本文整理汇总了Python中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transformers.BertModel
的用法示例。
在下文中一共展示了BertModel.from_pretrained方法的12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 transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, pretrained_model_name_or_path=None, cache_dir=None,
finetune_ebd=False, return_seq=False):
'''
pretrained_model_name_or_path, cache_dir: check huggingface's codebase for details
finetune_ebd: finetuning bert representation or not during
meta-training
return_seq: return a sequence of bert representations, or [cls]
'''
super(CXTEBD, self).__init__()
self.finetune_ebd = finetune_ebd
self.return_seq = return_seq
print("{}, Loading pretrained bert".format(
datetime.datetime.now().strftime('%02y/%02m/%02d %H:%M:%S')), flush=True)
self.model = BertModel.from_pretrained(pretrained_model_name_or_path,
cache_dir=cache_dir)
self.embedding_dim = self.model.config.hidden_size
self.ebd_dim = self.model.config.hidden_size
示例2: main
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def main(raw_args=None):
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, required=True, help="model name e.g. bert-base-uncased")
parser.add_argument(
"--cache_dir", type=str, default=None, required=False, help="Directory containing pytorch model"
)
parser.add_argument("--pytorch_model_path", type=str, required=True, help="/path/to/<pytorch-model-name>.bin")
parser.add_argument("--tf_cache_dir", type=str, required=True, help="Directory in which to save tensorflow model")
args = parser.parse_args(raw_args)
model = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name,
state_dict=torch.load(args.pytorch_model_path),
cache_dir=args.cache_dir,
)
convert_pytorch_checkpoint_to_tf(model=model, ckpt_dir=args.tf_cache_dir, model_name=args.model_name)
示例3: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, temp_dir, model_class, pretrained_model_name, pretrained_config):
super(Transformer, self).__init__()
if(pretrained_model_name):
self.model = model_class.from_pretrained(pretrained_model_name,
cache_dir=temp_dir)
#self.model = BertModel.from_pretrained('bert-base-uncased', cache_dir=temp_dir)
else:
self.model = model_class(pretrained_config)
示例4: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, max_length, pretrain_path, blank_padding=True, mask_entity=False):
"""
Args:
max_length: max length of sentence
pretrain_path: path of pretrain model
"""
super().__init__()
self.max_length = max_length
self.blank_padding = blank_padding
self.hidden_size = 768
self.mask_entity = mask_entity
logging.info('Loading BERT pre-trained checkpoint.')
self.bert = BertModel.from_pretrained(pretrain_path)
self.tokenizer = BertTokenizer.from_pretrained(pretrain_path)
示例5: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, model_config, device, slot_dim, intent_dim, intent_weight=None):
super(JointBERT, self).__init__()
self.slot_num_labels = slot_dim
self.intent_num_labels = intent_dim
self.device = device
self.intent_weight = intent_weight if intent_weight is not None else torch.tensor([1.]*intent_dim)
self.bert = BertModel.from_pretrained(model_config['pretrained_weights'])
self.dropout = nn.Dropout(model_config['dropout'])
self.context = model_config['context']
self.finetune = model_config['finetune']
self.context_grad = model_config['context_grad']
if self.context:
self.intent_classifier = nn.Linear(2 * self.bert.config.hidden_size, self.intent_num_labels)
self.slot_classifier = nn.Linear(2 * self.bert.config.hidden_size, self.slot_num_labels)
self.intent_hidden = nn.Linear(2 * self.bert.config.hidden_size, 2 * self.bert.config.hidden_size)
self.slot_hidden = nn.Linear(2 * self.bert.config.hidden_size, 2 * self.bert.config.hidden_size)
else:
self.intent_classifier = nn.Linear(self.bert.config.hidden_size, self.intent_num_labels)
self.slot_classifier = nn.Linear(self.bert.config.hidden_size, self.slot_num_labels)
self.intent_hidden = nn.Linear(self.bert.config.hidden_size, self.bert.config.hidden_size)
self.slot_hidden = nn.Linear(self.bert.config.hidden_size, self.bert.config.hidden_size)
nn.init.xavier_uniform_(self.intent_hidden.weight)
nn.init.xavier_uniform_(self.slot_hidden.weight)
nn.init.xavier_uniform_(self.intent_classifier.weight)
nn.init.xavier_uniform_(self.slot_classifier.weight)
self.intent_loss_fct = torch.nn.BCEWithLogitsLoss(pos_weight=self.intent_weight)
self.slot_loss_fct = torch.nn.CrossEntropyLoss()
示例6: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, model_name_or_path: str, max_seq_length: int = 128, do_lower_case: Optional[bool] = None, model_args: Dict = {}, tokenizer_args: Dict = {}):
super(BERT, self).__init__()
self.config_keys = ['max_seq_length', 'do_lower_case']
self.do_lower_case = do_lower_case
if max_seq_length > 510:
logging.warning("BERT only allows a max_seq_length of 510 (512 with special tokens). Value will be set to 510")
max_seq_length = 510
