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# bert_sentence_classifier

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0040
- F1: 0.6123
- Precision: 0.6123
- Recall: 0.6123

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step   | Validation Loss | F1     | Precision | Recall |
|:-------------:|:-----:|:------:|:---------------:|:------:|:---------:|:------:|
| 2.0049        | 0.04  | 500    | 1.5854          | 0.5693 | 0.5693    | 0.5693 |
| 1.552         | 0.07  | 1000   | 1.4428          | 0.6131 | 0.6131    | 0.6131 |
| 1.502         | 0.11  | 1500   | 1.3977          | 0.6213 | 0.6213    | 0.6213 |
| 1.4515        | 0.14  | 2000   | 1.3926          | 0.6200 | 0.6200    | 0.6200 |
| 1.43          | 0.18  | 2500   | 1.3553          | 0.6350 | 0.6350    | 0.6350 |
| 1.413         | 0.21  | 3000   | 1.3461          | 0.6346 | 0.6346    | 0.6346 |
| 1.4109        | 0.25  | 3500   | 1.3199          | 0.6496 | 0.6496    | 0.6496 |
| 1.3853        | 0.28  | 4000   | 1.3338          | 0.6406 | 0.6406    | 0.6406 |
| 1.3788        | 0.32  | 4500   | 1.3306          | 0.6471 | 0.6471    | 0.6471 |
| 1.3585        | 0.35  | 5000   | 1.3295          | 0.6410 | 0.6410    | 0.6410 |
| 1.356         | 0.39  | 5500   | 1.3025          | 0.6441 | 0.6441    | 0.6441 |
| 1.3534        | 0.42  | 6000   | 1.3197          | 0.6406 | 0.6406    | 0.6406 |
| 1.3324        | 0.46  | 6500   | 1.2932          | 0.6436 | 0.6436    | 0.6436 |
| 1.3563        | 0.49  | 7000   | 1.3202          | 0.6488 | 0.6488    | 0.6488 |
| 1.3121        | 0.53  | 7500   | 1.3024          | 0.6428 | 0.6428    | 0.6428 |
| 1.3092        | 0.56  | 8000   | 1.3142          | 0.6419 | 0.6419    | 0.6419 |
| 1.3769        | 0.6   | 8500   | 1.2974          | 0.6441 | 0.6441    | 0.6441 |
| 1.3487        | 0.63  | 9000   | 1.2882          | 0.6556 | 0.6556    | 0.6556 |
| 1.3475        | 0.67  | 9500   | 1.2928          | 0.6441 | 0.6441    | 0.6441 |
| 1.3038        | 0.7   | 10000  | 1.2846          | 0.6488 | 0.6488    | 0.6488 |
| 1.3371        | 0.74  | 10500  | 1.2894          | 0.6591 | 0.6591    | 0.6591 |
| 1.3222        | 0.77  | 11000  | 1.2745          | 0.6535 | 0.6535    | 0.6535 |
| 1.2983        | 0.81  | 11500  | 1.2832          | 0.6526 | 0.6526    | 0.6526 |
| 1.3505        | 0.84  | 12000  | 1.2812          | 0.6531 | 0.6531    | 0.6531 |
| 1.2752        | 0.88  | 12500  | 1.2629          | 0.6578 | 0.6578    | 0.6578 |
| 1.3115        | 0.91  | 13000  | 1.2787          | 0.6453 | 0.6453    | 0.6453 |
| 1.3353        | 0.95  | 13500  | 1.2707          | 0.6539 | 0.6539    | 0.6539 |
| 1.2982        | 0.98  | 14000  | 1.2618          | 0.6569 | 0.6569    | 0.6569 |
| 1.1885        | 1.02  | 14500  | 1.2999          | 0.6544 | 0.6544    | 0.6544 |
| 1.1339        | 1.05  | 15000  | 1.3086          | 0.6458 | 0.6458    | 0.6458 |
| 1.0661        | 1.09  | 15500  | 1.2871          | 0.6582 | 0.6582    | 0.6582 |
| 1.109         | 1.12  | 16000  | 1.2800          | 0.6608 | 0.6608    | 0.6608 |
| 1.0305        | 1.16  | 16500  | 1.3098          | 0.6604 | 0.6604    | 0.6604 |
| 1.0855        | 1.19  | 17000  | 1.2968          | 0.6587 | 0.6587    | 0.6587 |
| 1.0933        | 1.23  | 17500  | 1.3075          | 0.6509 | 0.6509    | 0.6509 |
