Advanced Suicidality Classifier Model Introduction Welcome to the Suicidality Detection AI Model! This project aims to provide a machine learning solution for detecting sequences of words indicative of suicidality in text. By utilizing the ELECTRA architecture and fine tuning on a diverse dataset, we have created a powerful classification model that can distinguish between suicidal and non suicidal text expressions. Labels The model classifies input text into two labels: LABEL 0 : Indicates that the text is non suicidal. LABEL 1 : Indicates that the text is indicative of suicidality. Training The model was fine tuned using the ELECTRA architecture on a carefully curated dataset. Our training process involved cleaning and preprocessing various text sources to create a comprehensive training set. The training results indicate promising performance, with metrics including: Performance The model's performance on the validation dataset is as follows: Accuracy: 0.939432 Recall: 0.937164 Precision: 0.92822 F1 Score: 0.932672 These metrics demonstrate the model's ability to accurately classify sequences of text as either indicative of suicidality or non suicidal. Data Sources We collected…
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