Fine-grained sentiment (5-class) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the dataset for fine-grained sentiment analysis.

NLP

from datasets import load_dataset

dataset = load_dataset('sst', '[1]')

Drag options to blanks, or click blank then click option'

Atrain

Btest

Call

Dvalidation

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'test' instead of 'train' to load training data.

Using 'all' which is not a valid split.

The 'train' split is used to load the training data for fine-grained sentiment analysis.

2fill in blank

medium

Complete the code to tokenize the input sentences for the model.

NLP

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
tokens = tokenizer([1], padding=True, truncation=True, return_tensors='pt')

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Adataset['sentence']

Bdataset['label']

Cdataset['text']

Ddataset['input']

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to tokenize labels instead of sentences.

Using a wrong field name like 'text' or 'input' which may not exist.

The 'sentence' field contains the text to tokenize for sentiment analysis.

3fill in blank

hard

Fix the error in the model output layer to match 5 sentiment classes.

NLP

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=[1])

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A2

B4

C3

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Setting num_labels to 2 or 3 which is for binary or ternary sentiment.

Using 4 which is incorrect for 5-class sentiment.

Fine-grained sentiment analysis has 5 classes, so num_labels must be 5.

4fill in blank

hard

Fill both blanks to compute accuracy and macro F1 score for the 5-class sentiment model.

NLP

from sklearn.metrics import [1], [2]

accuracy = [1](true_labels, predictions)
macro_f1 = [2](true_labels, predictions, average='macro')

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Aaccuracy_score

Bf1_score

Cprecision_score

Drecall_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using precision_score or recall_score instead of f1_score for F1 metric.

Not specifying average='macro' for multi-class F1.

Use accuracy_score for accuracy and f1_score with macro average for balanced F1 across classes.

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions with labels and filter positive sentiments.

NLP

pred_dict = [1](label: pred for label, pred in zip(labels, predictions) if pred [2] [3])

# Filter only positive sentiments (labels 3 and 4)

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Adict

B>=

C3

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list instead of dict for comprehension.

Using wrong comparison operator like '<' or '==' instead of '>='.

Filtering with wrong threshold value.

Use dict comprehension to map labels to predictions and filter predictions >= 3 for positive sentiments.

Practice

1. What does a fine-grained sentiment analysis with 5 classes typically represent?

easy

A. It translates text into five different languages.

B. It detects whether the text is about five different topics.

C. It summarizes text into five key points.

D. It classifies text into five levels from very negative to very positive feelings.

Solution

1. Step 1: Understand sentiment analysis levels

   Fine-grained sentiment analysis divides feelings into multiple levels, often five, ranging from very negative to very positive.

2. Step 2: Match the description to options

   It classifies text into five levels from very negative to very positive feelings correctly describes this as classifying text by sentiment levels. Other options describe unrelated tasks.

3. Final Answer:

   It classifies text into five levels from very negative to very positive feelings. -> Option D

4. Quick Check:

   Fine-grained sentiment = 5-level sentiment classification [OK]

Hint: Fine-grained means detailed sentiment levels, not topics or languages [OK]

Common Mistakes:

• Confusing sentiment classes with topic categories
• Thinking it translates text instead of analyzing feelings
• Assuming it summarizes text instead of classifying sentiment

2. Which of the following is the correct way to represent sentiment labels for a 5-class fine-grained sentiment model in Python?

easy

A. labels = {1: 'positive', 2: 'neutral', 3: 'negative'}

B. labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive']

C. labels = ['happy', 'sad', 'angry', 'excited']

D. labels = ['positive', 'negative']

Solution

1. Step 1: Identify correct label list for 5-class sentiment

   The 5-class sentiment labels should cover very negative to very positive, exactly five classes.

2. Step 2: Check each option

   labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'] lists five sentiment levels correctly. Options B, C, and D have wrong counts or unrelated labels.

3. Final Answer:

   labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'] -> Option B

4. Quick Check:

   5-class sentiment labels = labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'] [OK]

Hint: Five classes must cover full sentiment range, not fewer or unrelated words [OK]

Common Mistakes:

• Using fewer than five labels
• Using unrelated emotion words
• Confusing label types with numeric codes

3. Given the following Python code snippet for a fine-grained sentiment model prediction, what will be the printed output?

import numpy as np
predictions = np.array([[0.1, 0.2, 0.4, 0.2, 0.1]])
predicted_class = np.argmax(predictions)
print(predicted_class)

medium

A. 2

B. 3

C. 1

D. 0

Solution

1. Step 1: Understand np.argmax on prediction array

   np.argmax returns the index of the highest value in the array. Here, predictions are [0.1, 0.2, 0.4, 0.2, 0.1].

2. Step 2: Find the index of max value

   The max value is 0.4 at index 2 (0-based). So predicted_class = 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Max probability index = 2 [OK]

Hint: np.argmax returns index of max value, count from zero [OK]

Common Mistakes:

• Confusing index with value
• Counting indices from 1 instead of 0
• Misreading the prediction array

4. You trained a fine-grained sentiment model with 5 classes but your evaluation shows accuracy stuck at 20%. What is the most likely cause?

medium

A. The model is randomly guessing because the output layer has 5 units but the loss function expects 2 classes.

B. The model is overfitting the training data perfectly.

C. The input text is too long for the model to process.

D. The optimizer learning rate is too high.

Solution

1. Step 1: Analyze low accuracy with 5-class output

   Accuracy near 20% suggests random guessing among 5 classes (1/5 = 20%).

2. Step 2: Check mismatch between output and loss

   If the model output layer has 5 units but the loss function expects 2 classes (binary), the model cannot learn properly, causing random predictions.

3. Final Answer:

   Output layer and loss function class count mismatch causing random guessing. -> Option A

4. Quick Check:

   Mismatch output vs loss classes = random 20% accuracy [OK]

Hint: Check output units match loss classes to avoid random guessing [OK]

Common Mistakes:

• Assuming overfitting causes low accuracy
• Blaming input length without evidence
• Ignoring loss function and output layer mismatch

5. You want to improve a fine-grained sentiment model's performance on imbalanced data where 'neutral' class is very common. Which approach is best?

hard

A. Increase the batch size to speed up training.

B. Remove the 'neutral' class from the dataset to balance classes.

C. Use class weights in the loss function to give more importance to rare classes.

D. Use a simpler model with fewer layers.

Solution

1. Step 1: Understand class imbalance problem

   When one class dominates, the model may ignore rare classes, hurting performance on them.

2. Step 2: Choose method to handle imbalance

   Using class weights in the loss function tells the model to pay more attention to rare classes, improving balanced learning.

3. Final Answer:

   Use class weights in loss to handle imbalanced classes effectively. -> Option C

4. Quick Check:

   Class weights improve learning on rare classes [OK]

Hint: Apply class weights to balance rare vs common classes [OK]

Common Mistakes:

• Removing common classes loses important data
• Changing batch size doesn't fix imbalance
• Simpler models may underfit complex data