furkandrms / text-explorating-bert

This script leverages the BERT model for text exploration by predicting masked tokens within a given text.

Data Scientist & Engineer | Machine Learning Practitioner | AI Enthusiast

📍 Based in the historical and vibrant city of Istanbul, I am passionately deepening my expertise in Data Science & Engineering. My journey is fueled by a relentless quest to transform complex data into actionable insights that drive strategic decision-making. With a keen interest in exploring the intricacies of big data, I aspire to harness its full potential to innovate and solve real-world problems.

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📈 GitHub Stats

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text-explorating-bert's Issues

Enhance Text Exploration with BERT for Multiple [MASK] Tokens and Performance Evaluation

The current implementation of the Text Exploration with BERT project provides a solid foundation for predicting words for a single [MASK] token within a given piece of text. However, there are two significant areas where the project could be enhanced to increase its utility and applicability: handling multiple [MASK] tokens and introducing a performance evaluation function.

Feature 1: Handling Multiple [MASK] Tokens
Problem Statement: The predict function currently does not explicitly support sentences with multiple [MASK] tokens. In real-world scenarios, users might want to predict multiple masked words within the same context, which is not currently feasible with the existing setup.

Proposed Solution: Enhance the predict function to allow for the handling and prediction of multiple [MASK] tokens within a single input text. This would involve adjusting the function to iteratively or simultaneously predict words for each [MASK] token, taking into account the context provided by other tokens in the sentence.

Feature 2: Performance Evaluation Function
Problem Statement: After training or fine-tuning the BERT model, users currently do not have a built-in method to evaluate the model's performance. Key metrics such as accuracy, precision, recall, or F1 score are essential for understanding the effectiveness of the model on a given dataset.

Proposed Solution: Introduce a utility function that allows users to calculate and report key performance metrics. This function should support evaluation on a validation set and report metrics that are relevant for masked token prediction tasks, such as accuracy for correctly predicted tokens.

Impact
Implementing these features would significantly enhance the project's capabilities, making it more versatile and user-friendly. Users would be able to explore texts with multiple masked tokens more effectively and have clear insights into the model's performance, facilitating better decision-making for improvements or deployments.