本文整理汇总了Python中pytorch_pretrained_bert.modeling.BertConfig方法的典型用法代码示例。如果您正苦于以下问题：Python modeling.BertConfig方法的具体用法？Python modeling.BertConfig怎么用？Python modeling.BertConfig使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pytorch_pretrained_bert.modeling的用法示例。
在下文中一共展示了modeling.BertConfig方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_span_word_attention
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def test_span_word_attention(self):
config_file = 'tests/fixtures/bert/bert_config.json'
with open(config_file) as fin:
json_config = json.load(fin)
vocab_size = json_config.pop("vocab_size")
config = BertConfig(vocab_size, **json_config)
span_attn = SpanWordAttention(config)
batch_size = 7
timesteps = 29
hidden_states = torch.rand(batch_size, timesteps, config.hidden_size)
num_entity_embeddings = 11
entity_embeddings = torch.rand(batch_size, num_entity_embeddings, config.hidden_size)
entity_mask = entity_embeddings[:, :, 0] > 0.5
span_attn, attention_probs = span_attn(hidden_states, entity_embeddings, entity_mask)
self.assertEqual(list(span_attn.shape), [batch_size, timesteps, config.hidden_size]) 示例2: test_span_attention_layer
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def test_span_attention_layer(self):
config_file = 'tests/fixtures/bert/bert_config.json'
with open(config_file) as fin:
json_config = json.load(fin)
vocab_size = json_config.pop("vocab_size")
config = BertConfig(vocab_size, **json_config)
batch_size = 7
timesteps = 29
hidden_states = torch.rand(batch_size, timesteps, config.hidden_size)
num_entity_embeddings = 11
entity_embeddings = torch.rand(batch_size, num_entity_embeddings, config.hidden_size)
entity_mask = entity_embeddings[:, :, 0] > 0.5
span_attention_layer = SpanAttentionLayer(config)
output = span_attention_layer(hidden_states, entity_embeddings, entity_mask)
self.assertEqual(list(output["output"].shape), [batch_size, timesteps, config.hidden_size]) 示例3: __init__
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def __init__(
self,
bert_model,
output_dim,
add_transformer_layer=False,
layer_pulled=-1,
aggregation="first",
):
super(BertWrapper, self).__init__()
self.layer_pulled = layer_pulled
self.aggregation = aggregation
self.add_transformer_layer = add_transformer_layer
# deduce bert output dim from the size of embeddings
bert_output_dim = bert_model.embeddings.word_embeddings.weight.size(1)
if add_transformer_layer:
config_for_one_layer = BertConfig(
0,
hidden_size=bert_output_dim,
num_attention_heads=int(bert_output_dim / 64),
intermediate_size=3072,
hidden_act='gelu',
)
self.additional_transformer_layer = BertLayer(config_for_one_layer)
self.additional_linear_layer = torch.nn.Linear(bert_output_dim, output_dim)
self.bert_model = bert_model 示例4: __init__
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def __init__(self, vocab_size, original_hidden_size, num_layers, tau=1):
super().__init__()
self.bert_layer = BertLayer(BertConfig(
vocab_size_or_config_json_file=vocab_size,
hidden_size=original_hidden_size * num_layers,
))
self.linear_layer = nn.Linear(original_hidden_size * num_layers, 1)
self.log_sigmoid = nn.LogSigmoid()
self.tau = tau 示例5: __init__
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def __init__(self,
vocab: Vocabulary,
entity_linker: Model,
span_attention_config: Dict[str, int],
should_init_kg_to_bert_inverse: bool = True,
freeze: bool = False,
regularizer: RegularizerApplicator = None):
super().__init__(vocab, regularizer)
self.entity_linker = entity_linker
self.entity_embedding_dim = self.entity_linker.disambiguator.entity_embedding_dim
self.contextual_embedding_dim = self.entity_linker.disambiguator.contextual_embedding_dim
self.weighted_entity_layer_norm = BertLayerNorm(self.entity_embedding_dim, eps=1e-5)
init_bert_weights(self.weighted_entity_layer_norm, 0.02)
self.dropout = torch.nn.Dropout(0.1)
# the span attention layers
assert len(span_attention_config) == 4
config = BertConfig(
0, # vocab size, not used
hidden_size=span_attention_config['hidden_size'],
num_hidden_layers=span_attention_config['num_hidden_layers'],
num_attention_heads=span_attention_config['num_attention_heads'],
intermediate_size=span_attention_config['intermediate_size']
)
self.span_attention_layer = SpanAttentionLayer(config)
# already init inside span attention layer
# for the output!
self.output_layer_norm = BertLayerNorm(self.contextual_embedding_dim, eps=1e-5)
self.kg_to_bert_projection = torch.nn.Linear(
self.entity_embedding_dim, self.contextual_embedding_dim
)
self.should_init_kg_to_bert_inverse = should_init_kg_to_bert_inverse
self._init_kg_to_bert_projection()
self._freeze_all = freeze 示例6: __init__
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def __init__(self, config: BertConfig):
super().__init__()
self.embeddings = BertEmbeddings(config)
self.encoder = SanEncoder(
config.hidden_size,
config.num_hidden_layers,
True,
config.hidden_dropout_prob,
)
self.pooler = SanPooler(config.hidden_size, config.hidden_dropout_prob)
self.config = config 示例7: __init__
# 需要导入模块: from pytorch_pretrained_bert import modeling [as 别名]
# 或者: from pytorch_pretrained_bert.modeling import BertConfig [as 别名]
def __init__(self, config: BertConfig):
super().__init__()
self.embeddings = BertEmbeddings(config)
self.encoder = SanEncoder(config.hidden_size, config.num_hidden_layers, True,
config.hidden_dropout_prob)
self.pooler = SanPooler(config.hidden_size, config.hidden_dropout_prob)
self.config = config