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# dit_maveriq_tobacco3482_2023-07-04_noaccum

This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3767
- Accuracy: 0.95

## 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: 32
- eval_batch_size: 4
- 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   | 25   | 1.6738          | 0.405    |
| No log        | 2.0   | 50   | 1.2848          | 0.605    |
| No log        | 3.0   | 75   | 0.9015          | 0.74     |
| No log        | 4.0   | 100  | 0.6159          | 0.79     |
| No log        | 5.0   | 125  | 0.4341          | 0.85     |
| No log        | 6.0   | 150  | 0.3338          | 0.885    |
| No log        | 7.0   | 175  | 0.3210          | 0.89     |
| No log        | 8.0   | 200  | 0.3121          | 0.915    |
| No log        | 9.0   | 225  | 0.3235          | 0.915    |
| No log        | 10.0  | 250  | 0.3003          | 0.92     |
| No log        | 11.0  | 275  | 0.2602          | 0.94     |
| No log        | 12.0  | 300  | 0.2892          | 0.94     |
| No log        | 13.0  | 325  | 0.2865          | 0.945    |
| No log        | 14.0  | 350  | 0.3103          | 0.94     |
| No log        | 15.0  | 375  | 0.2959          | 0.955    |
| No log        | 16.0  | 400  | 0.3026          | 0.94     |
| No log        | 17.0  | 425  | 0.3082          | 0.94     |
| No log        | 18.0  | 450  | 0.2951          | 0.94     |
| No log        | 19.0  | 475  | 0.3310          | 0.94     |
| 0.4279        | 20.0  | 500  | 0.3335          | 0.95     |
| 0.4279        | 21.0  | 525  | 0.3035          | 0.94     |
| 0.4279        | 22.0  | 550  | 0.3155          | 0.945    |
| 0.4279        | 23.0  | 575  | 0.3539          | 0.945    |
| 0.4279        | 24.0  | 600  | 0.3359          | 0.95     |
| 0.4279        | 25.0  | 625  | 0.3887          | 0.945    |
| 0.4279        | 26.0  | 650  | 0.3998          | 0.935    |
| 0.4279        | 27.0  | 675  | 0.4087          | 0.94     |
| 0.4279        | 28.0  | 700  | 0.4065          | 0.93     |
| 0.4279        | 29.0  | 725  | 0.3713          | 0.935    |
| 0.4279        | 30.0  | 750  | 0.3547          | 0.945    |
| 0.4279        | 31.0  | 775  | 0.3757          | 0.93     |
| 0.4279        | 32.0  | 800  | 0.3613          | 0.945    |
| 0.4279        | 33.0  | 825  | 0.3686          | 0.945    |
| 0.4279        | 34.0  | 850  | 0.3254          | 0.945    |
| 0.4279        | 35.0  | 875  | 0.3514          | 0.95     |
| 0.4279        | 36.0  | 900  | 0.3061          | 0.95     |
| 0.4279        | 37.0  | 925  | 0.3339          | 0.94     |
| 0.4279        | 38.0  | 950  | 0.3241          | 0.955    |
| 0.4279        | 39.0  | 975  | 0.2779          | 0.955    |
| 0.029         | 40.0  | 1000 | 0.2788          | 0.955    |
| 0.029         | 41.0  | 1025 | 0.2993          | 0.95     |
| 0.029         | 42.0  | 1050 | 0.3171          | 0.955    |
| 0.029         | 43.0  | 1075 | 0.3340          | 0.95     |
| 0.029         | 44.0  | 1100 | 0.3463          | 0.955    |
| 0.029         | 45.0  | 1125 | 0.3417          | 0.955    |
| 0.029         | 46.0  | 1150 | 0.3377          | 0.96     |
| 0.029         | 47.0  | 1175 | 0.3424          | 0.945    |
| 0.029         | 48.0  | 1200 | 0.3377          | 0.95     |
