NLP Engineer
NLP Engineer Interview Questions and Answers
Q1. What is text embedding?
Text embedding is a technique to convert text data into numerical vectors for machine learning models.
Text embedding captures semantic meaning of words in a continuous vector space.
Popular methods include Word2Vec, GloVe, and BERT.
Embeddings can be pre-trained or learned from scratch depending on the task.
They are used in NLP tasks like sentiment analysis, text classification, and machine translation.
Q2. Challenges in project
One of the challenges in the project was integrating multiple NLP models with different architectures.
Ensuring compatibility and consistency between models
Handling different input formats and output structures
Optimizing performance and computational resources
Addressing potential conflicts or biases between models
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