Bumblebee v0.1.2

View Source Bumblebee.Text.Bert (Bumblebee v0.1.2)

BERT model family.

architectures

 Architectures

  • :base - plain BERT without any head on top

  • :for_masked_language_modeling - BERT with a language modeling head. The head returns logits for each token in the original sequence

  • :for_sequence_classification - BERT with a sequence classification head. The head returns logits corresponding to possible classes

  • :for_token_classification - BERT with a token classification head. The head returns logits for each token in the original sequence

  • :for_question_answering - BERT with a span classification head. The head returns logits for the span start and end positions

  • :for_multiple_choice - BERT with a multiple choice prediction head. Each input in the batch consists of several sequences to choose from and the model returns logits corresponding to those choices

  • :for_next_sentence_prediction - BERT with a next sentence prediction head. The head returns logits predicting whether the second sentence is random or in context

  • :for_pre_training - BERT with both MLM and NSP heads as done during the pre-training

  • :for_causal_language_modeling - BERT working as a decoder with a language modeling head. The head returns logits for each token in the original sequence

inputs

 Inputs

  • "input_ids" - {batch_size, sequence_length}

    Indices of input sequence tokens in the vocabulary.

  • "attention_mask" - {batch_size, sequence_length}

    Mask indicating which tokens to attend to. This is used to ignore padding tokens, which are added when processing a batch of sequences with different length.

  • "token_type_ids" - {batch_size, sequence_length}

    Mask distinguishing groups in the input sequence. This is used in when the input sequence is a semantically a pair of sequences.

  • "position_ids" - {batch_size, sequence_length}

    Indices of positions of each input sequence tokens in the position embeddings.

  • "attention_head_mask" - {num_blocks, num_attention_heads}

    Mask to nullify selected heads of the self-attention blocks in the encoder.

exceptions

 Exceptions

The :for_multiple_choice model accepts groups of sequences, so the expected sequence shape is {batch_size, num_choices, sequence_length}.

The :for_causal_language_modeling model is a decoder and accepts the following additional inputs: "encoder_hidden_state", "encoder_attention_mask", "cross_attention_head_mask", "cache".

configuration

 Configuration

  • :vocab_size - the vocabulary size of the token embedding. This corresponds to the number of distinct tokens that can be represented in model input and output . Defaults to 30522

  • :max_positions - the vocabulary size of the position embedding. This corresponds to the maximum sequence length that this model can process. Typically this is set to a large value just in case, such as 512, 1024 or 2048 . Defaults to 512

  • :max_token_types - the vocabulary size of the token type embedding (also referred to as segment embedding). This corresponds to how many different token groups can be distinguished in the input . Defaults to 2

  • :hidden_size - the dimensionality of hidden layers. Defaults to 768

  • :num_blocks - the number of Transformer blocks in the encoder. Defaults to 12

  • :num_attention_heads - the number of attention heads for each attention layer in the encoder. Defaults to 12

  • :intermediate_size - the dimensionality of the intermediate (often named feed-forward) layer in the encoder. Defaults to 3072

  • :activation - the activation function. Defaults to :gelu

  • :dropout_rate - the dropout rate for embedding and encoder. Defaults to 0.1

  • :attention_dropout_rate - the dropout rate for attention weights. Defaults to 0.1

  • :classifier_dropout_rate - the dropout rate for the classification head. If not specified, the value of :dropout_rate is used instead

  • :layer_norm_epsilon - the epsilon used by the layer normalization layers. Defaults to 1.0e-12

  • :initializer_scale - the standard deviation of the normal initializer used for initializing kernel parameters. Defaults to 0.02

  • :output_hidden_states - whether the model should return all hidden states. Defaults to false

  • :output_attentions - whether the model should return all attentions. Defaults to false

  • :num_labels - the number of labels to use in the last layer for the classification task. Defaults to 2

  • :id_to_label - a map from class index to label. Defaults to %{}

  • :use_cross_attention - whether cross-attention layers should be added to the model.This is only relevant for decoder models. Defaults to false

references

 References