Text Classification with BERT
HuggingFace transformers, AutoTokenizer, AutoModelForSequenceClassification, fine-tuning.
The Limits of Static Embeddings
Word2Vec and GloVe give each word one fixed vector. But "bank" means different things in "river bank" and "savings bank". Contextual models like BERT fix this.
What Is BERT
BERT is a transformer model that reads a whole sentence at once and produces context-aware embeddings. Pretrained on huge text, it can be fine-tuned for tasks like classification.
All lessons in this course
- Word2Vec: Skip-gram and CBOW
- GloVe and FastText Embeddings
- Text Classification with BERT
- Semantic Similarity and Sentence Embeddings