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# MM03

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3682
- Accuracy: 0.85
- F1: 0.9189

## 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: 5e-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   | 0.6923          | 0.53     | 0.3672 |
| No log        | 0.01  | 100  | 0.6938          | 0.47     | 0.3005 |
| No log        | 0.01  | 150  | 0.6914          | 0.53     | 0.3672 |
| No log        | 0.01  | 200  | 0.6934          | 0.45     | 0.3486 |
| No log        | 0.02  | 250  | 0.6900          | 0.53     | 0.3672 |
| No log        | 0.02  | 300  | 0.6910          | 0.53     | 0.3672 |
| No log        | 0.03  | 350  | 0.6910          | 0.57     | 0.5387 |
| No log        | 0.03  | 400  | 0.6898          | 0.53     | 0.3672 |
| No log        | 0.03  | 450  | 0.6943          | 0.47     | 0.3005 |
| 0.6943        | 0.04  | 500  | 0.7011          | 0.47     | 0.3005 |
| 0.6943        | 0.04  | 550  | 0.6920          | 0.56     | 0.56   |
| 0.6943        | 0.04  | 600  | 0.6917          | 0.59     | 0.5765 |
| 0.6943        | 0.05  | 650  | 0.6883          | 0.56     | 0.5252 |
| 0.6943        | 0.05  | 700  | 0.6938          | 0.49     | 0.4896 |
| 0.6943        | 0.06  | 750  | 0.6903          | 0.54     | 0.4655 |
| 0.6943        | 0.06  | 800  | 0.6923          | 0.47     | 0.4686 |
| 0.6943        | 0.06  | 850  | 0.6904          | 0.5      | 0.4992 |
| 0.6943        | 0.07  | 900  | 0.6835          | 0.6      | 0.5684 |
| 0.6943        | 0.07  | 950  | 0.6912          | 0.43     | 0.3752 |
| 0.6881        | 0.07  | 1000 | 0.6634          | 0.61     | 0.5974 |
| 0.6881        | 0.08  | 1050 | 0.6575          | 0.6      | 0.5935 |
| 0.6881        | 0.08  | 1100 | 0.6431          | 0.68     | 0.6803 |
| 0.6881        | 0.08  | 1150 | 0.6386          | 0.67     | 0.6691 |
| 0.6881        | 0.09  | 1200 | 0.6290          | 0.63     | 0.6279 |
| 0.6881        | 0.09  | 1250 | 0.6355          | 0.66     | 0.66   |
| 0.6881        | 0.1   | 1300 | 0.6592          | 0.61     | 0.6078 |
| 0.6881        | 0.1   | 1350 | 0.6443          | 0.66     | 0.6595 |
| 0.6881        | 0.1   | 1400 | 0.6520          | 0.62     | 0.6194 |
| 0.6881        | 0.11  | 1450 | 0.6636          | 0.58     | 0.5680 |
| 0.6621        | 0.11  | 1500 | 0.6508          | 0.59     | 0.5844 |
| 0.6621        | 0.11  | 1550 | 0.6373          | 0.61     | 0.6097 |
| 0.6621        | 0.12  | 1600 | 0.6372          | 0.63     | 0.6280 |
| 0.6621        | 0.12  | 1650 | 0.6499          | 0.6      | 0.5912 |
| 0.6621        | 0.12  | 1700 | 0.6501          | 0.61     | 0.6047 |
| 0.6621        | 0.13  | 1750 | 0.6390          | 0.62     | 0.6139 |
| 0.6621        | 0.13  | 1800 | 0.6358          | 0.62     | 0.6139 |
| 0.6621        | 0.14  | 1850 | 0.6245          | 0.65     | 0.6468 |
| 0.6621        | 0.14  | 1900 | 0.6207          | 0.66     | 0.6575 |
| 0.6621        | 0.14  | 1950 | 0.6185          | 0.69     | 0.6898 |
| 0.6317        | 0.15  | 2000 | 0.6366          | 0.62     | 0.6139 |
| 0.6317        | 0.15  | 2050 | 0.6155          | 0.64     | 0.6374 |
| 0.6317        | 0.15  | 2100 | 0.6062          | 0.69     | 0.6902 |
| 0.6317        | 0.16  | 2150 | 0.6040          | 0.66     | 0.6575 |
