roberthsu2003/for_classification

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for_classification

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: 0.2366
• —Accuracy: 0.9189
• —F1: 0.9415

模型實作

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
• —trainbatchsize: 64
• —evalbatchsize: 128
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 3

Training results

Framework versions

• —Transformers 4.50.0
• —Pytorch 2.6.0+cu124
• —Datasets 3.5.0
• —Tokenizers 0.21.1