def __init__(
self,
task: str = None,
load_checkpoint: str = None,
label_map: Dict = None,
num_classes: int = 2,
**kwargs,
):
super(Bert, self).__init__()
if label_map:
self.label_map = label_map
self.num_classes = len(label_map)
else:
self.num_classes = num_classes
if task == 'sequence_classification':
task = 'seq-cls'
logger.warning(
"current task name 'sequence_classification' was renamed to 'seq-cls', "
"'sequence_classification' has been deprecated and will be removed in the future.",
)
if task == 'seq-cls':
self.model = BertForSequenceClassification.from_pretrained(
pretrained_model_name_or_path='bert-base-uncased',
num_classes=self.num_classes,
**kwargs)
self.criterion = paddle.nn.loss.CrossEntropyLoss()
self.metric = paddle.metric.Accuracy()
elif task == 'token-cls':
self.model = BertForTokenClassification.from_pretrained(
pretrained_model_name_or_path='bert-base-uncased',
num_classes=self.num_classes,
**kwargs)
self.criterion = paddle.nn.loss.CrossEntropyLoss()
self.metric = ChunkEvaluator(label_list=[
self.label_map[i] for i in sorted(self.label_map.keys())
])
elif task == 'text-matching':
self.model = BertModel.from_pretrained(
pretrained_model_name_or_path='bert-base-uncased', **kwargs)
self.dropout = paddle.nn.Dropout(0.1)
self.classifier = paddle.nn.Linear(
self.model.config['hidden_size'] * 3, 2)
self.criterion = paddle.nn.loss.CrossEntropyLoss()
self.metric = paddle.metric.Accuracy()
elif task is None:
self.model = BertModel.from_pretrained(
pretrained_model_name_or_path='bert-base-uncased', **kwargs)
else:
raise RuntimeError(
"Unknown task {}, task should be one in {}".format(
task, self._tasks_supported))
self.task = task
if load_checkpoint is not None and os.path.isfile(load_checkpoint):
state_dict = paddle.load(load_checkpoint)
self.set_state_dict(state_dict)
logger.info('Loaded parameters from %s' %
os.path.abspath(load_checkpoint))
def __init__(self, config: SimpleConfig):
model = BertModel.from_pretrained(config.pretrained_model)
super().__init__(model, config.num_labels, config.dropout)