61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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from typing import Dict
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import torch
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import torch.nn.functional as F
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from torch import nn
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from modelscope.metainfo import Heads
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from modelscope.models.base import TorchHead
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from modelscope.models.builder import HEADS
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from modelscope.outputs import (AttentionTextClassificationModelOutput,
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ModelOutputBase, OutputKeys)
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from modelscope.utils.constant import Tasks
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@HEADS.register_module(
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Tasks.text_classification, module_name=Heads.text_classification)
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@HEADS.register_module(
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Tasks.sentence_similarity, module_name=Heads.text_classification)
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@HEADS.register_module(Tasks.nli, module_name=Heads.text_classification)
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@HEADS.register_module(
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Tasks.sentiment_classification, module_name=Heads.text_classification)
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class TextClassificationHead(TorchHead):
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def __init__(self,
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hidden_size=768,
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classifier_dropout=0.1,
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num_labels=None,
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**kwargs):
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super().__init__(
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hidden_size=hidden_size,
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classifier_dropout=classifier_dropout,
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num_labels=num_labels,
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)
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assert num_labels is not None
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self.dropout = nn.Dropout(classifier_dropout)
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self.classifier = nn.Linear(hidden_size, num_labels)
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def forward(self,
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inputs: ModelOutputBase,
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attention_mask=None,
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labels=None,
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**kwargs):
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pooler_output = inputs.pooler_output
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pooler_output = self.dropout(pooler_output)
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logits = self.classifier(pooler_output)
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loss = None
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if labels is not None:
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loss = self.compute_loss(logits, labels)
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return AttentionTextClassificationModelOutput(
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loss=loss,
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logits=logits,
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hidden_states=inputs.hidden_states,
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attentions=inputs.attentions,
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)
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def compute_loss(self, logits: torch.Tensor, labels) -> torch.Tensor:
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return F.cross_entropy(logits, labels)
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