Bumblebee.Text.ModernBert (Bumblebee v0.7.0)

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ModernBERT model family.

Architectures

  • :base - plain ModernBERT without any head on top

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

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

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

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.

  • "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.

Global layer options

  • :output_hidden_states - when true, the model output includes all hidden states

  • :output_attentions - when true, the model output includes all attention weights

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 50368

  • :max_positions - the maximum sequence length that this model can process. ModernBERT uses RoPE (Rotary Position Embedding) instead of absolute position embeddings . Defaults to 8192

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

  • :num_blocks - the number of Transformer blocks in the encoder. Defaults to 22

  • :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 layer in the transformer feed-forward network (FFN) in the encoder. Defaults to 1152

  • :activation - the activation function used in the gated FFN. Defaults to :gelu

  • :dropout_rate - the dropout rate for embedding and encoder. Defaults to 0.0

  • :attention_dropout_rate - the dropout rate for attention weights. Defaults to 0.0

  • :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-5

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

  • :local_attention_window - the window size for local attention layers. Defaults to 128

  • :layer_types - a list of layer types for each layer, where each element is either :sliding_attention (local attention with sliding window) or :full_attention (global attention)

  • :rotary_embedding_base_local - base for computing rotary embedding frequency for local (sliding) attention layers. Defaults to 10000.0

  • :rotary_embedding_base - base for computing rotary embedding frequency for global attention layers. Defaults to 160000.0

  • :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 %{}

References