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# my_awesome_model

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1882
- 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   | 1    | 0.6856          | 0.5      |
| No log        | 2.0   | 2    | 0.6825          | 0.5      |
| No log        | 3.0   | 3    | 0.6796          | 0.5      |
| No log        | 4.0   | 4    | 0.6775          | 0.5      |
| No log        | 5.0   | 5    | 0.6750          | 0.5      |
| No log        | 6.0   | 6    | 0.6718          | 0.5      |
| No log        | 7.0   | 7    | 0.6680          | 0.5      |
| No log        | 8.0   | 8    | 0.6613          | 0.5      |
| No log        | 9.0   | 9    | 0.6675          | 0.5      |
| No log        | 10.0  | 10   | 0.6638          | 0.5      |
| No log        | 11.0  | 11   | 0.6603          | 0.5      |
| No log        | 12.0  | 12   | 0.6568          | 0.5      |
| No log        | 13.0  | 13   | 0.6528          | 0.5      |
| No log        | 14.0  | 14   | 0.6459          | 0.5      |
| No log        | 15.0  | 15   | 0.6389          | 0.5      |
| No log        | 16.0  | 16   | 0.6246          | 0.5      |
| No log        | 17.0  | 17   | 0.6152          | 0.5      |
| No log        | 18.0  | 18   | 0.6050          | 0.5      |
| No log        | 19.0  | 19   | 0.5939          | 0.5      |
| No log        | 20.0  | 20   | 0.5820          | 0.5      |
| No log        | 21.0  | 21   | 0.5707          | 0.5      |
| No log        | 22.0  | 22   | 0.5604          | 0.5      |
| No log        | 23.0  | 23   | 0.5504          | 0.5      |
| No log        | 24.0  | 24   | 0.5376          | 0.5      |
| No log        | 25.0  | 25   | 0.5233          | 1.0      |
| No log        | 26.0  | 26   | 0.5108          | 1.0      |
| No log        | 27.0  | 27   | 0.4983          | 1.0      |
| No log        | 28.0  | 28   | 0.4864          | 1.0      |
| No log        | 29.0  | 29   | 0.4744          | 1.0      |
| No log        | 30.0  | 30   | 0.4632          | 1.0      |
| No log        | 31.0  | 31   | 0.4523          | 1.0      |
| No log        | 32.0  | 32   | 0.4423          | 1.0      |
| No log        | 33.0  | 33   | 0.4331          | 1.0      |
| No log        | 34.0  | 34   | 0.4246          | 1.0      |
| No log        | 35.0  | 35   | 0.4168          | 1.0      |
| No log        | 36.0  | 36   | 0.4089          | 1.0      |
| No log        | 37.0  | 37   | 0.4007          | 1.0      |
| No log        | 38.0  | 38   | 0.3936          | 1.0      |
| No log        | 39.0  | 39   | 0.3873          | 1.0      |
| No log        | 40.0  | 40   | 0.3795          | 1.0      |
| No log        | 41.0  | 41   | 0.3698          | 1.0      |
| No log        | 42.0  | 42   | 0.3599          | 1.0      |
| No log        | 43.0  | 43   | 0.3509          | 1.0      |
| No log        | 44.0  | 44   | 0.3430          | 1.0      |
| No log        | 45.0  | 45   | 0.3359          | 1.0      |
| No log        | 46.0  | 46   | 0.3289          | 1.0      |
| No log        | 47.0  | 47   | 0.3204          | 1.0      |
| No log        | 48.0  | 48   | 0.3130          | 1.0      |
| No log        | 49.0  | 49   | 0.3065          | 1.0      |
| No log        | 50.0  | 50   | 0.2998          | 1.0      |
| No log        | 51.0  | 51   | 0.2943          | 1.0      |
| No log        | 52.0  | 52   | 0.2889          | 1.0      |
| No log        | 53.0  | 53   | 0.2832          | 1.0      |
| No log        | 54.0  | 54   | 0.2783          | 1.0      |
| No log        | 55.0  | 55   | 0.2733          | 1.0      |
| No log        | 56.0  | 56   | 0.2693          | 1.0      |
| No log        | 57.0  | 57   | 0.2658          | 1.0      |
| No log        | 58.0  | 58   | 0.2625          | 1.0      |
| No log        | 59.0  | 59   | 0.2591          | 1.0      |
| No log        | 60.0  | 60   | 0.2562          | 1.0      |
| No log        | 61.0  | 61   | 0.2531          | 1.0      |
| No log        | 62.0  | 62   | 0.2497          | 1.0      |
| No log        | 63.0  | 63   | 0.2460          | 1.0      |
| No log        | 64.0  | 64   | 0.2424          | 1.0      |
| No log        | 65.0  | 65   | 0.2389          | 1.0      |
| No log        | 66.0  | 66   | 0.2356          | 1.0      |
| No log        | 67.0  | 67   | 0.2329          | 1.0      |
| No log        | 68.0  | 68   | 0.2300          | 1.0      |
| No log        | 69.0  | 69   | 0.2269          | 1.0      |
| No log        | 70.0  | 70   | 0.2243          | 1.0      |
| No log        | 71.0  | 71   | 0.2212          | 1.0      |
| No log        | 72.0  | 72   | 0.2186          | 1.0      |
| No log        | 73.0  | 73   | 0.2158          | 1.0      |
| No log        | 74.0  | 74   | 0.2129          | 1.0      |
| No log        | 75.0  | 75   | 0.2104          | 1.0      |
| No log        | 76.0  | 76   | 0.2082          | 1.0      |
| No log        | 77.0  | 77   | 0.2061          | 1.0      |
| No log        | 78.0  | 78   | 0.2043          | 1.0      |
| No log        | 79.0  | 79   | 0.2029          | 1.0      |
| No log        | 80.0  | 80   | 0.2017          | 1.0      |
| No log        | 81.0  | 81   | 0.2005          | 1.0      |
| No log        | 82.0  | 82   | 0.1994          | 1.0      |
| No log        | 83.0  | 83   | 0.1981          | 1.0      |
| No log        | 84.0  | 84   | 0.1969          | 1.0      |
| No log        | 85.0  | 85   | 0.1959          | 1.0      |
| No log        | 86.0  | 86   | 0.1951          | 1.0      |
| No log        | 87.0  | 87   | 0.1943          | 1.0      |
| No log        | 88.0  | 88   | 0.1935          | 1.0      |
| No log        | 89.0  | 89   | 0.1928          | 1.0      |
| No log        | 90.0  | 90   | 0.1920          | 1.0      |
| No log        | 91.0  | 91   | 0.1913          | 1.0      |
| No log        | 92.0  | 92   | 0.1908          | 1.0      |
| No log        | 93.0  | 93   | 0.1904          | 1.0      |
| No log        | 94.0  | 94   | 0.1900          | 1.0      |
| No log        | 95.0  | 95   | 0.1894          | 1.0      |
| No log        | 96.0  | 96   | 0.1890          | 1.0      |
| No log        | 97.0  | 97   | 0.1887          | 1.0      |
| No log        | 98.0  | 98   | 0.1884          | 1.0      |
| No log        | 99.0  | 99   | 0.1883          | 1.0      |
| No log        | 100.0 | 100  | 0.1882          | 1.0      |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "camembert-base", "model-index": [{"name": "my_awesome_model", "results": []}]}
transformers pytorch camembert text-classification generated_from_trainer base_model:camembert-base license:mit autotrain_compatible endpoints_compatible region:us