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.

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

```bash
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:

```json
{
  "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
  }
}

```


You can use our command-line tool [deepctl](/docs/getting-started) to run
inferences:

```bash
deepctl infer \
    -m 'sentence-transformers/paraphrase-MiniLM-L6-v2'  \
    -i 'inputs=["I like chocolate"]'
```

which will give you back something similar to:

```json
{
  "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
  }
}

```


inputs

whether to normalize the computed embeddings

normalize

image

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

webhook

Image

Inputs

Normalize

Webhook

EmbeddingsIn

estimated cost billed for the request in USD

Cost

Runtime Ms

Status

Tokens Generated

Tokens Input

the computed embeddings for each input sequence or a single embedding if an image was passed

sentence-transformers/paraphrase-MiniLM-L6-v2

HTTP/cURL API

Input fields

`inputs`array

`normalize`boolean

`image`string

`webhook`file

Input Schema

Output Schema

sentence-transformers/paraphrase-MiniLM-L6-v2

HTTP/cURL API

Input fields

inputsarray

normalizeboolean

imagestring