Inference value is different from train value

[unolife]

when I train model, I got this kind of prediction:

0_probability 1_probability prediction
0 0.849047 0.150953 0
1 0.847665 0.152334 0
2 0.848109 0.151891 0
3 0.849097 0.150903 0
4 0.843085 0.156915 0
... ... ... ...
4399 0.819843 0.180157 0
4400 0.656908 0.343092 0
4401 0.831712 0.168288 0
4402 0.778614 0.221386 0
4403 0.848845 0.151155 0

but when I do inference, I got this kind of result:

tensor([[ 1.2595, -0.8684],
[ 2.6755, -1.3752],
[ 2.2348, -1.2487],
...,
[ 3.6170, -1.6117],
[ 1.2994, -0.9014],
[ 1.6549, -1.0630]], grad_fn=)

why suddenly - value(<0) poped up??
here is my train code

continuous_cols = train_x.select_dtypes(include=['float64', 'int64']).columns.tolist()
categorical_cols = train_x.select_dtypes(include=['object', 'category']).columns.tolist()
target_column = params.get('target_column')
data_config = DataConfig(
target=[target_column],
continuous_cols=continuous_cols,
categorical_cols=categorical_cols,
)
optimizer = params.get('optimizer')
if optimizer is None:
optimizer = 'Adam'
optimizer_config = OptimizerConfig(
optimizer=optimizer,
lr_scheduler=params.get('lr_scheduler'),
)
trainer_config = TrainerConfig(
auto_lr_find=params.get('auto_lr_find'),
batch_size=params.get('batch_size'),
max_epochs=params.get('max_epochs'),
seed=params.get('random_state'),
early_stopping_patience=params.get('early_stopping_patience'),
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config,
)
train = pd.concat([train_x, pd.DataFrame(train_y, columns=[target_column])], axis=1)
valid = pd.concat([valid_x, pd.DataFrame(valid_y, columns=[target_column])], axis=1)
tabular_model.fit(train=train, validation=valid)
res = tabular_model.predict(valid)
print(res)
tabular_model.save_model_for_inference(path=f"./model.pt")

and here is my inference code

prediction_model = torch.load('./model.pt')
prediction_model.eval()
continuous_cols = before_fe_x.select_dtypes(include=['float64', 'int64']).columns.tolist()
categorical_cols = before_fe_x.select_dtypes(include=['object', 'category']).columns.tolist()
x = {"continuous": torch.tensor(before_fe_x[continuous_cols].values, dtype=torch.float32), "categorical": torch.tensor(before_fe_x[categorical_cols].values, dtype=torch.float32)}
res = prediction_model.predict(x)
print(res)