---
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
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# emotion-classification

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: 0.4979
- Model Preparation Time: 0.0019
- Accuracy: 0.8718
- F1: 0.8701

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 | Model Preparation Time | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|:------:|
| No log        | 1.0   | 364  | 0.4835          | 0.0019                 | 0.8510   | 0.8457 |
| 0.734         | 2.0   | 728  | 0.4865          | 0.0019                 | 0.8638   | 0.8604 |
| 0.2323        | 3.0   | 1092 | 0.4782          | 0.0019                 | 0.8830   | 0.8814 |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2