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README.md
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@@ -18,7 +18,7 @@ During the fine-tuning process, a batch size of 8 for efficient computation and
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Additionally, a learning rate (2e-5) was selected to strike a balance between rapid convergence and steady optimization,
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ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
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This model has been trained on a rather small dataset of under 50k, specifically designed for user intent classification.
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The dataset consists of text samples, each labeled with different user intents, such as "information seeking," "question asking," or "opinion expressing." The diversity within the dataset allowed the model to learn to identify user intent accurately. This dataset was carefully curated from a variety of sources.
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The goal of this meticulous training process is to equip the model with the ability to classify user intent in text data effectively, making it ready to contribute to a wide range of applications involving user interaction analysis and personalization.
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Additionally, a learning rate (2e-5) was selected to strike a balance between rapid convergence and steady optimization,
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ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
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This model has been trained on a rather small dataset of under 50k, 100 epochs, specifically designed for user intent classification.
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The dataset consists of text samples, each labeled with different user intents, such as "information seeking," "question asking," or "opinion expressing." The diversity within the dataset allowed the model to learn to identify user intent accurately. This dataset was carefully curated from a variety of sources.
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The goal of this meticulous training process is to equip the model with the ability to classify user intent in text data effectively, making it ready to contribute to a wide range of applications involving user interaction analysis and personalization.
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