NLP and transformers

Text processing, embeddings, attention and transformer models.

All 118 notes, A to Z

BART for Sequence Classification in Python6 min
Introduction to BART for Sequence Classification
BERT for Question Answering7 min
BERT (Bidirectional Encoder Representations from Transformers) is a powerful language model that has revolutionized natural language processing tasks...
Context-Independent Word Embeddings in Python10 min
Introduction to Context-Independent Word Embeddings
Contextual Awareness in AI and Language Models11 min
Context plays a crucial role in the functioning and effectiveness of artificial intelligence, particularly in large language models. It provides the...
Essential Language Model Terms for Everyday Use13 min
Transformer models are the foundation of modern Natural Language Processing (NLP). They process text efficiently by handling multiple parts of a sentence...
Exploring Byte Pair Encoding Tokenization with Python11 min
Byte Pair Encoding is a data compression technique that iteratively replaces the most frequent pair of bytes in a sequence with a single, unused byte. In the...
Exploring Efficient Small Language Models10 min
Small Language Models (SLMs) are compact versions of larger language models, designed to be more efficient and deployable in resource-constrained...
Exploring Vector Embeddings in Language Models14 min
Vector embeddings form the foundation of modern language models by transforming discrete tokens into continuous vector spaces. These dense representations...
Extended Mind Transformers for Long Context NLP in Python5 min
Extended Mind Transformers (EMT) are a novel approach to handling long-context tasks in natural language processing without the need for extensive...
Generating Structured Outputs with Language Models Using Python7 min
Structured LLM outputs allow us to generate specific formats of text from language models, making it easier to parse and use the results in various...
Generating Text with Language Models10 min
Language models are powerful tools for generating text, but the process is not as straightforward as it might seem. These models are trained to predict the...
Introduction to Context Evolution in Language Models7 min
Context evolution in language models refers to the dynamic process of updating and adapting the contextual understanding of a model as it processes...
Introduction to Small Language Models (SLMs) in Python10 min
Small Language Models (SLMs) are compact, efficient versions of larger language models, designed to perform specific tasks with reduced computational...
Introduction to Vision-Language Models in Python7 min
Introduction to Vision-Language Models (VLMs)
Language Modeling in NLP with Python11 min
Language modeling is a fundamental task in Natural Language Processing (NLP) that involves predicting the probability of a sequence of words. It forms the...
Mastering BERT Transforming NLP with Bidirectional Understanding10 min
BERT (Bidirectional Encoder Representations from Transformers) revolutionized natural language processing by introducing true bidirectional understanding...
Mastering Temperature and Top_p in Python Language Models10 min
Temperature and top\p are crucial parameters in language models that control the randomness and diversity of generated text. These settings affect how the...
Optimizing Language Model Prompts8 min
Neural networks form the backbone of modern language models, requiring careful optimization across multiple dimensions including architecture, training data...
Optimizing Transformer Models with KV Cache and Advanced Techniques10 min
KV Cache, short for Key-Value Cache, is a technique used to optimize the performance of transformer models. It stores precomputed key and value tensors from...
Physics of Language Models in Python9 min
Language models are statistical models that predict the probability of sequences of words. They form the backbone of many natural language processing tasks....
Quantization Effects on Multilingual Language Models12 min
Quantization is a technique used to reduce the computational and memory requirements of large language models (LLMs) while maintaining their performance. In...
Reinforcement Learning from Human Feedback! Aligning Language Models Using Python:8 min
Reinforcement Learning from Human Feedback (RLHF) is a technique used to align language models with human preferences. It involves training two key...
Scaling Laws for Neural Language Models in Python7 min
Neural language models have shown remarkable improvements in recent years, largely due to increases in model size and training data. This slide introduces...
Sequence-to-Sequence for Unordered Sets in Python7 min
In traditional sequence-to-sequence models, the order of input elements is crucial. However, when dealing with sets, where order is irrelevant, we need a...
Sequence-to-Sequence Models in Python8 min
Sequence-to-Sequence (Seq2Seq) models are a type of neural network architecture designed to transform an input sequence into an output sequence. They are...
Temperature's Role in Language Model Output8 min
Temperature is a crucial hyperparameter in language models that controls the randomness of the model's output. It affects the probability distribution of the...
The Power of Word Embeddings in Language Models11 min
Token embeddings form the foundation of modern language models by transforming discrete tokens into continuous vector representations. These dense vectors...
Training Custom Tokenizers with Hugging Face Transformers in Python3 min
Tokenization is the process of breaking down text into smaller units called tokens. It is a crucial step in natural language processing (NLP) tasks, as it...
Understanding BERT A Cornerstone of NLP with Python12 min
BERT (Bidirectional Encoder Representations from Transformers) is a revolutionary natural language processing model that has significantly improved the way...
Understanding Embeddings and Sentence Transformers in NLP12 min
Embeddings are dense vector representations of words, sentences, or documents in a continuous vector space. They capture semantic meaning and relationships...