Sentence-transformers
Rank:Average Model Cost: $0.0000
Number of Runs: 12,966,395
Models by this creator
all-MiniLM-L6-v2
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4.6M
Huggingface
paraphrase-multilingual-MiniLM-L12-v2
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2.9M
Huggingface
all-mpnet-base-v2
all-mpnet-base-v2
The all-mpnet-base-v2 model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional vector space. It can be used for tasks like clustering or semantic search. The model was trained using a self-supervised contrastive learning objective on a 1 billion sentence pairs dataset. It is intended to be used as a sentence and short paragraph encoder for information retrieval, clustering, and sentence similarity tasks. The model was fine-tuned using a contrastive objective and trained on a TPU v3-8 with a batch size of 1024 and a sequence length of 128 tokens. The training script and data configuration details are available in the repository.
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2.7M
Huggingface
multi-qa-mpnet-base-dot-v1
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790.3K
Huggingface
paraphrase-MiniLM-L6-v2
$-/run
510.4K
Huggingface
all-MiniLM-L12-v2
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469.4K
Huggingface
bert-base-nli-mean-tokens
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327.9K
Huggingface
multi-qa-MiniLM-L6-cos-v1
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287.2K
Huggingface
paraphrase-multilingual-mpnet-base-v2
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222.2K
Huggingface
all-distilroberta-v1
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202.0K
Huggingface