Natural Language Processing

Colab notebooks exploring different Machine Learning topics.

Notebooks for teaching Named Entity Recognition at the Cultural Heritage Data School, run by Cambridge Digital Humanities

A Jupyter notebook demonstrating entity extraction on headlines with SpaCy.

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

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

Jupyter notebook for geoparsing French historical documents with the PERDIDO Geoparser

Training Notebooks

This repository contains codes and notebooks used in various competitions

Atelier d'initiation à la reconnaissance d'entités nommées avec spaCy

Forks, Models, and Notebooks

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.

This repository contains the social media data scraper and the notebooks of this analysis. Where we analise the Social Media posts - tweets with Sentiment Analysis then we analyse this results with Named Entity Recognition (NER) and Information Extraction methods to get a more accurate and detailed picture of this sentiment results.

Python code and Notebooks of NLP practices and MOOC

Embark on your NLP journey by learning essential techniques through a series of notebooks designed to kickstart your career in this field.

Notebook for Flan-T5 – an alternative to large language models like GPT-3 & GPT-4 for NLP tasks like named entity recognition and text generation.

Jupyter notebook for geoparsing French hike descriptions with the PERDIDO Geoparser

Notebooks of our research and modelling for Named Entity Recognition (Location Only)

A Jupyter Notebook containing methods for common tasks related to the field of Natural Language Processing

A comprehensive set of Jupyter notebooks that take you from NLP fundamentals to advanced techniques. Covers text preprocessing, POS tagging, NER, sentiment analysis (with VADER), text classification, word embeddings, and transformer models like BERT. Built with real-world datasets using NLTK, spaCy, scikit-learn, and Hugging Face Transformers.