| --- |
| base_model: bert-base-chinese |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: bert-finetuned-weibo-luobokuaipao |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bert-finetuned-weibo-luobokuaipao |
|
|
| This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 3.3065 |
| - Accuracy: 0.5833 |
|
|
| ## 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: 5e-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: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | No log | 1.0 | 270 | 0.9893 | 0.5852 | |
| | 1.0044 | 2.0 | 540 | 1.0391 | 0.5907 | |
| | 1.0044 | 3.0 | 810 | 1.2162 | 0.6130 | |
| | 0.5461 | 4.0 | 1080 | 1.3702 | 0.5667 | |
| | 0.5461 | 5.0 | 1350 | 1.8272 | 0.5704 | |
| | 0.349 | 6.0 | 1620 | 2.1860 | 0.5741 | |
| | 0.349 | 7.0 | 1890 | 2.1618 | 0.5685 | |
| | 0.2502 | 8.0 | 2160 | 2.5620 | 0.5593 | |
| | 0.2502 | 9.0 | 2430 | 2.6044 | 0.5667 | |
| | 0.1651 | 10.0 | 2700 | 3.0138 | 0.5778 | |
| | 0.1651 | 11.0 | 2970 | 3.1734 | 0.5481 | |
| | 0.1153 | 12.0 | 3240 | 3.0025 | 0.5759 | |
| | 0.0893 | 13.0 | 3510 | 3.1646 | 0.5889 | |
| | 0.0893 | 14.0 | 3780 | 3.0978 | 0.5833 | |
| | 0.0659 | 15.0 | 4050 | 3.1681 | 0.5741 | |
| | 0.0659 | 16.0 | 4320 | 3.1982 | 0.5778 | |
| | 0.0433 | 17.0 | 4590 | 3.2583 | 0.5778 | |
| | 0.0433 | 18.0 | 4860 | 3.2408 | 0.5778 | |
| | 0.0396 | 19.0 | 5130 | 3.2881 | 0.5852 | |
| | 0.0396 | 20.0 | 5400 | 3.3065 | 0.5833 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.42.4 |
| - Pytorch 2.3.1+cu121 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
|
|
