ALL AI DEVELOPERS MUST KNOW - TOKENIZATION IN LLMs

The video delves into the intricacies of tokenization, a crucial step in Natural Language Processing (NLP) pipelines, focusing on its types and significance. It highlights the role of tokenization in converting text into tokens, which are essential for NLP tasks, as models require numerical inputs. The speaker explores various tokenization methods, including character, word, and subword tokenization, with a detailed emphasis on Byte Pair Encoding (BPE) and its variant, byte-level BPE. The discussion covers the benefits of these methods, such as vocabulary management and handling rare words, and includes a practical demonstration of creating a tokenizer using Hugging Face.

• Tokenization is the process of breaking down text into smaller units known as tokens. These can be words, characters, subwords, or any other entity.
• It is a fundamental step in NLP, crucial for any NLP task, as models require discrete numerical inputs for processing.

"Tokenization is the process of breaking down a text into a smaller unit which is known as token."

• Tokenization is essential for converting text into a format suitable for NLP models, facilitating the embedding stage where text is converted into numbers.

• Tokenization should be done after text cleaning and before vectorization, as models expect numerical inputs.
• Text cleaning can be viewed as text formatting or processing, ensuring data is in a common format for training.

"Tokenization is usually done once you feel that your Ro Text data is clean and then you are ready for providing a model input."

• The process helps in vocabulary management, feature extraction, and dimensionality reduction.

• NLP models need discrete inputs, which are numbers, to convert into embeddings.
• Tokenization controls the granularity level of the model, affecting performance and vocabulary management.

"NLP models require discrete inputs which is just numbers so that it can convert into embeddings and then drain itself."

• Effective tokenization ensures language understanding, vocabulary management, and supports multilingual applications.

• Text normalization is crucial, involving converting text to lowercase, removing punctuation, and handling special characters.
• Boundary detection identifies where tokens start and end, which can be at whitespace, character, or punctuation levels.

"Make sure that your text is normalized for that use case."

• Token extraction involves separating tokens from the original text, which can be customized using rules like regex.

• Breaks text into individual characters, including spaces and punctuation.
• Simple and avoids out-of-vocabulary issues, useful for character-level analysis.

"A character tokenizer as the name suggest will break down character into sorry text into individual character."

• It loses word-level semantics and is computationally expensive.

• Splits text into words, often using spaces or punctuation as delimiters.
• Preserves word-level semantics and is easier to interpret for many NLP tasks.

"If it is split it as words then it is Word level and here what we'll do is we usually split it as spaces."

• Challenges include large vocabulary size, out-of-vocabulary words, and handling compound words or unconventional spellings.

• Similar to word tokenization but specifically splits text at whitespace, not considering punctuation separately.
• Simple and fast, but shares the same advantages and disadvantages as word-level tokenization.

"This wh space tokenization won't handle punctuation well because it is together maintained."

• Does not handle punctuation well, as it is included within tokens.

These notes provide a comprehensive overview of tokenization, its importance, process, and types, emphasizing its role in NLP and language models.

• Subword tokenization breaks words into meaningful units, balancing vocabulary size and semantic representation.
• It bridges the gap between character-level and word-level tokenization, capturing common patterns in word variations.
• Examples include handling variations like "play," "playing," "played," etc., by identifying common roots.

"Subword tokenization is a method, as the name suggests, to break words into meaningful units so that you'll be balancing the vocabulary size and semantic representation."

• Subword tokenization aims to balance vocabulary size and semantic representation by breaking words into smaller meaningful units.

"Subword works on that. What it will do is it will try to split the word itself into smaller words, making sure that the semantic representation and vocabulary size both are balanced together."

• The method splits words into smaller units to maintain balance between semantic representation and vocabulary size.

• BPE starts with a vocabulary of individual characters and merges the most frequent adjacent character pairs.
• It balances vocabulary size and token semantics, solving issues like out-of-vocabulary words.
• BPE can sometimes fail to capture morphological structures effectively.

"The byte pair encoding is a method which starts with a vocabulary of initial characters, which is individual characters, and what we'll do is we'll go in an additive approach where we will merge the most frequent adjacent pair of characters."

• BPE begins with individual characters and merges frequent pairs to form a balanced vocabulary.

"Whatever issues we have addressed as main issues of tokenizer, everything will be solved here: balance between vocabulary size and token semantics and then handling rare words, out-of-vocabulary words."

• BPE addresses main tokenizer issues, balancing vocabulary size and semantics, and handling rare words.

