Custom NER training basics in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does NER stand for in NLP?

NER stands for Named Entity Recognition. It is a process to find and classify names, places, dates, and other important information in text.

beginner

Why do we train a custom NER model?

We train a custom NER model to recognize specific types of entities that are important for our task but not covered by general models, like product names or medical terms.

beginner

What is the role of annotated data in custom NER training?

Annotated data is text where entities are marked with their correct labels. This data teaches the model what to look for during training.

intermediate

Name one common metric used to evaluate NER models.

F1 score is commonly used. It balances precision (correctness) and recall (completeness) to measure model performance.

beginner

What is a typical step after training a custom NER model?

After training, you test the model on new text to see how well it finds entities, then improve it by adding more data or tuning settings.

What is the main purpose of Named Entity Recognition?

A To translate text from one language to another

B To generate new text automatically

C To summarize long documents

D To find and label important words like names and places in text

NER identifies and labels entities such as names, places, and dates in text.

Why might you need to train a custom NER model instead of using a pre-trained one?

A To reduce the size of the model

B Because pre-trained models are always inaccurate

C To recognize special entity types not in general models

D To translate text faster

Custom training helps the model learn to find entities specific to your needs.

What is annotated data in the context of NER training?

A Text translated into another language

B Text with entities marked and labeled

C Text summarized into short sentences

D Text generated by the model

Annotated data shows the model examples of correct entity labels.

Which metric combines precision and recall to evaluate NER models?

A F1 score

B Accuracy

C Loss

D BLEU score

F1 score balances precision and recall for a fair evaluation.

What is a common next step after training a custom NER model?

A Test it on new text and improve if needed

B Delete the training data

C Use it to translate languages

D Convert it into a chatbot

Testing helps check how well the model works and guides improvements.

Explain the process and importance of annotating data for custom NER training.

Think about how the model knows what to find in text.

Describe how you would evaluate a custom NER model after training.

Consider how to check if the model is good at finding entities.