Predictive Analytics with TensorFlow 6.2: Transformers and Estimators

The video tutorial covers NLP analytics pipelines, focusing on transformers, which are functions that transform datasets. It distinguishes between standard and estimator transformers, providing examples of each. The tutorial also delves into tokenization techniques, including regex tokenizers and stop words removal, and explains the concept of n-grams, demonstrating their application in text processing.

What is the primary function of a standard transformer in NLP?

• To generate a new data set based on input data

• To apply a transformation function to input data

• To estimate the output data set

• To remove stop words from the input data

How does an estimator transformer differ from a standard transformer?

• It does not require any input data

• It is used for removing stop words

• It generates a transformer based on the input data set

• It only works with numerical data

Which of the following is an example of an estimator transformer?

• Regex Tokenizer

• Tokenizer

• Stop Words Remover

• TF-IDF

What is the purpose of a regex tokenizer?

• To transform numerical data

• To combine words into n-grams

• To remove stop words from text

• To split text into words using a regex pattern

What does the 'N' in n-grams stand for?

• Number of characters

• Number of paragraphs

• Number of sentences

• Number of words in a sequence