self.max_seq_length = max_seq_length
if self.do_lower_case is not None:
tokenizer_args['do_lower_case'] = do_lower_case
self.bert = BertModel.from_pretrained(model_name_or_path, **model_args)
self.tokenizer = BertTokenizer.from_pretrained(model_name_or_path, **tokenizer_args)
示例7: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, model, n_layers, n_out, requires_grad=False):
super(BertEmbedding, self).__init__()
self.bert = BertModel.from_pretrained(model, output_hidden_states=True)
self.bert = self.bert.requires_grad_(requires_grad)
self.n_layers = n_layers
self.n_out = n_out
self.requires_grad = requires_grad
self.hidden_size = self.bert.config.hidden_size
self.scalar_mix = ScalarMix(n_layers)
self.projection = nn.Linear(self.hidden_size, n_out, False)
示例8: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, model_config, device, slot_dim, intent_dim, intent_weight=None):
super(JointBERT, self).__init__()
self.slot_num_labels = slot_dim
self.intent_num_labels = intent_dim
self.device = device
self.intent_weight = intent_weight if intent_weight is not None else torch.tensor([1.]*intent_dim)
self.bert = BertModel.from_pretrained(model_config['pretrained_weights'])
self.dropout = nn.Dropout(model_config['dropout'])
self.context = model_config['context']
self.finetune = model_config['finetune']
self.context_grad = model_config['context_grad']
self.hidden_units = model_config['hidden_units']
if self.hidden_units > 0:
if self.context:
self.intent_classifier = nn.Linear(self.hidden_units, self.intent_num_labels)
self.slot_classifier = nn.Linear(self.hidden_units, self.slot_num_labels)
self.intent_hidden = nn.Linear(2 * self.bert.config.hidden_size, self.hidden_units)
self.slot_hidden = nn.Linear(2 * self.bert.config.hidden_size, self.hidden_units)
else:
self.intent_classifier = nn.Linear(self.hidden_units, self.intent_num_labels)
self.slot_classifier = nn.Linear(self.hidden_units, self.slot_num_labels)
self.intent_hidden = nn.Linear(self.bert.config.hidden_size, self.hidden_units)
self.slot_hidden = nn.Linear(self.bert.config.hidden_size, self.hidden_units)
nn.init.xavier_uniform_(self.intent_hidden.weight)
nn.init.xavier_uniform_(self.slot_hidden.weight)
else:
if self.context:
self.intent_classifier = nn.Linear(2 * self.bert.config.hidden_size, self.intent_num_labels)
self.slot_classifier = nn.Linear(2 * self.bert.config.hidden_size, self.slot_num_labels)
else:
self.intent_classifier = nn.Linear(self.bert.config.hidden_size, self.intent_num_labels)
self.slot_classifier = nn.Linear(self.bert.config.hidden_size, self.slot_num_labels)
nn.init.xavier_uniform_(self.intent_classifier.weight)
nn.init.xavier_uniform_(self.slot_classifier.weight)
self.intent_loss_fct = torch.nn.BCEWithLogitsLoss(pos_weight=self.intent_weight)
self.slot_loss_fct = torch.nn.CrossEntropyLoss()
示例9: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(
self,
class_size=None,
pretrained_model="gpt2-medium",
classifier_head=None,
cached_mode=False,
device='cpu'
):
super(Discriminator, self).__init__()
if pretrained_model.startswith("gpt2"):
self.tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
self.encoder = GPT2LMHeadModel.from_pretrained(pretrained_model)
self.embed_size = self.encoder.transformer.config.hidden_size
elif pretrained_model.startswith("bert"):
self.tokenizer = BertTokenizer.from_pretrained(pretrained_model)
self.encoder = BertModel.from_pretrained(pretrained_model)
self.embed_size = self.encoder.config.hidden_size
else:
raise ValueError(
"{} model not yet supported".format(pretrained_model)
)
if classifier_head:
self.classifier_head = classifier_head
else:
if not class_size:
raise ValueError("must specify class_size")
self.classifier_head = ClassificationHead(
class_size=class_size,
embed_size=self.embed_size
)
self.cached_mode = cached_mode
self.device = device
示例10: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, config: BertEmbeddingLayerConfig):
super(BertEmbeddingLayer, self).__init__(config)
self.embedding = BertModel.from_pretrained(self.config.model_dir)
示例11: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, pretrain_path, max_length, cat_entity_rep=False):
nn.Module.__init__(self)
self.bert = BertModel.from_pretrained(pretrain_path)
self.max_length = max_length
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.cat_entity_rep = cat_entity_rep
示例12: __init__
# 需要导入模块: from transformers import BertModel [as 别名]
# 或者: from transformers.BertModel import from_pretrained [as 别名]
def __init__(self, max_length, pretrain_path, blank_padding=True):
"""
Args:
max_length: max length of sentence
pretrain_path: path of pretrain model
"""
super().__init__()
self.max_length = max_length
self.blank_padding = blank_padding
self.bert = BertModel.from_pretrained(pretrain_path)
self.hidden_size = self.bert.config.hidden_size
self.tokenizer = BertTokenizer.from_pretrained(pretrain_path)