Custom QA model fine-tuning in NLP - Model Pipeline Trace

Model Pipeline - Custom QA model fine-tuning

This pipeline fine-tunes a question-answering (QA) model on a custom dataset. It starts with raw text and questions, processes the data, trains the model to understand context and answer questions, and finally evaluates its performance.

Data Flow - 5 Stages

1Raw Data Input

1000 samples (context-question-answer triplets)→Load raw text passages, questions, and answers→1000 samples (context-question-answer triplets)

{"context": "The sky is blue.", "question": "What color is the sky?", "answer": "blue"}

↓

2Preprocessing

1000 samples (context-question-answer triplets)→Tokenize text and align answer spans with tokens→1000 samples (tokenized inputs with answer span indices)

{"input_ids": [101, 1996, 3712, 2003, 2631, 1012, 102], "start_position": 4, "end_position": 4}

↓

3Feature Engineering

1000 samples (tokenized inputs)→Create attention masks and segment IDs for model input→1000 samples (input_ids, attention_mask, token_type_ids, start_position, end_position)

{"input_ids": [...], "attention_mask": [1,1,1,1,1,1,1], "token_type_ids": [0,0,0,0,0,0,0]}

↓

4Model Training

800 samples (train set)→Fine-tune pretrained QA model on training data→Trained QA model

Model weights updated after each batch

↓

5Evaluation

200 samples (validation set)→Calculate loss and accuracy metrics on validation data→Validation loss and accuracy scores

Loss=0.12, Accuracy=0.85

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |*** 
0.3 |**  
0.2 |*   
0.1 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Model starts learning, loss decreases, accuracy improves
2	0.30	0.72	Loss continues to drop, accuracy rises steadily
3	0.20	0.80	Model shows good learning progress
4	0.15	0.84	Loss decreases further, accuracy improves
5	0.12	0.87	Training converges with stable improvements

Prediction Trace - 3 Layers

Layer 1: Input Encoding

Layer 2: Model Forward Pass

Layer 3: Answer Span Selection

Model Quiz - 3 Questions

What happens to the loss value as training progresses?

A It increases steadily

B It stays the same

C It decreases steadily

D It randomly jumps up and down

Key Insight

Fine-tuning a QA model involves converting text into tokens, training the model to predict answer spans, and observing steady loss decrease and accuracy increase as the model learns to answer questions accurately.