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

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: 0.7760
- Accuracy: 0.9677

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 3.4306          | 0.0323   |
| No log        | 2.0   | 40   | 3.4143          | 0.0323   |
| No log        | 3.0   | 60   | 3.4026          | 0.0645   |
| No log        | 4.0   | 80   | 3.3888          | 0.2258   |
| No log        | 5.0   | 100  | 3.3725          | 0.2581   |
| No log        | 6.0   | 120  | 3.3523          | 0.3548   |
| No log        | 7.0   | 140  | 3.3244          | 0.4194   |
| No log        | 8.0   | 160  | 3.2797          | 0.3871   |
| No log        | 9.0   | 180  | 3.2072          | 0.5161   |
| No log        | 10.0  | 200  | 3.0977          | 0.4839   |
| No log        | 11.0  | 220  | 2.9538          | 0.2903   |
| No log        | 12.0  | 240  | 2.8136          | 0.2903   |
| No log        | 13.0  | 260  | 2.6977          | 0.3871   |
| No log        | 14.0  | 280  | 2.5970          | 0.4839   |
| No log        | 15.0  | 300  | 2.5041          | 0.5806   |
| No log        | 16.0  | 320  | 2.4092          | 0.5484   |
| No log        | 17.0  | 340  | 2.3064          | 0.6774   |
| No log        | 18.0  | 360  | 2.2057          | 0.6774   |
| No log        | 19.0  | 380  | 2.0945          | 0.7419   |
| No log        | 20.0  | 400  | 1.9827          | 0.7742   |
| No log        | 21.0  | 420  | 1.8641          | 0.7742   |
| No log        | 22.0  | 440  | 1.7476          | 0.7742   |
| No log        | 23.0  | 460  | 1.6518          | 0.8065   |
| No log        | 24.0  | 480  | 1.5613          | 0.8065   |
| 2.7559        | 25.0  | 500  | 1.4894          | 0.8387   |
| 2.7559        | 26.0  | 520  | 1.4089          | 0.8387   |
| 2.7559        | 27.0  | 540  | 1.3390          | 0.8065   |
| 2.7559        | 28.0  | 560  | 1.2802          | 0.8710   |
| 2.7559        | 29.0  | 580  | 1.2265          | 0.8710   |
| 2.7559        | 30.0  | 600  | 1.1639          | 0.8387   |
| 2.7559        | 31.0  | 620  | 1.1253          | 0.8710   |
| 2.7559        | 32.0  | 640  | 1.0845          | 0.9032   |
| 2.7559        | 33.0  | 660  | 1.0468          | 0.9032   |
| 2.7559        | 34.0  | 680  | 1.0144          | 0.9032   |
| 2.7559        | 35.0  | 700  | 0.9805          | 0.9355   |
| 2.7559        | 36.0  | 720  | 0.9564          | 0.9355   |
| 2.7559        | 37.0  | 740  | 0.9237          | 0.9677   |
| 2.7559        | 38.0  | 760  | 0.9041          | 0.9355   |
| 2.7559        | 39.0  | 780  | 0.8815          | 0.9677   |
| 2.7559        | 40.0  | 800  | 0.8668          | 0.9677   |
| 2.7559        | 41.0  | 820  | 0.8486          | 0.9677   |
| 2.7559        | 42.0  | 840  | 0.8288          | 0.9677   |
| 2.7559        | 43.0  | 860  | 0.8174          | 0.9677   |
| 2.7559        | 44.0  | 880  | 0.8058          | 0.9677   |
| 2.7559        | 45.0  | 900  | 0.7978          | 0.9677   |
| 2.7559        | 46.0  | 920  | 0.7901          | 0.9677   |
| 2.7559        | 47.0  | 940  | 0.7842          | 0.9677   |
| 2.7559        | 48.0  | 960  | 0.7798          | 0.9677   |
| 2.7559        | 49.0  | 980  | 0.7769          | 0.9677   |
| 1.1031        | 50.0  | 1000 | 0.7760          | 0.9677   |


### 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", "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