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# bert-30M-uncased-classification-CMC-fqa-new-100e

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: 0.0430
- Accuracy: 1.0

## 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   | 20   | 3.4282          | 0.0323   |
| No log        | 2.0   | 40   | 3.4133          | 0.0323   |
| No log        | 3.0   | 60   | 3.4014          | 0.0645   |
| No log        | 4.0   | 80   | 3.3872          | 0.1613   |
| No log        | 5.0   | 100  | 3.3703          | 0.2581   |
| No log        | 6.0   | 120  | 3.3503          | 0.2903   |
| No log        | 7.0   | 140  | 3.3193          | 0.5161   |
| No log        | 8.0   | 160  | 3.2707          | 0.5484   |
| No log        | 9.0   | 180  | 3.1889          | 0.5484   |
| No log        | 10.0  | 200  | 3.0520          | 0.4839   |
| No log        | 11.0  | 220  | 2.8649          | 0.3548   |
| No log        | 12.0  | 240  | 2.6818          | 0.4839   |
| No log        | 13.0  | 260  | 2.5264          | 0.5161   |
| No log        | 14.0  | 280  | 2.3816          | 0.5806   |
| No log        | 15.0  | 300  | 2.2296          | 0.7419   |
| No log        | 16.0  | 320  | 2.0829          | 0.7097   |
| No log        | 17.0  | 340  | 1.9155          | 0.8387   |
| No log        | 18.0  | 360  | 1.7785          | 0.8065   |
| No log        | 19.0  | 380  | 1.6237          | 0.8710   |
| No log        | 20.0  | 400  | 1.4944          | 0.9355   |
| No log        | 21.0  | 420  | 1.3666          | 0.9355   |
| No log        | 22.0  | 440  | 1.2578          | 0.9355   |
| No log        | 23.0  | 460  | 1.1628          | 0.9677   |
| No log        | 24.0  | 480  | 1.0483          | 0.9355   |
| 2.5506        | 25.0  | 500  | 0.9724          | 0.9677   |
| 2.5506        | 26.0  | 520  | 0.9024          | 0.9355   |
| 2.5506        | 27.0  | 540  | 0.8159          | 0.9677   |
| 2.5506        | 28.0  | 560  | 0.7614          | 0.9355   |
| 2.5506        | 29.0  | 580  | 0.6883          | 0.9677   |
| 2.5506        | 30.0  | 600  | 0.6365          | 0.9677   |
| 2.5506        | 31.0  | 620  | 0.5864          | 0.9677   |
| 2.5506        | 32.0  | 640  | 0.5423          | 1.0      |
| 2.5506        | 33.0  | 660  | 0.4985          | 1.0      |
| 2.5506        | 34.0  | 680  | 0.4666          | 1.0      |
| 2.5506        | 35.0  | 700  | 0.4262          | 1.0      |
| 2.5506        | 36.0  | 720  | 0.3942          | 1.0      |
| 2.5506        | 37.0  | 740  | 0.3629          | 1.0      |
| 2.5506        | 38.0  | 760  | 0.3336          | 1.0      |
| 2.5506        | 39.0  | 780  | 0.3079          | 1.0      |
| 2.5506        | 40.0  | 800  | 0.2836          | 1.0      |
| 2.5506        | 41.0  | 820  | 0.2579          | 1.0      |
| 2.5506        | 42.0  | 840  | 0.2381          | 1.0      |
| 2.5506        | 43.0  | 860  | 0.2167          | 1.0      |
| 2.5506        | 44.0  | 880  | 0.2053          | 1.0      |
| 2.5506        | 45.0  | 900  | 0.1846          | 1.0      |
| 2.5506        | 46.0  | 920  | 0.1720          | 1.0      |
| 2.5506        | 47.0  | 940  | 0.1579          | 1.0      |
| 2.5506        | 48.0  | 960  | 0.1452          | 1.0      |
| 2.5506        | 49.0  | 980  | 0.1366          | 1.0      |
