sup-simcse-ja-large
| Property | Value |
|---|---|
| License | CC-BY-SA-4.0 |
| Language | Japanese |
| Base Model | cl-tohoku/bert-large-japanese-v2 |
| Hidden Size | 1024 |
| Training Dataset | JSNLI |
What is sup-simcse-ja-large?
sup-simcse-ja-large is a Japanese language model specifically designed for semantic similarity tasks and sentence embeddings. Built using the Supervised SimCSE approach, it's based on the BERT-large-japanese-v2 architecture and trained on the JSNLI dataset. This model specializes in generating high-quality sentence embeddings for Japanese text, making it particularly useful for tasks like semantic search and text similarity analysis.
Implementation Details
The model implements a sophisticated architecture combining a BERT-based transformer with specialized pooling mechanisms. It's trained using supervised learning with a temperature of 0.05 and uses BFloat16 for efficient computation. The model processes sequences up to 64 tokens and was trained with a batch size of 512 and learning rate of 5e-5.
- Uses CLS token pooling strategy with an additional MLP layer during training
- Implements sentence-transformers framework for easy deployment
- Supports both sentence-transformers and HuggingFace Transformers implementations
- Trained on 2^20 examples with warmup ratio of 0.1
Core Capabilities
- Generation of semantic sentence embeddings for Japanese text
- Semantic similarity computation between Japanese sentences
- Support for both batch processing and individual sentence encoding
- Integration with popular NLP frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Japanese language understanding, using supervised SimCSE training on the JSNLI dataset. The combination of BERT-large architecture with supervised learning makes it particularly effective for semantic similarity tasks in Japanese.
Q: What are the recommended use cases?
The model is ideal for applications requiring semantic understanding of Japanese text, including: semantic search systems, document similarity analysis, text clustering, and information retrieval systems. It's particularly well-suited for production environments due to its integration with sentence-transformers.
