This model is a multilingual version of the OpenAI CLIP-ViT-B32 model, which maps text and images to a common dense vector space. It includes a text embedding model that works for 50+ languages and an image encoder from CLIP. The model was trained using Multilingual Knowledge Distillation, where a multilingual DistilBERT model was trained as a student model to align the vector space of the original CLIP image encoder across many languages.

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/clip-ViT-B-32-multilingual-v1'
```

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/clip-ViT-B-32-multilingual-v1'  \
    -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/clip-ViT-B-32-multilingual-v1

Web inference not supported yet, please check API tab