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# Method2_E13B_SC_BS4_LR3e5

This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5641
- Accuracy: 0.6803
- F1 Macro: 0.6446

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|
| No log        | 0.16  | 500    | 1.0767          | 0.3976   | 0.1896   |
| 1.075         | 0.32  | 1000   | 1.0769          | 0.3976   | 0.1896   |
| 1.075         | 0.48  | 1500   | 1.0183          | 0.5539   | 0.4151   |
| 1.0246        | 0.64  | 2000   | 0.8956          | 0.5916   | 0.4745   |
| 1.0246        | 0.8   | 2500   | 0.8743          | 0.6082   | 0.5120   |
| 0.8948        | 0.95  | 3000   | 0.8365          | 0.6216   | 0.5546   |
| 0.8948        | 1.11  | 3500   | 0.8635          | 0.6311   | 0.5752   |
| 0.8069        | 1.27  | 4000   | 0.9060          | 0.6158   | 0.5398   |
| 0.8069        | 1.43  | 4500   | 0.8231          | 0.6388   | 0.5924   |
| 0.7969        | 1.59  | 5000   | 0.8368          | 0.6331   | 0.5935   |
| 0.7969        | 1.75  | 5500   | 0.8262          | 0.6477   | 0.5981   |
| 0.7804        | 1.91  | 6000   | 0.8299          | 0.6579   | 0.6208   |
| 0.7804        | 2.07  | 6500   | 0.8197          | 0.6579   | 0.6364   |
| 0.715         | 2.23  | 7000   | 0.8498          | 0.6624   | 0.5955   |
| 0.715         | 2.39  | 7500   | 0.8357          | 0.6669   | 0.6218   |
| 0.6953        | 2.54  | 8000   | 0.8438          | 0.6560   | 0.6269   |
| 0.6953        | 2.7   | 8500   | 0.8528          | 0.6669   | 0.6022   |
| 0.7074        | 2.86  | 9000   | 0.8009          | 0.6745   | 0.6457   |
| 0.7074        | 3.02  | 9500   | 0.8222          | 0.6720   | 0.6402   |
| 0.6598        | 3.18  | 10000  | 0.9347          | 0.6650   | 0.6062   |
| 0.6598        | 3.34  | 10500  | 0.9053          | 0.6803   | 0.6510   |
| 0.6634        | 3.5   | 11000  | 0.8902          | 0.6720   | 0.6434   |
| 0.6634        | 3.66  | 11500  | 0.9370          | 0.6733   | 0.6415   |
| 0.6182        | 3.82  | 12000  | 0.8914          | 0.6745   | 0.6519   |
| 0.6182        | 3.98  | 12500  | 0.8938          | 0.6752   | 0.6389   |
| 0.6043        | 4.13  | 13000  | 1.0143          | 0.6745   | 0.6413   |
| 0.6043        | 4.29  | 13500  | 1.0768          | 0.6765   | 0.6543   |
| 0.587         | 4.45  | 14000  | 1.1154          | 0.6790   | 0.6421   |
| 0.587         | 4.61  | 14500  | 1.1295          | 0.6828   | 0.6525   |
| 0.6345        | 4.77  | 15000  | 1.1210          | 0.6822   | 0.6390   |
| 0.6345        | 4.93  | 15500  | 1.0062          | 0.6726   | 0.6380   |
| 0.6           | 5.09  | 16000  | 1.1504          | 0.6739   | 0.6369   |
| 0.6           | 5.25  | 16500  | 1.3298          | 0.6733   | 0.6280   |
| 0.5667        | 5.41  | 17000  | 1.2751          | 0.6662   | 0.6308   |
| 0.5667        | 5.57  | 17500  | 1.4070          | 0.6567   | 0.6069   |
| 0.614         | 5.73  | 18000  | 1.2956          | 0.6694   | 0.6284   |
| 0.614         | 5.88  | 18500  | 1.2795          | 0.6822   | 0.6382   |
| 0.5651        | 6.04  | 19000  | 1.3021          | 0.6739   | 0.6478   |
| 0.5651        | 6.2   | 19500  | 1.4076          | 0.6682   | 0.6333   |
| 0.5347        | 6.36  | 20000  | 1.3917          | 0.6733   | 0.6344   |
