» Full List of NLI & STS Models
Performance Comparison
Here are the performances on the STS benchmark for other sentence embeddings methods. They were also computed by using cosine-similarity and Spearman rank correlation:
- Avg. GloVe embeddings:  58.02 
- BERT-as-a-service avg. embeddings:  46.35 
- BERT-as-a-service CLS-vector: 16.50 
- InferSent - GloVe: 68.03 
- Universal Sentence Encoder: 74.92
Applications
This model works well in accessing the coarse-grained similarity between sentences. For application examples, see semantic_textual_similarity and semantic search.