• BPE has variants like B-level BPE, which operates at byte level instead of token level.
• It captures common suffixes and stop words, but may not split effectively at character or word level.

"There is a variant of this tokenizer which is B-level BP, where instead of working at these token levels, it will be working on its byte level."

• B-level BPE operates at the byte level, providing an alternative approach to tokenization.

• WordPiece is similar to BPE but selects tokens based on maximum likelihood in training data.
• It is computationally expensive and may generate unknown tokens (Unk).
• Preferred for multilingual models despite higher computational cost.

"WordPiece is similar to BP but instead of choosing the most frequent pair, it will try to identify which token has the maximum likelihood to be there in the training data."

• WordPiece selects tokens based on likelihood in training data, differing from BPE's frequency-based approach.

"The likelihood of an unknown token being occurring is higher with WordPiece tokenization, but still, it can be handled by providing all the character possibility character set."

• WordPiece may generate more unknown tokens, but this can be managed with comprehensive character sets.

• Unigram starts with a large vocabulary and removes tokens to achieve the desired vocabulary size.
• It is robust and optimal for vocabulary selection, but complex to implement and train.
• Works by calculating loss for each token and removing those with low real-world occurrence probability.

"Unigram will start with a large vocabulary and then it will remove tokens to reach the vocabulary."

• Unigram reduces vocabulary size by removing less probable tokens, starting from a large initial set.

"It is very optimal to find the vocabulary because you are working down from above, which means your vocabulary is good, but you are removing the tokens which have very less possibility to be there in the real world."

• Unigram optimizes vocabulary by eliminating tokens with low real-world likelihood.

• SentencePiece is a framework that applies BPE or Unigram, treating space as part of the token set.
• It processes raw text streams, learning vocabulary with space-separated tokens.
• It offers consistency and reversibility but may not be as effective as other methods.

"SentencePiece is not a tokenization algorithm by itself; it is a framework where you can use BP or Unigram."

• SentencePiece provides a framework for applying BPE or Unigram tokenization methods.

"It will have input as a raw string where space is also considered to be a part of the token set."

• SentencePiece treats spaces as part of the token set, processing raw text streams.

• BPE is suitable for general-purpose models and common in many large language models.
• WordPiece is preferred for models requiring detailed morphological understanding.
• SentencePiece and Unigram are optimal for handling multilingual data and ambiguity.

"If you're going to use for a general-purpose model, BPE is good. If you want to work it with encoder models, basically when morphology is the most important thing, then you go for WordPiece."

• BPE is recommended for general models, while WordPiece suits morphology-focused encoder models.

"For multilingual, go for SentencePiece, and Unigram is working on top of anything to handle ambiguity."

• SentencePiece and Unigram are recommended for multilingual applications and ambiguity handling.

• Tokenization is the process of converting text into smaller units called tokens, which can be words or characters.
• In NLP, a tokenizer constructs a vocabulary by splitting words and adding special symbols like underscores to denote word boundaries.
• The vocabulary construction involves identifying unique tokens and their frequencies in the text.

"It will split the word by spaces and once it is done, it will add a special symbol underscore to say this is the boundary of the word."

• This quote explains the initial step of tokenization where words are split by spaces, and underscores are added to indicate boundaries.

• The first step in tokenization is creating a base vocabulary from unique characters.
• Tokens are represented as characters, and frequent character pairs are merged to form new tokens.
• The merging process stops when the desired vocabulary size is achieved.

"First, you'll create a base vocabulary, which is character level... and then representing these words as characters and then merging the most frequent pair."

• This quote outlines the process of creating a base vocabulary and merging frequent character pairs to construct tokens.

• BPE is a technique used to merge frequent character pairs to create a compact and efficient vocabulary.
• The process involves identifying frequent character pairs and merging them to form new tokens.
• BPE is useful for handling unseen words by breaking them into known tokens.

"This is just an example working of how BP basically works behind while it is trying to construct the vocabulary."

• This quote provides an example of how BPE works by merging frequent character pairs to construct a vocabulary.

• Byte-Level BPE operates at the byte level, converting words or tokens into raw bytes.
• It allows for the encoding of any character, including Unicode characters, making it suitable for multilingual languages.
• Byte-Level BPE ensures a lossless tokenization process by not losing any information during encoding.

"Instead of working at character or word level, it will operate at byte level."

• This quote highlights the difference between traditional BPE and Byte-Level BPE, emphasizing its byte-level operation.

