sentence-transformers/paraphrase-MiniLM-L6-v2 cover image

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.

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

HTTP/cURL API

You can use cURL or any other http client to run inferences:

curl -X POST \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -F 'inputs=["I like chocolate"]'  \
    'https://api.deepinfra.com/v1/inference/sentence-transformers/paraphrase-MiniLM-L6-v2'

which will give you back something similar to:

{
  "embeddings": [
    [
      0.0,
      0.5,
      1.0
    ],
    [
      1.0,
      0.5,
      0.0
    ]
  ],
  "input_tokens": 42,
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

Input fields

inputsarray

sequences to embed

Default value:


normalizeboolean

whether to normalize the computed embeddings

Default value: false


imagestring

image to embed


webhookfile

The webhook to call when inference is done, by default you will get the output in the response of your inference request

Input Schema

Output Schema