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820c190 33c3f19 820c190 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from torch import nn
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.models.roberta import RobertaModel, RobertaPreTrainedModel
from .configuration_alignscore import AlignscoreConfig
@dataclass
class ModelOutput:
loss: Optional[torch.FloatTensor] = None
all_loss: Optional[list] = None
loss_nums: Optional[list] = None
prediction_logits: torch.FloatTensor = None
seq_relationship_logits: torch.FloatTensor = None
tri_label_logits: torch.FloatTensor = None
reg_label_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class AlignscoreModel(RobertaPreTrainedModel):
config_class = AlignscoreConfig
# COPIED FROM transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification
def __init__(self, config):
super().__init__(config)
# NUM_LABELS WILL BE IGNOREDD
# self.num_labels = config.num_labels
self.config = config
self.roberta = RobertaModel(config, add_pooling_layer=True)
self.bin_layer = nn.Linear(config.hidden_size, 2)
self.tri_layer = nn.Linear(config.hidden_size, 3)
self.reg_layer = nn.Linear(config.hidden_size, 1)
if config.hidden_dropout_prob != 0.1:
print(
"Warning: The hidden_dropout_prob is not set to 0.1, which may affect the model's performance."
)
self.dropout = nn.Dropout(config.hidden_dropout_prob) # should be 0.1
self.softmax = nn.Softmax(dim=-1)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = 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,
return_dict=return_dict,
)
seq_relationship_score = self.bin_layer(
self.dropout(outputs.pooler_output)
) ## pooled output for classification
tri_label_score = self.tri_layer(self.dropout(outputs.pooler_output))
reg_label_score = self.reg_layer(outputs.pooler_output)
if labels is not None:
raise NotImplementedError(
"AlignscoreModel does not support labels for training. "
"Please use the model for inference only."
)
return ModelOutput(
loss=None,
all_loss=None,
loss_nums=None,
prediction_logits=None,
seq_relationship_logits=seq_relationship_score,
tri_label_logits=tri_label_score,
reg_label_logits=reg_label_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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