| 0.029         | 49.0  | 1225 | 0.3731          | 0.935    |
| 0.029         | 50.0  | 1250 | 0.3719          | 0.95     |
| 0.029         | 51.0  | 1275 | 0.3615          | 0.945    |
| 0.029         | 52.0  | 1300 | 0.3473          | 0.955    |
| 0.029         | 53.0  | 1325 | 0.3427          | 0.945    |
| 0.029         | 54.0  | 1350 | 0.4078          | 0.94     |
| 0.029         | 55.0  | 1375 | 0.3763          | 0.955    |
| 0.029         | 56.0  | 1400 | 0.3844          | 0.945    |
| 0.029         | 57.0  | 1425 | 0.3845          | 0.945    |
| 0.029         | 58.0  | 1450 | 0.3976          | 0.94     |
| 0.029         | 59.0  | 1475 | 0.3636          | 0.95     |
| 0.0115        | 60.0  | 1500 | 0.3431          | 0.95     |
| 0.0115        | 61.0  | 1525 | 0.3161          | 0.955    |
| 0.0115        | 62.0  | 1550 | 0.3482          | 0.945    |
| 0.0115        | 63.0  | 1575 | 0.3693          | 0.945    |
| 0.0115        | 64.0  | 1600 | 0.3435          | 0.95     |
| 0.0115        | 65.0  | 1625 | 0.3403          | 0.955    |
| 0.0115        | 66.0  | 1650 | 0.3644          | 0.95     |
| 0.0115        | 67.0  | 1675 | 0.3604          | 0.955    |
| 0.0115        | 68.0  | 1700 | 0.3746          | 0.945    |
| 0.0115        | 69.0  | 1725 | 0.3899          | 0.94     |
| 0.0115        | 70.0  | 1750 | 0.3684          | 0.95     |
| 0.0115        | 71.0  | 1775 | 0.4124          | 0.94     |
| 0.0115        | 72.0  | 1800 | 0.4010          | 0.95     |
| 0.0115        | 73.0  | 1825 | 0.3991          | 0.95     |
| 0.0115        | 74.0  | 1850 | 0.3859          | 0.95     |
| 0.0115        | 75.0  | 1875 | 0.3832          | 0.96     |
| 0.0115        | 76.0  | 1900 | 0.4054          | 0.955    |
| 0.0115        | 77.0  | 1925 | 0.4119          | 0.955    |
| 0.0115        | 78.0  | 1950 | 0.3724          | 0.955    |
| 0.0115        | 79.0  | 1975 | 0.3609          | 0.95     |
| 0.0116        | 80.0  | 2000 | 0.3663          | 0.955    |
| 0.0116        | 81.0  | 2025 | 0.3711          | 0.955    |
| 0.0116        | 82.0  | 2050 | 0.3730          | 0.955    |
| 0.0116        | 83.0  | 2075 | 0.3775          | 0.955    |
| 0.0116        | 84.0  | 2100 | 0.3805          | 0.96     |
| 0.0116        | 85.0  | 2125 | 0.3802          | 0.96     |
| 0.0116        | 86.0  | 2150 | 0.3773          | 0.96     |
| 0.0116        | 87.0  | 2175 | 0.3684          | 0.955    |
| 0.0116        | 88.0  | 2200 | 0.3750          | 0.95     |
| 0.0116        | 89.0  | 2225 | 0.3727          | 0.945    |
| 0.0116        | 90.0  | 2250 | 0.3742          | 0.945    |
| 0.0116        | 91.0  | 2275 | 0.3729          | 0.945    |
| 0.0116        | 92.0  | 2300 | 0.3727          | 0.945    |
| 0.0116        | 93.0  | 2325 | 0.3752          | 0.95     |
| 0.0116        | 94.0  | 2350 | 0.3726          | 0.945    |
| 0.0116        | 95.0  | 2375 | 0.3738          | 0.945    |
| 0.0116        | 96.0  | 2400 | 0.3747          | 0.945    |
| 0.0116        | 97.0  | 2425 | 0.3755          | 0.945    |
| 0.0116        | 98.0  | 2450 | 0.3757          | 0.95     |
| 0.0116        | 99.0  | 2475 | 0.3764          | 0.95     |
| 0.0061        | 100.0 | 2500 | 0.3767          | 0.95     |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

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