Model Pipeline - Context window handling
This pipeline shows how text data is processed in chunks called context windows to help a language model understand and predict words better.
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Context window handling in NLP - Model Pipeline Trace
Data Flow - 4 Stages
1Raw Text Input
1 document with 1000 words→Receive full text document→1 document with 1000 words
"The quick brown fox jumps over the lazy dog ..."
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2Tokenization
1 document with 1000 words→Split text into tokens (words or subwords)→1 document with 1200 tokens
["The", "quick", "brown", "fox", "jump", "##s", ...]
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3Context Windowing
1 document with 1200 tokens→Split tokens into overlapping windows of 100 tokens each→23 windows x 100 tokens
[Window 1: tokens 1-100, Window 2: tokens 51-150, ...]
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4Model Input Preparation
23 windows x 100 tokens→Convert tokens to numerical IDs and add special tokens→23 windows x 100 token IDs
[[101, 2003, 2204, ...], [101, 2204, 2024, ...], ...]
Training Trace - Epoch by Epoch
Loss
2.5 |****
2.0 |***
1.5 |**
1.0 |*
0.5 |
+----
1 2 3 4 5 Epochs
| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 2.3 | 0.30 | Model starts learning basic word patterns |
| 2 | 1.8 | 0.45 | Loss decreases as model understands context windows better |
| 3 | 1.4 | 0.60 | Model improves predictions using overlapping windows |
| 4 | 1.1 | 0.70 | Context window handling helps capture longer dependencies |
| 5 | 0.9 | 0.78 | Training converges with good understanding of context |
Prediction Trace - 4 Layers
Layer 1: Input Window Selection
Layer 2: Token Embedding Layer
Layer 3: Transformer Layers
Layer 4: Output Layer
Model Quiz - 3 Questions
Test your understanding
Why do we split text into overlapping context windows?
Key Insight
Handling text in overlapping context windows helps language models understand longer passages by focusing on smaller, manageable chunks. This improves prediction accuracy as the model learns relationships within each window and across windows.