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Class BertConfig

examples/tensorflow/bert/bert-quantization/modeling.py:36–109  ·  view source on GitHub ↗

Configuration for `BertModel`.

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34
35
36class BertConfig(object):
37 """Configuration for `BertModel`."""
38
39 def __init__(self,
40 vocab_size,
41 hidden_size=768,
42 num_hidden_layers=12,
43 num_attention_heads=12,
44 intermediate_size=3072,
45 hidden_act="gelu",
46 hidden_dropout_prob=0.1,
47 attention_probs_dropout_prob=0.1,
48 max_position_embeddings=512,
49 type_vocab_size=16,
50 initializer_range=0.02):
51 """Constructs BertConfig.
52
53 Args:
54 vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
55 hidden_size: Size of the encoder layers and the pooler layer.
56 num_hidden_layers: Number of hidden layers in the Transformer encoder.
57 num_attention_heads: Number of attention heads for each attention layer in
58 the Transformer encoder.
59 intermediate_size: The size of the "intermediate" (i.e., feed-forward)
60 layer in the Transformer encoder.
61 hidden_act: The non-linear activation function (function or string) in the
62 encoder and pooler.
63 hidden_dropout_prob: The dropout probability for all fully connected
64 layers in the embeddings, encoder, and pooler.
65 attention_probs_dropout_prob: The dropout ratio for the attention
66 probabilities.
67 max_position_embeddings: The maximum sequence length that this model might
68 ever be used with. Typically set this to something large just in case
69 (e.g., 512 or 1024 or 2048).
70 type_vocab_size: The vocabulary size of the `token_type_ids` passed into
71 `BertModel`.
72 initializer_range: The stdev of the truncated_normal_initializer for
73 initializing all weight matrices.
74 """
75 self.vocab_size = vocab_size
76 self.hidden_size = hidden_size
77 self.num_hidden_layers = num_hidden_layers
78 self.num_attention_heads = num_attention_heads
79 self.hidden_act = hidden_act
80 self.intermediate_size = intermediate_size
81 self.hidden_dropout_prob = hidden_dropout_prob
82 self.attention_probs_dropout_prob = attention_probs_dropout_prob
83 self.max_position_embeddings = max_position_embeddings
84 self.type_vocab_size = type_vocab_size
85 self.initializer_range = initializer_range
86
87 @classmethod
88 def from_dict(cls, json_object):
89 """Constructs a `BertConfig` from a Python dictionary of parameters."""
90 config = BertConfig(vocab_size=None)
91 for (key, value) in six.iteritems(json_object):
92 config.__dict__[key] = value
93 return config

Callers 1

from_dictMethod · 0.70

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