1. Request access through the model page on Hugging Face and wait for approval from the model authors.
2. Create a Custom keys workspace connection in Microsoft Foundry or Azure Machine Learning named HuggingFaceTokenConnection with the key HF_TOKEN and value your Hugging Face read or fine-grained token (marked as secret).
3. Create the Managed Online Endpoint with the property enforce_access_to_default_secret_stores set to enabled so it can access the secret connection value.
4. Once access is approved, the connection is configured, and the endpoint is created with read access to the token, you can deploy and use the model in Microsoft Foundry or Azure Machine Learning.

• Original Model Card
• text-classification Task on Hugging Face

Hugging Face API

Send Request

curl <AZUREML_ENDPOINT_URL> \\
    -X POST \\
    -H "Authorization: Bearer <AZUREML_TOKEN>" \\
    -H "Content-Type: application/json" \\
    -d '{"inputs":"http://images.cocodataset.org/val2017/000000077595.jpg","parameters":{"mask_threshold":0.5}}'

Supported Parameters

• inputs (string): The text to classify.
• parameters (object, optional):
  • function_to_apply (any of sigmoid, softmax, none, optional): The function to apply to the model outputs in order to retrieve the scores.
  • top_k (int, optional): When specified, limits the output to the top K most probable classes.

Expected output

• label (str): The predicted class label out of the ones the model was trained with.
• score (float): The probability for the predicted class.