See how to do topic modeling using Roberta and transformers. We will use a pre-trained Roberta model finetuned on the NLI dataset for getting embeddings and then do topic modelling.
Cluster text documents using BERT embeddings and Kmeans. See how you can apply the K-means algorithm on the embedding to cluster documents.
Summarize text document using Huggingface transformers and BERT. Use different transformer models for summary and findout the performance.