You can run text evaluations using descriptors directly in the user interface.

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1. Prepare the Dataset

Before you start, create a Project and prepare the Dataset to evaluate. There are two options:
  • Upload a CSV. Enter the “Dataset” menu, click on “Create new dataset from CSV”. Drag and drop your Dataset. You must also specify the data definition when you upload it.
  • Use an existing Dataset. Select a Dataset you previously uploaded to the platform or one collected through Tracing.
What are Datasets? Learn how to manage and upload Datasets to the platform.
What is Data Definition? Understand how to set your dataset schema in the Data Definition.

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2. Start an evaluation

While you are viewing the Dataset, you can click on “Add descriptors” on the right. (Optional) Add the LLM provider API key. Add a token in the “Secrets” menu section if you plan to use an LLM for evaluations. You can proceed without it, using other types of evals.

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3. Configure the evaluation

You must choose which column to evaluate and how. You can choose from the following methods:
  • Model-based: use built-in machine learning models, like sentiment analysis.
  • Regular expressions: check for specific words or patterns.
  • Text stats: measure stats like the number of symbols or sentences.
  • LLM-based: use external LLMs to evaluate your text data.
Select specific checks one by one: Each evaluation result is called a Descriptor. No matter the method, you’ll get a label or score for every evaluated text. Some, like “Sentiment,” work instantly, while others may need setup.
What other evaluators are there? Check the list of All Descriptors.
Here are few examples of Descriptors and how to configure them:

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Words presence

Include Words. This Descriptor checks for listed words and returns “True” or “False.” Set up these parameters:
  • Add a list of words.
  • Choose whether to check for “any” or “all” of the words present.
  • Set the lemmatize parameter to check for inflected and variant words automatically.
  • Give your check a name so you can easily find it in your results.
Example setup:

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Semantic Similarity

Semantic Similarity. This descriptor converts texts to embeddings and calculates Cosine Similarity between your evaluated column and another column. It scores from 0 to 1 (0: completely different, 0.5: unrelated, 1: identical). It’s useful for checking if responses are semantically similar to a question or reference. Select the column to compare against:

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LLM as a judge

Custom LLM evaluator. If you’ve added your token, use LLM-based evals (built-in or custom) to send your texts to LLMs for grading or scoring. You can choose a specific LLM model from the provider. For example, you can create a custom evaluator to classify texts as “cheerful” or “neutral.” Fill in the parameters, and Evidently will generate the evaluation prompt: For a binary classification template, you can configure:
  • Criteria: define custom criteria in free text to clarify the classification task.
  • Target/Non-target Category: labels you want to use.
  • Uncertain Category: how the model should respond when it can’t decide.
  • Reasoning: choose to include explanation (Recommended).
  • Category and/or Score: have the LLM respond with the category (Recommended) or score.
  • Visualize as: when both Category and Score are computed, choose which to display in the Report.
To add evaluations for another column in the same Report, click “Add Preset,” select “Text Evals,” and follow the same steps for the new column. You can include evals for multiple columns at once.

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4. Run the evaluation

Click “Run calculation”, and the calculation will start! It may take some time to process, especially on a large dataset. You can check the status of the evaluation in the “Tasks“ (use the left menu to navigate). Once your evaluation is complete, you can view the new dataset with the results.