This repository contains small-scale machine learning (ML) models trained on dummy datasets for exploratory and trial purposes. The purpose of this repository is to demonstrate the potential of different ML techniques, including their strengths and limitations, in handling specific tasks. The models included in this repository are:
- DistilBERT: A lightweight, distilled version of BERT for natural language processing tasks.
- Logistic Regression: A simple yet effective linear model for binary classification.
- Random Forest Classifier: An ensemble model that combines multiple decision trees for robust classification results.
The models provided in this repository offer diverse capabilities for query classification tasks. Here's how each can assist in classifying queries effectively:
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DistilBERT:
- Capable of understanding and processing natural language queries with contextual awareness.
- Useful for classifying queries into categories based on their semantic meaning.
- Works well in scenarios involving complex or conversational query structures.
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Logistic Regression:
- Provides a straightforward method for binary classification tasks.
- Suitable for classifying simple queries where the feature space is limited and well-structured.
- Offers interpretability, allowing insights into which features contribute most to the classification.
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Random Forest Classifier:
- An excellent choice for handling high-dimensional and diverse datasets.
- Capable of managing noisy data and extracting important features for classification.
- Performs well for queries with structured data or a mix of categorical and numerical features.
By leveraging these models, you can classify queries accurately in various scenarios, ranging from natural language processing to structured data analysis. They serve as foundational tools that can be further customized for specific tasks, ensuring efficient query categorization without the need for additional guardrails in many cases