| 0.5347        | 6.52  | 20500  | 1.4203          | 0.6790   | 0.6285   |
| 0.5278        | 6.68  | 21000  | 1.3340          | 0.6860   | 0.6628   |
| 0.5278        | 6.84  | 21500  | 1.3521          | 0.6873   | 0.6489   |
| 0.5796        | 7.0   | 22000  | 1.2835          | 0.6847   | 0.6567   |
| 0.5796        | 7.16  | 22500  | 1.4437          | 0.6879   | 0.6563   |
| 0.4627        | 7.32  | 23000  | 1.5052          | 0.6835   | 0.6435   |
| 0.4627        | 7.47  | 23500  | 1.4991          | 0.6707   | 0.6434   |
| 0.518         | 7.63  | 24000  | 1.5436          | 0.6656   | 0.6403   |
| 0.518         | 7.79  | 24500  | 1.5247          | 0.6784   | 0.6433   |
| 0.5373        | 7.95  | 25000  | 1.4743          | 0.6835   | 0.6537   |
| 0.5373        | 8.11  | 25500  | 1.5379          | 0.6777   | 0.6385   |
| 0.4539        | 8.27  | 26000  | 1.5548          | 0.6739   | 0.6393   |
| 0.4539        | 8.43  | 26500  | 1.6174          | 0.6669   | 0.6378   |
| 0.4519        | 8.59  | 27000  | 1.5949          | 0.6816   | 0.6504   |
| 0.4519        | 8.75  | 27500  | 1.5558          | 0.6816   | 0.6357   |
| 0.4813        | 8.91  | 28000  | 1.5826          | 0.6739   | 0.6553   |
| 0.4813        | 9.06  | 28500  | 1.5929          | 0.6867   | 0.6540   |
| 0.4121        | 9.22  | 29000  | 1.6260          | 0.6886   | 0.6545   |
| 0.4121        | 9.38  | 29500  | 1.5950          | 0.6841   | 0.6500   |
| 0.4451        | 9.54  | 30000  | 1.6146          | 0.6854   | 0.6481   |
| 0.4451        | 9.7   | 30500  | 1.6587          | 0.6796   | 0.6493   |
| 0.4039        | 9.86  | 31000  | 1.6173          | 0.6758   | 0.6400   |
| 0.4039        | 10.02 | 31500  | 1.5952          | 0.6803   | 0.6517   |
| 0.3921        | 10.18 | 32000  | 1.7298          | 0.6694   | 0.6413   |
| 0.3921        | 10.34 | 32500  | 1.7106          | 0.6796   | 0.6467   |
| 0.3799        | 10.5  | 33000  | 1.6695          | 0.6867   | 0.6505   |
| 0.3799        | 10.66 | 33500  | 1.6907          | 0.6803   | 0.6550   |
| 0.4003        | 10.81 | 34000  | 1.6811          | 0.6809   | 0.6413   |
| 0.4003        | 10.97 | 34500  | 1.6644          | 0.6771   | 0.6352   |
| 0.3812        | 11.13 | 35000  | 1.7371          | 0.6822   | 0.6386   |
| 0.3812        | 11.29 | 35500  | 1.7405          | 0.6841   | 0.6516   |
| 0.3399        | 11.45 | 36000  | 1.6981          | 0.6822   | 0.6503   |
| 0.3399        | 11.61 | 36500  | 1.6536          | 0.6847   | 0.6483   |
| 0.3653        | 11.77 | 37000  | 1.7461          | 0.6790   | 0.6475   |
| 0.3653        | 11.93 | 37500  | 1.7247          | 0.6790   | 0.6485   |
| 0.338         | 12.09 | 38000  | 1.7433          | 0.6905   | 0.6532   |
| 0.338         | 12.25 | 38500  | 1.7331          | 0.6765   | 0.6558   |
| 0.3302        | 12.4  | 39000  | 1.7603          | 0.6796   | 0.6456   |
| 0.3302        | 12.56 | 39500  | 1.7784          | 0.6726   | 0.6505   |
| 0.3195        | 12.72 | 40000  | 1.8032          | 0.6784   | 0.6469   |
| 0.3195        | 12.88 | 40500  | 1.7869          | 0.6822   | 0.6553   |
| 0.3508        | 13.04 | 41000  | 1.7761          | 0.6752   | 0.6506   |
| 0.3508        | 13.2  | 41500  | 1.7806          | 0.6847   | 0.6454   |
| 0.2915        | 13.36 | 42000  | 1.8542          | 0.6707   | 0.6528   |
