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import torch.nn as nn
from transformers import XLMRobertaModel
from transformers.models.xlm_roberta.modeling_xlm_roberta import XLMRobertaPreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
class Smish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x * (x.sigmoid() + 1).log().tanh()
class NoRefER(XLMRobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
hidden_size = 32
self.config = config
self.roberta = XLMRobertaModel(config)
self.dense = nn.Sequential(
nn.Dropout(config.hidden_dropout_prob),
nn.Linear(config.hidden_size, hidden_size, bias = False),
nn.Dropout(config.hidden_dropout_prob), Smish(),
nn.Linear(hidden_size, 1, bias = False)
)
self.post_init()
def forward(self, positive_input_ids, positive_attention_mask, negative_input_ids, negative_attention_mask, labels, weight=None):
# positive processing
positive_inputs = {
"input_ids": positive_input_ids #, "attention_mask": positive_attention_mask
}
positive = self.dense(self.roberta(**positive_inputs).pooler_output).squeeze(-1)
# negative processing
negative_inputs = {
"input_ids": negative_input_ids #, "attention_mask": negative_attention_mask
}
negative = self.dense(self.roberta(**negative_inputs).pooler_output).squeeze(-1)
if weight is None:
bce = nn.BCEWithLogitsLoss()
else:
bs = len(positive)
weights = (weight.float() * bs) / weight.sum()
bce = nn.BCEWithLogitsLoss(weight = weights)
loss = bce(positive - negative, labels.float())
return SequenceClassifierOutput(
loss=loss,
logits=positive.sigmoid()-negative.sigmoid(),
)
def score(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
):
h = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,).pooler_output
return self.dense(h).sigmoid().squeeze(-1)
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