ContextEntityRecall

Evaluates the context entity recall for dataset entries and visualizes the results.

Overview

This metric gives the measure of recall of the retrieved context, based on the number of entities present in both ground_truths and contexts relative to the number of entities present in the ground_truths alone. Simply put, it is a measure of what fraction of entities are recalled from ground_truths. This metric is useful in fact-based use cases like tourism help desk, historical QA, etc. This metric can help evaluate the retrieval mechanism for entities, based on comparison with entities present in ground_truths, because in cases where entities matter, we need the contexts which cover them.

Formula

To compute this metric, we use two sets, \(GE\) and \(CE\), representing the set of entities present in ground_truths and set of entities present in contexts respectively. We then take the number of elements in intersection of these sets and divide it by the number of elements present in the \(GE\), given by the formula:

\[ \\text{context entity recall} = \\frac{| CE \\cap GE |}{| GE |} \]

Configuring Columns

This metric requires the following columns in your dataset: - contexts (List[str]): A list of text contexts which will be evaluated to make sure if they contain the entities present in the ground truth. - ground_truth (str): The ground truth text from which the entities will be extracted and compared with the entities in the contexts.

If the above data is not in the appropriate column, you can specify different column names for these fields using the parameters contexts_column, and ground_truth_column.

For example, if your dataset has this data stored in different columns, you can pass the following parameters:

{
contexts_column": "context_info
ground_truth_column": "my_ground_truth_col",
}

If the data is stored as a dictionary in another column, specify the column and key like this:

pred_col = dataset.prediction_column(model)
params = {
contexts_column": f"{pred_col}.contexts",
ground_truth_column": "my_ground_truth_col",
}

For more complex situations, you can use a function to extract the data:

pred_col = dataset.prediction_column(model)
params = {
contexts_column": lambda row: [row[pred_col]["context_message"]],
ground_truth_column": "my_ground_truth_col",
}