def train_model_bert(args):
# need remake config with device option for train with another cuda device
config = BertConfig.from_pretrained(args.folder_model)
config = config.to_dict()
config.update({"device": args.device})
config.update({"use_pooler": args.use_pooler})
config.update({"weight_class": args.weight_class})
config.update({"output_hidden_states": args.output_hidden_states})
config = BertConfig.from_dict(config)
tokenizer = BertTokenizer.from_pretrained(args.folder_model)
model = BERTQa.from_pretrained(args.folder_model, config=config)
model = model.to(args.device)
train_squad(args, tokenizer, model)
def loss(self, input_ids, attention_mask, token_type_ids, label):
target = label
final_output = self.compute(input_ids, attention_mask, token_type_ids)
if self.use_pooler:
logits = self.qa_outputs(final_output)
else:
logits = self.qa_outputs_cat(final_output)
class_weights = torch.FloatTensor(self.weight_class).to(self.device)
loss = F.cross_entropy(logits, target, weight=class_weights)
predict_value = torch.max(logits, 1)[1]
list_predict = predict_value.cpu().numpy().tolist()
list_target = target.cpu().numpy().tolist()
return loss, list_predict, list_target
if __name__ == '__main__':
from transformers.configuration_bert import BertConfig
config = BertConfig.from_pretrained("bert-base-multilingual-uncased",
cache_dir="../resources/cache_model")
config = config.to_dict()
config.update({"weight_class": [1, 1]})
config = BertConfig.from_dict(config)
# model = BERTQa.from_pretrained("bert-base-multilingual-uncased",
# cache_dir="../resources/cache_model", config=config)