metadata
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: for_classification
results: []
license: apache-2.0
datasets:
- roberthsu2003/data_for_classification
language:
- zh
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
- 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