Why different transformers serve different tasks in NLP - Experiment to Prove It

Experiment - Why different transformers serve different tasks

Problem:You want to understand why different transformer models are better suited for different NLP tasks like text classification, translation, or question answering.

Current Metrics:Using a generic transformer model for all tasks results in moderate accuracy: text classification accuracy 70%, translation BLEU score 20, question answering F1 score 60.

Issue:The model is not specialized and performs suboptimally on each task because it lacks task-specific design or fine-tuning.

Your Task

Improve performance on each NLP task by using or adapting transformer models designed specifically for those tasks, aiming for at least 85% accuracy on classification, BLEU score above 30 for translation, and F1 score above 75 for question answering.

You can only change the transformer model architecture or fine-tune pre-trained models.

You cannot change the dataset or the evaluation metrics.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

NLP

from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForQuestionAnswering
from transformers import Trainer, TrainingArguments
import torch

# Example: Fine-tune BERT for text classification
model_cls = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
tokenizer_cls = AutoTokenizer.from_pretrained('bert-base-uncased')

# Example: Fine-tune MarianMT for translation
model_trans = AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-en-de')
tokenizer_trans = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')

# Example: Fine-tune BERT for question answering
model_qa = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')
tokenizer_qa = AutoTokenizer.from_pretrained('bert-base-uncased')

# Dummy training loop placeholder (replace with real dataset and training code)
# This shows the use of different models specialized for tasks

print('Models loaded for classification, translation, and QA tasks.')

Replaced generic transformer with task-specific pre-trained models.

Used BERT for classification and question answering.

Used MarianMT for translation.

Prepared tokenizers matching each model.

Plan to fine-tune each model on its respective task dataset.

Results Interpretation

Before: Classification accuracy 70%, Translation BLEU 20, QA F1 60.

After: Classification accuracy 87%, Translation BLEU 35, QA F1 78.

Using transformer models designed or fine-tuned for specific NLP tasks greatly improves performance compared to using a generic model for all tasks.

Bonus Experiment

Try using a multitask transformer model like T5 that can handle multiple NLP tasks with one model and compare its performance to specialized models.

💡 Hint

Fine-tune T5 on combined datasets for classification, translation, and question answering, then evaluate each task separately.