Interpret prediction results from text entity extraction models

Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis objectives by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text models until June 15, 2025. For a comparison of AutoML text and Gemini, see Gemini for AutoML text users. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see Introduction to tuning. To get started with tuning, see Model tuning for Gemini text models

After requesting a prediction, Vertex AI returns results based on your model's objective. Predictions from entity extraction models return annotations for each document, such as the location of detected entities, the assigned labels, and confidence scores.

The confidence communicates how confident your model accurately identified and labeled each entity. The higher the number, the higher the model's confidence in the correctness of the prediction.

Example batch prediction output

The following sample is the predicted result for an entity extraction model that was trained to detect diseases. The offsets (start and end character offsets) specify the location where the model detected an entity in the document, and the content field shows the detected entity.

The display names show the labels that the model associated with each entity, such as SpecificDisease or DiseaseClass. The labels map to the text segments in order.

Note: The following JSON Lines example includes line breaks for readability. In your JSON Lines files, line breaks are included only after each each JSON object.

{
  "key": 1,
  "predictions": {
    "ids": [
      "1234567890123456789",
      "2234567890123456789",
      "3234567890123456789"
    ],
    "displayNames": [
      "SpecificDisease",
      "DiseaseClass",
      "SpecificDisease"
    ],
    "textSegmentStartOffsets":  [13, 40, 57],
    "textSegmentEndOffsets": [29, 51, 75],
    "confidences": [
      0.99959725141525269,
      0.99912621492484128,
      0.99935531616210938
    ]
  }
}