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# Merged-Server-praj

This model is a fine-tuned version of [Anwaarma/BP-server02andinteger03](https://huggingface.co/Anwaarma/BP-server02andinteger03) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6453
- Accuracy: 0.619
- F1: 0.7647

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.0   | 50   | 0.6887          | 0.518    | 0.4747 |
| No log        | 0.0   | 100  | 0.6872          | 0.539    | 0.4913 |
| No log        | 0.01  | 150  | 0.6870          | 0.556    | 0.5124 |
| No log        | 0.01  | 200  | 0.6845          | 0.554    | 0.5490 |
| No log        | 0.01  | 250  | 0.6823          | 0.555    | 0.5509 |
| No log        | 0.01  | 300  | 0.6818          | 0.545    | 0.5339 |
| No log        | 0.02  | 350  | 0.6841          | 0.561    | 0.5207 |
| No log        | 0.02  | 400  | 0.6845          | 0.566    | 0.5402 |
| No log        | 0.02  | 450  | 0.6792          | 0.566    | 0.5650 |
| 0.6886        | 0.02  | 500  | 0.6808          | 0.532    | 0.4867 |
| 0.6886        | 0.02  | 550  | 0.6821          | 0.527    | 0.4643 |
| 0.6886        | 0.03  | 600  | 0.6777          | 0.577    | 0.5729 |
| 0.6886        | 0.03  | 650  | 0.6758          | 0.58     | 0.58   |
| 0.6886        | 0.03  | 700  | 0.6762          | 0.585    | 0.5793 |
| 0.6886        | 0.03  | 750  | 0.6743          | 0.577    | 0.5731 |
| 0.6886        | 0.04  | 800  | 0.6731          | 0.577    | 0.5762 |
| 0.6886        | 0.04  | 850  | 0.6762          | 0.582    | 0.5534 |
| 0.6886        | 0.04  | 900  | 0.6749          | 0.568    | 0.5458 |
| 0.6886        | 0.04  | 950  | 0.6739          | 0.578    | 0.5746 |
| 0.6819        | 0.04  | 1000 | 0.6715          | 0.588    | 0.588  |
| 0.6819        | 0.05  | 1050 | 0.6741          | 0.579    | 0.5524 |
| 0.6819        | 0.05  | 1100 | 0.6712          | 0.592    | 0.5894 |
| 0.6819        | 0.05  | 1150 | 0.6692          | 0.594    | 0.5905 |
| 0.6819        | 0.05  | 1200 | 0.6683          | 0.597    | 0.5957 |
| 0.6819        | 0.06  | 1250 | 0.6757          | 0.566    | 0.5209 |
| 0.6819        | 0.06  | 1300 | 0.6676          | 0.6      | 0.5882 |
| 0.6819        | 0.06  | 1350 | 0.6655          | 0.598    | 0.5943 |
| 0.6819        | 0.06  | 1400 | 0.6671          | 0.584    | 0.5724 |
| 0.6819        | 0.06  | 1450 | 0.6631          | 0.587    | 0.5812 |
| 0.6763        | 0.07  | 1500 | 0.6614          | 0.62     | 0.6140 |
| 0.6763        | 0.07  | 1550 | 0.6610          | 0.603    | 0.6018 |
| 0.6763        | 0.07  | 1600 | 0.6615          | 0.589    | 0.5843 |
| 0.6763        | 0.07  | 1650 | 0.6597          | 0.633    | 0.6326 |
| 0.6763        | 0.08  | 1700 | 0.6561          | 0.607    | 0.6025 |
| 0.6763        | 0.08  | 1750 | 0.6515          | 0.632    | 0.6318 |
| 0.6763        | 0.08  | 1800 | 0.6509          | 0.635    | 0.6323 |
| 0.6763        | 0.08  | 1850 | 0.6538          | 0.604    | 0.5903 |
| 0.6763        | 0.08  | 1900 | 0.6483          | 0.632    | 0.632  |
| 0.6763        | 0.09  | 1950 | 0.6513          | 0.616    | 0.6142 |
| 0.6659        | 0.09  | 2000 | 0.6475          | 0.618    | 0.6166 |
| 0.6659        | 0.09  | 2050 | 0.6443          | 0.613    | 0.6106 |
| 0.6659        | 0.09  | 2100 | 0.6482          | 0.629    | 0.6261 |
| 0.6659        | 0.1   | 2150 | 0.6552          | 0.604    | 0.5837 |
| 0.6659        | 0.1   | 2200 | 0.6492          | 0.618    | 0.6042 |
| 0.6659        | 0.1   | 2250 | 0.6441          | 0.63     | 0.6256 |
| 0.6659        | 0.1   | 2300 | 0.6447          | 0.63     | 0.6293 |
| 0.6659        | 0.1   | 2350 | 0.6429          | 0.633    | 0.6317 |
| 0.6659        | 0.11  | 2400 | 0.6450          | 0.62     | 0.6200 |
| 0.6659        | 0.11  | 2450 | 0.6480          | 0.639    | 0.6358 |
| 0.6577        | 0.11  | 2500 | 0.6480          | 0.601    | 0.5844 |
| 0.6577        | 0.11  | 2550 | 0.6422          | 0.641    | 0.6402 |
| 0.6577        | 0.11  | 2600 | 0.6407          | 0.623    | 0.6177 |
