Implements BERT version of Adam algorithm with weight decay fix. Params: lr: learning rate warmup: portion of t_total for the

Each head is a self-attention operation. self-attention refers to https://arxiv.org/pdf/1706.03762.pdf

spo_files - list of Path of *.spo files, or default kg name. e.g., ['HowNet']

Transformer layer mainly consists of two parts: multi-headed self-attention and feed forward layer.

Runs basic tokenization (punctuation splitting, lower casing, etc.).

BERT embedding consists of three parts: word embedding, position embedding, and segment embedding.

BertModel consists of three parts: - embedding: token embedding, position embedding, segment embedding - encoder: multi-layer tra

Feed Forward Layer

Runs WordPiece tokenization.

BERT encoder exploits 12 or 24 transformer layers to extract features.

Construct dataset for MLM and NSP tasks from the given corpus. Each document consists of multiple sentences, and each sentence occupies

BERT encoder exploits 12 or 24 transformer layers to extract features.

BertModel consists of three parts: - embedding: token embedding, position embedding, segment embedding - encoder: multi-layer tra

BERT exploits masked language modeling (MLM) and next sentence prediction (NSP) for pretraining.

Runs end-to-end tokenization: punctuation splitting + wordpiece

Construct dataset for MLM and NSP tasks from the given corpus. Each document consists of multiple sentences, and each sentence occupies

Construct dataset for MLM and NSP tasks from the given corpus. Each document consists of multiple sentences, and each sentence occupies

BERT encoder exploits 12 or 24 transformer layers to extract features.

Construct dataset for MLM and NSP tasks from the given corpus. Each document consists of multiple sentences, and each sentence occupies

Construct dataset for MLM and NSP tasks from the given corpus. Each document consists of multiple sentences, and each sentence occupies

BERT exploits masked language modeling (MLM) and next sentence prediction (NSP) for pretraining.

Construct dataset for MLM and NSP tasks from the given corpus. Each document consists of multiple sentences, and each sentence occupies