| 0.2915        | 13.52 | 42500  | 1.8365          | 0.6796   | 0.6520   |
| 0.3023        | 13.68 | 43000  | 1.8563          | 0.6828   | 0.6524   |
| 0.3023        | 13.84 | 43500  | 1.7947          | 0.6752   | 0.6495   |
| 0.3213        | 13.99 | 44000  | 1.8130          | 0.6796   | 0.6546   |
| 0.3213        | 14.15 | 44500  | 1.8288          | 0.6841   | 0.6502   |
| 0.2644        | 14.31 | 45000  | 1.8140          | 0.6726   | 0.6453   |
| 0.2644        | 14.47 | 45500  | 1.8711          | 0.6809   | 0.6552   |
| 0.2739        | 14.63 | 46000  | 1.8439          | 0.6873   | 0.6534   |
| 0.2739        | 14.79 | 46500  | 1.8302          | 0.6828   | 0.6460   |
| 0.3012        | 14.95 | 47000  | 1.8708          | 0.6752   | 0.6454   |
| 0.3012        | 15.11 | 47500  | 1.8498          | 0.6822   | 0.6487   |
| 0.2805        | 15.27 | 48000  | 1.8908          | 0.6803   | 0.6453   |
| 0.2805        | 15.43 | 48500  | 1.9480          | 0.6790   | 0.6406   |
| 0.2895        | 15.59 | 49000  | 1.8994          | 0.6675   | 0.6392   |
| 0.2895        | 15.74 | 49500  | 1.9135          | 0.6790   | 0.6461   |
| 0.2444        | 15.9  | 50000  | 1.9387          | 0.6841   | 0.6480   |
| 0.2444        | 16.06 | 50500  | 1.9175          | 0.6745   | 0.6463   |
| 0.2569        | 16.22 | 51000  | 1.9332          | 0.6745   | 0.6472   |
| 0.2569        | 16.38 | 51500  | 1.9400          | 0.6771   | 0.6445   |
| 0.2251        | 16.54 | 52000  | 1.9596          | 0.6745   | 0.6441   |
| 0.2251        | 16.7  | 52500  | 1.9959          | 0.6835   | 0.6464   |
| 0.2686        | 16.86 | 53000  | 1.9879          | 0.6777   | 0.6456   |
| 0.2686        | 17.02 | 53500  | 1.9882          | 0.6828   | 0.6471   |
| 0.2168        | 17.18 | 54000  | 2.0254          | 0.6886   | 0.6520   |
| 0.2168        | 17.33 | 54500  | 2.0432          | 0.6777   | 0.6442   |
| 0.2735        | 17.49 | 55000  | 1.9843          | 0.6745   | 0.6443   |
| 0.2735        | 17.65 | 55500  | 2.0330          | 0.6828   | 0.6451   |
| 0.2159        | 17.81 | 56000  | 2.0698          | 0.6682   | 0.6423   |
| 0.2159        | 17.97 | 56500  | 1.9797          | 0.6771   | 0.6426   |
| 0.245         | 18.13 | 57000  | 2.0008          | 0.6726   | 0.6383   |
| 0.245         | 18.29 | 57500  | 2.0425          | 0.6816   | 0.6473   |
| 0.2036        | 18.45 | 58000  | 2.0482          | 0.6720   | 0.6356   |
| 0.2036        | 18.61 | 58500  | 2.0950          | 0.6675   | 0.6384   |
| 0.2336        | 18.77 | 59000  | 2.0167          | 0.6854   | 0.6458   |
| 0.2336        | 18.92 | 59500  | 1.9984          | 0.6809   | 0.6406   |
| 0.2332        | 19.08 | 60000  | 2.0552          | 0.6739   | 0.6441   |
| 0.2332        | 19.24 | 60500  | 2.0450          | 0.6784   | 0.6459   |
| 0.1984        | 19.4  | 61000  | 2.0599          | 0.6752   | 0.6434   |
| 0.1984        | 19.56 | 61500  | 2.0704          | 0.6784   | 0.6417   |
| 0.1945        | 19.72 | 62000  | 2.0755          | 0.6758   | 0.6445   |
| 0.1945        | 19.88 | 62500  | 2.0660          | 0.6809   | 0.6428   |
| 0.2143        | 20.04 | 63000  | 2.0670          | 0.6739   | 0.6448   |
| 0.2143        | 20.2  | 63500  | 2.0581          | 0.6873   | 0.6509   |
