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Continuum Knowledge
  • Continuum
  • Data
    • Datasets
      • Pre Training Data
      • Types of Fine Tuning
      • Self Instruct Paper
      • Self-Alignment with Instruction Backtranslation
      • Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets
      • Instruction Tuning
      • Instruction Fine Tuning - Alpagasus
      • Less is More For Alignment
      • Enhanced Supervised Fine Tuning
      • Visualising Data using t-SNE
      • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
      • Training and Evaluation Datasets
      • What is perplexity?
  • MODELS
    • Foundation Models
      • The leaderboard
      • Foundation Models
      • LLama 2 - Analysis
      • Analysis of Llama 3
      • Llama 3.1 series
      • Google Gemini 1.5
      • Platypus: Quick, Cheap, and Powerful Refinement of LLMs
      • Mixtral of Experts
      • Mixture-of-Agents (MoA)
      • Phi 1.5
        • Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach
      • Phi 2.0
      • Phi-3 Technical Report
  • Training
    • The Fine Tuning Process
      • Why fine tune?
        • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
        • Explanations in Fine Tuning
      • Tokenization
        • Tokenization Is More Than Compression
        • Tokenization - SentencePiece
        • Tokenization explore
        • Tokenizer Choice For LLM Training: Negligible or Crucial?
        • Getting the most out of your tokenizer for pre-training and domain adaptation
        • TokenMonster
      • Parameter Efficient Fine Tuning
        • P-Tuning
          • The Power of Scale for Parameter-Efficient Prompt Tuning
        • Prefix-Tuning: Optimizing Continuous Prompts for Generation
        • Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models
        • What is Low-Rank Adaptation (LoRA) - explained by the inventor
        • Low Rank Adaptation (Lora)
        • Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)
        • QLORA: Efficient Finetuning of Quantized LLMs
        • Bits and Bytes
        • The Magic behind Qlora
        • Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation
        • The quantization constant
        • QLORA: Efficient Finetuning of Quantized Language Models
        • QLORA and Fine-Tuning of Quantized Language Models (LMs)
        • ReLoRA: High-Rank Training Through Low-Rank Updates
        • SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
        • GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection
      • Hyperparameters
        • Batch Size
        • Padding Tokens
        • Mixed precision training
        • FP8 Formats for Deep Learning
        • Floating Point Numbers
        • Batch Size and Model loss
        • Batch Normalisation
        • Rethinking Learning Rate Tuning in the Era of Language Models
        • Sample Packing
        • Gradient accumulation
        • A process for choosing the learning rate
        • Learning Rate Scheduler
        • Checkpoints
        • A Survey on Efficient Training of Transformers
        • Sequence Length Warmup
        • Understanding Training vs. Evaluation Data Splits
        • Cross-entropy loss
        • Weight Decay
        • Optimiser
        • Caching
      • Training Processes
        • Extending the context window
        • PyTorch Fully Sharded Data Parallel (FSDP)
        • Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
        • YaRN: Efficient Context Window Extension of Large Language Models
        • Sliding Window Attention
        • LongRoPE
        • Reinforcement Learning
        • An introduction to reinforcement learning
        • Reinforcement Learning from Human Feedback (RLHF)
        • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • INFERENCE
    • Why is inference important?
      • Grouped Query Attention
      • Key Value Cache
      • Flash Attention
      • Flash Attention 2
      • StreamingLLM
      • Paged Attention and vLLM
      • TensorRT-LLM
      • Torchscript
      • NVIDIA L40S GPU
      • Triton Inference Server - Introduction
      • Triton Inference Server
      • FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference
      • Is PUE a useful measure of data centre performance?
      • SLORA
  • KNOWLEDGE
    • Vector Databases
      • A Comprehensive Survey on Vector Databases
      • Vector database management systems: Fundamental concepts, use-cases, and current challenges
      • Using the Output Embedding to Improve Language Models
      • Decoding Sentence-BERT
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • SimCSE: Simple Contrastive Learning of Sentence Embeddings
      • Questions Are All You Need to Train a Dense Passage Retriever
      • Improving Text Embeddings with Large Language Models
      • Massive Text Embedding Benchmark
      • RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
      • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
      • Embedding and Fine-Tuning in Neural Language Models
      • Embedding Model Construction
      • Demystifying Embedding Spaces using Large Language Models
      • Fine-Tuning Llama for Multi-Stage Text Retrieval
      • Large Language Model Based Text Augmentation Enhanced Personality Detection Model
      • One Embedder, Any Task: Instruction-Finetuned Text Embeddings
      • Vector Databases are not the only solution
      • Knowledge Graphs
        • Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration
        • Unifying Large Language Models and Knowledge Graphs: A Roadmap
      • Approximate Nearest Neighbor (ANN)
      • High Dimensional Data
      • Principal Component Analysis (PCA)
      • Vector Similarity Search - HNSW
      • FAISS (Facebook AI Similarity Search)
      • Unsupervised Dense Retrievers
    • Retrieval Augmented Generation
      • Retrieval-Augmented Generation for Large Language Models: A Survey
      • Fine-Tuning or Retrieval?
      • Revolutionising Information Retrieval: The Power of RAG in Language Models
      • A Survey on Retrieval-Augmented Text Generation
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • Retrieve Anything To Augment Large Language Models
      • Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators
