Sequence classification in PyTorch - Model Pipeline Trace

Model Pipeline - Sequence classification

This pipeline takes sequences of words or tokens as input and trains a model to classify the entire sequence into categories. It shows how data flows from raw text to predictions, how the model learns over time, and how a single sequence is processed step-by-step.

Data Flow - 5 Stages

1Raw text input

1000 sequences x variable length→Collect raw text sequences (e.g., sentences or short paragraphs)→1000 sequences x variable length

["I love this movie", "This is bad"]

↓

2Tokenization and padding

1000 sequences x variable length→Convert words to numbers (tokens) and pad sequences to fixed length 10→1000 sequences x 10 tokens

[[12, 45, 78, 0, 0, 0, 0, 0, 0, 0], [34, 56, 0, 0, 0, 0, 0, 0, 0, 0]]

↓

3Embedding layer

1000 sequences x 10 tokens→Map tokens to 50-dimensional vectors→1000 sequences x 10 tokens x 50 features

[[[0.1, -0.2, ...], [0.05, 0.3, ...], ...], ...]

↓

4Sequence model (LSTM)

1000 sequences x 10 tokens x 50 features→Process sequence to capture order and context→1000 sequences x 64 features

[[0.2, -0.1, ..., 0.05], [0.3, 0.0, ..., -0.02], ...]

↓

5Fully connected + softmax

1000 sequences x 64 features→Classify sequence into 3 categories→1000 sequences x 3 classes

[[0.7, 0.2, 0.1], [0.1, 0.8, 0.1], ...]

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | *      
0.7 |  *     
0.5 |   *    
0.4 |    *   
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, loss is high, accuracy low
2	0.9	0.60	Loss decreases, accuracy improves
3	0.7	0.72	Model learns better sequence patterns
4	0.5	0.80	Loss continues to drop, accuracy rises
5	0.4	0.85	Model converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Tokenization and padding

Layer 2: Embedding layer

Layer 3: LSTM layer

Layer 4: Fully connected + softmax

Model Quiz - 3 Questions

What happens to the sequence length after tokenization and padding?

A All sequences become the same fixed length

B Sequences keep their original variable length

C Sequences become shorter than original

D Sequences are converted to single numbers

Key Insight

Sequence classification models transform variable-length text into fixed-size vectors using embeddings and LSTM layers. Training reduces loss and improves accuracy steadily, showing the model learns to recognize patterns in sequences to classify them correctly.