103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
import json
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import copy
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# import six
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import logging
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class BertConfig(object):
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"""BertModel的配置类"""
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def __init__(
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self,
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vocab_size=21128,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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pad_token_id=0,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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):
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"""
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Args:
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vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
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hidden_size: Size of the encoder layers and the pooler layer.
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num_hidden_layers: Number of hidden layers in the Transformer encoder.
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num_attention_heads: Number of attention heads for each attention\
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layer in the Transformer encoder.
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intermediate_size: The size of the `intermediate` (i.e., feed-forward)\
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layer in the Transformer encoder.
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hidden_act: The non-linear activation function (function or string)\
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in the encoder and pooler.
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hidden_dropout_prob: The dropout probability for all fully connected\
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layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob: The dropout ratio for the attention\
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might\
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ever be used with. Typically set this to something large just in case\
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(e.g., 512 or 1024 or 2048).
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type_vocab_size: The vocabulary size of the `token_type_ids` passed into\
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`BertModel`.
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initializer_range: The stdev of the `truncated_normal_initializer` for\
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initializing all weight matrices.
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"""
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.pad_token_id = pad_token_id
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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@classmethod
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def from_json_file(cls, json_file):
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"""从json格式配置文件读取配置信息"""
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with open(json_file, "r") as reader:
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json_obj = reader.read()
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logging.info(f"成功导入BERT配置文件 {json_file}")
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return cls.from_dict(json.loads(json_obj))
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@classmethod
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def from_dict(cls, dict_obj):
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"""从Python字典中读取配置信息"""
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config = BertConfig(vocab_size=None) # 创建Config对象
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for key, value in dict_obj.items(): # 从字典中读取配置信息
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config.__dict__[key] = value
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return config
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def to_json_str(self):
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"""把对象转换为json格式字符串"""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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def to_dict(self):
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"""把对象转换为字典"""
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out = copy.deepcopy(self.__dict__)
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return out
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if __name__ == "__main__":
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import sys, os
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sys.path.append(os.getcwd())
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json_file = "./archive/bert_base_chinese/config.json"
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config = BertConfig.from_json_file(json_file)
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for key, value in config.__dict__.items():
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print(f"{key} = {value}")
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print("=" * 20)
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print(config.to_json_str())
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