Welcome to the Embeddings for NLP repository. This repository contains a Jupyter Notebook, main.ipynb, which covers various concepts related to embeddings in Natural Language Processing (NLP). This is based on the book Build a Large Language Model (From Scratch)
Have you ever wondered how machines understand the nuances of human language? It all starts with tokenization, the foundational step in training language models to grasp our complex languages. This repository delves deep into this foundational element, demonstrating how tokenization enables machines to decode and process human speech through the lens of NLP.
Join us for an engaging exploration of the initial challenges of segmenting text into manageable pieces to the sophisticated techniques that enable deeper language understanding. Whether you’re just starting out or looking to brush up on the latest in NLP, this repository promises a blend of foundational knowledge and advanced insights.
- Introduction to Tokenization: Understanding the basics and importance of tokenization in NLP.
- Impact of Tokenization on Language Models: Exploring how tokenization affects the performance and efficiency of language models.
- Text Splitting for Deeper Analysis: Techniques for splitting text into meaningful segments for better analysis.
- Byte Pair Encoding (BPE): Exploring the efficiency and benefits of BPE in tokenization.
- Sliding Windows for Better Training Data: Utilizing sliding windows to enhance the quality of training data.
- Converting Tokens into Vectors: Methods for converting tokens into numerical vectors for model training.
To get started with this repository, follow these steps:
- Clone the repository:
git clone https://github.com/debnsuma/nlp-embeddings
- Navigate to the repository directory:
cd embeddings-for-nlp - Install the required dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook main.ipynb
- Python 3.x
- Jupyter Notebook
- Libraries: numpy, torch