Falconsai
/

intent_classification

@@ -14,11 +14,11 @@ The model, named "distilbert-base-uncased," is pre-trained on a substantial amou
 which allows it to capture semantic nuances and contextual information present in natural language text.
 It has been fine-tuned with meticulous attention to hyperparameter settings, including batch size and learning rate, to ensure optimal model performance for the user intent classification task.
-During the fine-tuning process, a batch size of 16 for efficient computation and learning was chosen.
 Additionally, a learning rate (2e-5) was selected to strike a balance between rapid convergence and steady optimization,
 ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
-This model has been trained on a proprietary dataset < 100k, specifically designed for user intent classification.
 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.
 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.
@@ -65,4 +65,4 @@ It is essential to use this model responsibly and ethically, adhering to content
 - [DistilBERT Paper](https://arxiv.org/abs/1910.01108)
 **Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
-```

 which allows it to capture semantic nuances and contextual information present in natural language text.
 It has been fine-tuned with meticulous attention to hyperparameter settings, including batch size and learning rate, to ensure optimal model performance for the user intent classification task.
+During the fine-tuning process, a batch size of 8 for efficient computation and learning was chosen.
 Additionally, a learning rate (2e-5) was selected to strike a balance between rapid convergence and steady optimization,
 ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
+This model has been trained on a rather small dataset of under 50k, specifically designed for user intent classification.
 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.
 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.
 - [DistilBERT Paper](https://arxiv.org/abs/1910.01108)
 **Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.