sentence_similarity

This model is a fine-tuned version of google-bert/bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3474
• Accuracy: 0.897
• F1: 0.8652

模型使用

# 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