Class: Transformers::Mpnet::MPNetForSequenceClassification

Inherits:
MPNetPreTrainedModel show all
Defined in:
lib/transformers/models/mpnet/modeling_mpnet.rb

Instance Attribute Summary

Attributes inherited from PreTrainedModel

#config

Instance Method Summary collapse

Methods inherited from MPNetPreTrainedModel

#_init_weights

Methods inherited from PreTrainedModel

#_backward_compatibility_gradient_checkpointing, #_init_weights, #_initialize_weights, #base_model, #can_generate, #dequantize, #dummy_inputs, #framework, from_pretrained, #get_input_embeddings, #get_output_embeddings, #init_weights, #post_init, #prune_heads, #set_input_embeddings, #tie_weights, #warn_if_padding_and_no_attention_mask

Methods included from ClassAttribute

#class_attribute

Methods included from Transformers::ModuleUtilsMixin

#device, #get_extended_attention_mask, #get_head_mask

Constructor Details

#initialize(config) ⇒ MPNetForSequenceClassification

Returns a new instance of MPNetForSequenceClassification.



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# File 'lib/transformers/models/mpnet/modeling_mpnet.rb', line 542

def initialize(config)
  super(config)

  @num_labels = config.num_labels
  @mpnet = MPNetModel.new(config, add_pooling_layer: false)
  @classifier = MPNetClassificationHead.new(config)

  # Initialize weights and apply final processing
  post_init
end

Instance Method Details

#forward(input_ids: nil, attention_mask: nil, position_ids: nil, head_mask: nil, inputs_embeds: nil, labels: nil, output_attentions: nil, output_hidden_states: nil, return_dict: nil) ⇒ Object 



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# File 'lib/transformers/models/mpnet/modeling_mpnet.rb', line 553

def forward(
  input_ids: nil,
  attention_mask: nil,
  position_ids: nil,
  head_mask: nil,
  inputs_embeds: nil,
  labels: nil,
  output_attentions: nil,
  output_hidden_states: nil,
  return_dict: nil
)
  return_dict = !return_dict.nil? ? return_dict : @config.use_return_dict

  outputs = @mpnet.(input_ids, attention_mask: attention_mask, position_ids: position_ids, head_mask: head_mask, inputs_embeds: inputs_embeds, output_attentions: output_attentions, output_hidden_states: output_hidden_states, return_dict: return_dict)
  sequence_output = outputs[0]
  logits = @classifier.(sequence_output)

  loss = nil
  if !labels.nil?
    if @config.problem_type.nil?
      if @num_labels == 1
        @problem_type = "regression"
      elsif @num_labels > 1 && labels.dtype == Torch.long || labels.dtype == Torch.int
        @problem_type = "single_label_classification"
      else
        @problem_type = "multi_label_classification"
      end
    end

    if @config.problem_type == "regression"
      loss_fct = Torch::NN::MSELoss.new
      if @num_labels == 1
        loss = loss_fct.(logits.squeeze, labels.squeeze)
      else
        loss = loss_fct.(logits, labels)
      end
    elsif @config.problem_type == "single_label_classification"
      loss_fct = Torch::NN::CrossEntropyLoss.new
      loss = loss_fct.(logits.view(-1, @num_labels), labels.view(-1))
    elsif @config.problem_type == "multi_label_classification"
      loss_fct = Torch::NN::BCEWithLogitsLoss.new
      loss = loss_fct.(logits, labels)
    end
  end
  if !return_dict
    output = [logits] + outputs[2..]
    return !loss.nil? ? [loss] + output : output
  end

  SequenceClassifierOutput.new(loss: loss, logits: logits, hidden_states: outputs.hidden_states, attentions: outputs.attentions)
end