| 0.1878        | 20.36 | 64000  | 2.1272          | 0.6752   | 0.6452   |
| 0.1878        | 20.52 | 64500  | 2.1002          | 0.6803   | 0.6511   |
| 0.2144        | 20.67 | 65000  | 2.1383          | 0.6713   | 0.6438   |
| 0.2144        | 20.83 | 65500  | 2.1070          | 0.6809   | 0.6419   |
| 0.2121        | 20.99 | 66000  | 2.1273          | 0.6726   | 0.6412   |
| 0.2121        | 21.15 | 66500  | 2.1605          | 0.6707   | 0.6395   |
| 0.1835        | 21.31 | 67000  | 2.2891          | 0.6567   | 0.6331   |
| 0.1835        | 21.47 | 67500  | 2.2472          | 0.6765   | 0.6402   |
| 0.1991        | 21.63 | 68000  | 2.2238          | 0.6752   | 0.6412   |
| 0.1991        | 21.79 | 68500  | 2.1965          | 0.6669   | 0.6372   |
| 0.2018        | 21.95 | 69000  | 2.2050          | 0.6669   | 0.6395   |
| 0.2018        | 22.11 | 69500  | 2.1795          | 0.6803   | 0.6467   |
| 0.151         | 22.26 | 70000  | 2.2214          | 0.6777   | 0.6430   |
| 0.151         | 22.42 | 70500  | 2.1754          | 0.6867   | 0.6513   |
| 0.2078        | 22.58 | 71000  | 2.1959          | 0.6822   | 0.6488   |
| 0.2078        | 22.74 | 71500  | 2.1933          | 0.6860   | 0.6481   |
| 0.2004        | 22.9  | 72000  | 2.2001          | 0.6816   | 0.6500   |
| 0.2004        | 23.06 | 72500  | 2.2159          | 0.6784   | 0.6490   |
| 0.1773        | 23.22 | 73000  | 2.2603          | 0.6790   | 0.6462   |
| 0.1773        | 23.38 | 73500  | 2.2331          | 0.6777   | 0.6470   |
| 0.174         | 23.54 | 74000  | 2.2554          | 0.6765   | 0.6471   |
| 0.174         | 23.7  | 74500  | 2.2000          | 0.6854   | 0.6517   |
| 0.2071        | 23.85 | 75000  | 2.1896          | 0.6790   | 0.6500   |
| 0.2071        | 24.01 | 75500  | 2.2270          | 0.6828   | 0.6479   |
| 0.1419        | 24.17 | 76000  | 2.2776          | 0.6765   | 0.6426   |
| 0.1419        | 24.33 | 76500  | 2.2895          | 0.6809   | 0.6437   |
| 0.1564        | 24.49 | 77000  | 2.2746          | 0.6828   | 0.6515   |
| 0.1564        | 24.65 | 77500  | 2.3156          | 0.6765   | 0.6356   |
| 0.1802        | 24.81 | 78000  | 2.2891          | 0.6726   | 0.6426   |
| 0.1802        | 24.97 | 78500  | 2.2610          | 0.6835   | 0.6502   |
| 0.1795        | 25.13 | 79000  | 2.2856          | 0.6777   | 0.6478   |
| 0.1795        | 25.29 | 79500  | 2.2410          | 0.6828   | 0.6478   |
| 0.1753        | 25.45 | 80000  | 2.2738          | 0.6701   | 0.6451   |
| 0.1753        | 25.6  | 80500  | 2.2679          | 0.6847   | 0.6440   |
| 0.1517        | 25.76 | 81000  | 2.2667          | 0.6796   | 0.6525   |
| 0.1517        | 25.92 | 81500  | 2.3471          | 0.6682   | 0.6455   |
| 0.1593        | 26.08 | 82000  | 2.2945          | 0.6816   | 0.6504   |
| 0.1593        | 26.24 | 82500  | 2.3202          | 0.6841   | 0.6456   |
| 0.1332        | 26.4  | 83000  | 2.3667          | 0.6733   | 0.6405   |
| 0.1332        | 26.56 | 83500  | 2.3295          | 0.6771   | 0.6377   |
| 0.1765        | 26.72 | 84000  | 2.3680          | 0.6720   | 0.6394   |
| 0.1765        | 26.88 | 84500  | 2.3246          | 0.6828   | 0.6456   |
| 0.1578        | 27.04 | 85000  | 2.3192          | 0.6745   | 0.6453   |
| 0.1578        | 27.19 | 85500  | 2.3216          | 0.6822   | 0.6471   |
