Using a pre-trained BERT model, we generate embeddings for each token:
deep_feature = [0.23, 0.41, ..., 0.57]
Tokenized text:
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
This is a dense vector representation of the input text, which can be used for downstream tasks such as text classification, clustering, or information retrieval. Using a pre-trained BERT model
Let's use mean pooling:
Using a pre-trained BERT model, we generate embeddings for each token:
deep_feature = [0.23, 0.41, ..., 0.57]
Tokenized text:
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.
This is a dense vector representation of the input text, which can be used for downstream tasks such as text classification, clustering, or information retrieval.
Let's use mean pooling: