Jupyter Notebook describing named entity recognition (NER) using conditional random fields (CRFs), implemented using scikit-learn / sklearn-crfsuite.

Contains jupyter notebooks, presentations and examples for Keras, Google AI Platform and Kubeflow.

Name Entity Recognition toolkit lunches Entator class - inline annotator within your Jupyter notebook in Python for name entities in text.

Notebooks for fine-tuning and running of PubMedBERT-based classification models, used in the ANDDigest tool

Chinese NER problem that needs to capture 18 types of entities in medical conversation text. The process is divided into 4 parts that are encapsulated in high-level abstract classes. We control the workflow in a single Jupyter notebook.

Training Notebooks

Jupyter notebook used to generate the fine-tuned models for korean natural language processing

NLP notebooks

If you want to learn these topics, i refer you to go through this notebook.

Contains the notebook used during the shared task SMM4H-ProfNER

Contains relevant notebooks for the hands-on NLP workshop by organized Analytics India Magazine Plugin Conference-2020 Edition

NER Training on BERT model using Transformers on medical data, continual learning over 3 datasets; Notebooks and Scripts

short and simple notebook which demonstrates the creation of a graph which visualizes the connections network between the main places and people in a book

Classification of acronyms and their long forms using an RNN (LSTM), CNN, and FFNN model. The experiments focused on the RNN and used different vectorisation methods and hyperparameters. Models were built with Keras and the notebook code runs on Google Colab.