from pytorch_transformers import BertConfig
from pytorch_transformers import BertModel

config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)

class BertForSequenceClassification(nn.Module):
    def __init__(self, num_labels=2):
        super(BertForSequenceClassification, self).__init__()
        self.num_labels = num_labels
        self.bert = BertModel.from_pretrained('bert_based_uncased')
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, num_labels)

        nn.init.xavier_normal_(self.classifier.weight)

    def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
        _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask)
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

    def freeze_bert_encoder(self):
        for param in self.bert.parameters():
            param.requires_grad = False

    def unfreeze_bert_encoder(self):
        for param in self.bert.parameters():
            param.requires_grad = True

num_labels = 11
model = BertForSequenceClassification(num_labels)
model = torch.nn.DataParallel(model)