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# bert-30M-uncased-classification-CMC-fqa

This model is a fine-tuned version of [vietgpt/bert-30M-uncased](https://huggingface.co/vietgpt/bert-30M-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4703
- Accuracy: 0.8739

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log        | 1.0   | 130   | 5.3723          | 0.0043   |
| No log        | 2.0   | 260   | 5.3750          | 0.0043   |
| No log        | 3.0   | 390   | 5.3555          | 0.0      |
| 5.3407        | 4.0   | 520   | 5.2599          | 0.0174   |
| 5.3407        | 5.0   | 650   | 4.9584          | 0.0261   |
| 5.3407        | 6.0   | 780   | 4.7092          | 0.0609   |
| 5.3407        | 7.0   | 910   | 4.4852          | 0.1087   |
| 4.7451        | 8.0   | 1040  | 4.2827          | 0.1696   |
| 4.7451        | 9.0   | 1170  | 4.0794          | 0.2478   |
| 4.7451        | 10.0  | 1300  | 3.8857          | 0.2913   |
| 4.7451        | 11.0  | 1430  | 3.6879          | 0.4      |
| 3.8815        | 12.0  | 1560  | 3.5051          | 0.4435   |
| 3.8815        | 13.0  | 1690  | 3.3316          | 0.4652   |
| 3.8815        | 14.0  | 1820  | 3.1648          | 0.4913   |
| 3.8815        | 15.0  | 1950  | 2.9991          | 0.5261   |
| 3.1326        | 16.0  | 2080  | 2.8421          | 0.5652   |
| 3.1326        | 17.0  | 2210  | 2.6915          | 0.5913   |
| 3.1326        | 18.0  | 2340  | 2.5519          | 0.6043   |
| 3.1326        | 19.0  | 2470  | 2.4192          | 0.6478   |
| 2.4835        | 20.0  | 2600  | 2.2918          | 0.6870   |
| 2.4835        | 21.0  | 2730  | 2.1705          | 0.7043   |
| 2.4835        | 22.0  | 2860  | 2.0567          | 0.7261   |
| 2.4835        | 23.0  | 2990  | 1.9522          | 0.7261   |
| 1.9554        | 24.0  | 3120  | 1.8542          | 0.7391   |
| 1.9554        | 25.0  | 3250  | 1.7546          | 0.7696   |
| 1.9554        | 26.0  | 3380  | 1.6647          | 0.7609   |
| 1.5347        | 27.0  | 3510  | 1.5819          | 0.7739   |
| 1.5347        | 28.0  | 3640  | 1.5082          | 0.7870   |
| 1.5347        | 29.0  | 3770  | 1.4383          | 0.7957   |
| 1.5347        | 30.0  | 3900  | 1.3742          | 0.8      |
| 1.1984        | 31.0  | 4030  | 1.3075          | 0.8043   |
| 1.1984        | 32.0  | 4160  | 1.2476          | 0.8043   |
| 1.1984        | 33.0  | 4290  | 1.1953          | 0.8043   |
| 1.1984        | 34.0  | 4420  | 1.1515          | 0.8087   |
| 0.9448        | 35.0  | 4550  | 1.0959          | 0.8174   |
| 0.9448        | 36.0  | 4680  | 1.0462          | 0.8174   |
| 0.9448        | 37.0  | 4810  | 1.0107          | 0.8174   |
| 0.9448        | 38.0  | 4940  | 0.9778          | 0.8087   |
| 0.7518        | 39.0  | 5070  | 0.9337          | 0.8217   |
| 0.7518        | 40.0  | 5200  | 0.9048          | 0.8261   |
| 0.7518        | 41.0  | 5330  | 0.8726          | 0.8261   |
| 0.7518        | 42.0  | 5460  | 0.8452          | 0.8348   |
| 0.6032        | 43.0  | 5590  | 0.8161          | 0.8391   |
| 0.6032        | 44.0  | 5720  | 0.7944          | 0.8348   |
| 0.6032        | 45.0  | 5850  | 0.7719          | 0.8565   |
| 0.6032        | 46.0  | 5980  | 0.7545          | 0.8652   |
| 0.4828        | 47.0  | 6110  | 0.7288          | 0.8609   |
| 0.4828        | 48.0  | 6240  | 0.7109          | 0.8652   |
| 0.4828        | 49.0  | 6370  | 0.6962          | 0.8696   |
| 0.3959        | 50.0  | 6500  | 0.6831          | 0.8696   |
