| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-chinese |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: emotion-classification |
| results: [] |
| language: |
| - zh |
| pipeline_tag: text-classification |
| --- |
|
|
| <!-- 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. -->
|
|
|
| # emotion-classification
|
|
|
| This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.4979
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| - Model Preparation Time: 0.0019
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| - Accuracy: 0.8718
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| - F1: 0.8701
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|
|
| ## Model description
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|
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| More information needed
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|
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| ## Intended uses & limitations
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|
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| More information needed
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|
|
| ## Training and evaluation data
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| More information needed
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|
|
| ## Training procedure
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|
|
| ### Training hyperparameters
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| The following hyperparameters were used during training:
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| - learning_rate: 2e-05
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| - train_batch_size: 8
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| - eval_batch_size: 8
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| - seed: 42
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| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| - lr_scheduler_type: linear
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| - num_epochs: 3
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|
|
| ### Training results
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|
|
| | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy | F1 |
|
| |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|:------:|
|
| | No log | 1.0 | 364 | 0.4835 | 0.0019 | 0.8510 | 0.8457 |
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| | 0.734 | 2.0 | 728 | 0.4865 | 0.0019 | 0.8638 | 0.8604 |
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| | 0.2323 | 3.0 | 1092 | 0.4782 | 0.0019 | 0.8830 | 0.8814 |
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| ### Framework versions
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| - Transformers 4.57.3
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| - Pytorch 2.11.0+cu128
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| - Datasets 5.0.0
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| - Tokenizers 0.22.2 |
