecture, designed to convert multilingual text into dense vector representations. The model maps sentences and paragraphs into a 768-dimensional vector space, making it particularly effective for semantic search, clustering, and similarity comparisons across different languages.
Implementation Details
The model was trained using the sentence-transformers framework with careful optimization parameters including a batch size of 8, MultipleNegativesRankingLoss with a scale of 20.0, and AdamW optimizer with a learning rate of 2e-05. The training process ran for 5 epochs with 465 warmup steps.
- Maximum sequence length: 512 tokens
- Pooling strategy: Mean tokens pooling
- Architecture: XLMRobertaModel with normalization layer
- Training optimization: Weight decay of 0.01 and maximum gradient norm of 1
Core Capabilities
- Multilingual text embedding generation
- Semantic similarity computation
- Cross-lingual information retrieval
- Document clustering and classification
- Zero-shot transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
The model combines the robust multilingual capabilities of XLM-RoBERTa with optimized training for sentence embedding tasks, making it particularly effective for cross-lingual applications while maintaining competitive performance with 278M parameters.
Q: What are the recommended use cases?
The model excels in semantic search applications, document similarity analysis, clustering tasks, and any application requiring multilingual text understanding. It's particularly useful when you need to compare or analyze text across different languages.
