sentence_similarity_spanish_es
| Property | Value |
|---|---|
| Parameter Count | 110M |
| Model Type | Sentence Transformer |
| License | Apache 2.0 |
| Embedding Dimension | 768 |
What is sentence_similarity_spanish_es?
sentence_similarity_spanish_es is a specialized Spanish language model designed for semantic similarity tasks. Built on the sentence-transformers framework, it transforms Spanish text into dense 768-dimensional vector representations, enabling powerful semantic search and clustering capabilities. The model is based on BERT architecture and has been specifically optimized for Spanish language understanding.
Implementation Details
The model utilizes a BERT-based architecture (dccuchile/bert-base-spanish-wwm-cased) with mean pooling strategy. It achieves impressive performance metrics, including a 0.828 Pearson correlation on similarity tasks. The model was trained using CosineSimilarityLoss with careful optimization parameters including a learning rate of 2e-05 and warmup steps of 144.
- Pre-trained on extensive Spanish language data
- Implements efficient mean pooling for sentence embeddings
- Supports maximum sequence length of 512 tokens
- Optimized with AdamW optimizer and WarmupLinear scheduler
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Clustering of Spanish text
- Cross-sentence semantic comparison
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized optimization for Spanish language sentence similarity tasks, achieving strong correlation scores (82.8% Pearson) while maintaining efficient processing with 110M parameters.
Q: What are the recommended use cases?
The model excels in applications requiring semantic understanding of Spanish text, including document similarity analysis, semantic search systems, text clustering, and automated content organization in Spanish language contexts.
