Assignment
In the previous section, we used traditional embedding with TF-IDF to transform text data into numerical representations, which can then be used for various natural language processing (NLP) tasks such as clustering. Let’s now try and improve the clustering results by using a more advanced embedding technique. We will use the Hugging Face Transformers library to get pre-trained embeddings for our product descriptions:
- Ask ChatGPT to explain Hugging Face Transformers’ advantages over TF-IDF vectorization for clustering use cases.
- Use ChatGPT to generate and create product clusters using Hugging Face Transformers embeddings.
- Compare the results with the previous clustering results using TF-IDF vectorization.