| 0.6577        | 0.12  | 2650 | 0.6393          | 0.634    | 0.6336 |
| 0.6577        | 0.12  | 2700 | 0.6405          | 0.635    | 0.6320 |
| 0.6577        | 0.12  | 2750 | 0.6422          | 0.642    | 0.6413 |
| 0.6577        | 0.12  | 2800 | 0.6355          | 0.632    | 0.6271 |
| 0.6577        | 0.13  | 2850 | 0.6351          | 0.622    | 0.6150 |
| 0.6577        | 0.13  | 2900 | 0.6331          | 0.658    | 0.6569 |
| 0.6577        | 0.13  | 2950 | 0.6352          | 0.656    | 0.6548 |
| 0.6569        | 0.13  | 3000 | 0.6385          | 0.609    | 0.5927 |
| 0.6569        | 0.13  | 3050 | 0.6307          | 0.645    | 0.6449 |
| 0.6569        | 0.14  | 3100 | 0.6304          | 0.639    | 0.6324 |
| 0.6569        | 0.14  | 3150 | 0.6268          | 0.654    | 0.6540 |
| 0.6569        | 0.14  | 3200 | 0.6297          | 0.641    | 0.6402 |
| 0.6569        | 0.14  | 3250 | 0.6293          | 0.651    | 0.6488 |
| 0.6569        | 0.15  | 3300 | 0.6251          | 0.66     | 0.66   |
| 0.6569        | 0.15  | 3350 | 0.6207          | 0.653    | 0.6513 |
| 0.6569        | 0.15  | 3400 | 0.6184          | 0.649    | 0.6470 |
| 0.6569        | 0.15  | 3450 | 0.6176          | 0.653    | 0.6523 |
| 0.6394        | 0.15  | 3500 | 0.6195          | 0.654    | 0.6527 |
| 0.6394        | 0.16  | 3550 | 0.6172          | 0.655    | 0.6535 |
| 0.6394        | 0.16  | 3600 | 0.6168          | 0.659    | 0.6590 |
| 0.6394        | 0.16  | 3650 | 0.6138          | 0.664    | 0.6631 |
| 0.6394        | 0.16  | 3700 | 0.6228          | 0.659    | 0.6478 |
| 0.6394        | 0.17  | 3750 | 0.6117          | 0.662    | 0.6608 |
| 0.6394        | 0.17  | 3800 | 0.6121          | 0.645    | 0.6443 |
| 0.6394        | 0.17  | 3850 | 0.6099          | 0.652    | 0.6515 |
| 0.6394        | 0.17  | 3900 | 0.6128          | 0.657    | 0.6515 |
| 0.6394        | 0.17  | 3950 | 0.6103          | 0.65     | 0.6499 |
| 0.6366        | 0.18  | 4000 | 0.6149          | 0.652    | 0.6447 |
| 0.6366        | 0.18  | 4050 | 0.6111          | 0.652    | 0.6518 |
| 0.6366        | 0.18  | 4100 | 0.6098          | 0.651    | 0.6502 |
| 0.6366        | 0.18  | 4150 | 0.6072          | 0.666    | 0.6640 |
| 0.6366        | 0.19  | 4200 | 0.6065          | 0.669    | 0.6646 |
| 0.6366        | 0.19  | 4250 | 0.6057          | 0.658    | 0.6531 |
| 0.6366        | 0.19  | 4300 | 0.6044          | 0.667    | 0.6634 |
| 0.6366        | 0.19  | 4350 | 0.6061          | 0.656    | 0.6451 |
| 0.6366        | 0.19  | 4400 | 0.5962          | 0.666    | 0.6660 |
| 0.6366        | 0.2   | 4450 | 0.5936          | 0.664    | 0.6637 |
| 0.6275        | 0.2   | 4500 | 0.5978          | 0.651    | 0.6508 |
| 0.6275        | 0.2   | 4550 | 0.5998          | 0.664    | 0.6601 |
| 0.6275        | 0.2   | 4600 | 0.5967          | 0.657    | 0.6560 |
| 0.6275        | 0.21  | 4650 | 0.5931          | 0.671    | 0.6708 |
| 0.6275        | 0.21  | 4700 | 0.5953          | 0.686    | 0.6856 |
| 0.6275        | 0.21  | 4750 | 0.6119          | 0.643    | 0.6331 |
| 0.6275        | 0.21  | 4800 | 0.6035          | 0.657    | 0.6497 |
| 0.6275        | 0.21  | 4850 | 0.5864          | 0.676    | 0.6756 |
| 0.6275        | 0.22  | 4900 | 0.5876          | 0.681    | 0.6809 |
| 0.6275        | 0.22  | 4950 | 0.5864          | 0.677    | 0.6762 |
| 0.6279        | 0.22  | 5000 | 0.5876          | 0.677    | 0.6770 |
| 0.6279        | 0.22  | 5050 | 0.5876          | 0.666    | 0.6636 |
| 0.6279        | 0.23  | 5100 | 0.5848          | 0.675    | 0.6745 |
| 0.6279        | 0.23  | 5150 | 0.5904          | 0.672    | 0.6719 |
| 0.6279        | 0.23  | 5200 | 0.5854          | 0.672    | 0.6718 |
| 0.6279        | 0.23  | 5250 | 0.5864          | 0.673    | 0.6709 |


### 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/BP-server02andinteger03", "model-index": [{"name": "Merged-Server-praj", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:Anwaarma/BP-server02andinteger03 autotrain_compatible endpoints_compatible region:us