Custom NER training basics in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library needed for Named Entity Recognition (NER) training.

NLP

import [1]

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

Aspacy

Bnumpy

Cmatplotlib

Dpandas

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated libraries like numpy or pandas.

Forgetting to import the NLP library before training.

We use spacy for training custom Named Entity Recognition models.

2fill in blank

medium

Complete the code to load a blank English model for NER training.

NLP

nlp = spacy.blank('[1]')

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

Afr

Ben

Cde

Des

Attempts:

3 left

💡 Hint

Common Mistakes

Using other language codes like 'fr' or 'de' when training English NER.

Passing full language names instead of codes.

We use 'en' to load a blank English model for training.

3fill in blank

hard

Fix the error in adding the NER pipeline component to the model.

NLP

ner = nlp.add_pipe('[1]')

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Atextcat

Btagger

Cparser

Dner

Attempts:

3 left

💡 Hint

Common Mistakes

Using unrelated pipeline components like 'textcat' or 'parser'.

Misspelling the component name.

The pipeline component for Named Entity Recognition is called 'ner'.

4fill in blank

hard

Fill both blanks to add a new entity label and prepare the optimizer.

NLP

ner.add_label('[1]')
optimizer = nlp.[2]()

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APERSON

Btrain

Cbegin_training

DORG

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or unrelated strings as labels.

Calling a non-existent method like 'train()' instead of 'begin_training()'.

We add the entity label 'PERSON' and start training with begin_training().

5fill in blank

hard

Fill all three blanks to update the model with training data and print the loss.

NLP

for text, annotations in TRAIN_DATA:
    doc = nlp.make_doc(text)
    example = spacy.training.Example.from_dict(doc, [1])
    nlp.update([example], sgd=[2], losses=[3])

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

Aannotations

Boptimizer

Closses

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable for annotations or optimizer.

Not tracking losses correctly.

We use annotations for labels, optimizer for training, and losses to track errors.

Practice

1. What is the main goal of custom NER training in NLP?

easy

A. To summarize long documents automatically

B. To teach the model to recognize specific words or phrases you label

C. To translate text from one language to another

D. To generate new text based on a prompt

Solution

1. Step 1: Understand what NER means

   NER stands for Named Entity Recognition, which means finding specific words or phrases in text.

2. Step 2: Identify the purpose of custom training

   Custom NER training teaches the model to find your special labeled words, not general tasks like translation or summarization.

3. Final Answer:

   To teach the model to recognize specific words or phrases you label -> Option B

4. Quick Check:

   Custom NER = Recognize labeled words [OK]

Hint: Custom NER means teaching model your special words [OK]

Common Mistakes:

• Confusing NER with translation or summarization
• Thinking NER generates new text
• Assuming NER works without labeled data

2. Which of the following is the correct way to label a sentence for custom NER training in Python spaCy format?

easy

A. ('Apple is a company', {'entities': [(0, 5, 'ORG')]})

B. ('Apple is a company', {'labels': [(0, 5, 'ORG')]})

C. ('Apple is a company', {'entities': [(6, 7, 'ORG')]})

D. ('Apple is a company', {'entities': [(0, 5, 'PERSON')]})

Solution

1. Step 1: Check the labeling key

   spaCy uses the 'entities' key, not 'labels', to hold labeled spans.

2. Step 2: Verify the span and label

   Span (0,5) covers 'Apple' correctly, and label 'ORG' (organization) fits. A span like (6,7,'ORG') points to the wrong position, and 'PERSON' is incorrect for a company.

3. Final Answer:

   ('Apple is a company', {'entities': [(0, 5, 'ORG')]}) -> Option A

4. Quick Check:

   Correct key and span = ('Apple is a company', {'entities': [(0, 5, 'ORG')]}) [OK]

Hint: Use 'entities' key with correct span and label [OK]

Common Mistakes:

• Using 'labels' instead of 'entities'
• Incorrect character span for entity
• Wrong entity type label

3. Given this training data snippet for custom NER:

TRAIN_DATA = [
  ('I love Paris', {'entities': [(7, 12, 'GPE')]})
]

What will the model predict for the sentence 'I love Paris' after training?

medium

A. [] (no entities)

B. [('I', 'GPE')]

C. [('Paris', 'GPE')]

D. [('love', 'GPE')]

Solution

1. Step 1: Understand the labeled entity

   The training data labels 'Paris' from character 7 to 12 as 'GPE' (Geopolitical entity).

2. Step 2: Predict model output after training

   The model learns to recognize 'Paris' as 'GPE' and should predict [('Paris', 'GPE')] for the same sentence.

3. Final Answer:

   [('Paris', 'GPE')] -> Option C

4. Quick Check:

   Entity span matches 'Paris' = [('Paris', 'GPE')] [OK]

Hint: Model predicts labeled spans from training data [OK]

Common Mistakes:

• Confusing entity span with other words
• Expecting no entities if training is done
• Mixing entity labels

4. You wrote this code to add a new entity label to your NER model:

ner.add_label('ANIMAL')

But after training, the model never detects 'ANIMAL' entities. What is the most likely mistake?

medium

A. The label 'ANIMAL' is reserved and cannot be used

B. You used the wrong method name; it should be add_entity()

C. You need to call ner.remove_label('ANIMAL') before adding

D. You forgot to include training examples with 'ANIMAL' labels

Solution

1. Step 1: Check the method usage

   ner.add_label('ANIMAL') is correct to add a new label. There is no add_entity() method, no need to call remove_label first, and 'ANIMAL' is not reserved.

2. Step 2: Verify training data

   Model learns from examples. Without training examples labeled 'ANIMAL', model cannot detect it.

3. Final Answer:

   You forgot to include training examples with 'ANIMAL' labels -> Option D

4. Quick Check:

   Training data needed for new labels = You forgot to include training examples with 'ANIMAL' labels [OK]

Hint: Add labeled examples for new entity labels [OK]

Common Mistakes:

• Assuming adding label alone trains model
• Using wrong method names
• Thinking labels are reserved keywords

5. You want to train a custom NER model to recognize two new entity types: 'FOOD' and 'DRINK'. You have labeled training data for both. Which of the following is the best approach to ensure the model learns both correctly?

hard

A. Add both labels with ner.add_label(), include balanced training examples for each, and train in multiple iterations

B. Add only 'FOOD' label first, train fully, then add 'DRINK' label and train again

C. Train the model without adding labels explicitly; it will learn automatically

D. Add labels but use only examples for 'FOOD' to avoid confusion

Solution

1. Step 1: Add all new labels before training

   Adding both 'FOOD' and 'DRINK' labels upfront ensures model knows what to learn.

2. Step 2: Provide balanced training data and train iteratively

   Balanced examples for both labels and multiple training loops help model learn both well.

3. Final Answer:

   Add both labels with ner.add_label(), include balanced training examples for each, and train in multiple iterations -> Option A

4. Quick Check:

   All labels + balanced data + training = Add both labels with ner.add_label(), include balanced training examples for each, and train in multiple iterations [OK]

Hint: Add all labels and balanced data before training [OK]

Common Mistakes:

• Adding labels one by one with separate training
• Skipping label addition
• Training with unbalanced or missing examples