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In the bustling world of natural language processing (NLP), Transformer-based models like BERT and GPT have become go-to tools for a myriad of applications. However, as these models scale up to billions of parameters, they face challenges that require innovative solutions. Enter the tokenizer, a fundamental component that is pivotal in these teachable models. This article delves into the tokenizer's role and the application of model parallelism in the context of NLP.

Tokenizer: The Foundation of NLP Models

The tokenizer serves as the cornerstone of NLP models, breaking down the vast expanse of natural language into digestible tokens. These tokens can range from single characters to words or subwords, depending on the tokenizer's architecture. The choice of tokenizer significantly influences how a model comprehends and processes language.

Tokenization Techniques

1. Word Tokenization: This approach splits text into words based on whitespace and punctuation.
2. Character Tokenization: It divides text into individual characters.
3. Subword Tokenization: This method breaks text into subwords, aiding in handling out-of-vocabulary words and varying word lengths.

Teachable Models and Tokenizers

Teachable models are machine learning models designed to be trained and learned from data, a concept particularly relevant in NLP. These models heavily depend on tokenizers to process and understand text data.

Tokenizer Implementation in Teachable Models

• Preprocessing: This involves normalizing text and converting it into a format the model can comprehend, which includes tokenization.
• Feature Extraction: This step extracts features from tokens, such as word embeddings or subword embeddings.
• Model Training: The model utilizes these features to train itself.

Model Parallelism: A Solution for Large-scale Models

Model parallelism is a technique used to train massive models by dividing them into smaller pieces that can be executed on different GPUs or TPUs. This enables the training of models that are too large to fit into a single GPU or TPU.

Tokenizers in the Era of Model Parallelism

In the realm of model parallelism, tokenizers must be meticulously designed to ensure tokens are correctly distributed across various GPUs or TPUs. This involves:

• Mapping Tokens to GPUs/TPUs: Deciding which tokens will be processed on which GPUs/TPUs.
• Maintaining Consistency: Ensuring token distributions between GPUs/TPUs are consistent to guarantee the model's output remains accurate.

Applications and Implementation of Tokenizers

Word-level Tokenization

• BERT: This tokenizer is adept at handling web content and documents.
• GPT-3: It employs word-level tokenization, allowing for the processing of long sequences of text.

Subword-level Tokenization

• Byte Pair Encoding (BPE): This partial tokenizer works well with extended linguistic resources.
• PaddleNLP: It supports various subword tokenization methods, including BPE and SentencePiece.

Conclusion

Tokenizers are indispensable components of teachable models, particularly in large-scale NLP tasks. They are responsible for preprocessing input data and extracting features that feed into the model. As NLP continues to advance, tokenizers will become increasingly critical in training and deploying models. The successful application of model parallelism hinges on a well-designed tokenizer that can effectively distribute tokens across multiple GPUs or TPUs.