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6316722 e90dc4c 6316722 cdb2325 6316722 e90dc4c 6316722 | 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 | from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from .configuration_suave_multitask import SuaveMultitaskConfig
@dataclass
class SuaveMultitaskOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits_binary: Optional[torch.FloatTensor] = None
logits_multiclass: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
class SuaveMultitaskModel(PreTrainedModel):
config_class = SuaveMultitaskConfig
base_model_prefix = "encoder"
def __init__(self, config: SuaveMultitaskConfig):
super().__init__(config)
base_config = AutoConfig.from_pretrained(config.base_model_name)
self.encoder = AutoModel.from_config(base_config)
hidden_size = self.encoder.config.hidden_size
self.dropout = nn.Dropout(config.classifier_dropout)
self.classifier_binary = nn.Linear(hidden_size, 2)
self.classifier_multiclass = nn.Linear(hidden_size, config.num_ai_classes)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
labels_binary=None,
labels_multiclass=None,
**kwargs,
):
outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=kwargs.get("output_hidden_states", False),
output_attentions=kwargs.get("output_attentions", False),
)
pooled = outputs.last_hidden_state[:, 0]
pooled = self.dropout(pooled)
logits_binary = self.classifier_binary(pooled)
logits_multiclass = self.classifier_multiclass(pooled)
loss = None
if labels_binary is not None and labels_multiclass is not None:
loss_binary = nn.CrossEntropyLoss()(logits_binary, labels_binary)
loss_multiclass = nn.CrossEntropyLoss(ignore_index=-1)(
logits_multiclass, labels_multiclass
)
loss = loss_binary + 0.5 * loss_multiclass
return SuaveMultitaskOutput(
loss=loss,
logits_binary=logits_binary,
logits_multiclass=logits_multiclass,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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