Do inference on one of your models.
Resource URL
https://api.app.labelf.ai/v2/models/{model_id}/inference
Resource information
Response formats | JSON |
Requires authentication? | Yes - Bearer token |
Path parameters
Name | Required | Type | Description |
model_id | required | int | The model id for your model |
JSON body parameters
Name | Required | Type | Description | Example |
texts | required | List of strings | Specifies the texts that should be inferenced on. | ["Breakfast was not tasty", "breakfast was very good"] |
max_predictions | NOT required | int | Specifies the amount of predictions you want back. For example, setting this to three will yield the three class predictions with the highest probability. You can skip sending this parameter and you will instead get predictions for all classes. | 3 |
label_filter | NOT required | List of strings | If you only want the predictions for certain classes and ignore all other classes, then list those here. Partial matches will also be filtered here, so "po" will filter yield the class "positive". Leaving this out will yield predictions for all classes. | ["positive", "negative"]" |
Example Request
POST /v2/models/{model_id}/inference HTTP/1.1 Host: api.app.labelf.ai Authorization: Bearer YOUR_BEARER_TOKEN Content-Type: application/json;charset=UTF-8 { "texts": ["Breakfast was not tasty"], "max_predictions": 2 }Example Curl Request
curl --location --request POST 'https://api.app.labelf.ai/v2/models/inference \ --header 'Authorization: Bearer YOUR_BEARER_TOKEN' \ --header 'Content-Type: application/json' \ --data-raw '{ "texts": ["Breakfast was not tasty"], "max_predictions": 2, } 'Example Response
HTTP/1.1 200 OK Status: 200 OK Content-Type: application/json; charset=utf-8 ... [ { "text": "Breakfast was not tasty", "predictions": [ { "label": "positive", "score": 0.93 }, { "label": "neutral", "score": 0.03 } ] } ]