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Types & classes14 in github.com/ProHiryu/albert-chinese-ner

↓ 2 callersClassTrainingInstance
A single training instance (sentence pair).
create_pretraining_data.py:71
↓ 1 callersClassAdamWeightDecayOptimizer
A basic Adam optimizer that includes "correct" L2 weight decay.
optimization_finetuning.py:87
↓ 1 callersClassBasicTokenizer
Runs basic tokenization (punctuation splitting, lower casing, etc.).
tokenization.py:187
↓ 1 callersClassBertConfig
Configuration for `BertModel`.
modeling.py:31
↓ 1 callersClassInputExample
A single training/test example for simple sequence classification.
albert_ner.py:132
↓ 1 callersClassInputFeatures
A single set of features of data.
albert_ner.py:162
↓ 1 callersClassLAMBOptimizer
LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. -
optimization.py:178
↓ 1 callersClassPaddingInputExample
Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of examples
albert_ner.py:151
↓ 1 callersClassWordpieceTokenizer
Runs WordPiece tokenziation.
tokenization.py:302
ClassAdamWeightDecayOptimizer
A basic Adam optimizer that includes "correct" L2 weight decay.
optimization.py:87
ClassBertModel
BERT model ("Bidirectional Encoder Representations from Transformers"). Example usage: ```python # Already been converted into WordPiece token
modeling.py:107
ClassDataProcessor
Base class for data converters for sequence classification data sets.
albert_ner.py:177
ClassFullTokenizer
Runs end-to-end tokenziation.
tokenization.py:163
ClassNerProcessor
albert_ner.py:222