# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="roberthsu2003/sentence_similarity")
pipe({"text":"我喜歡台北", "text_pair":"台北是我喜歡的地方"})

#=======output=====
{'label': '相似', 'score': 0.8854433298110962}

More information needed

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: 32
eval_batch_size: 32
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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2928	1.0	250	0.2737	0.887	0.8546
0.1815	2.0	500	0.2596	0.8985	0.8741
0.1203	3.0	750	0.3474	0.897	0.8652

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Tokenizers 0.21.1