      • Active Retrieval Augmented Generation
      • DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints
      • DSPy: Compiling Declarative Language Model Calls
      • DSPy: In-Context Learning for Extreme Multi-Label Classification
      • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
      • HYDE: Revolutionising Search with Hypothetical Document Embeddings
      • Enhancing Recommender Systems with Large Language Model Reasoning Graphs
      • Retrieval Augmented Generation (RAG) versus fine tuning
      • RAFT: Adapting Language Model to Domain Specific RAG
      • Summarisation Methods and RAG
      • Lessons Learned on LLM RAG Solutions
      • Stanford: Retrieval Augmented Language Models
      • Overview of RAG Approaches with Vector Databases
      • Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems
    • Semantic Routing
    • Resource Description Framework (RDF)
  • AGENTS
    • What is agency?
      • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
      • Types of Agents
      • The risk of AI agency
      • Understanding Personality in Large Language Models: A New Frontier in AI Psychology
      • AI Agents - Reasoning, Planning, and Tool Calling
      • Personality and Brand
      • Agent Interaction via APIs
      • Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon
      • A Survey on Language Model based Autonomous Agents
      • Large Language Models as Agents
      • AI Reasoning: A Deep Dive into Chain-of-Thought Prompting
      • Enhancing AI Reasoning with Self-Taught Reasoner (STaR)
      • Exploring the Frontier of AI: The "Tree of Thoughts" Framework
      • Toolformer: Revolutionising Language Models with API Integration - An Analysis
      • TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion
      • Unleashing the Power of LLMs in API Integration: The Rise of Gorilla
      • Andrew Ng's presentation on AI agents
      • Making AI accessible with Andrej Karpathy and Stephanie Zhan
  • Regulation and Ethics
    • Regulation and Ethics
      • Privacy
      • Detecting AI Generated content
      • Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs
      • Adverse Reactions to generative AI
      • Navigating the Ethical Minefield: The Challenge of Security in Large Language Models
      • Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making
  • DISRUPTION
    • Data Architecture
      • What is a data pipeline?
      • What is Reverse ETL?
      • Unstructured Data and Generatve AI
      • Resource Description Framework (RDF)
      • Integrating generative AI with the Semantic Web
    • Search
      • BM25 - Search Engine Ranking Function
      • BERT as a reranking engine
      • BERT and Google
      • Generative Engine Optimisation (GEO)
      • Billion-scale similarity search with GPUs
      • FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
      • Neural Collaborative Filtering
      • Federated Neural Collaborative Filtering
      • Latent Space versus Embedding Space
      • Improving Text Embeddings with Large Language Models
    • Recommendation Engines
      • On Interpretation and Measurement of Soft Attributes for Recommendation
      • A Survey on Large Language Models for Recommendation
      • Model driven recommendation systems
      • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
      • Foundation Models for Recommender Systems
      • Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
      • AI driven recommendations - harming autonomy?
    • Logging
      • A Taxonomy of Anomalies in Log Data
      • Deeplog
      • LogBERT: Log Anomaly Detection via BERT
      • Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
      • Log-based Anomaly Detection with Deep Learning: How Far Are We?
      • Deep Learning for Anomaly Detection in Log Data: A Survey
      • LogGPT
      • Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis
  • Infrastructure
    • The modern data centre
      • Enhancing Data Centre Efficiency: Strategies to Improve PUE
      • TCO of NVIDIA GPUs and falling barriers to entry
      • Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator
      • Data Centres
      • Liquid Cooling
    • Servers and Chips
      • The NVIDIA H100 GPU
      • NVIDIA H100 NVL
      • Lambda Hyperplane 8-H100
      • NVIDIA DGX Servers
      • NVIDIA DGX-2
      • NVIDIA DGX H-100 System
      • NVLink Switch
      • Tensor Cores
      • NVIDIA Grace Hopper Superchip
      • NVIDIA Grace CPU Superchip
      • NVIDIA GB200 NVL72
      • Hopper versus Blackwell
      • HGX: High-Performance GPU Platforms
      • ARM Chips
      • ARM versus x86
      • RISC versus CISC
      • Introduction to RISC-V
    • Networking and Connectivity
      • Infiniband versus Ethernet
      • NVIDIA Quantum InfiniBand
      • PCIe (Peripheral Component Interconnect Express)
      • NVIDIA ConnectX InfiniBand adapters
      • NVMe (Non-Volatile Memory Express)
      • NVMe over Fabrics (NVMe-oF)
      • NVIDIA Spectrum-X
      • NVIDIA GPUDirect
      • Evaluating Modern GPU Interconnect
      • Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)
      • Next-generation networking in AI environments
      • NVIDIA Collective Communications Library (NCCL)
    • Data and Memory
      • NVIDIA BlueField Data Processing Units (DPUs)
      • Remote Direct Memory Access (RDMA)
      • High Bandwidth Memory (HBM3)
      • Flash Memory
      • Model Requirements
      • Calculating GPU memory for serving LLMs
      • Transformer training costs
      • GPU Performance Optimisation
    • Libraries and Complements
      • NVIDIA Base Command
      • NVIDIA AI Enterprise
      • CUDA - NVIDIA GTC 2024 presentation
      • RAPIDs
      • RAFT
    • Vast Data Platform
      • Vast Datastore
      • Vast Database
      • Vast Data Engine
      • DASE (Disaggregated and Shared Everything)
      • Dremio and VAST Data
    • Storage
      • WEKA: A High-Performance Storage Solution for AI Workloads
      • Introduction to NVIDIA GPUDirect Storage (GDS)
        • GDS cuFile API
      • NVIDIA Magnum IO GPUDirect Storage (GDS)
      • Vectors in Memory
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Copyright Continuum Labs - 2023

