from transformers import pipeline

id2_label = {'LABEL_0':"負評",'LABEL_1':"正評"}
pipe = pipeline('text-classification', model="roberthsu2003/for_classification")

sen="服務人員都很親切"
print(sen,id2_label[pipe(sen)[0]['label']])

sen1="服務人員都不親切"
print(sen1,id2_label[pipe(sen1)[0]['label']])

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 128
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2886	1.0	110	0.2269	0.9009	0.9272
0.1799	2.0	220	0.2218	0.9112	0.9356
0.1395	3.0	330	0.2366	0.9189	0.9415

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1