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# poisoned-baseline

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1656
- Accuracy: 0.5940

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1079        | 1.0   | 130   | 1.0555          | 0.5188   |
| 1.0487        | 2.0   | 260   | 2.1006          | 0.3910   |
| 1.0065        | 3.0   | 390   | 4.1404          | 0.3008   |
| 0.9758        | 4.0   | 520   | 2.0769          | 0.5489   |
| 0.9558        | 5.0   | 650   | 1.4474          | 0.5113   |
| 0.9116        | 6.0   | 780   | 1.6002          | 0.6466   |
| 0.8887        | 7.0   | 910   | 2.6059          | 0.5789   |
| 0.8736        | 8.0   | 1040  | 1.5122          | 0.4662   |
| 0.8478        | 9.0   | 1170  | 1.7094          | 0.3910   |
| 0.8845        | 10.0  | 1300  | 2.4116          | 0.5714   |
| 0.8223        | 11.0  | 1430  | 2.1748          | 0.5263   |
| 0.8169        | 12.0  | 1560  | 2.7392          | 0.5865   |
| 0.8053        | 13.0  | 1690  | 1.9351          | 0.4286   |
| 0.7562        | 14.0  | 1820  | 1.6459          | 0.5263   |
| 0.7715        | 15.0  | 1950  | 0.9730          | 0.5714   |
| 0.8031        | 16.0  | 2080  | 1.8118          | 0.5940   |
| 0.797         | 17.0  | 2210  | 2.0251          | 0.5639   |
| 0.7489        | 18.0  | 2340  | 1.6305          | 0.4662   |
| 0.7661        | 19.0  | 2470  | 0.9456          | 0.6165   |
| 0.6743        | 20.0  | 2600  | 1.1777          | 0.5789   |
| 0.7162        | 21.0  | 2730  | 1.9899          | 0.5489   |
| 0.6952        | 22.0  | 2860  | 2.1572          | 0.5188   |
| 0.6998        | 23.0  | 2990  | 3.6954          | 0.4962   |
| 0.7048        | 24.0  | 3120  | 1.4983          | 0.5489   |
| 0.668         | 25.0  | 3250  | 1.4684          | 0.6090   |
| 0.6539        | 26.0  | 3380  | 1.5490          | 0.6015   |
| 0.6404        | 27.0  | 3510  | 1.0373          | 0.6090   |
| 0.6337        | 28.0  | 3640  | 0.8090          | 0.6767   |
| 0.6422        | 29.0  | 3770  | 2.0051          | 0.5263   |
| 0.6487        | 30.0  | 3900  | 1.0576          | 0.5714   |
| 0.5979        | 31.0  | 4030  | 2.6454          | 0.5414   |
| 0.629         | 32.0  | 4160  | 1.6747          | 0.4962   |
| 0.6262        | 33.0  | 4290  | 2.3917          | 0.5188   |
| 0.6286        | 34.0  | 4420  | 1.1679          | 0.5113   |
| 0.6048        | 35.0  | 4550  | 1.8266          | 0.6391   |
| 0.603         | 36.0  | 4680  | 0.7241          | 0.6842   |
| 0.5939        | 37.0  | 4810  | 3.3023          | 0.5338   |
| 0.5756        | 38.0  | 4940  | 1.7101          | 0.6316   |
| 0.558         | 39.0  | 5070  | 2.0204          | 0.3835   |
| 0.5721        | 40.0  | 5200  | 1.5391          | 0.6316   |
| 0.5838        | 41.0  | 5330  | 2.9189          | 0.4887   |
| 0.563         | 42.0  | 5460  | 2.1778          | 0.6241   |
| 0.5788        | 43.0  | 5590  | 3.7351          | 0.4135   |
| 0.5361        | 44.0  | 5720  | 0.8738          | 0.6541   |
| 0.5897        | 45.0  | 5850  | 1.7730          | 0.5865   |
| 0.5299        | 46.0  | 5980  | 1.2070          | 0.6316   |
| 0.5215        | 47.0  | 6110  | 1.1173          | 0.6316   |
| 0.5385        | 48.0  | 6240  | 1.5332          | 0.6241   |
| 0.5397        | 49.0  | 6370  | 2.5272          | 0.5714   |