• Universal coverage: Byte-Level BPE can represent any character in any language, making it highly versatile.
• Efficient representation: It provides an efficient way to encode a wide range of characters without losing information.
• Lossless tokenization: Ensures that no information is lost during the tokenization process.

"The benefits is like I said, like, you know, Universal coverage, efficient representation... you'll not lose any information."

• This quote summarizes the advantages of using Byte-Level BPE, including universal coverage and efficient representation.

• The implementation involves creating a BP tokenizer from scratch, initializing it with a corpus file path, and training it.
• The tokenizer builds a vocabulary by mapping integers to byte-level tokens and saving the merged tokens.
• The process includes overriding functions such as train, encode, and decode to customize the tokenizer's behavior.

"I'll just walk you through these codes... I'm creating the BP tokenizer and then OS is there, I'm providing my Corpus file path."

• This quote describes the initial steps in implementing a BP tokenizer, including setting up the corpus file path and training the tokenizer.

• BP (Byte Pair Encoding) tokenizer is a method to tokenize text into byte-level tokens, facilitating vocabulary building.
• The tokenizer reads merges and constructs vocabulary by splitting text at the byte level.
• It iteratively merges byte pairs to reach a desired vocabulary size, creating new tokens and assigning IDs.

"You'll read your merges and then based on those merges you will build your vocabulary."

• Explanation: The process involves reading predefined merges to build a vocabulary, essential for tokenization.

• The train function takes arguments such as text, vocabulary size, and verbosity.
• Converts input strings to byte-level integers, merging byte pairs iteratively until the vocabulary size is reached.
• New tokens are created and assigned IDs beyond the initial 256.

"It will convert the input string into byte level integers and then it will iteratively merge together the consecutive byte pair until the vocab size is reached."

• Explanation: The train function is essential for preparing text data into a format conducive for tokenization by merging byte pairs.

• Utilized in the merging process to determine the frequency of byte pairs.
• Generates pairs and finds occurrences, adding the most frequent pair to merges and vocabulary.

"Get stats, what it will do is for example you have IDs 1 2 312 it will generate pairs like 1 2 2 3 3 1 1 2 and then it will find the occurrence."

• Explanation: The function is crucial for identifying the most frequent byte pairs, guiding the merging process.

• Involves computing byte sequences and merging based on byte pair frequency.
• Uses the get stats function to determine the best pair to merge.
• Decoding refers to referencing the vocabulary for token interpretation.

"Given a new sentence, let's say your tokenizer restrain, how you will compute the tokens at inference time is you will again calculate the bytes."

• Explanation: The tokenization process includes recalculating byte sequences and determining optimal merges for efficient tokenization.

• Encourages understanding and creating a BP tokenizer by facing multiple scenarios, including handling five languages.
• Participants are challenged to create a tokenizer capable of processing tokens from diverse languages.

"Let's say I'll take English German French Hindi Tamil okay let's say these are my five languages and this is a challenge."

• Explanation: The exercise is designed to test the ability to create a versatile tokenizer capable of handling multiple languages.

• Instead of manual training, Hugging Face's pre-tokenizer and BP tokenizer can be used.
• The process involves loading a dataset, writing it line by line, and constructing a tokenizer with a specified vocabulary size.

"Rather what you are going to do is you're going to use hugging, you'll call the pre-tokenizer byte level."

• Explanation: Hugging Face offers a streamlined approach to tokenization, leveraging pre-built tools for efficiency.

• Configures special tokens like EOS (End of Sequence) and manages tokenization settings.
• The tokenizer can be saved and loaded, facilitating reuse and adaptation.

"You'll save the tokenizer by calling the save function you'll provide a save path."

• Explanation: Saving and configuring the tokenizer ensures it can be reused and adapted to various text processing tasks.

• Demonstrates the tokenization pipeline, including training from scratch and using Hugging Face tools.
• Shows byte-level operations and merging processes, highlighting the efficiency of using pre-built libraries.

"Here you can see this is how the tokenizer file will be these are my tokens."

• Explanation: The demonstration provides a practical look at tokenization processes, showcasing the effectiveness of different approaches.

• Encourages viewers to engage with the content, experiment with tokenization, and utilize available resources.
• Highlights the comprehensive nature of the video on tokenization and invites feedback.

"I hope you all like this video I hope you all find this informative."

• Explanation: The conclusion wraps up the content, encouraging further learning and interaction with the material.