To generate a deep feature for the text, we can use a text embedding technique such as Word2Vec or BERT. Let's assume we're using a pre-trained BERT model to generate embeddings.
Using a pre-trained BERT model, we generate embeddings for each token: To generate a deep feature for the text,
The input text is tokenized into subwords: To generate a deep feature for the text,