ToxicityScore

Computes and visualizes the toxicity score for input text, true text, and predicted text, assessing content quality and potential risk.

Purpose: The ToxicityScore metric is designed to evaluate the toxicity levels of texts generated by models. This is crucial for identifying and mitigating harmful or offensive content in machine-generated texts.

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

Signs of High Risk: - Drastic spikes in toxicity scores indicate potentially toxic content within the associated text segment. - Persistent high toxicity scores across multiple texts may suggest systemic issues in the model’s text generation process.

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

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