sbert-jsnli-luke-japanese-base-lite
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Language | Japanese |
| Vector Dimensions | 768 |
| Base Model | studio-ousia/luke-japanese-base-lite |
What is sbert-jsnli-luke-japanese-base-lite?
This is a specialized sentence transformer model designed for Japanese text processing, built on the LUKE (Language Understanding with Knowledge-based Embeddings) architecture. It's specifically engineered to convert Japanese sentences and paragraphs into 768-dimensional dense vector representations, making it ideal for semantic search and clustering applications.
Implementation Details
The model is built upon studio-ousia/luke-japanese-base-lite and has been fine-tuned on the JSNLI (Japanese Natural Language Inference) dataset for one epoch. Training was completed on Google Colab Pro using an A100 GPU in approximately 40 minutes. The implementation supports both sentence-transformers and HuggingFace Transformers frameworks.
- Provides dense 768-dimensional embeddings for Japanese text
- Trained on JSNLI dataset for semantic understanding
- Supports both sentence-level and paragraph-level encoding
- Implements mean pooling for token aggregation
Core Capabilities
- Semantic sentence similarity computation
- Text clustering and classification
- Document similarity analysis
- Semantic search functionality
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Japanese language processing, combining the power of LUKE architecture with JSNLI dataset training. It offers a lightweight alternative while maintaining strong semantic understanding capabilities.
Q: What are the recommended use cases?
The model is particularly well-suited for Japanese text applications requiring semantic similarity matching, document clustering, and information retrieval. It's ideal for projects needing efficient sentence-level embeddings without heavy computational requirements.
