Transformer Embedding

TransformerEmbedding is based on bert4keras. The embeddings itself are wrapped into our simple embedding interface so that they can be used like any other embedding.

TransformerEmbedding support models:

Model	Author	Link		Example
BERT	Google	https://github.com/google-research/bert
ALBERT	Google	https://github.com/google-research/ALBERT
ALBERT	brightmart	https://github.com/brightmart/albert_zh
RoBERTa	brightmart	https://github.com/brightmart/roberta_zh
RoBERTa	哈工大	https://github.com/ymcui/Chinese-BERT-wwm
RoBERTa	苏剑林	https://github.com/ZhuiyiTechnology/pretrained-models
NEZHA	Huawei	https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA

!!! tip When using pre-trained embedding, remember to use same tokenize tool with the embedding model, this will allow to access the full power of the embedding

kashgari.embeddings.TransformerEmbedding(vocab_path: str,
                                          config_path: str,
                                          checkpoint_path: str,
                                          bert_type: str = 'bert',
                                          task: str = None,
                                          sequence_length: Union[str, int] = 'auto',
                                          processor: Optional[BaseProcessor] = None,
                                          from_saved_model: bool = False):

Arguments

• vocab_path: path of model’s vacab.txt file
• config_path: path of model’s model.json file
• checkpoint_path: path of model’s checkpoint file
• bert_type: bert, albert, nezha, electra, gpt2_ml, t5. Type of BERT model.
• task: kashgari.CLASSIFICATION kashgari.LABELING. Downstream task type, If you only need to feature extraction, just set it as kashgari.CLASSIFICATION.
• sequence_length: 'auto' or integer. When using 'auto', use the 95% of corpus length as sequence length. If using an integer, let’s say 50, the input output sequence length will set to 50.

Example Usage - Text Classification

Let’s run a text classification model with BERT.

sentences = [
    "Jim Henson was a puppeteer.",
    "This here's an example of using the BERT tokenizer.",
    "Why did the chicken cross the road?"
            ]
labels = [
    "class1",
    "class2",
    "class1"
]
# ------------ Load Bert Embedding ------------
import os
import kashgari
from kashgari.embeddings import TransformerEmbedding
from kashgari.tokenizer import BertTokenizer

# Setup paths
model_folder = '/Users/brikerman/Desktop/nlp/language_models/albert_base'
checkpoint_path = os.path.join(model_folder, 'model.ckpt-best')
config_path = os.path.join(model_folder, 'albert_config.json')
vocab_path = os.path.join(model_folder, 'vocab_chinese.txt')

tokenizer = BertTokenizer.load_from_vacob_file(vocab_path)
embed = TransformerEmbedding(vocab_path, config_path, checkpoint_path,
                             bert_type='albert',
                             task=kashgari.CLASSIFICATION,
                             sequence_length=100)

sentences_tokenized = [tokenizer.tokenize(s) for s in sentences]
"""
The sentences will become tokenized into:
[
    ['jim', 'henson', 'was', 'a', 'puppet', '##eer', '.'],
    ['this', 'here', "'", 's', 'an', 'example', 'of', 'using', 'the', 'bert', 'token', '##izer', '.'],
    ['why', 'did', 'the', 'chicken', 'cross', 'the', 'road', '?']
]
"""

train_x, train_y = sentences_tokenized[:2], labels[:2]
validate_x, validate_y = sentences_tokenized[2:], labels[2:]

# ------------ Build Model Start ------------
from kashgari.tasks.classification import CNNLSTMModel
model = CNNLSTMModel(embed)

# ------------ Build Model End ------------

model.fit(
    train_x, train_y,
    validate_x, validate_y,
    epochs=3,
    batch_size=32
)
# save model
model.save('path/to/save/model/to')