bert_embedding(varicad) = [0.1, 0.2, ..., 0.768] bert_embedding(-) = [0.05, 0.05, ..., 0.05] bert_embedding(v2) = [0.3, 0.4, ..., 0.9] ... bert_embedding(2022) = [0.8, 0.9, ..., 0.1]
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.
Tokenized text:
The final deep feature representation for the input text is:
deep_feature = [0.23, 0.41, ..., 0.57]
To get a fixed-size vector representation for the entire text, we can use a pooling technique such as mean pooling or max pooling.
['varicad', '-', 'v2', '-', '07', '-', 'crack', '-', 'keygen', '-', 'full', '-', 'torrent', '-', 'free', '-', 'download', '-', 'latest', '-', '2022'] bert_embedding(varicad) = [0
The input text is tokenized into subwords: