Method: Transformers::Mpnet::MPNetForTokenClassification#forward

Defined in:

lib/transformers/models/mpnet/modeling_mpnet.rb

#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

# File 'lib/transformers/models/mpnet/modeling_mpnet.rb', line 672

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]

  sequence_output = @dropout.(sequence_output)
  logits = @classifier.(sequence_output)

  loss = nil
  if !labels.nil?
    loss_fct = Torch::NN::CrossEntropyLoss.new
    loss = loss_fct.(logits.view(-1, @num_labels), labels.view(-1))
  end

  if !return_dict
    output = [logits] + outputs[2..]
    return !loss.nil? ? [loss] + output : output
  end

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