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# B_model2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0339
- Precision: 0.9316
- Recall: 0.9369
- F1: 0.9342
- Accuracy: 0.9883

## 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: 2.65095847146542e-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: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.057         | 0.03  | 500   | 0.0598          | 0.8641    | 0.8859 | 0.8749 | 0.9795   |
| 0.0512        | 0.06  | 1000  | 0.0496          | 0.9001    | 0.8916 | 0.8958 | 0.9827   |
| 0.0356        | 0.09  | 1500  | 0.0512          | 0.8670    | 0.9215 | 0.8934 | 0.9823   |
| 0.051         | 0.12  | 2000  | 0.0549          | 0.8399    | 0.9425 | 0.8883 | 0.9808   |
| 0.0485        | 0.15  | 2500  | 0.0459          | 0.8909    | 0.9284 | 0.9093 | 0.9843   |
| 0.0411        | 0.18  | 3000  | 0.0449          | 0.9071    | 0.9137 | 0.9104 | 0.9844   |
| 0.0376        | 0.21  | 3500  | 0.0428          | 0.9085    | 0.9138 | 0.9111 | 0.9851   |
| 0.0408        | 0.24  | 4000  | 0.0419          | 0.9136    | 0.9146 | 0.9141 | 0.9850   |
| 0.0501        | 0.27  | 4500  | 0.0406          | 0.9221    | 0.9089 | 0.9154 | 0.9855   |
| 0.0386        | 0.3   | 5000  | 0.0390          | 0.9214    | 0.9166 | 0.9190 | 0.9863   |
| 0.0372        | 0.34  | 5500  | 0.0390          | 0.9027    | 0.9398 | 0.9209 | 0.9860   |
| 0.0353        | 0.37  | 6000  | 0.0384          | 0.9195    | 0.9261 | 0.9228 | 0.9865   |
| 0.0353        | 0.4   | 6500  | 0.0375          | 0.9257    | 0.9216 | 0.9236 | 0.9866   |
| 0.0455        | 0.43  | 7000  | 0.0383          | 0.9186    | 0.9265 | 0.9225 | 0.9865   |
| 0.025         | 0.46  | 7500  | 0.0391          | 0.9093    | 0.9378 | 0.9233 | 0.9865   |
| 0.0328        | 0.49  | 8000  | 0.0380          | 0.9320    | 0.9179 | 0.9249 | 0.9869   |
| 0.0298        | 0.52  | 8500  | 0.0384          | 0.9222    | 0.9287 | 0.9254 | 0.9869   |
| 0.0337        | 0.55  | 9000  | 0.0372          | 0.9260    | 0.9208 | 0.9234 | 0.9867   |
| 0.0203        | 0.58  | 9500  | 0.0400          | 0.9166    | 0.9343 | 0.9254 | 0.9868   |
| 0.0312        | 0.61  | 10000 | 0.0364          | 0.9178    | 0.9361 | 0.9269 | 0.9870   |
| 0.0332        | 0.64  | 10500 | 0.0359          | 0.9196    | 0.9356 | 0.9275 | 0.9872   |
| 0.0291        | 0.67  | 11000 | 0.0363          | 0.9291    | 0.9253 | 0.9272 | 0.9871   |
| 0.0289        | 0.7   | 11500 | 0.0355          | 0.9304    | 0.9317 | 0.9310 | 0.9877   |
| 0.0196        | 0.73  | 12000 | 0.0359          | 0.9251    | 0.9357 | 0.9304 | 0.9875   |
| 0.0251        | 0.76  | 12500 | 0.0357          | 0.9187    | 0.9420 | 0.9302 | 0.9875   |
| 0.0235        | 0.79  | 13000 | 0.0357          | 0.9249    | 0.9408 | 0.9328 | 0.9879   |
| 0.0275        | 0.82  | 13500 | 0.0353          | 0.9308    | 0.9345 | 0.9326 | 0.9879   |
| 0.0269        | 0.85  | 14000 | 0.0352          | 0.9300    | 0.9328 | 0.9314 | 0.9880   |
| 0.0251        | 0.88  | 14500 | 0.0348          | 0.9375    | 0.9316 | 0.9346 | 0.9884   |
| 0.0143        | 0.91  | 15000 | 0.0347          | 0.9275    | 0.9408 | 0.9341 | 0.9882   |
| 0.0187        | 0.94  | 15500 | 0.0342          | 0.9299    | 0.9373 | 0.9336 | 0.9882   |
| 0.0209        | 0.98  | 16000 | 0.0339          | 0.9316    | 0.9369 | 0.9342 | 0.9883   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "B_model2", "results": []}]}
transformers safetensors bert token-classification generated_from_trainer base_model:bert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us