On this page
  • Understanding SentencePiece
  • The Role of Tokenization
  • Key Components of SentencePiece
  • Key technical aspects
  • SentencePiece makes the process of tokenization easier in several ways

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  1. Training
  2. The Fine Tuning Process
  3. Tokenization

Tokenization - SentencePiece

The Unsupervised Text Tokenizer for Neural Networks

PreviousTokenization Is More Than CompressionNextTokenization explore

Last updated 1 year ago

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SentencePiece is a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, particularly Neural Machine Translation (NMT).

Its main goal is to provide a simple, efficient, and reproducible preprocessing and postprocessing tool that can be easily integrated into neural network-based NLP systems.

Its core strength lies in its ability to manage vocabulary size before training neural models, a critical factor in the efficiency and effectiveness of these systems.

Understanding SentencePiece

SentencePiece is a language-independent subword tokenizer and detokenizer, engineered for neural-based text processing.

Unlike conventional tokenizers, it doesn't rely on whitespaces for tokenization, making it versatile for languages like Chinese and Japanese.

It implements subword units, such as byte-pair-encoding (BPE) and unigram language models, directly from raw sentences. This approach ensures that important words are captured within a fixed vocabulary list, minimising redundancy.

The Role of Tokenization

Tokenization, the process of breaking down text into words or subwords, is fundamental in NLP.

SentencePiece excels in splitting words into subwords, capturing frequent and diverse subwords within a predetermined vocabulary size.

