Custom NER training basics in NLP - Model Pipeline Trace

Model Pipeline - Custom NER training basics

This pipeline trains a model to recognize custom named entities in text, like names or places. It starts with raw text, adds labels, trains a model, and then checks how well it learned.

Data Flow - 5 Stages

1Raw Text Input

1000 sentences→Collect sentences with entities to label→1000 sentences

"Apple is looking at buying U.K. startup for $1 billion."

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2Annotation

1000 sentences→Label entities in sentences (e.g., 'Apple' as ORG)→1000 sentences with entity labels

"Apple" labeled as ORG, "U.K." labeled as GPE

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3Data Preprocessing

1000 sentences with labels→Convert text and labels into token-level format→1000 token sequences with entity tags

[('Apple', 'ORG'), ('is', 'O'), ('looking', 'O'), ...]

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4Model Training

1000 token sequences with tags→Train NER model to predict entity tags→Trained NER model

Model learns to tag 'Apple' as ORG

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5Evaluation

Validation sentences with labels→Calculate accuracy and F1 score→Performance metrics

Accuracy: 0.92, F1 score: 0.89

Training Trace - Epoch by Epoch

Loss
1.0 |****
0.8 |*** 
0.6 |**  
0.4 |*   
0.2 |    
    +-----
     1 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning entity patterns
2	0.60	0.75	Loss decreases, accuracy improves
3	0.45	0.82	Model better at recognizing entities
4	0.35	0.87	Training converging, accuracy rising
5	0.28	0.90	Good balance of loss and accuracy

Prediction Trace - 4 Layers

Layer 1: Tokenization

Layer 2: Feature Extraction

Layer 3: NER Model Prediction

Layer 4: Post-processing

Model Quiz - 3 Questions

What happens during the annotation stage?

A Splitting sentences into tokens

B Training the model

C Labeling entities in sentences

D Calculating accuracy

Key Insight

Training a custom NER model involves labeling text with entities, converting text into tokens, and teaching the model to recognize patterns. As training progresses, loss decreases and accuracy improves, showing the model learns to identify entities better.