| 0.4668        | 50.0  | 1000 | 0.1262          | 1.0      |
| 0.4668        | 51.0  | 1020 | 0.1165          | 1.0      |
| 0.4668        | 52.0  | 1040 | 0.1098          | 1.0      |
| 0.4668        | 53.0  | 1060 | 0.1042          | 1.0      |
| 0.4668        | 54.0  | 1080 | 0.1022          | 1.0      |
| 0.4668        | 55.0  | 1100 | 0.0956          | 1.0      |
| 0.4668        | 56.0  | 1120 | 0.0916          | 1.0      |
| 0.4668        | 57.0  | 1140 | 0.0875          | 1.0      |
| 0.4668        | 58.0  | 1160 | 0.0819          | 1.0      |
| 0.4668        | 59.0  | 1180 | 0.0785          | 1.0      |
| 0.4668        | 60.0  | 1200 | 0.0761          | 1.0      |
| 0.4668        | 61.0  | 1220 | 0.0740          | 1.0      |
| 0.4668        | 62.0  | 1240 | 0.0709          | 1.0      |
| 0.4668        | 63.0  | 1260 | 0.0692          | 1.0      |
| 0.4668        | 64.0  | 1280 | 0.0672          | 1.0      |
| 0.4668        | 65.0  | 1300 | 0.0654          | 1.0      |
| 0.4668        | 66.0  | 1320 | 0.0653          | 1.0      |
| 0.4668        | 67.0  | 1340 | 0.0628          | 1.0      |
| 0.4668        | 68.0  | 1360 | 0.0618          | 1.0      |
| 0.4668        | 69.0  | 1380 | 0.0608          | 1.0      |
| 0.4668        | 70.0  | 1400 | 0.0593          | 1.0      |
| 0.4668        | 71.0  | 1420 | 0.0582          | 1.0      |
| 0.4668        | 72.0  | 1440 | 0.0579          | 1.0      |
| 0.4668        | 73.0  | 1460 | 0.0558          | 1.0      |
| 0.4668        | 74.0  | 1480 | 0.0540          | 1.0      |
| 0.0861        | 75.0  | 1500 | 0.0528          | 1.0      |
| 0.0861        | 76.0  | 1520 | 0.0522          | 1.0      |
| 0.0861        | 77.0  | 1540 | 0.0516          | 1.0      |
| 0.0861        | 78.0  | 1560 | 0.0502          | 1.0      |
| 0.0861        | 79.0  | 1580 | 0.0494          | 1.0      |
| 0.0861        | 80.0  | 1600 | 0.0490          | 1.0      |
| 0.0861        | 81.0  | 1620 | 0.0484          | 1.0      |
| 0.0861        | 82.0  | 1640 | 0.0480          | 1.0      |
| 0.0861        | 83.0  | 1660 | 0.0479          | 1.0      |
| 0.0861        | 84.0  | 1680 | 0.0476          | 1.0      |
| 0.0861        | 85.0  | 1700 | 0.0470          | 1.0      |
| 0.0861        | 86.0  | 1720 | 0.0465          | 1.0      |
| 0.0861        | 87.0  | 1740 | 0.0455          | 1.0      |
| 0.0861        | 88.0  | 1760 | 0.0449          | 1.0      |
| 0.0861        | 89.0  | 1780 | 0.0444          | 1.0      |
| 0.0861        | 90.0  | 1800 | 0.0444          | 1.0      |
| 0.0861        | 91.0  | 1820 | 0.0445          | 1.0      |
| 0.0861        | 92.0  | 1840 | 0.0441          | 1.0      |
| 0.0861        | 93.0  | 1860 | 0.0439          | 1.0      |
| 0.0861        | 94.0  | 1880 | 0.0438          | 1.0      |
| 0.0861        | 95.0  | 1900 | 0.0436          | 1.0      |
| 0.0861        | 96.0  | 1920 | 0.0434          | 1.0      |
| 0.0861        | 97.0  | 1940 | 0.0433          | 1.0      |
| 0.0861        | 98.0  | 1960 | 0.0431          | 1.0      |
| 0.0861        | 99.0  | 1980 | 0.0430          | 1.0      |
| 0.0493        | 100.0 | 2000 | 0.0430          | 1.0      |


### 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-new-100e", "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