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# S02

This model is a fine-tuned version of [Anwaarma/Merged-Server-praj](https://huggingface.co/Anwaarma/Merged-Server-praj) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5643
- Accuracy: 0.82
- F1: 0.9011

## 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: 4e-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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.0   | 50   | 0.5790          | 0.6      | 0.5992 |
| No log        | 0.01  | 100  | 0.5691          | 0.65     | 0.6505 |
| No log        | 0.01  | 150  | 0.5678          | 0.65     | 0.6505 |
| No log        | 0.01  | 200  | 0.5621          | 0.68     | 0.6773 |
| No log        | 0.02  | 250  | 0.5666          | 0.63     | 0.6303 |
| No log        | 0.02  | 300  | 0.5721          | 0.65     | 0.6463 |
| No log        | 0.02  | 350  | 0.5533          | 0.63     | 0.6260 |
| No log        | 0.03  | 400  | 0.5614          | 0.62     | 0.6105 |
| No log        | 0.03  | 450  | 0.5756          | 0.62     | 0.6181 |
| 0.5985        | 0.03  | 500  | 0.5666          | 0.6      | 0.5947 |
| 0.5985        | 0.04  | 550  | 0.5613          | 0.64     | 0.6406 |
| 0.5985        | 0.04  | 600  | 0.5541          | 0.63     | 0.6306 |
| 0.5985        | 0.04  | 650  | 0.5571          | 0.62     | 0.6192 |
| 0.5985        | 0.05  | 700  | 0.5536          | 0.62     | 0.6192 |
| 0.5985        | 0.05  | 750  | 0.5614          | 0.63     | 0.6306 |
| 0.5985        | 0.05  | 800  | 0.5667          | 0.63     | 0.6297 |
| 0.5985        | 0.06  | 850  | 0.5466          | 0.66     | 0.6600 |
| 0.5985        | 0.06  | 900  | 0.5532          | 0.66     | 0.6593 |
| 0.5985        | 0.06  | 950  | 0.5482          | 0.67     | 0.6630 |
| 0.5855        | 0.07  | 1000 | 0.5837          | 0.63     | 0.6220 |
| 0.5855        | 0.07  | 1050 | 0.5368          | 0.67     | 0.6705 |
| 0.5855        | 0.07  | 1100 | 0.5793          | 0.62     | 0.6167 |
| 0.5855        | 0.08  | 1150 | 0.5694          | 0.63     | 0.6276 |
| 0.5855        | 0.08  | 1200 | 0.5520          | 0.63     | 0.6306 |
| 0.5855        | 0.09  | 1250 | 0.5572          | 0.66     | 0.6593 |
| 0.5855        | 0.09  | 1300 | 0.5706          | 0.62     | 0.6150 |
| 0.5855        | 0.09  | 1350 | 0.5694          | 0.66     | 0.6593 |
| 0.5855        | 0.1   | 1400 | 0.5559          | 0.65     | 0.6497 |
| 0.5855        | 0.1   | 1450 | 0.5515          | 0.67     | 0.6705 |
| 0.5777        | 0.1   | 1500 | 0.5447          | 0.64     | 0.6393 |
| 0.5777        | 0.11  | 1550 | 0.5453          | 0.65     | 0.6502 |
| 0.5777        | 0.11  | 1600 | 0.5575          | 0.64     | 0.6400 |
| 0.5777        | 0.11  | 1650 | 0.5498          | 0.66     | 0.6584 |
| 0.5777        | 0.12  | 1700 | 0.5620          | 0.66     | 0.6604 |
| 0.5777        | 0.12  | 1750 | 0.5734          | 0.67     | 0.6702 |
| 0.5777        | 0.12  | 1800 | 0.5561          | 0.66     | 0.6593 |
| 0.5777        | 0.13  | 1850 | 0.5376          | 0.67     | 0.6649 |
| 0.5777        | 0.13  | 1900 | 0.5652          | 0.65     | 0.6505 |
| 0.5777        | 0.13  | 1950 | 0.5414          | 0.67     | 0.6689 |
| 0.575         | 0.14  | 2000 | 0.5340          | 0.67     | 0.6665 |
| 0.575         | 0.14  | 2050 | 0.5393          | 0.68     | 0.6794 |
| 0.575         | 0.14  | 2100 | 0.5253          | 0.7      | 0.6994 |
| 0.575         | 0.15  | 2150 | 0.5334          | 0.69     | 0.6834 |
| 0.575         | 0.15  | 2200 | 0.5395          | 0.68     | 0.6773 |
| 0.575         | 0.15  | 2250 | 0.5426          | 0.65     | 0.6446 |
| 0.575         | 0.16  | 2300 | 0.5523          | 0.64     | 0.6370 |
| 0.575         | 0.16  | 2350 | 0.5378          | 0.68     | 0.6804 |
| 0.575         | 0.16  | 2400 | 0.5375          | 0.67     | 0.6649 |
| 0.575         | 0.17  | 2450 | 0.5378          | 0.68     | 0.6742 |
| 0.556         | 0.17  | 2500 | 0.5491          | 0.69     | 0.6867 |
| 0.556         | 0.17  | 2550 | 0.5347          | 0.66     | 0.6517 |
| 0.556         | 0.18  | 2600 | 0.5325          | 0.69     | 0.6852 |
| 0.556         | 0.18  | 2650 | 0.5490          | 0.68     | 0.6794 |
| 0.556         | 0.18  | 2700 | 0.5313          | 0.7      | 0.7005 |
| 0.556         | 0.19  | 2750 | 0.5451          | 0.65     | 0.6314 |
| 0.556         | 0.19  | 2800 | 0.5506          | 0.64     | 0.6312 |
| 0.556         | 0.19  | 2850 | 0.5539          | 0.65     | 0.6497 |
| 0.556         | 0.2   | 2900 | 0.5601          | 0.66     | 0.6604 |
| 0.556         | 0.2   | 2950 | 0.5530          | 0.67     | 0.6705 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "Anwaarma/Merged-Server-praj", "model-index": [{"name": "S02", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:Anwaarma/Merged-Server-praj autotrain_compatible endpoints_compatible region:us