Description:
To help users understand the differences in Named Entity Recognition (NER) models, add a notebook that applies multiple NER techniques on the same dataset and compares results.
Tasks:
- Create a notebook to compare Spacy, NLTK, Stanford NER, and transformer-based models (e.g., BERT, RoBERTa, GPT-4 for NER).
- Provide visual comparisons and discuss precision, recall, and real-world use cases.
- Summarize key takeaways for each method.
- Name the notebook ner_comparison.ipynb.
- Update the README file with relevant references.
Description:
To help users understand the differences in Named Entity Recognition (NER) models, add a notebook that applies multiple NER techniques on the same dataset and compares results.
Tasks: