sbert-base-ja
| Property | Value |
|---|---|
| License | CC BY-SA 4.0 |
| Base Model | colorfulscoop/bert-base-ja |
| Training Data | Japanese SNLI Dataset (523,005 samples) |
| Paper | Sentence BERT Paper |
What is sbert-base-ja?
sbert-base-ja is a Japanese Sentence BERT model specifically designed for semantic similarity tasks. Built by Colorful Scoop, it's trained on the Japanese SNLI dataset and achieves an impressive 85.29% accuracy on test sets. The model leverages the sentence-transformers framework and is optimized for Japanese text processing.
Implementation Details
The model is implemented using the SentenceTransformer architecture with a BERT base model and mean pooling. It uses a max sequence length of 512 and was trained using AdamW optimizer with a learning rate of 2e-05, including a 10% linear warm-up period. Training was conducted on a single RTX 2080 Ti GPU with a batch size of 8.
- Transformer backbone with 768-dimensional word embeddings
- Mean pooling strategy for sentence representation
- Trained for 1 epoch on 523,005 training samples
- Validated on 10,000 samples with 3,916 test samples
Core Capabilities
- Japanese sentence embedding generation
- Semantic similarity computation
- Support for up to 512 token sequences
- Efficient mean pooling for sentence representations
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Japanese language processing and is one of the few publicly available Japanese Sentence BERT models. It's trained on a large-scale Japanese SNLI dataset and provides state-of-the-art performance for Japanese sentence similarity tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring Japanese text similarity comparison, semantic search, clustering of Japanese sentences, and natural language understanding tasks. It's particularly suitable for applications needing to understand semantic relationships between Japanese text segments.