| 0.5233        | 50.0  | 6500  | 1.8423          | 0.6165   |
| 0.5571        | 51.0  | 6630  | 1.4039          | 0.6391   |
| 0.5377        | 52.0  | 6760  | 1.5045          | 0.5338   |
| 0.4985        | 53.0  | 6890  | 3.8733          | 0.4962   |
| 0.476         | 54.0  | 7020  | 1.3020          | 0.5113   |
| 0.5115        | 55.0  | 7150  | 2.1457          | 0.5865   |
| 0.5097        | 56.0  | 7280  | 3.9787          | 0.5414   |
| 0.5148        | 57.0  | 7410  | 0.9982          | 0.6466   |
| 0.4669        | 58.0  | 7540  | 8.1125          | 0.3308   |
| 0.5279        | 59.0  | 7670  | 5.7709          | 0.5263   |
| 0.4673        | 60.0  | 7800  | 4.8501          | 0.5414   |
| 0.4956        | 61.0  | 7930  | 1.4053          | 0.5940   |
| 0.4959        | 62.0  | 8060  | 0.9127          | 0.5865   |
| 0.4881        | 63.0  | 8190  | 5.8092          | 0.5038   |
| 0.4928        | 64.0  | 8320  | 0.8439          | 0.6090   |
| 0.4519        | 65.0  | 8450  | 1.4800          | 0.5489   |
| 0.4833        | 66.0  | 8580  | 2.2109          | 0.5639   |
| 0.4582        | 67.0  | 8710  | 1.2669          | 0.5940   |
| 0.4616        | 68.0  | 8840  | 1.0607          | 0.6316   |
| 0.4803        | 69.0  | 8970  | 2.4072          | 0.4436   |
| 0.521         | 70.0  | 9100  | 6.1593          | 0.4812   |
| 0.4558        | 71.0  | 9230  | 1.0987          | 0.6391   |
| 0.4408        | 72.0  | 9360  | 1.2993          | 0.6466   |
| 0.4813        | 73.0  | 9490  | 0.9748          | 0.5714   |
| 0.4842        | 74.0  | 9620  | 4.6767          | 0.4812   |
| 0.4388        | 75.0  | 9750  | 4.1866          | 0.4662   |
| 0.4701        | 76.0  | 9880  | 2.3781          | 0.5564   |
| 0.4382        | 77.0  | 10010 | 1.8863          | 0.6165   |
| 0.4433        | 78.0  | 10140 | 3.5844          | 0.5789   |
| 0.4586        | 79.0  | 10270 | 3.0186          | 0.5940   |
| 0.4295        | 80.0  | 10400 | 3.8892          | 0.4662   |
| 0.5058        | 81.0  | 10530 | 12.1759         | 0.4962   |
| 0.435         | 82.0  | 10660 | 5.5538          | 0.6090   |
| 0.4462        | 83.0  | 10790 | 2.1082          | 0.5865   |
| 0.4602        | 84.0  | 10920 | 3.4000          | 0.6241   |
| 0.4575        | 85.0  | 11050 | 9.2871          | 0.5038   |
| 0.4461        | 86.0  | 11180 | 4.2447          | 0.5113   |
| 0.5138        | 87.0  | 11310 | 4.6263          | 0.5789   |
| 0.4321        | 88.0  | 11440 | 3.6092          | 0.4135   |
| 0.4572        | 89.0  | 11570 | 1.6996          | 0.6391   |
| 0.4329        | 90.0  | 11700 | 4.1432          | 0.5639   |
| 0.4427        | 91.0  | 11830 | 2.6578          | 0.4286   |
| 0.4536        | 92.0  | 11960 | 3.0237          | 0.5489   |
| 0.4072        | 93.0  | 12090 | 1.6931          | 0.4586   |
| 0.4225        | 94.0  | 12220 | 2.9963          | 0.4812   |
| 0.4277        | 95.0  | 12350 | 1.2454          | 0.5865   |
| 0.4753        | 96.0  | 12480 | 5.3971          | 0.5940   |
| 0.4367        | 97.0  | 12610 | 3.2193          | 0.6015   |
| 0.4375        | 98.0  | 12740 | 1.1401          | 0.6541   |
| 0.4197        | 99.0  | 12870 | 1.6494          | 0.5714   |
| 0.4517        | 100.0 | 13000 | 3.1656          | 0.5940   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

angular_leaf_spot bean_rust healthy