NLP to LLM from Basic Principles: NLPlanet NLP and ML pt.4

Photo by Nhu Nguyen on Unsplash

Preface:

I am undertaking a Natural Language Processing and Machine Learning course that uses NLTK and HuggingFace. I have taken notes on their lessons and would like to share them. Although I tried to be in-depth and transformative with my notebook, I strongly encourage following their course as it is detailed and well-formatted.

Credit:

Course Link:

Welcome to the Course — Practical NLP with Python (nlplanet.org)

(Section link provided further down)

Author:

Fabio Chiusano — Medium

Note Information:

This is a Jupyter Notebook uploaded to my Github as provided in the link further down.

Notes include definitions, examples, and explanations.

Uses Sentence Transformers, Hugging Face Hub, Pandas, NumPy, Scikit-learn, and Plotly.

Title:

Section 1.12

Representing Text as Vectors with Word Embeddings

Section Link:

1.12 Representing Texts as Vectors: Word Embeddings — Practical NLP with Python (nlplanet.org)

Information Covered (All Defined in Notes):

• Word Embeddings
• Word Embedding models
• Context-independent Embedding
• Context-dependent Embedding
• Pre-trained Models
• Finetuning
• Sentence Embeddings
• Sentence Transformers
• Cosine Similarity
• MTEB
• Analyzing Similarity
• Plotly Visualizations
• Datasets
• DataFrames
• Bag of Words vs Embeddings comparison

Note Link:

NLPlanet Lesson 1.12 Notes: Representing Text as Vectors with Word Embeddings