BertEmbedding

BERT embedding.

class bert_embedding.bert.BertEmbedding(ctx=cpu(0), dtype='float32', model='bert_12_768_12', dataset_name='book_corpus_wiki_en_uncased', params_path=None, max_seq_length=25, batch_size=256)

Bases: object

Encoding from BERT model.

Parameters:

• ctx (Context.) – running BertEmbedding on which gpu device id.
• dtype (str) – data type to use for the model.
• model (str, default bert_12_768_12.) – pre-trained BERT model
• dataset_name (str, default book_corpus_wiki_en_uncased.) – pre-trained model dataset
• params_path (str, default None) – path to a parameters file to load instead of the pretrained model.
• max_seq_length (int, default 25) – max length of each sequence
• batch_size (int, default 256) – batch size

__init__(ctx=cpu(0), dtype='float32', model='bert_12_768_12', dataset_name='book_corpus_wiki_en_uncased', params_path=None, max_seq_length=25, batch_size=256)

Encoding from BERT model.

Parameters:

• ctx (Context.) – running BertEmbedding on which gpu device id.
• dtype (str) –
• type to use for the model. (data) –
• model (str, default bert_12_768_12.) – pre-trained BERT model
• dataset_name (str, default book_corpus_wiki_en_uncased.) – pre-trained model dataset
• params_path (str, default None) – path to a parameters file to load instead of the pretrained model.
• max_seq_length (int, default 25) – max length of each sequence
• batch_size (int, default 256) – batch size

embedding(sentences, oov_way='avg', filter_spec_tokens=True)

Get tokens, tokens embedding

Parameters:

• sentences (List[str]) – sentences for encoding.
• oov_way (str, default avg.) – use avg, sum or last to get token embedding for those out of vocabulary words
• filter_spec_tokens (bool) – filter [CLS], [SEP] tokens.

Returns:

List of tokens, and tokens embedding

Return type:

List[(List[str], List[ndarray])]