RoBERTa and DistilBERT in NLP - Model Pipeline Trace

Model Pipeline - RoBERTa and DistilBERT

This pipeline shows how two popular language models, RoBERTa and DistilBERT, process text data to learn and make predictions. RoBERTa is a large, powerful model, while DistilBERT is a smaller, faster version that keeps much of RoBERTa's understanding.

Data Flow - 6 Stages

1Input Text

1000 sentences→Raw text sentences collected for training→1000 sentences

"The cat sat on the mat."

↓

2Tokenization

1000 sentences→Convert sentences into tokens (words or subwords)→1000 sequences x 50 tokens

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

↓

3Embedding Layer

1000 sequences x 50 tokens→Convert tokens into vectors of size 768→1000 sequences x 50 tokens x 768 features

[[0.12, -0.05, ..., 0.33], ..., [0.01, 0.22, ..., -0.11]]

↓

4Transformer Layers (RoBERTa/DistilBERT)

1000 sequences x 50 tokens x 768 features→Process embeddings through multiple attention layers→1000 sequences x 50 tokens x 768 features

Contextualized token vectors capturing sentence meaning

↓

5Pooling

1000 sequences x 50 tokens x 768 features→Aggregate token vectors into a single vector per sentence→1000 sequences x 768 features

[0.45, -0.12, ..., 0.67]

↓

6Classification Head

1000 sequences x 768 features→Feed pooled vectors into a classifier to predict labels→1000 sequences x number_of_classes

[[0.1, 0.9], [0.8, 0.2], ...]

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
0.2 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss high, accuracy moderate
2	0.48	0.75	Loss decreases, accuracy improves as model learns patterns
3	0.35	0.83	Model continues to improve, learning meaningful features
4	0.28	0.88	Loss lowers further, accuracy nearing good performance
5	0.22	0.91	Training converges with high accuracy and low loss

Prediction Trace - 5 Layers

Layer 1: Tokenization

Layer 2: Embedding Layer

Layer 3: Transformer Layers

Layer 4: Pooling

Layer 5: Classification Head

Model Quiz - 3 Questions

What happens to the data shape after tokenization?

A It reduces to fewer sentences

B It becomes a single vector per sentence

C It changes from sentences to sequences of tokens

D It changes to label predictions

Key Insight

RoBERTa and DistilBERT transform raw text into meaningful vectors through tokenization, embedding, and transformer layers. DistilBERT offers faster training with fewer layers, while RoBERTa provides deeper understanding. Training shows steady improvement in loss and accuracy, reflecting learning progress.