Named Entity Recognition basics in ML Python - Model Pipeline Trace

Model Pipeline - Named Entity Recognition basics

Named Entity Recognition (NER) finds important names in text, like people, places, or dates. It helps computers understand text by marking these special words.

Data Flow - 4 Stages

1Raw Text Input

1 sentence (variable length)→Input sentence with words→1 sentence (variable length)

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

↓

2Tokenization

1 sentence (variable length)→Split sentence into words or tokens→1 sentence x 10 tokens

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

↓

3Feature Extraction

1 sentence x 10 tokens→Convert tokens to numbers (word embeddings)→1 sentence x 10 tokens x 50 features

[[0.12, -0.03, ...], [0.05, 0.10, ...], ...]

↓

4Model Prediction

1 sentence x 10 tokens x 50 features→Model predicts entity label for each token→1 sentence x 10 tokens x 1 label

["ORG", "O", "O", "O", "O", "LOC", "O", "O", "MONEY", "MONEY"]

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.7 |**
0.5 |*
0.4 |

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.60	Model starts learning, loss is high, accuracy low
2	0.9	0.72	Loss decreases, accuracy improves
3	0.7	0.80	Model learns important patterns
4	0.5	0.86	Better recognition of entities
5	0.4	0.90	Model converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Tokenization

Layer 2: Feature Extraction

Layer 3: Model Prediction

Model Quiz - 3 Questions

What does the 'Tokenization' stage do in NER?

A Predicts entity labels

B Converts tokens into numbers

C Splits text into words or tokens

D Calculates model accuracy

Key Insight

Named Entity Recognition models learn to identify special words by breaking text into tokens, turning words into numbers, and then predicting labels. Training improves accuracy by lowering loss over time.