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Access global models in the Registry

Availability information

Global models are a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature.

Deploy pre-trained, global models for predictive or generative use cases. These high-quality, open-source models are trained and ready for deployment, allowing you to make predictions without additional setup. For LLM use cases, you can find classifiers to identify prompt injection, toxicity, and sentiment, as well as a regressor to output a refusal score.

Global model availability

Global models created by DataRobot are available to all users. Administrator-created global models are available based on the following rules:

  • If an organization administrator creates a global model, that global model is available to all users within the organization.
  • If a platform administrator creates a global model, it's available to all users of that DataRobot platform instance.

Only administrators have edit rights to global models. Deployed global models follow the deployment's sharing rules.

To identify global models on the Registry > Model directory page, locate the Global column and look for models with Yes:

You can filter the Registry > Model directory page to list only global models. Click Filter models, select the Global model check-box, then click Apply filters:

The following global models are available:

Model Type Target Description
Prompt Injection Classifier Binary injection Classifies text as prompt injection or legitimate. This model requires one column named text, containing the text to classify. For more information, see the deberta-v3-base-injection model details.
Toxicity Classifier Binary toxicity Classifies text as toxic or non-toxic. This model requires one column named text, containing the text to classify. For more information, see the toxic-comment-model details.
Sentiment Classifier Binary sentiment Classifies text sentiment as positive or negative. This model requires one column named text, containing the text to classify. For more information, see the distilbert-base-uncased-finetuned-sst-2-english model details.
Refusal Score Regression target Outputs a maximum similarity score, comparing the input to a list of cases where an LLM has refused to answer a query because the prompt is outside the limits of what the model is configured to answer.
Presidio PII Detection Binary contains_pii Detects and replaces Personally Identifiable Information (PII) in text. This model requires one column named text, containing the text to be classified. The types of PII to detect can optionally be specified in a column, 'entities', as a comma-separated string. If this column is not specified, all supported entities will be detected. Entity types can be found in the PII entities supported by Presidio documentation.

In addition to the detection result, the model returns an anonymized_text column, containing an updated version of the input with detected PII replaced with placeholders.

For more information, see the Presidio: Data Protection and De-identification SDK documentation.
Zero-shot Classifier Binary target Performs zero-shot classification on text with user-specified labels. This model requires classified text in a column named text and class labels as a comma-seperated string in a column named labels. It expects the same set of labels for all rows; therefore, the labels provided in the first row are used. For more information, see the deberta-v3-large-zeroshot-v1 model details.
Python Dummy Binary Classification Binary target Always yields 0.75 for the positive class. For more information, see the python3_dummy_binary model template.

To clear the global model filter, in the Filters applied row, click x on the Global filter badge. You can also click Clear all to remove every filter applied.

Updated April 5, 2024