transformers pytorch bert question-answering generated_from_trainer base_model:ckiplab/bert-base-chinese license:gpl-3.0 endpoints_compatible region:us
---
license: gpl-3.0
base_model: ckiplab/bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: bert-base-chinese-finetuned-QA-b4
  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-base-chinese-finetuned-QA-b4

This model is a fine-tuned version of [ckiplab/bert-base-chinese](https://huggingface.co/ckiplab/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4979

## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3371        | 0.07  | 500   | 1.3143          |
| 1.1681        | 0.14  | 1000  | 1.2073          |
| 1.019         | 0.22  | 1500  | 1.2466          |
| 0.9532        | 0.29  | 2000  | 0.9045          |
| 0.927         | 0.36  | 2500  | 0.8376          |
| 0.858         | 0.43  | 3000  | 0.8539          |
| 0.911         | 0.51  | 3500  | 0.7801          |
| 0.7875        | 0.58  | 4000  | 0.9157          |
| 0.8543        | 0.65  | 4500  | 0.8728          |
| 0.8743        | 0.72  | 5000  | 0.8431          |
| 0.8004        | 0.79  | 5500  | 0.7866          |
| 0.828         | 0.87  | 6000  | 0.7686          |
| 0.7597        | 0.94  | 6500  | 0.7637          |
| 0.728         | 1.01  | 7000  | 0.8749          |
| 0.4751        | 1.08  | 7500  | 0.8225          |
| 0.5158        | 1.16  | 8000  | 1.1740          |
| 0.5046        | 1.23  | 8500  | 0.8435          |
| 0.501         | 1.3   | 9000  | 0.9899          |
| 0.4962        | 1.37  | 9500  | 1.0151          |
| 0.5345        | 1.45  | 10000 | 0.9104          |
| 0.5102        | 1.52  | 10500 | 0.9287          |
| 0.519         | 1.59  | 11000 | 0.9018          |
| 0.5032        | 1.66  | 11500 | 0.9816          |
| 0.4776        | 1.73  | 12000 | 1.1031          |
| 0.5099        | 1.81  | 12500 | 0.8666          |
| 0.4616        | 1.88  | 13000 | 0.8950          |
| 0.5127        | 1.95  | 13500 | 0.8260          |
| 0.4322        | 2.02  | 14000 | 1.0062          |
| 0.258         | 2.1   | 14500 | 1.1331          |
| 0.2794        | 2.17  | 15000 | 1.2275          |
| 0.2325        | 2.24  | 15500 | 1.2657          |
| 0.2742        | 2.31  | 16000 | 1.0803          |
| 0.2315        | 2.38  | 16500 | 1.2236          |
| 0.2782        | 2.46  | 17000 | 1.1722          |
| 0.2706        | 2.53  | 17500 | 1.2902          |
| 0.2817        | 2.6   | 18000 | 1.1814          |
| 0.2541        | 2.67  | 18500 | 1.1149          |
| 0.2965        | 2.75  | 19000 | 1.0627          |
| 0.2511        | 2.82  | 19500 | 1.1695          |
| 0.2675        | 2.89  | 20000 | 1.1538          |
| 0.2743        | 2.96  | 20500 | 1.2044          |
| 0.183         | 3.04  | 21000 | 1.3375          |
| 0.1657        | 3.11  | 21500 | 1.3305          |
| 0.1112        | 3.18  | 22000 | 1.4240          |
| 0.1316        | 3.25  | 22500 | 1.3410          |
| 0.0971        | 3.32  | 23000 | 1.4676          |
| 0.1227        | 3.4   | 23500 | 1.4158          |
| 0.1064        | 3.47  | 24000 | 1.4724          |
| 0.1012        | 3.54  | 24500 | 1.5001          |
| 0.0755        | 3.61  | 25000 | 1.4947          |
| 0.1           | 3.69  | 25500 | 1.4799          |
| 0.0913        | 3.76  | 26000 | 1.4940          |
| 0.0962        | 3.83  | 26500 | 1.4657          |
| 0.0664        | 3.9   | 27000 | 1.4992          |
| 0.0724        | 3.97  | 27500 | 1.4979          |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.13.1+cu116
- Datasets 2.14.5
- Tokenizers 0.14.1