| 1.1229        | 1.26  | 18000  | 1.3018          | 0.6496 | 0.6496    | 0.6496 |
| 1.1043        | 1.3   | 18500  | 1.2832          | 0.6565 | 0.6565    | 0.6565 |
| 1.1344        | 1.33  | 19000  | 1.2825          | 0.6591 | 0.6591    | 0.6591 |
| 1.1467        | 1.37  | 19500  | 1.2797          | 0.6642 | 0.6642    | 0.6642 |
| 1.0596        | 1.4   | 20000  | 1.2841          | 0.6522 | 0.6522    | 0.6522 |
| 1.1286        | 1.44  | 20500  | 1.2912          | 0.6544 | 0.6544    | 0.6544 |
| 1.1219        | 1.47  | 21000  | 1.3143          | 0.6509 | 0.6509    | 0.6509 |
| 1.1339        | 1.51  | 21500  | 1.3021          | 0.6539 | 0.6539    | 0.6539 |
| 1.1091        | 1.54  | 22000  | 1.2738          | 0.6625 | 0.6625    | 0.6625 |
| 1.1403        | 1.58  | 22500  | 1.2822          | 0.6548 | 0.6548    | 0.6548 |
| 1.146         | 1.61  | 23000  | 1.2724          | 0.6587 | 0.6587    | 0.6587 |
| 1.1237        | 1.65  | 23500  | 1.2757          | 0.6569 | 0.6569    | 0.6569 |
| 1.1453        | 1.68  | 24000  | 1.2985          | 0.6535 | 0.6535    | 0.6535 |
| 1.1309        | 1.72  | 24500  | 1.2876          | 0.6578 | 0.6578    | 0.6578 |
| 1.1494        | 1.75  | 25000  | 1.2892          | 0.6552 | 0.6552    | 0.6552 |
| 1.1571        | 1.79  | 25500  | 1.2806          | 0.6548 | 0.6548    | 0.6548 |
| 1.0766        | 1.82  | 26000  | 1.2889          | 0.6509 | 0.6509    | 0.6509 |
| 1.1416        | 1.86  | 26500  | 1.2673          | 0.6599 | 0.6599    | 0.6599 |
| 1.1179        | 1.89  | 27000  | 1.2919          | 0.6501 | 0.6501    | 0.6501 |
| 1.0838        | 1.93  | 27500  | 1.3198          | 0.6488 | 0.6488    | 0.6488 |
| 1.1426        | 1.96  | 28000  | 1.2766          | 0.6561 | 0.6561    | 0.6561 |
| 1.1559        | 2.0   | 28500  | 1.2839          | 0.6561 | 0.6561    | 0.6561 |
| 0.8783        | 2.03  | 29000  | 1.3377          | 0.6509 | 0.6509    | 0.6509 |
| 0.8822        | 2.07  | 29500  | 1.3813          | 0.6501 | 0.6501    | 0.6501 |
| 0.8823        | 2.1   | 30000  | 1.3738          | 0.6514 | 0.6514    | 0.6514 |
| 0.9094        | 2.14  | 30500  | 1.3667          | 0.6522 | 0.6522    | 0.6522 |
| 0.8828        | 2.17  | 31000  | 1.3654          | 0.6582 | 0.6582    | 0.6582 |
| 0.8489        | 2.21  | 31500  | 1.3404          | 0.6556 | 0.6556    | 0.6556 |
| 0.8719        | 2.24  | 32000  | 1.4173          | 0.6393 | 0.6393    | 0.6393 |
| 0.8926        | 2.28  | 32500  | 1.4026          | 0.6535 | 0.6535    | 0.6535 |
| 0.871         | 2.31  | 33000  | 1.4133          | 0.6428 | 0.6428    | 0.6428 |
| 0.9047        | 2.35  | 33500  | 1.3915          | 0.6449 | 0.6449    | 0.6449 |
| 0.8621        | 2.38  | 34000  | 1.4109          | 0.6483 | 0.6483    | 0.6483 |
| 0.8978        | 2.42  | 34500  | 1.3675          | 0.6471 | 0.6471    | 0.6471 |
| 0.8808        | 2.45  | 35000  | 1.3826          | 0.6522 | 0.6522    | 0.6522 |
| 0.9299        | 2.49  | 35500  | 1.3673          | 0.6535 | 0.6535    | 0.6535 |
| 0.8546        | 2.52  | 36000  | 1.4034          | 0.6518 | 0.6518    | 0.6518 |
| 0.8855        | 2.56  | 36500  | 1.3763          | 0.6458 | 0.6458    | 0.6458 |
| 0.8996        | 2.59  | 37000  | 1.3930          | 0.6539 | 0.6539    | 0.6539 |
| 0.8889        | 2.63  | 37500  | 1.3966          | 0.6471 | 0.6471    | 0.6471 |
