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The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. These included the Supportvectormachine (SVM) based models. 2014) Significant people : Geoffrey Hinton Yoshua Bengio Ilya Sutskever 5. 2018) “ Language models are few-shot learners ” by Brown et al.
Sentence embeddings can also be used in text classification by representing entire sentences as high-dimensional vectors and then feeding them into a classifier. Clustering — we can cluster our sentences, useful for topic modeling. How can we make the machine draw the inference between ‘crowded places’ and ‘busy cities’?
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