- Input
- $0.005
- Output
- $0.00
sentence-transformersListed Mar 3, 2023
paraphrase-MiniLM-L6-v2
We present a sentence similarity model based on the Sentence Transformers architecture, which maps sentences to a 384-dimensional dense vector space. The model uses a pre-trained BERT encoder and applies…
Source description
We present a sentence similarity model based on the Sentence Transformers architecture, which maps sentences to a 384-dimensional dense vector space. The model uses a pre-trained BERT encoder and applies mean pooling on top of the contextualized word embeddings to obtain sentence embeddings. We evaluate the model on the Sentence Embeddings Benchmark.
Measured performance
All leaderboards ↗Benchmark results
No published results in this index yet
Scores appear here when an evaluation matches this exact model.
Access & cost
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Model specifications
- Developer
- sentence-transformers
- Modality
- Embedding
- Context
- 512 tokens
- Maximum output
- 460 tokens
- Weights
- Open weights
- Provider listed
- Mar 3, 2023
- First discovered
- Oct 16, 2026
Exact identities
- openrouter
sentence-transformers/paraphrase-minilm-l6-v2 - deepinfra
sentence-transformers/paraphrase-MiniLM-L6-v2
Sources
Updated Oct 20, 2026
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