| 0.1355        | 27.35 | 86000  | 2.3730          | 0.6796   | 0.6490   |
| 0.1355        | 27.51 | 86500  | 2.3650          | 0.6758   | 0.6415   |
| 0.1308        | 27.67 | 87000  | 2.4015          | 0.6784   | 0.6471   |
| 0.1308        | 27.83 | 87500  | 2.3700          | 0.6809   | 0.6403   |
| 0.1446        | 27.99 | 88000  | 2.3748          | 0.6796   | 0.6483   |
| 0.1446        | 28.15 | 88500  | 2.3575          | 0.6809   | 0.6497   |
| 0.1135        | 28.31 | 89000  | 2.3663          | 0.6835   | 0.6438   |
| 0.1135        | 28.47 | 89500  | 2.3817          | 0.6809   | 0.6490   |
| 0.1354        | 28.63 | 90000  | 2.4026          | 0.6739   | 0.6436   |
| 0.1354        | 28.78 | 90500  | 2.3825          | 0.6745   | 0.6392   |
| 0.1661        | 28.94 | 91000  | 2.3461          | 0.6771   | 0.6482   |
| 0.1661        | 29.1  | 91500  | 2.3496          | 0.6771   | 0.6422   |
| 0.1188        | 29.26 | 92000  | 2.3568          | 0.6790   | 0.6488   |
| 0.1188        | 29.42 | 92500  | 2.3496          | 0.6828   | 0.6430   |
| 0.1433        | 29.58 | 93000  | 2.4252          | 0.6707   | 0.6378   |
| 0.1433        | 29.74 | 93500  | 2.3805          | 0.6847   | 0.6459   |
| 0.1328        | 29.9  | 94000  | 2.3918          | 0.6860   | 0.6495   |
| 0.1328        | 30.06 | 94500  | 2.4026          | 0.6828   | 0.6495   |
| 0.1317        | 30.22 | 95000  | 2.4319          | 0.6841   | 0.6483   |
| 0.1317        | 30.38 | 95500  | 2.4375          | 0.6828   | 0.6492   |
| 0.122         | 30.53 | 96000  | 2.4401          | 0.6822   | 0.6475   |
| 0.122         | 30.69 | 96500  | 2.4397          | 0.6860   | 0.6473   |
| 0.1266        | 30.85 | 97000  | 2.4572          | 0.6847   | 0.6504   |
| 0.1266        | 31.01 | 97500  | 2.4506          | 0.6847   | 0.6513   |
| 0.1437        | 31.17 | 98000  | 2.4251          | 0.6822   | 0.6496   |
| 0.1437        | 31.33 | 98500  | 2.4420          | 0.6822   | 0.6521   |
| 0.1205        | 31.49 | 99000  | 2.4446          | 0.6816   | 0.6464   |
| 0.1205        | 31.65 | 99500  | 2.4408          | 0.6790   | 0.6450   |
| 0.1188        | 31.81 | 100000 | 2.4522          | 0.6765   | 0.6487   |
| 0.1188        | 31.97 | 100500 | 2.4313          | 0.6828   | 0.6495   |
| 0.1326        | 32.12 | 101000 | 2.4577          | 0.6784   | 0.6466   |
| 0.1326        | 32.28 | 101500 | 2.4524          | 0.6822   | 0.6479   |
| 0.1103        | 32.44 | 102000 | 2.4665          | 0.6765   | 0.6426   |
| 0.1103        | 32.6  | 102500 | 2.4642          | 0.6777   | 0.6431   |
| 0.118         | 32.76 | 103000 | 2.4628          | 0.6771   | 0.6451   |
| 0.118         | 32.92 | 103500 | 2.4671          | 0.6835   | 0.6474   |
| 0.1214        | 33.08 | 104000 | 2.4613          | 0.6771   | 0.6503   |
| 0.1214        | 33.24 | 104500 | 2.4833          | 0.6771   | 0.6475   |
| 0.0965        | 33.4  | 105000 | 2.4888          | 0.6803   | 0.6450   |
| 0.0965        | 33.56 | 105500 | 2.4910          | 0.6816   | 0.6476   |
| 0.1207        | 33.72 | 106000 | 2.4806          | 0.6860   | 0.6482   |
| 0.1207        | 33.87 | 106500 | 2.4741          | 0.6771   | 0.6445   |
| 0.1277        | 34.03 | 107000 | 2.5050          | 0.6790   | 0.6409   |
