Intent Classification

🚀 Model Card: Fine-Tuned DistilBERT for User Intent Classification

A fine - tuned DistilBERT model for accurately classifying user intent in text data

📚 Documentation

Model Description

The Fine - Tuned DistilBERT is a variant of the BERT transformer model. It is distilled to achieve efficient performance while maintaining high accuracy. This model, named "distilbert - base - uncased", has been pre - trained on a large amount of text data, enabling it to capture semantic nuances and contextual information in natural language text.

It has been carefully fine - tuned for the task of classifying user intent in text data. During the fine - tuning process, a batch size of 8 was chosen for efficient computation and learning. A learning rate of 2e - 5 was selected to balance rapid convergence and steady optimization, ensuring that the model learns quickly and refines its capabilities steadily throughout training.

This model was trained on a relatively small dataset of under 50k for 100 epochs, specifically designed for user intent classification. The dataset consists of text samples labeled with different user intents like "information seeking", "question asking", or "opinion expressing". It was carefully curated from various sources, and its diversity allowed the model to learn to identify user intent accurately.

The goal of this training process is to enable the model to classify user intent in text data effectively, making it suitable for a wide range of applications involving user interaction analysis and personalization.

Intended Uses & Limitations

Intended Uses

• User Intent Classification: The main purpose of this model is to classify user intent in text data. It is well - suited for applications such as chatbots, virtual assistants, and recommendation systems that require understanding user intentions.

How to Use

To use this model for user intent classification, follow these steps:

from transformers import pipeline

classifier = pipeline("text - classification", model="Falconsai/intent_classification")
text = "Your text to classify here."
result = classifier(text)

Limitations

• Specialized Task Fine - Tuning: While the model performs well in user intent classification, its performance may vary when applied to other natural language processing tasks. Users interested in using this model for different tasks should explore fine - tuned versions available in the model hub for optimal results.

Training Data

The model's training data includes a proprietary dataset for user intent classification. This dataset contains a diverse collection of text samples categorized into various user intent classes. The training aimed to enable the model to classify user intent effectively.

Training Stats

Responsible Usage

It is crucial to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real - world applications, especially those involving potentially sensitive content.

References

• Hugging Face Model Hub
• DistilBERT Paper

⚠️ Important Note

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.

📄 License

This model is licensed under the Apache 2.0 license.