DistilBERT for Multilabel Text Classification

Natural Language Processing • Independent Project

Challenge

The goal of the project was to fine-tune DistilBERT for multilabel text classification, enabling documents to be categorized into multiple relevant categories simultaneously. The dataset was complex, with overlapping labels, requiring a model capable of efficiently handling multilabel classification.

Approach

I led the fine-tuning of DistilBERT, leveraging transfer learning to adapt the pre-trained model to the specific requirements of the dataset. We optimized the model for multilabel classification, fine-tuning it to improve its ability to predict multiple categories realted to toxic language for each document. Special care was taken to preprocess the data and optimize the model’s threshold for classification.

Results

The model significantly improved document categorization accuracy and efficiency, streamlining large-scale text processing. The enhanced accuracy enabled better decision-making and reduced the manual effort required for categorization, making it suitable for handling large datasets.

Future Plans

The next step is to integrate the model into a real-time classification pipeline, further optimizing the fine-tuning for more domain-specific applications. Exploring additional architectures and models to enhance performance is also a potential direction for future improvements.

Expertise

The project team had strong expertise in natural language processing and machine learning. My contributions included leading the fine-tuning process, optimizing the multilabel classification algorithm, as well as performing NLP to better understand the data set.