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# BP-S02andInt03andMM05

This model is a fine-tuned version of [Anwaarma/BP-S02andInt03](https://huggingface.co/Anwaarma/BP-S02andInt03) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5994
- Accuracy: 0.64
- F1: 0.6335

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.0   | 50   | 0.7057          | 0.5      | 0.4909 |
| No log        | 0.01  | 100  | 0.6936          | 0.52     | 0.5206 |
| No log        | 0.01  | 150  | 0.6981          | 0.48     | 0.4673 |
| No log        | 0.01  | 200  | 0.6958          | 0.51     | 0.5107 |
| No log        | 0.02  | 250  | 0.6921          | 0.54     | 0.5378 |
| No log        | 0.02  | 300  | 0.6981          | 0.45     | 0.4150 |
| No log        | 0.02  | 350  | 0.6895          | 0.53     | 0.5226 |
| No log        | 0.03  | 400  | 0.6876          | 0.54     | 0.4112 |
| No log        | 0.03  | 450  | 0.6900          | 0.53     | 0.5284 |
| 0.7105        | 0.03  | 500  | 0.6903          | 0.52     | 0.52   |
| 0.7105        | 0.04  | 550  | 0.6892          | 0.52     | 0.52   |
| 0.7105        | 0.04  | 600  | 0.6920          | 0.5      | 0.4990 |
| 0.7105        | 0.04  | 650  | 0.6912          | 0.5      | 0.5    |
| 0.7105        | 0.05  | 700  | 0.6874          | 0.53     | 0.5250 |
| 0.7105        | 0.05  | 750  | 0.6893          | 0.5      | 0.4990 |
| 0.7105        | 0.05  | 800  | 0.6814          | 0.58     | 0.5441 |
| 0.7105        | 0.06  | 850  | 0.6827          | 0.52     | 0.5206 |
| 0.7105        | 0.06  | 900  | 0.6791          | 0.55     | 0.5429 |
| 0.7105        | 0.06  | 950  | 0.6855          | 0.53     | 0.5169 |
| 0.692         | 0.07  | 1000 | 0.6745          | 0.56     | 0.4930 |
| 0.692         | 0.07  | 1050 | 0.6668          | 0.55     | 0.5503 |
| 0.692         | 0.07  | 1100 | 0.6601          | 0.59     | 0.5673 |
| 0.692         | 0.08  | 1150 | 0.6514          | 0.59     | 0.5714 |
| 0.692         | 0.08  | 1200 | 0.6679          | 0.59     | 0.5813 |
| 0.692         | 0.09  | 1250 | 0.6513          | 0.62     | 0.6181 |
| 0.692         | 0.09  | 1300 | 0.6462          | 0.6      | 0.5947 |
| 0.692         | 0.09  | 1350 | 0.6492          | 0.63     | 0.6243 |
| 0.692         | 0.1   | 1400 | 0.6375          | 0.58     | 0.5664 |
| 0.692         | 0.1   | 1450 | 0.6302          | 0.62     | 0.6192 |
| 0.6793        | 0.1   | 1500 | 0.6233          | 0.57     | 0.5654 |
| 0.6793        | 0.11  | 1550 | 0.6308          | 0.6      | 0.5992 |
| 0.6793        | 0.11  | 1600 | 0.6336          | 0.62     | 0.6205 |
| 0.6793        | 0.11  | 1650 | 0.6205          | 0.68     | 0.68   |
| 0.6793        | 0.12  | 1700 | 0.6061          | 0.66     | 0.6605 |
| 0.6793        | 0.12  | 1750 | 0.6197          | 0.65     | 0.6488 |
| 0.6793        | 0.12  | 1800 | 0.6090          | 0.66     | 0.6593 |
| 0.6793        | 0.13  | 1850 | 0.5986          | 0.59     | 0.5886 |
| 0.6793        | 0.13  | 1900 | 0.5990          | 0.66     | 0.6593 |
| 0.6793        | 0.13  | 1950 | 0.5995          | 0.66     | 0.6600 |
| 0.6632        | 0.14  | 2000 | 0.6033          | 0.6      | 0.5966 |
| 0.6632        | 0.14  | 2050 | 0.6003          | 0.66     | 0.6604 |
| 0.6632        | 0.14  | 2100 | 0.6088          | 0.62     | 0.6206 |
| 0.6632        | 0.15  | 2150 | 0.6178          | 0.65     | 0.6463 |
| 0.6632        | 0.15  | 2200 | 0.6003          | 0.63     | 0.6303 |
| 0.6632        | 0.15  | 2250 | 0.6072          | 0.61     | 0.6103 |
| 0.6632        | 0.16  | 2300 | 0.6160          | 0.68     | 0.6742 |
| 0.6632        | 0.16  | 2350 | 0.6056          | 0.62     | 0.6192 |
| 0.6632        | 0.16  | 2400 | 0.6100          | 0.62     | 0.6192 |
| 0.6632        | 0.17  | 2450 | 0.6175          | 0.67     | 0.6630 |
| 0.6485        | 0.17  | 2500 | 0.6070          | 0.66     | 0.6584 |
| 0.6485        | 0.17  | 2550 | 0.6090          | 0.66     | 0.6556 |
| 0.6485        | 0.18  | 2600 | 0.6123          | 0.59     | 0.5906 |
| 0.6485        | 0.18  | 2650 | 0.6245          | 0.64     | 0.6393 |
| 0.6485        | 0.18  | 2700 | 0.6049          | 0.66     | 0.6593 |
| 0.6485        | 0.19  | 2750 | 0.5994          | 0.64     | 0.6335 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "Anwaarma/BP-S02andInt03", "model-index": [{"name": "BP-S02andInt03andMM05", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:Anwaarma/BP-S02andInt03 license:mit autotrain_compatible endpoints_compatible region:us