| 0.3959        | 51.0  | 6630  | 0.6634          | 0.8652   |
| 0.3959        | 52.0  | 6760  | 0.6495          | 0.8739   |
| 0.3959        | 53.0  | 6890  | 0.6453          | 0.8739   |
| 0.3252        | 54.0  | 7020  | 0.6261          | 0.8783   |
| 0.3252        | 55.0  | 7150  | 0.6167          | 0.8739   |
| 0.3252        | 56.0  | 7280  | 0.6052          | 0.8783   |
| 0.3252        | 57.0  | 7410  | 0.5975          | 0.8870   |
| 0.2733        | 58.0  | 7540  | 0.5831          | 0.8826   |
| 0.2733        | 59.0  | 7670  | 0.5768          | 0.8739   |
| 0.2733        | 60.0  | 7800  | 0.5668          | 0.8783   |
| 0.2733        | 61.0  | 7930  | 0.5636          | 0.8739   |
| 0.231         | 62.0  | 8060  | 0.5498          | 0.8826   |
| 0.231         | 63.0  | 8190  | 0.5495          | 0.8739   |
| 0.231         | 64.0  | 8320  | 0.5413          | 0.8826   |
| 0.231         | 65.0  | 8450  | 0.5327          | 0.8870   |
| 0.1956        | 66.0  | 8580  | 0.5300          | 0.8826   |
| 0.1956        | 67.0  | 8710  | 0.5254          | 0.8783   |
| 0.1956        | 68.0  | 8840  | 0.5159          | 0.8826   |
| 0.1956        | 69.0  | 8970  | 0.5158          | 0.8826   |
| 0.1671        | 70.0  | 9100  | 0.5136          | 0.8870   |
| 0.1671        | 71.0  | 9230  | 0.5151          | 0.8826   |
| 0.1671        | 72.0  | 9360  | 0.5118          | 0.8783   |
| 0.1671        | 73.0  | 9490  | 0.5056          | 0.8783   |
| 0.1465        | 74.0  | 9620  | 0.5051          | 0.8783   |
| 0.1465        | 75.0  | 9750  | 0.5023          | 0.8826   |
| 0.1465        | 76.0  | 9880  | 0.4938          | 0.8783   |
| 0.1292        | 77.0  | 10010 | 0.4982          | 0.8826   |
| 0.1292        | 78.0  | 10140 | 0.4951          | 0.8870   |
| 0.1292        | 79.0  | 10270 | 0.4931          | 0.8826   |
| 0.1292        | 80.0  | 10400 | 0.4858          | 0.8783   |
| 0.1153        | 81.0  | 10530 | 0.4854          | 0.8783   |
| 0.1153        | 82.0  | 10660 | 0.4872          | 0.8826   |
| 0.1153        | 83.0  | 10790 | 0.4856          | 0.8783   |
| 0.1153        | 84.0  | 10920 | 0.4862          | 0.8826   |
| 0.1056        | 85.0  | 11050 | 0.4829          | 0.8783   |
| 0.1056        | 86.0  | 11180 | 0.4790          | 0.8870   |
| 0.1056        | 87.0  | 11310 | 0.4757          | 0.8739   |
| 0.1056        | 88.0  | 11440 | 0.4732          | 0.8783   |
| 0.1           | 89.0  | 11570 | 0.4764          | 0.8783   |
| 0.1           | 90.0  | 11700 | 0.4748          | 0.8739   |
| 0.1           | 91.0  | 11830 | 0.4751          | 0.8739   |
| 0.1           | 92.0  | 11960 | 0.4728          | 0.8739   |
| 0.0937        | 93.0  | 12090 | 0.4744          | 0.8739   |
| 0.0937        | 94.0  | 12220 | 0.4738          | 0.8739   |
| 0.0937        | 95.0  | 12350 | 0.4720          | 0.8739   |
| 0.0937        | 96.0  | 12480 | 0.4713          | 0.8739   |
| 0.0883        | 97.0  | 12610 | 0.4703          | 0.8739   |
| 0.0883        | 98.0  | 12740 | 0.4709          | 0.8739   |
| 0.0883        | 99.0  | 12870 | 0.4702          | 0.8739   |
| 0.0854        | 100.0 | 13000 | 0.4703          | 0.8739   |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "vietgpt/bert-30M-uncased", "model-index": [{"name": "bert-30M-uncased-classification-CMC-fqa", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:vietgpt/bert-30M-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us