IndicSBERT-STS
| Property | Value |
|---|---|
| License | cc-by-4.0 |
| Research Paper | L3Cube-IndicSBERT Paper |
| Languages Supported | 12 (Including English and major Indian languages) |
| Author | l3cube-pune |
What is indic-sentence-similarity-sbert?
IndicSBERT-STS is a sophisticated multilingual sentence similarity model specifically designed for Indian languages. Built on the SBERT architecture, it's trained on the STS dataset comprising ten major Indian languages plus English. The model excels at understanding semantic similarities between sentences across different Indian languages, making it a powerful tool for cross-lingual applications.
Implementation Details
The model is implemented using the sentence-transformers framework and can be easily integrated using either the sentence-transformers library or HuggingFace Transformers. It employs mean pooling for generating sentence embeddings and supports both monolingual and cross-lingual similarity computations.
- Built on BERT architecture with specialized Indian language training
- Supports sentence embedding generation for 12 languages
- Implements cross-lingual similarity detection
- Uses advanced pooling techniques for embedding generation
Core Capabilities
- Multilingual sentence embedding generation
- Cross-lingual similarity detection
- Support for 11 Indian languages plus English
- Efficient semantic similarity computation
- Easy integration with popular NLP frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive coverage of Indian languages and cross-lingual capabilities, making it particularly valuable for applications requiring semantic understanding across multiple Indian languages.
Q: What are the recommended use cases?
The model is ideal for cross-lingual information retrieval, semantic similarity detection in Indian languages, text clustering, and multilingual document comparison applications.
