---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_model012
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# populism_model012

This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3847
- Accuracy: 0.9816
- 1-f1: 0.3529
- 1-recall: 0.3
- 1-precision: 0.4286
- Balanced Acc: 0.6466

## 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: 32
- eval_batch_size: 32
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1   | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.333         | 1.0   | 225  | 0.3551          | 0.9833   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.1579        | 2.0   | 450  | 0.3008          | 0.9839   | 0.3830 | 0.3      | 0.5294      | 0.6477       |
| 0.2232        | 3.0   | 675  | 0.3847          | 0.9816   | 0.3529 | 0.3      | 0.4286      | 0.6466       |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0