In this project, I fine-tuned a BERT model to generate embeddings for text data and employed t-SNE for dimensionality reduction, enabling effective visualization of complex linguistic patterns. This approach facilitates a deeper understanding of textual relationships and structures within the dataset.
BERT Fine-Tuning: Adapted a pre-trained BERT model to produce contextually rich embeddings tailored to the specific text corpus.
Dimensionality Reduction: Applied t-SNE to reduce the high-dimensional embeddings into two or three dimensions, preserving the intrinsic structure of the data.
Data Visualization: Created visual representations that reveal clusters and patterns in the text data, aiding in the interpretation of linguistic relationships.
Analytical Insights: Utilized visualizations to identify trends, anomalies, and groupings within the text corpus, supporting data-driven decision-making.
Tool Integration: Leveraged libraries such as Hugging Face Transformers and scikit-learn to streamline the fine-tuning and visualization processes.