Which of the following is NOT a commonly used pre-trained language model for NLP tasks?Question 14Answera.BERT (Bidirectional Encoder Representations from Transformers)b.ELMO (Embeddings from Language Models)c.GPT (Generative Pre-trained Transformer)d.SVM (Support Vector Machine)
Question
Which of the following is NOT a commonly used pre-trained language model for NLP tasks?Question 14Answera.BERT (Bidirectional Encoder Representations from Transformers)b.ELMO (Embeddings from Language Models)c.GPT (Generative Pre-trained Transformer)d.SVM (Support Vector Machine)
Solution
Para responder a esta pregunta, primero debemos identificar qué modelos son comúnmente utilizados en tareas de procesamiento de lenguaje natural (NLP) y cuál no lo es.
a. BERT (Bidirectional Encoder Representations from Transformers): BERT es un modelo de lenguaje preentrenado muy popular en NLP, desarrollado por Google. Es ampliamente utilizado para diversas tareas de NLP.
b. ELMO (Embeddings from Language Models): ELMO es otro modelo de lenguaje preentrenado que también es muy conocido y utilizado en tareas de NLP. Fue desarrollado por Allen Institute for AI.
c. GPT (Generative Pre-trained Transformer): GPT es un modelo de lenguaje preentrenado desarrollado por OpenAI. Es muy popular y se utiliza en una variedad de tareas de NLP.
d. SVM (Support Vector Machine): SVM es un algoritmo de aprendizaje supervisado utilizado para clasificación y regresión
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What is the primary advantage of using Bidirectional Encoder Representations from Transformers (BERT) in NLP?*1 pointIt can generate new text from scratchIt considers the context of a word from both directions.It reduces computational complexity.It focuses solely on syntactic parsing.
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