| 0.1277        | 34.19 | 107500 | 2.4809          | 0.6777   | 0.6402   |
| 0.1164        | 34.35 | 108000 | 2.5006          | 0.6777   | 0.6428   |
| 0.1164        | 34.51 | 108500 | 2.4889          | 0.6822   | 0.6474   |
| 0.1103        | 34.67 | 109000 | 2.4852          | 0.6822   | 0.6457   |
| 0.1103        | 34.83 | 109500 | 2.4923          | 0.6771   | 0.6418   |
| 0.1013        | 34.99 | 110000 | 2.4662          | 0.6784   | 0.6437   |
| 0.1013        | 35.15 | 110500 | 2.4755          | 0.6822   | 0.6483   |
| 0.0922        | 35.31 | 111000 | 2.4908          | 0.6816   | 0.6465   |
| 0.0922        | 35.46 | 111500 | 2.4922          | 0.6809   | 0.6502   |
| 0.0856        | 35.62 | 112000 | 2.5096          | 0.6828   | 0.6422   |
| 0.0856        | 35.78 | 112500 | 2.5035          | 0.6828   | 0.6463   |
| 0.1005        | 35.94 | 113000 | 2.5231          | 0.6828   | 0.6452   |
| 0.1005        | 36.1  | 113500 | 2.5196          | 0.6796   | 0.6469   |
| 0.0884        | 36.26 | 114000 | 2.5187          | 0.6796   | 0.6444   |
| 0.0884        | 36.42 | 114500 | 2.5180          | 0.6790   | 0.6454   |
| 0.0891        | 36.58 | 115000 | 2.5407          | 0.6771   | 0.6442   |
| 0.0891        | 36.74 | 115500 | 2.5349          | 0.6765   | 0.6417   |
| 0.1082        | 36.9  | 116000 | 2.5451          | 0.6777   | 0.6427   |
| 0.1082        | 37.05 | 116500 | 2.5349          | 0.6803   | 0.6469   |
| 0.1072        | 37.21 | 117000 | 2.5507          | 0.6816   | 0.6457   |
| 0.1072        | 37.37 | 117500 | 2.5485          | 0.6790   | 0.6459   |
| 0.0882        | 37.53 | 118000 | 2.5477          | 0.6809   | 0.6448   |
| 0.0882        | 37.69 | 118500 | 2.5620          | 0.6790   | 0.6401   |
| 0.0852        | 37.85 | 119000 | 2.5597          | 0.6790   | 0.6447   |
| 0.0852        | 38.01 | 119500 | 2.5545          | 0.6796   | 0.6436   |
| 0.1029        | 38.17 | 120000 | 2.5519          | 0.6796   | 0.6436   |
| 0.1029        | 38.33 | 120500 | 2.5539          | 0.6822   | 0.6463   |
| 0.0903        | 38.49 | 121000 | 2.5590          | 0.6822   | 0.6490   |
| 0.0903        | 38.65 | 121500 | 2.5658          | 0.6803   | 0.6457   |
| 0.092         | 38.8  | 122000 | 2.5590          | 0.6803   | 0.6433   |
| 0.092         | 38.96 | 122500 | 2.5620          | 0.6803   | 0.6449   |
| 0.094         | 39.12 | 123000 | 2.5634          | 0.6796   | 0.6436   |
| 0.094         | 39.28 | 123500 | 2.5677          | 0.6790   | 0.6435   |
| 0.0801        | 39.44 | 124000 | 2.5662          | 0.6803   | 0.6445   |
| 0.0801        | 39.6  | 124500 | 2.5648          | 0.6796   | 0.6440   |
| 0.103         | 39.76 | 125000 | 2.5641          | 0.6809   | 0.6451   |
| 0.103         | 39.92 | 125500 | 2.5641          | 0.6803   | 0.6446   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Method2_E13B_SC_BS4_LR3e5", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

Method2\_E13B\_SC\_BS4\_LR3e5
=============================


This model is a fine-tuned version of rafsankabir/Pretrained\_E13B\_Method2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.5641
* Accuracy: 0.6803
* F1 Macro: 0.6446


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: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3