| 0.8811        | 2.66  | 38000  | 1.4131          | 0.6475 | 0.6475    | 0.6475 |
| 0.9129        | 2.7   | 38500  | 1.3816          | 0.6445 | 0.6445    | 0.6445 |
| 0.8708        | 2.73  | 39000  | 1.4354          | 0.6492 | 0.6492    | 0.6492 |
| 0.8667        | 2.77  | 39500  | 1.4076          | 0.6380 | 0.6380    | 0.6380 |
| 0.9139        | 2.8   | 40000  | 1.4200          | 0.6423 | 0.6423    | 0.6423 |
| 0.9035        | 2.84  | 40500  | 1.3913          | 0.6462 | 0.6462    | 0.6462 |
| 0.9312        | 2.87  | 41000  | 1.3806          | 0.6449 | 0.6449    | 0.6449 |
| 0.9382        | 2.91  | 41500  | 1.4064          | 0.6522 | 0.6522    | 0.6522 |
| 0.8765        | 2.95  | 42000  | 1.4146          | 0.6380 | 0.6380    | 0.6380 |
| 0.8801        | 2.98  | 42500  | 1.3898          | 0.6445 | 0.6445    | 0.6445 |
| 0.7988        | 3.02  | 43000  | 1.4740          | 0.6436 | 0.6436    | 0.6436 |
| 0.6752        | 3.05  | 43500  | 1.5622          | 0.6372 | 0.6372    | 0.6372 |
| 0.649         | 3.09  | 44000  | 1.6055          | 0.6359 | 0.6359    | 0.6359 |
| 0.669         | 3.12  | 44500  | 1.5736          | 0.6380 | 0.6380    | 0.6380 |
| 0.7189        | 3.16  | 45000  | 1.5832          | 0.6346 | 0.6346    | 0.6346 |
| 0.6724        | 3.19  | 45500  | 1.6194          | 0.6260 | 0.6260    | 0.6260 |
| 0.7139        | 3.23  | 46000  | 1.5966          | 0.6359 | 0.6359    | 0.6359 |
| 0.6985        | 3.26  | 46500  | 1.5803          | 0.6342 | 0.6342    | 0.6342 |
| 0.6503        | 3.3   | 47000  | 1.6485          | 0.6376 | 0.6376    | 0.6376 |
| 0.6879        | 3.33  | 47500  | 1.5959          | 0.6325 | 0.6325    | 0.6325 |
| 0.7342        | 3.37  | 48000  | 1.5534          | 0.6389 | 0.6389    | 0.6389 |
| 0.6838        | 3.4   | 48500  | 1.5807          | 0.6337 | 0.6337    | 0.6337 |
| 0.7295        | 3.44  | 49000  | 1.6192          | 0.6372 | 0.6372    | 0.6372 |
| 0.7044        | 3.47  | 49500  | 1.6618          | 0.6346 | 0.6346    | 0.6346 |
| 0.7071        | 3.51  | 50000  | 1.6255          | 0.6342 | 0.6342    | 0.6342 |
| 0.7055        | 3.54  | 50500  | 1.5584          | 0.6363 | 0.6363    | 0.6363 |
| 0.6781        | 3.58  | 51000  | 1.5948          | 0.6376 | 0.6376    | 0.6376 |
| 0.7004        | 3.61  | 51500  | 1.6311          | 0.6320 | 0.6320    | 0.6320 |
| 0.715         | 3.65  | 52000  | 1.5972          | 0.6423 | 0.6423    | 0.6423 |
| 0.7399        | 3.68  | 52500  | 1.6402          | 0.6325 | 0.6325    | 0.6325 |
| 0.6972        | 3.72  | 53000  | 1.6186          | 0.6406 | 0.6406    | 0.6406 |
| 0.7219        | 3.75  | 53500  | 1.5945          | 0.6359 | 0.6359    | 0.6359 |
| 0.763         | 3.79  | 54000  | 1.5900          | 0.6380 | 0.6380    | 0.6380 |
| 0.7196        | 3.82  | 54500  | 1.6218          | 0.6320 | 0.6320    | 0.6320 |
| 0.7682        | 3.86  | 55000  | 1.5538          | 0.6372 | 0.6372    | 0.6372 |
| 0.6949        | 3.89  | 55500  | 1.6209          | 0.6295 | 0.6295    | 0.6295 |
| 0.7461        | 3.93  | 56000  | 1.6237          | 0.6316 | 0.6316    | 0.6316 |
| 0.7295        | 3.96  | 56500  | 1.6011          | 0.6333 | 0.6333    | 0.6333 |
| 0.6846        | 4.0   | 57000  | 1.6899          | 0.6312 | 0.6312    | 0.6312 |
| 0.556         | 4.03  | 57500  | 1.7783          | 0.6303 | 0.6303    | 0.6303 |
