本文整理汇总了Python中pytorch_transformers.BertModel.from_pretrained方法的典型用法代码示例。如果您正苦于以下问题：Python BertModel.from_pretrained方法的具体用法？Python BertModel.from_pretrained怎么用？Python BertModel.from_pretrained使用的例子？那么, 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_transformers.BertModel的用法示例。
在下文中一共展示了BertModel.from_pretrained方法的11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: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(
self, token_makers, num_tags, ignore_tag_idx, pretrained_model_name=None, dropout=0.2
):
super(BertForTokCls, self).__init__(token_makers)
self.use_pytorch_transformers = True # for optimizer's model parameters
self.ignore_tag_idx = ignore_tag_idx
self.num_tags = num_tags
self._model = BertModel.from_pretrained(
pretrained_model_name, cache_dir=str(CachePath.ROOT)
)
self.classifier = nn.Sequential(
nn.Dropout(dropout), nn.Linear(self._model.config.hidden_size, num_tags)
)
self.classifier.apply(self._model.init_weights)
self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_tag_idx) 示例2: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, token_makers, num_classes, pretrained_model_name=None, dropout=0.2):
super(BertForSeqCls, self).__init__(token_makers)
self.use_pytorch_transformers = True # for optimizer's model parameters
self.num_classes = num_classes
self._model = BertModel.from_pretrained(
pretrained_model_name, cache_dir=str(CachePath.ROOT)
)
self.classifier = nn.Sequential(
nn.Dropout(dropout), nn.Linear(self._model.config.hidden_size, num_classes)
)
self.classifier.apply(self._model.init_weights)
self.criterion = nn.CrossEntropyLoss() 示例3: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, token_makers, tasks, pretrained_model_name=None, dropouts=None):
super(BertForMultiTask, self).__init__(token_makers)
self.use_pytorch_transformers = True # for optimizer's model parameters
self.tasks = tasks
assert len(tasks) == len(dropouts)
self.curr_task_category = None
self.curr_dataset = None
self.shared_layers = BertModel.from_pretrained(
pretrained_model_name, cache_dir=str(CachePath.ROOT)
)
self._init_task_layers(tasks, dropouts)
self._init_criterions(tasks) 示例4: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, token_makers, pretrained_model_name=None, dropout=0.2):
super(BertForRegression, self).__init__(token_makers)
self.use_pytorch_transformers = True # for optimizer's model parameters
NUM_CLASSES = 1
self._model = BertModel.from_pretrained(
pretrained_model_name, cache_dir=str(CachePath.ROOT)
)
self.classifier = nn.Sequential(
nn.Dropout(dropout), nn.Linear(self._model.config.hidden_size, NUM_CLASSES)
)
self.classifier.apply(self._model.init_weights)
self.criterion = nn.MSELoss() 示例5: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, device, config, index_to_tag, tag_to_index):
super().__init__()
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.FIXED_NR_OUTPUTS = 8 # FIXME: We may want to make this dynamic?
# For now it is handcoded to the mapping below.
self.fc = nn.Linear(768, self.FIXED_NR_OUTPUTS).to(device)
self.device = device
self.index_to_tag = index_to_tag
self.tag_to_index = tag_to_index
self.mapping = {'<pad>': [0, 0, 0, 0, 0, 0, 1, 0], # <pad>
'NA' : [0, 0, 0, 0, 0, 1, 0, 0], # NA
'2' : [1, 1, 1, 1, 1, 0, 0, 0], # prosody value 2
'0' : [1, 1, 1, 0, 0, 0, 0, 0], # prosody value 0
'1' : [1, 1, 1, 1, 0, 0, 0, 0]} # prosody value 1 示例6: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, mode: str = 'bert-base-uncased'):
""":class:`BertModule` constructor."""
super().__init__()
self.bert = BertModel.from_pretrained(mode) 示例7: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if(large):
self.model = BertModel.from_pretrained('bert-large-uncased', cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained('bert-base-uncased', cache_dir=temp_dir)
self.finetune = finetune 示例8: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, pretrained_model_name_for_tokenizer, max_vocabulary_size,
max_tokenization_length, embedding_dim, num_classes=1, num_recurrent_layers=1,
use_bidirectional=False, hidden_size=128, dropout_rate=0.10, use_gpu=False):
super(SimpleRNN, self).__init__()
self.num_recurrent_layers = num_recurrent_layers
self.use_bidirectional = use_bidirectional
self.hidden_size = hidden_size
self.use_gpu = use_gpu
# Configure tokenizer
self.tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_for_tokenizer)
self.tokenizer.max_len = max_tokenization_length
# Define additional layers & utilities specific to the finetuned task
# Embedding Layer
self.embedding = nn.Embedding(num_embeddings=max_vocabulary_size,
embedding_dim=embedding_dim)
# Dropout to prevent overfitting
self.dropout = nn.Dropout(p=dropout_rate)
# Recurrent Layer
self.lstm = nn.LSTM(input_size=embedding_dim,
hidden_size=hidden_size,
num_layers=num_recurrent_layers,
bidirectional=use_bidirectional,
batch_first=True)
# Dense Layer for Classification
self.clf = nn.Linear(in_features=hidden_size*2 if use_bidirectional else hidden_size,
out_features=num_classes) 示例9: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, vocab, pretrained_model_name=None, trainable=False, unit="subword"):
super(BertEmbedding, self).__init__(vocab)
self.trainable = trainable
self.pad_index = vocab.get_index(vocab.pad_token)
self.sep_index = vocab.get_index(vocab.sep_token)
if unit != "subword":
raise NotImplementedError("BertEmbedding is only available 'subword' unit, right now.")
self.bert_model = BertModel.from_pretrained(pretrained_model_name) # BertModel with config 示例10: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, config, gpu_list, *args, **params):
super(BertEncoder, self).__init__()
self.bert = BertModel.from_pretrained(config.get("model", "bert_path")) 示例11: __init__
# 需要导入模块: from pytorch_transformers import BertModel [as 别名]
# 或者: from pytorch_transformers.BertModel import from_pretrained [as 别名]
def __init__(self, pretrain_path, max_length):
nn.Module.__init__(self)
# self.bert = BertModel.from_pretrained(pretrain_path)
self.bert = BertForSequenceClassification.from_pretrained(
pretrain_path,
num_labels=2)
self.max_length = max_length
self.tokenizer = BertTokenizer.from_pretrained(os.path.join(
pretrain_path, 'bert_vocab.txt'))
self.modelName = 'Bert'