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@@ -20,7 +20,7 @@ ensuring the model not only learns quickly but also steadily refines its capabil
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  This model has been trained on a proprietary dataset < 100k, specifically designed for offensive/hate speech detection.
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  The dataset consists of text samples, each labeled as "non-offensive" or "offensive."
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- The diversity within the dataset allowed the model to learn to identify offensive content accurately.
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  The goal of this meticulous training process is to equip the model with the ability to detect offensive and hate speech in text data effectively. The result is a model ready to contribute significantly to content moderation and safety, while maintaining high standards of accuracy and reliability.
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  This model has been trained on a proprietary dataset < 100k, specifically designed for offensive/hate speech detection.
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  The dataset consists of text samples, each labeled as "non-offensive" or "offensive."
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+ The diversity within the dataset allowed the model to learn to identify offensive content accurately. THis dataset was combined from reseearch datasets on this topivc as well as news headliens. All duplicates were removed and meticulous effort was taken to ensure the dataset quality.
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  The goal of this meticulous training process is to equip the model with the ability to detect offensive and hate speech in text data effectively. The result is a model ready to contribute significantly to content moderation and safety, while maintaining high standards of accuracy and reliability.
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