RegardScore

Computes and visualizes the regard score for each text instance, assessing sentiment and potential biases.

Purpose: The RegardScore metric is designed to evaluate the regard levels (positive, negative, neutral, or other) of texts generated by models. This helps in understanding the sentiment and biases in the generated content.

Test Mechanism: The function starts by extracting the true and predicted values from the provided dataset and model. The regard scores are computed for each text using a preloaded regard evaluation tool. The scores are compiled into dataframes, and histograms and bar charts are generated to visualize the distribution of regard scores. Additionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and maximum) is compiled for the regard scores, providing a comprehensive summary of the model’s performance.

Signs of High Risk: - Noticeable skewness in the histogram, especially when comparing the predicted regard scores with the target regard scores, could indicate biases or inconsistencies in the model. - Lack of neutral scores in the model’s predictions, despite a balanced distribution in the target data, might signal an issue.

Strengths: - Provides a clear evaluation of regard levels in generated texts, helping to ensure content appropriateness. - Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of regard scores. - Descriptive statistics offer a concise summary of the model’s performance in generating texts with balanced sentiments.

Limitations: - The accuracy of the regard scores is contingent upon the underlying regard tool. - The scores provide a broad overview but do not specify which portions or tokens of the text are responsible for high regard. - Supplementary, in-depth analysis might be needed for granular insights.