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Transformers like BERT (Bidirectional Encoder Representations from Transformers) have revolutionized NLP by capturing contextual word representations. Unlike traditional techniques, BERT processes words in both their preceding and succeeding contexts, enabling nuanced understanding. 1. Contextual Embeddings: BERT generates embeddings that vary depending on the surrounding words, addressing issues like polysemy (e.g., "bank" as a financial institution vs. a riverbank). 2. Bidirectionality: By analyzing text in both directions, BERT captures deeper linguistic patterns and relationships. 3. Pretraining and Fine-Tuning: BERT is pretrained on vast corpora and fine-tuned for specific NLP tasks, making it versatile for applications like sentiment analysis, question answering, and translation. Why Other Options Are Incorrect: • A) Bag of Words: Ignores word order and context, treating sentences as a collection of words. • B) One-Hot Encoding: Fails to capture semantic relationships between words. • C) Word2Vec: Generates static word embeddings, lacking context sensitivity. • D) TF-IDF: Focuses on word importance across documents but overlooks word order and meaning.
Food quality management is based on
_________is a physical or chemical method of food preservation where all microorganisms present in food are destroyed.
How many countries are members of FAO, WHO, and CODEX?
(i) saponification
(a) triglycerides
(ii) milk fat
(b) alkali
(iii) ...
The microbes, which can grow at high concentration of sugar, called
Heat required to change the state of commodity is known as
a) Specific heat
b) Latent heat
c) �...
Which of the following is not a result of uncontrolled freezing
Bacillus is an example of
Which of the following is true with regard to growth of microorganisms:
Milk is an example of
a) Water dispersed in fat
b) Fat dispersed in water
c) Oil in water...