metadata
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-finetuned-weibo-luobokuaipao
results: []
bert-finetuned-weibo-luobokuaipao
This model is a fine-tuned version of bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.2017
- Accuracy: 0.5815
Model description
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 243 | 1.0137 | 0.5852 |
| No log | 2.0 | 486 | 0.9878 | 0.5815 |
| 1.0413 | 3.0 | 729 | 1.0463 | 0.6056 |
| 1.0413 | 4.0 | 972 | 1.1697 | 0.6130 |
| 0.5875 | 5.0 | 1215 | 1.3835 | 0.5852 |
| 0.5875 | 6.0 | 1458 | 1.5941 | 0.5722 |
| 0.3069 | 7.0 | 1701 | 1.9360 | 0.5796 |
| 0.3069 | 8.0 | 1944 | 2.0863 | 0.6093 |
| 0.1782 | 9.0 | 2187 | 2.2601 | 0.5833 |
| 0.1782 | 10.0 | 2430 | 2.4810 | 0.5926 |
| 0.12 | 11.0 | 2673 | 2.5233 | 0.6 |
| 0.12 | 12.0 | 2916 | 2.5486 | 0.5833 |
| 0.089 | 13.0 | 3159 | 2.6555 | 0.5704 |
| 0.089 | 14.0 | 3402 | 2.6093 | 0.6019 |
| 0.0718 | 15.0 | 3645 | 2.6888 | 0.5907 |
| 0.0718 | 16.0 | 3888 | 2.9839 | 0.5722 |
| 0.061 | 17.0 | 4131 | 2.8104 | 0.5778 |
| 0.061 | 18.0 | 4374 | 2.9843 | 0.5685 |
| 0.062 | 19.0 | 4617 | 3.1577 | 0.5648 |
| 0.062 | 20.0 | 4860 | 3.1641 | 0.5722 |
| 0.0553 | 21.0 | 5103 | 3.1004 | 0.5611 |
| 0.0553 | 22.0 | 5346 | 3.0974 | 0.5778 |
| 0.0417 | 23.0 | 5589 | 3.0206 | 0.5759 |
| 0.0417 | 24.0 | 5832 | 3.0191 | 0.5667 |
| 0.0374 | 25.0 | 6075 | 3.0920 | 0.5722 |
| 0.0374 | 26.0 | 6318 | 2.9696 | 0.5852 |
| 0.0335 | 27.0 | 6561 | 3.0100 | 0.5889 |
| 0.0335 | 28.0 | 6804 | 3.1014 | 0.5667 |
| 0.0313 | 29.0 | 7047 | 3.2620 | 0.5574 |
| 0.0313 | 30.0 | 7290 | 3.0502 | 0.5889 |
| 0.032 | 31.0 | 7533 | 3.0984 | 0.5833 |
| 0.032 | 32.0 | 7776 | 3.1546 | 0.5704 |
| 0.0329 | 33.0 | 8019 | 3.0977 | 0.5741 |
| 0.0329 | 34.0 | 8262 | 3.0975 | 0.5796 |
| 0.0276 | 35.0 | 8505 | 3.1124 | 0.5870 |
| 0.0276 | 36.0 | 8748 | 3.1204 | 0.5926 |
| 0.0276 | 37.0 | 8991 | 3.1556 | 0.5833 |
| 0.026 | 38.0 | 9234 | 3.1909 | 0.5815 |
| 0.026 | 39.0 | 9477 | 3.1959 | 0.5815 |
| 0.0245 | 40.0 | 9720 | 3.2017 | 0.5815 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1