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import torch.nn as nn |
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from transformers import BertConfig, BertModel, BertTokenizer |
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from modules.build import LANGUAGE_REGISTRY |
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@LANGUAGE_REGISTRY.register() |
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class BERTLanguageEncoder(nn.Module): |
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def __init__(self, cfg, weights="bert-base-uncased", hidden_size=768, |
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num_hidden_layers=4, num_attention_heads=12, type_vocab_size=2): |
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super().__init__() |
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self.tokenizer = BertTokenizer.from_pretrained( |
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weights, do_lower_case=True |
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) |
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self.bert_config = BertConfig( |
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hidden_size=hidden_size, |
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num_hidden_layers=num_hidden_layers, |
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num_attention_heads=num_attention_heads, |
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type_vocab_size=type_vocab_size |
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) |
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self.model = BertModel.from_pretrained( |
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weights, config=self.bert_config |
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) |
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def forward(self, txt_ids, txt_masks, **kwargs): |
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return self.model(txt_ids, txt_masks).last_hidden_state |
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