---
license: gpl-3.0
base_model: ckiplab/bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: bert_model-1
  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_model-1

This model is a fine-tuned version of [ckiplab/bert-base-chinese](https://huggingface.co/ckiplab/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4135

## 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3994        | 0.01  | 100  | 1.6845          |
| 1.8101        | 0.03  | 200  | 1.6963          |
| 1.7742        | 0.04  | 300  | 1.6679          |
| 1.8425        | 0.05  | 400  | 1.6657          |
| 1.8452        | 0.06  | 500  | 1.6369          |
| 1.8109        | 0.08  | 600  | 1.6471          |
| 1.8469        | 0.09  | 700  | 1.6350          |
| 1.7709        | 0.1   | 800  | 1.6302          |
| 1.7848        | 0.12  | 900  | 1.6346          |
| 1.7955        | 0.13  | 1000 | 1.6345          |
| 1.79          | 0.14  | 1100 | 1.6356          |
| 1.7655        | 0.16  | 1200 | 1.6116          |
| 1.7826        | 0.17  | 1300 | 1.6248          |
| 1.7651        | 0.18  | 1400 | 1.6262          |
| 1.7639        | 0.19  | 1500 | 1.6078          |
| 1.7743        | 0.21  | 1600 | 1.6105          |
| 1.7672        | 0.22  | 1700 | 1.5910          |
| 1.7054        | 0.23  | 1800 | 1.6060          |
| 1.6777        | 0.25  | 1900 | 1.6253          |
| 1.748         | 0.26  | 2000 | 1.5970          |
| 1.7503        | 0.27  | 2100 | 1.5893          |
| 1.7329        | 0.29  | 2200 | 1.5883          |
| 1.6826        | 0.3   | 2300 | 1.5781          |
| 1.7237        | 0.31  | 2400 | 1.5716          |
| 1.7358        | 0.32  | 2500 | 1.5671          |
| 1.7093        | 0.34  | 2600 | 1.5689          |
| 1.6771        | 0.35  | 2700 | 1.5654          |
| 1.6924        | 0.36  | 2800 | 1.5729          |
| 1.6768        | 0.38  | 2900 | 1.5545          |
| 1.7158        | 0.39  | 3000 | 1.5471          |
| 1.6808        | 0.4   | 3100 | 1.5415          |
| 1.6547        | 0.42  | 3200 | 1.5444          |
| 1.6557        | 0.43  | 3300 | 1.5400          |
| 1.6491        | 0.44  | 3400 | 1.5358          |
| 1.6757        | 0.45  | 3500 | 1.5244          |
| 1.6473        | 0.47  | 3600 | 1.5268          |
| 1.5987        | 0.48  | 3700 | 1.5201          |
| 1.6386        | 0.49  | 3800 | 1.5121          |
| 1.6568        | 0.51  | 3900 | 1.5004          |
| 1.6454        | 0.52  | 4000 | 1.4895          |
| 1.6175        | 0.53  | 4100 | 1.4974          |
| 1.6036        | 0.55  | 4200 | 1.4964          |
| 1.5785        | 0.56  | 4300 | 1.4882          |
| 1.6009        | 0.57  | 4400 | 1.4858          |
| 1.5723        | 0.58  | 4500 | 1.4755          |
| 1.6133        | 0.6   | 4600 | 1.4751          |
| 1.5683        | 0.61  | 4700 | 1.4692          |
| 1.5773        | 0.62  | 4800 | 1.4677          |
| 1.6005        | 0.64  | 4900 | 1.4645          |
| 1.5812        | 0.65  | 5000 | 1.4596          |
| 1.577         | 0.66  | 5100 | 1.4506          |
| 1.591         | 0.68  | 5200 | 1.4507          |
| 1.5609        | 0.69  | 5300 | 1.4474          |
| 1.5437        | 0.7   | 5400 | 1.4441          |
| 1.5535        | 0.71  | 5500 | 1.4430          |
| 1.5882        | 0.73  | 5600 | 1.4398          |
| 1.5731        | 0.74  | 5700 | 1.4328          |
| 1.5511        | 0.75  | 5800 | 1.4280          |
| 1.5455        | 0.77  | 5900 | 1.4358          |
| 1.5194        | 0.78  | 6000 | 1.4321          |
| 1.5524        | 0.79  | 6100 | 1.4207          |
| 1.5406        | 0.81  | 6200 | 1.4215          |
| 1.4811        | 0.82  | 6300 | 1.4293          |
| 1.5117        | 0.83  | 6400 | 1.4282          |
| 1.5197        | 0.84  | 6500 | 1.4109          |
| 1.558         | 0.86  | 6600 | 1.4241          |
| 1.5277        | 0.87  | 6700 | 1.4116          |
| 1.5346        | 0.88  | 6800 | 1.4190          |
| 1.4974        | 0.9   | 6900 | 1.4105          |
| 1.5345        | 0.91  | 7000 | 1.4163          |
| 1.5578        | 0.92  | 7100 | 1.4099          |
| 1.496         | 0.94  | 7200 | 1.4120          |
| 1.5192        | 0.95  | 7300 | 1.4073          |
| 1.456         | 0.96  | 7400 | 1.4105          |
| 1.4821        | 0.97  | 7500 | 1.4175          |
| 1.5331        | 0.99  | 7600 | 1.4135          |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3