Update modeling_hf_nomic_bert.py
Browse files- modeling_hf_nomic_bert.py +2 -13
modeling_hf_nomic_bert.py
CHANGED
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@@ -1694,7 +1694,6 @@ class NomicBertModel(NomicBertPreTrainedModel):
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return_dict=None,
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matryoshka_dim=None,
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inputs_embeds=None,
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head_mask=None,
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):
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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@@ -1918,10 +1917,6 @@ class NomicBertForMultipleChoice(NomicBertPreTrainedModel):
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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unpad_inputs=unpad_inputs,
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)
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pooled_output = outputs[1]
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@@ -1987,9 +1982,6 @@ class NomicBertForTokenClassification(NomicBertPreTrainedModel):
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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sequence_output = outputs[0]
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@@ -1999,7 +1991,7 @@ class NomicBertForTokenClassification(NomicBertPreTrainedModel):
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loss = None
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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if not return_dict:
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@@ -2057,9 +2049,6 @@ class NomicBertForQuestionAnswering(NomicBertPreTrainedModel):
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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sequence_output = outputs[0]
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@@ -2081,7 +2070,7 @@ class NomicBertForQuestionAnswering(NomicBertPreTrainedModel):
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start_positions = start_positions.clamp(0, ignored_index)
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end_positions = end_positions.clamp(0, ignored_index)
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loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
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start_loss = loss_fct(start_logits, start_positions)
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end_loss = loss_fct(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2
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return_dict=None,
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matryoshka_dim=None,
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inputs_embeds=None,
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):
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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)
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pooled_output = outputs[1]
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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)
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sequence_output = outputs[0]
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loss = None
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if labels is not None:
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loss_fct = nn.CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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if not return_dict:
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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)
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sequence_output = outputs[0]
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start_positions = start_positions.clamp(0, ignored_index)
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end_positions = end_positions.clamp(0, ignored_index)
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loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
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start_loss = loss_fct(start_logits, start_positions)
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end_loss = loss_fct(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2
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