A data scientist trains a text sentiment classifier using SAS and wants to use pre-trained word embeddings. Which SAS capability best supports loading and using pre-trained word vectors such as Word2Vec or GloVe?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you use SAS DLPy when you need deep learning NLP, because it lets you initialize a word embedding layer from a pre-trained weight file like Word2Vec or GloVe. Those weights load into the CAS deep learning engine so the model can use them.
Full Explanation
The correct capability is SAS DLPy with a word embedding layer initialized from a pre-trained weight file. SAS DLPy supports deep learning-based NLP and allows embedding layers to be initialized with pre-trained weights, such as Word2Vec or GloVe, which are loaded as weight tensors into the CAS deep learning engine. The PROC TEXTMINE with EMBED=YES option is wrong because PROC TEXTMINE does not have an EMBED=YES option. The PROC HPNEURAL with PRETRAINED= option for NLP tasks is wrong because PROC HPNEURAL does not support a PRETRAINED= option for NLP. The PROC FACTOR with METHOD=EMBED for word vector extraction is wrong because PROC FACTOR performs factor/PCA analysis, not word embedding.