BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
BERT-based Models:
BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
Why Option D is Correct:
Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
Why Other Options are Incorrect:
A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.
