yongbowin/pytorch-pretrained-BERT_annotation

bert annotation, input and output for people from scratch, 代码注释, 有每一步的输入和输出, 适合初学者

PyTorch Pretrained Bert Annotation

This BERT annotation repo is for my personal study.

• The raw README of PyTorch Pretrained Bert is here.
• A very nice PPT to help understanding.
• Synthetic Self-Training PPT.

Arch

The BertModel and BertForMaskedLM arch.

BertModel Arch

• BertEmbeddings
  • word_embeddings: Embedding(30522, 768)
  • position_embeddings: Embedding(512, 768)
  • token_type_embeddings: Embedding(2, 768)
  • LayerNorm: BertLayerNorm()
  • dropout: Dropout(p=0.1)
• BertEncoder
  • BertLayer: (12 layers)
    • BertAttention
      • BertSelfAttention
        • query: Linear(in_features=768, out_features=768, bias=True)
        • key: Linear(in_features=768, out_features=768, bias=True)
        • value: Linear(in_features=768, out_features=768, bias=True)
        • dropout: Dropout(p=0.1)
      • BertSelfOutput
        • dense: Linear(in_features=768, out_features=768, bias=True)
        • LayerNorm: BertLayerNorm()
        • dropout: Dropout(p=0.1)
    • BertIntermediate
      • dense: Linear(in_features=768, out_features=3072, bias=True)
      • activation: gelu
    • BertOutput
      • dense: Linear(in_features=3072, out_features=768, bias=True)
      • LayerNorm: BertLayerNorm()
      • dropout: Dropout(p=0.1)
• BertPooler
  • dense: Linear(in_features=768, out_features=768, bias=True)
  • activation: Tanh()

BertForMaskedLM Arch

• BertModel
  • BertEmbeddings
    • word_embeddings: Embedding(30522, 768)
    • position_embeddings: Embedding(512, 768)
    • token_type_embeddings: Embedding(2, 768)
    • LayerNorm: BertLayerNorm()
    • dropout: Dropout(p=0.1)
  • BertEncoder
    • BertLayer: (12 layers)
      • BertAttention
        • BertSelfAttention
          • query: Linear(in_features=768, out_features=768, bias=True)
          • key: Linear(in_features=768, out_features=768, bias=True)
          • value: Linear(in_features=768, out_features=768, bias=True)
          • dropout: Dropout(p=0.1)
        • BertSelfOutput
          • dense: Linear(in_features=768, out_features=768, bias=True)
          • LayerNorm: BertLayerNorm()
          • dropout: Dropout(p=0.1)
      • BertIntermediate
        • dense: Linear(in_features=768, out_features=3072, bias=True)
        • activation: gelu
      • BertOutput
        • dense: Linear(in_features=3072, out_features=768, bias=True)
        • LayerNorm: BertLayerNorm()
        • dropout: Dropout(p=0.1)
  • BertPooler
    • dense: Linear(in_features=768, out_features=768, bias=True)
    • activation: Tanh()
• BertOnlyMLMHead
  • BertLMPredictionHead
    • transform: BertPredictionHeadTransform
      • dense: Linear(in_features=768, out_features=768, bias=True)
      • LayerNorm: BertLayerNorm()
    • decoder: Linear(in_features=768, out_features=30522, bias=False)