LLM Embedding-Based Named Entity Recognition


This example visualizes BERT-based named entity recognition (NER). It works by splitting text into tokens, feeding the token sequence into a large language model (BERT) to retrieve embeddings per token. The embeddings are then classified.

To run this example use

pip install -r examples/python/llm_embedding_ner/requirements.txt python examples/python/llm_embedding_ner/main.py

You can specify your own text using

main.py [--text TEXT]