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sentence-transformers/

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 mean pooling on top of the contextualized word embeddings to obtain sentence embeddings. We evaluate the model on the Sentence Embeddings Benchmark.

Public
$0.005 / Mtoken
512
sentence-transformers/paraphrase-MiniLM-L6-v2 cover image

Input

inputs
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Settings

The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority. 3

whether to normalize the computed embeddings 2

The number of dimensions in the embedding. If not provided, the model's default will be used.If provided bigger than model's default, the embedding will be padded with zeros. (Default: empty, 32 ≤ dimensions ≤ 8192)

Output

[
  [
    0,
    0.5,
    1
  ],
  [
    1,
    0.5,
    0
  ]
]
Model Information

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