Understanding Input Embedding in Transformers

Introduction

When processing natural language, neural networks cannot directly interpret raw text. Instead, words, subwords, or characters must be converted into numerical representations before being input into a machine learning model.

The Input Embedding Layer facilitates this conversion by transforming tokenized words into dense numerical vectors that encode their semantic meaning and contextual information.

This article covers:

• The definition and significance of input embeddings.
• The process of converting tokenized words into embeddings.
• The functioning of embedding layers in Transformers such as BERT, GPT, and T5.
• The differences between learned embeddings and pretrained embeddings.

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1. Definition of Input Embedding

Purpose and Necessity

Input embedding refers to the process of mapping each token (word, subword, or character) to a fixed-length vector representation in a continuous numerical space.

Why is Input Embedding Important?

• Neural networks require numerical input rather than textual representations.
• Token IDs alone (e.g., 101, 2345, 5678) lack semantic meaning.
• Embeddings capture word relationships, enabling models to understand context effectively.

Example: Given the sentence:

"The cat sat on the mat."

Tokenization results in:

["The", "cat", "sat", "on", "the", "mat"]

Each token is mapped to a corresponding Token ID:

[101, 2345, 5678, 3456, 101, 6789]

The Embedding Layer then converts these token IDs into numerical vectors:

[
  [0.2, 0.8, -0.5, 0.1],  # "The"
  [0.5, 0.3, 0.9, -0.7],  # "cat"
  [0.1, 0.9, 0.4, -0.3],  # "sat"
  ...
]

At this stage, each token is represented numerically, allowing further processing by the model.

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2. Generation of Input Embeddings

The embedding layer functions as a lookup table that stores vector representations of words in a high-dimensional space.

Steps in the Embedding Process

1. Tokenization → Splitting text into tokens.
2. Mapping Tokens to IDs → Assigning unique numerical identifiers to tokens based on a predefined vocabulary.
3. Embedding Lookup → Replacing token IDs with their corresponding dense vector representations.

Example: Mapping Token IDs to Embeddings

Token	Token ID	Embedding Vector
“The”	101	[0.2, 0.8, -0.5, 0.1]
“cat”	2345	[0.5, 0.3, 0.9, -0.7]
“sat”	5678	[0.1, 0.9, 0.4, -0.3]

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3. Input Embeddings in Transformer Models

In Transformer architectures such as BERT and GPT, input embedding is composed of:

1. Token Embedding → Represents word meanings.
2. Positional Encoding → Adds positional information to preserve word order.
3. Segment Embedding (used in BERT) → Differentiates between multiple sentences.

Formula for Final Input Representation

Final Input Vector = Token Embedding + Positional Encoding + Segment Embedding

Example in a Transformer Model:

"The cat sat" → Token Embeddings → Positional Encoding → Final Transformer Input

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4. Learned vs. Pretrained Embeddings

Two primary types of embeddings are used in NLP:

Embedding Type	Description	Examples
Pretrained Embeddings	Word vectors trained separately on large datasets.	Word2Vec, GloVe, FastText
Learned Embeddings	Word embeddings adjusted during model training.	BERT, GPT, T5

Differences Between Pretrained and Learned Embeddings

• Pretrained embeddings remain fixed after training, whereas learned embeddings update dynamically during model training.
• Transformers predominantly utilize learned embeddings, enabling adaptability to varying contexts.

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5. Benefits of Input Embeddings in NLP

• Preserves Word Semantics → Facilitates understanding of word relationships.
• Efficient Representation of Large Vocabularies → Supports extensive word coverage.
• Enhances Contextual Awareness → Assists models in grasping sentence structures.
• Computational Efficiency → More compact than one-hot encoding, improving processing speed.

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6. Applications of Input Embeddings

1. Conversational AI

• Utilized in models such as ChatGPT, Google Bard, and virtual assistants.

2. Search Engines & Recommendation Systems

• Applied in Google Search, YouTube recommendations, and content filtering.

3. Machine Translation

• Enables translation models (e.g., Google Translate) to encode multilingual text.

4. AI Coding Assistants

• Used in AI-powered tools like GitHub Copilot and AlphaCode for code generation.

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7. Conclusion

The Input Embedding Layer is fundamental to modern NLP models, facilitating the transformation of raw text into meaningful numerical representations.

Key Takeaways

• Input embeddings map tokens to dense numerical vectors.
• Embedding layers store semantic word representations.
• Transformers rely on dynamically updated learned embeddings.
• Positional encoding helps preserve word order.
• Embeddings play a vital role in diverse NLP applications.

For a deeper dive into Transformers and advanced NLP concepts, stay tuned for further articles.

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Further Reading & References

📖 Research Paper: Attention Is All You Need (Vaswani et al., 2017)
📌 Illustrated Transformer Guide: Jay Alammar’s Illustrated Transformer
📌 Hugging Face Transformer Library: Hugging Face Guide

For more insights into NLP and AI advancements, visit EasyExamNotes.com.

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Note: The text and visual content presented here were created using AI-driven tools, including large language models (LLMs) and generative AI. The information has been reviewed for accuracy and clarity.