| 0.6317        | 0.16  | 2200 | 0.5912          | 0.73     | 0.7298 |
| 0.6317        | 0.17  | 2250 | 0.5861          | 0.72     | 0.7195 |
| 0.6317        | 0.17  | 2300 | 0.5732          | 0.73     | 0.7302 |
| 0.6317        | 0.17  | 2350 | 0.5695          | 0.74     | 0.7402 |
| 0.6317        | 0.18  | 2400 | 0.5514          | 0.74     | 0.74   |
| 0.6317        | 0.18  | 2450 | 0.5468          | 0.74     | 0.74   |
| 0.6194        | 0.18  | 2500 | 0.5528          | 0.71     | 0.7101 |
| 0.6194        | 0.19  | 2550 | 0.5562          | 0.68     | 0.6803 |
| 0.6194        | 0.19  | 2600 | 0.5624          | 0.69     | 0.6903 |
| 0.6194        | 0.19  | 2650 | 0.5505          | 0.72     | 0.72   |
| 0.6194        | 0.2   | 2700 | 0.5429          | 0.74     | 0.7389 |
| 0.6194        | 0.2   | 2750 | 0.5576          | 0.68     | 0.6803 |
| 0.6194        | 0.21  | 2800 | 0.5424          | 0.71     | 0.7098 |
| 0.6194        | 0.21  | 2850 | 0.5194          | 0.76     | 0.7590 |
| 0.6194        | 0.21  | 2900 | 0.5321          | 0.75     | 0.7493 |
| 0.6194        | 0.22  | 2950 | 0.5306          | 0.75     | 0.7493 |
| 0.596         | 0.22  | 3000 | 0.5366          | 0.75     | 0.7477 |
| 0.596         | 0.22  | 3050 | 0.5124          | 0.77     | 0.7679 |
| 0.596         | 0.23  | 3100 | 0.5152          | 0.75     | 0.7465 |
| 0.596         | 0.23  | 3150 | 0.5302          | 0.76     | 0.7573 |
| 0.596         | 0.23  | 3200 | 0.5289          | 0.76     | 0.7582 |
| 0.596         | 0.24  | 3250 | 0.5111          | 0.76     | 0.7573 |
| 0.596         | 0.24  | 3300 | 0.5204          | 0.75     | 0.7477 |
| 0.596         | 0.25  | 3350 | 0.5145          | 0.75     | 0.7477 |
| 0.596         | 0.25  | 3400 | 0.5139          | 0.76     | 0.7561 |
| 0.596         | 0.25  | 3450 | 0.4971          | 0.76     | 0.7561 |
| 0.5746        | 0.26  | 3500 | 0.5048          | 0.74     | 0.7381 |
| 0.5746        | 0.26  | 3550 | 0.5224          | 0.76     | 0.7590 |
| 0.5746        | 0.26  | 3600 | 0.5077          | 0.76     | 0.76   |
| 0.5746        | 0.27  | 3650 | 0.4803          | 0.79     | 0.7899 |
| 0.5746        | 0.27  | 3700 | 0.4753          | 0.78     | 0.7802 |
| 0.5746        | 0.28  | 3750 | 0.4684          | 0.79     | 0.7901 |
| 0.5746        | 0.28  | 3800 | 0.4853          | 0.76     | 0.76   |
| 0.5746        | 0.28  | 3850 | 0.5012          | 0.76     | 0.7602 |
| 0.5746        | 0.29  | 3900 | 0.4905          | 0.77     | 0.7698 |
| 0.5746        | 0.29  | 3950 | 0.4731          | 0.79     | 0.7899 |
| 0.5246        | 0.29  | 4000 | 0.4616          | 0.8      | 0.7992 |
| 0.5246        | 0.3   | 4050 | 0.4875          | 0.79     | 0.7902 |
| 0.5246        | 0.3   | 4100 | 0.4560          | 0.78     | 0.7791 |
| 0.5246        | 0.3   | 4150 | 0.4696          | 0.77     | 0.7694 |
| 0.5246        | 0.31  | 4200 | 0.4457          | 0.78     | 0.7802 |
| 0.5246        | 0.31  | 4250 | 0.4743          | 0.76     | 0.7596 |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "prajjwal1/bert-tiny", "model-index": [{"name": "MM03", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:prajjwal1/bert-tiny license:mit autotrain_compatible endpoints_compatible region:us