本文整理汇总了Python中transformers.BertForSequenceClassification.from_pretrained方法的典型用法代码示例。如果您正苦于以下问题：Python BertForSequenceClassification.from_pretrained方法的具体用法？Python BertForSequenceClassification.from_pretrained怎么用？Python BertForSequenceClassification.from_pretrained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transformers.BertForSequenceClassification的用法示例。
在下文中一共展示了BertForSequenceClassification.from_pretrained方法的5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: set_classifier
# 需要导入模块: from transformers import BertForSequenceClassification [as 别名]
# 或者: from transformers.BertForSequenceClassification import from_pretrained [as 别名]
def set_classifier(self, classifier_type=CLASSIFIER_TYPE_RNN, classifier=None):
""" Set the classifier from prepackaged option or provide custom classifier
:param classifier_type: One of ['BERT', 'RNN', 'BERT_RNN']
:type: str
:param classifier: Custom provided classifier
:type: Any
"""
if classifier_type == CLASSIFIER_TYPE_RNN or classifier_type == CLASSIFIER_TYPE_BERT_RNN:
self.classifier = ClassifierModule(self.model_config, self.preprocessor.word_vocab)
elif classifier_type == CLASSIFIER_TYPE_BERT:
self.classifier = BertForSequenceClassification.from_pretrained(
"bert-base-uncased",
num_labels=self.model_config.num_labels,
output_hidden_states=False,
output_attentions=False,
)
else:
self.classifier = classifier 示例2: set_anti_classifier
# 需要导入模块: from transformers import BertForSequenceClassification [as 别名]
# 或者: from transformers.BertForSequenceClassification import from_pretrained [as 别名]
def set_anti_classifier(self, classifier_type=CLASSIFIER_TYPE_RNN, anti_classifier=None):
""" Set anti classifier from prepackaged option or provide custom anti classifier
:param classifier_type: One of ['BERT', 'RNN', 'BERT_RNN']
:type str
:param anti_classifier: Custom provided anti classifier
:type Any
"""
if classifier_type == CLASSIFIER_TYPE_RNN or classifier_type == CLASSIFIER_TYPE_BERT_RNN:
self.anti_classifier = ClassifierModule(self.model_config, self.preprocessor.word_vocab)
elif classifier_type == CLASSIFIER_TYPE_BERT:
self.anti_classifier = BertForSequenceClassification.from_pretrained(
"bert-base-uncased",
num_labels=self.model_config.num_labels,
output_hidden_states=False,
output_attentions=False,
)
else:
self.anti_classifier = anti_classifier 示例3: set_generator_classifier
# 需要导入模块: from transformers import BertForSequenceClassification [as 别名]
# 或者: from transformers.BertForSequenceClassification import from_pretrained [as 别名]
def set_generator_classifier(self, classifier_type=CLASSIFIER_TYPE_RNN, generator_classifier=None):
""" Set classifier for the Generator
:param classifier_type: One of ['BERT', 'RNN', 'BERT_RNN']
:type classifier_type: str
:param generator_classifier: Custom provided classifier for generator
:type generator_classifier: Any
:return: Any
"""
if classifier_type == CLASSIFIER_TYPE_RNN:
self.generator_classifier = ClassifierModule(self.model_config, self.preprocessor.word_vocab)
elif classifier_type == CLASSIFIER_TYPE_BERT or classifier_type == CLASSIFIER_TYPE_BERT_RNN:
self.generator_classifier = BertForSequenceClassification.from_pretrained(
"bert-base-uncased",
num_labels=self.model_config.num_labels,
output_hidden_states=True,
output_attentions=True,
)
else:
self.generator_classifier = generator_classifier 示例4: __init__
# 需要导入模块: from transformers import BertForSequenceClassification [as 别名]
# 或者: from transformers.BertForSequenceClassification import from_pretrained [as 别名]
def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False):
from transformers import BertTokenizer, BertForSequenceClassification
# download the model or load the model path
path_emotion = download_model('bert.emotion', cache_dir,
process_func=_unzip_process_func,
verbose=verbose)
path_emotion = os.path.join(path_emotion,'bert.emotion')
path_reject = download_model('bert.noemotion', cache_dir,
process_func=_unzip_process_func,
verbose=verbose)
path_reject = os.path.join(path_reject,'bert.noemotion')
# load the models
self.tokenizer_rejct = BertTokenizer.from_pretrained(path_reject)
self.model_reject = BertForSequenceClassification.from_pretrained(path_reject)
self.tokenizer = BertTokenizer.from_pretrained(path_emotion)
self.model = BertForSequenceClassification.from_pretrained(path_emotion)
# load the class names mapping
self.catagories = {5: 'Foragt/Modvilje', 2: 'Forventning/Interrese',
0: 'Glæde/Sindsro', 3: 'Overasket/Målløs',
1: 'Tillid/Accept',
4: 'Vrede/Irritation', 6: 'Sorg/trist',
7: 'Frygt/Bekymret'} 示例5: __init__
# 需要导入模块: from transformers import BertForSequenceClassification [as 别名]
# 或者: from transformers.BertForSequenceClassification 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