sentence-bert-base-ja-mean-tokens
| Property | Value |
|---|---|
| Parameter Count | 111M |
| License | CC-BY-SA-4.0 |
| Tensor Type | F32 |
| Downloads | 72,450+ |
What is sentence-bert-base-ja-mean-tokens?
This is a specialized Japanese Sentence-BERT model designed for generating meaningful sentence embeddings and computing semantic similarity between Japanese texts. It implements the mean tokens pooling strategy and is built upon the BERT architecture, specifically optimized for Japanese language processing.
Implementation Details
The model utilizes the BertJapaneseTokenizer and BertModel components, implementing mean pooling for sentence embeddings. It supports batch processing and can run on both CPU and GPU environments through PyTorch.
- Efficient batch processing with customizable batch sizes
- Built-in mean pooling strategy for token embeddings
- Support for CUDA acceleration when available
- Automatic padding and truncation of input sequences
Core Capabilities
- Japanese text embedding generation
- Sentence similarity computation
- Feature extraction for downstream NLP tasks
- Support for both single sentence and batch processing
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Japanese language processing, offering state-of-the-art sentence embedding capabilities with mean tokens pooling strategy. It has been widely adopted with over 72,000 downloads and provides a robust foundation for Japanese NLP tasks.
Q: What are the recommended use cases?
The model is ideal for tasks such as semantic similarity comparison, document classification, clustering Japanese text, and as a feature extractor for downstream NLP applications. It's particularly effective for applications requiring understanding of semantic relationships between Japanese sentences.
