Language Modeling¶
Welcome to the Language Modeling module of NLP-101. This section covers the core concepts, techniques, and practical implementations in language modeling.
Module Topics & Resources¶
| Topic | Description | Resources |
|---|---|---|
| Byte Pair Encoding (BPE) | Subword tokenization algorithm for representing common character sequences | BPE Guide (implementation included in this README) |
| Resource Accounting | Tensor memory, precision types, and training efficiency in deep learning | Resource Accounting Guide |
| Architectures & Hyperparameters | Guidance on model architecture choices, normalization, activations, and hyperparameter trade-offs | Architectures & Hyperparameters |
What is Language Modeling?¶
Language modeling is a fundamental task in Natural Language Processing (NLP) that involves predicting the probability of a sequence of words. It is the backbone of many modern NLP applications, including machine translation and text generation.
Learning Objectives¶
| Objective | Description |
|---|---|
| Basic Concepts | Understand fundamental concepts of language modeling and probability distributions over text |
| Tokenization Techniques | Learn different tokenization approaches including character, word, and subword tokenization |
| Implementation | Implement and experiment with various language modeling techniques |