Example Code

import sentencepiece as spm

# Initialize SentencePiece
sp = spm.SentencePieceProcessor(model_file='your_model.model')

# Encode text into subwords
encoded_text = sp.encode_as_pieces('This is a sample text.')
print(encoded_text)

# Decode subwords back into text
decoded_text = sp.decode_pieces(encoded_text)
print(decoded_text)

Importance of Vocabulary Size Limit

Setting a vocabulary size limit is vital in preventing the inclusion of rare or complex words that may not be beneficial as separate vectors. This balance is key to efficient and effective models.

Key Components of SentencePiece

SentencePiece comprises four primary components:

  1. Normalizer: Standardises words into equivalent NFKC Unicode.

  2. Trainer: Builds vocabulary based on subword components using BPE and unigram language models.

  3. Encoder and Decoder: Handle encoding and decoding processes, ensuring lossless tokenization.

Key technical aspects

Subword segmentation

SentencePiece implements two subword segmentation algorithms - byte-pair-encoding (BPE) and unigram language model. These algorithms allow the tokenizer to break down words into smaller units (subwords) to reduce the vocabulary size and handle out-of-vocabulary words effectively.

Language independence

SentencePiece can directly train subword models from raw sentences without relying on language-specific pre-tokenization. This enables the creation of purely end-to-end and language-independent NMT systems.

Lossless tokenization

SentencePiece treats the input text as a sequence of Unicode characters, including whitespace, which is escaped with a meta symbol (e.g., "_"). This allows for reversible encoding and decoding without losing information, making the process language-agnostic.

Vocabulary management

SentencePiece manages the vocabulary-to-id mapping, enabling direct conversion of text into an id sequence and vice versa. This is particularly useful for NMT systems, as their input and output are typically id sequences.

Normalization

SentencePiece includes a normalizer module that canonicalizes semantically-equivalent Unicode characters, ensuring consistent input for the subword model training.

The paper demonstrates that SentencePiece can achieve comparable accuracy to direct subword training from raw sentences in an English-Japanese NMT task.

By providing a simple, language-independent, and reversible tokenization process, SentencePiece aims to standardise and simplify the preprocessing and postprocessing steps in neural network-based NLP systems.

SentencePiece makes the process of tokenization easier in several ways

Language Independence

SentencePiece is designed to be language-independent, meaning it can be applied to any language without requiring language-specific knowledge or preprocessing. This is particularly useful in multilingual NLP tasks or when working with low-resource languages. By treating the input text as a sequence of Unicode characters and directly learning subword units from raw sentences, SentencePiece eliminates the need for language-specific tokenization rules or tools.

Vocabulary Size Management

One of the key challenges in tokenization is managing the vocabulary size. A large vocabulary can lead to increased model complexity and computational costs, while a small vocabulary may not capture important words or subwords. SentencePiece allows you to specify a desired vocabulary size, and it automatically learns the most frequent and informative subwords to include in the vocabulary. This helps in striking a balance between model efficiency and expressiveness.

Subword Segmentation

SentencePiece implements subword segmentation algorithms, such as byte-pair encoding (BPE) and unigram language model, which break down words into smaller units (subwords). This approach has several advantages:

  • It reduces the vocabulary size by representing rare or out-of-vocabulary words as combinations of subwords.

  • It captures morphological and semantic information within words, as subwords often correspond to meaningful units like prefixes, suffixes, or roots.

  • It enables the model to handle unseen words by composing them from learned subwords.

Reversibility and Consistency

SentencePiece provides a lossless tokenization process, meaning that the original text can be perfectly reconstructed from the tokenized representation. It achieves this by treating whitespace and other special characters as separate tokens and escaping them with a meta symbol. This reversibility ensures that no information is lost during tokenization and detokenization, making it easier to integrate SentencePiece into existing NLP pipelines.

Simplicity and Ease of Use

SentencePiece offers a simple and intuitive API for tokenization and detokenization. It provides straightforward methods for training subword models, encoding text into subword sequences, and decoding subword sequences back into text. The library is well-documented and comes with pre-trained models for various languages, making it easy to get started with tokenization tasks.

Reproducibility

SentencePiece promotes reproducibility by providing a standardised and deterministic tokenization process.

Given the same input text and trained model, SentencePiece guarantees consistent tokenization results across different platforms and implementations. This is crucial for reproducible research and ensures that models trained using SentencePiece can be easily shared and deployed.

LogoSentencePiece: A simple and language independent subword tokenizer...arXiv.org
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