This repository demonstrates the implementation of Named Entity Recognition (NER) using both traditional machine learning approaches and deep learning approaches.
Specifically, it covers:
- Traditional NER using Conditional Random Fields (CRF).
- Deep Learning NER using Bidirectional Long Short-Term Memory (BiLSTM)
ner_traditional/
crf_model.ipynb
: Jupyter Notebook for CRF-based NER implementation.
ner_deep_learning/
bilstm_model.ipynb
: Jupyter Notebook for BiLSTM-based NER implementation.
saved_models/
: Directory to save trained models.requirements.txt
: List of required packages for the project.docs/
: Detailed project documentation.ner_models_developments.pdf
Ensure you have Python installed on your system. You can install the required packages using the requirements.txt
file.
pip install -r requirements.txt