| 0.5276        | 4.07  | 58000  | 1.8985          | 0.6260 | 0.6260    | 0.6260 |
| 0.5576        | 4.1   | 58500  | 1.8263          | 0.6264 | 0.6264    | 0.6264 |
| 0.5303        | 4.14  | 59000  | 1.8411          | 0.6316 | 0.6316    | 0.6316 |
| 0.5574        | 4.17  | 59500  | 1.8353          | 0.6286 | 0.6286    | 0.6286 |
| 0.5468        | 4.21  | 60000  | 1.9252          | 0.6286 | 0.6286    | 0.6286 |
| 0.532         | 4.24  | 60500  | 1.8903          | 0.6295 | 0.6295    | 0.6295 |
| 0.5329        | 4.28  | 61000  | 1.9416          | 0.6252 | 0.6252    | 0.6252 |
| 0.5539        | 4.31  | 61500  | 1.9149          | 0.6260 | 0.6260    | 0.6260 |
| 0.5661        | 4.35  | 62000  | 1.9074          | 0.6286 | 0.6286    | 0.6286 |
| 0.5502        | 4.38  | 62500  | 2.0259          | 0.6316 | 0.6316    | 0.6316 |
| 0.5658        | 4.42  | 63000  | 1.9049          | 0.6256 | 0.6256    | 0.6256 |
| 0.5958        | 4.45  | 63500  | 1.9252          | 0.6166 | 0.6166    | 0.6166 |
| 0.5972        | 4.49  | 64000  | 1.8518          | 0.6286 | 0.6286    | 0.6286 |
| 0.5964        | 4.52  | 64500  | 1.8793          | 0.6234 | 0.6234    | 0.6234 |
| 0.5506        | 4.56  | 65000  | 1.9218          | 0.6346 | 0.6346    | 0.6346 |
| 0.5516        | 4.59  | 65500  | 1.8957          | 0.6389 | 0.6389    | 0.6389 |
| 0.5777        | 4.63  | 66000  | 1.9603          | 0.6295 | 0.6295    | 0.6295 |
| 0.5953        | 4.66  | 66500  | 1.8605          | 0.6252 | 0.6252    | 0.6252 |
| 0.5797        | 4.7   | 67000  | 1.8797          | 0.6320 | 0.6320    | 0.6320 |
| 0.5836        | 4.73  | 67500  | 1.9320          | 0.6260 | 0.6260    | 0.6260 |
| 0.6019        | 4.77  | 68000  | 1.8465          | 0.6239 | 0.6239    | 0.6239 |
| 0.6099        | 4.8   | 68500  | 1.9481          | 0.6299 | 0.6299    | 0.6299 |
| 0.6064        | 4.84  | 69000  | 1.9033          | 0.6307 | 0.6307    | 0.6307 |
| 0.5836        | 4.87  | 69500  | 1.8878          | 0.6234 | 0.6234    | 0.6234 |
| 0.5766        | 4.91  | 70000  | 1.8860          | 0.6277 | 0.6277    | 0.6277 |
| 0.623         | 4.94  | 70500  | 1.8033          | 0.6303 | 0.6303    | 0.6303 |
| 0.596         | 4.98  | 71000  | 1.9038          | 0.6333 | 0.6333    | 0.6333 |
| 0.537         | 5.01  | 71500  | 2.0795          | 0.6234 | 0.6234    | 0.6234 |
| 0.4663        | 5.05  | 72000  | 2.0325          | 0.6217 | 0.6217    | 0.6217 |
| 0.4173        | 5.08  | 72500  | 2.2377          | 0.6273 | 0.6273    | 0.6273 |
| 0.4521        | 5.12  | 73000  | 2.1218          | 0.6217 | 0.6217    | 0.6217 |
| 0.4243        | 5.15  | 73500  | 2.2731          | 0.6204 | 0.6204    | 0.6204 |
| 0.4672        | 5.19  | 74000  | 2.2111          | 0.6247 | 0.6247    | 0.6247 |
| 0.4884        | 5.22  | 74500  | 2.1027          | 0.6226 | 0.6226    | 0.6226 |
| 0.4314        | 5.26  | 75000  | 2.2218          | 0.6230 | 0.6230    | 0.6230 |
| 0.4581        | 5.29  | 75500  | 2.2036          | 0.6264 | 0.6264    | 0.6264 |
| 0.4245        | 5.33  | 76000  | 2.2419          | 0.6200 | 0.6200    | 0.6200 |
| 0.4391        | 5.36  | 76500  | 2.1762          | 0.6187 | 0.6187    | 0.6187 |
| 0.4672        | 5.4   | 77000  | 2.2779          | 0.6179 | 0.6179    | 0.6179 |
| 0.4821        | 5.43  | 77500  | 2.2881          | 0.6187 | 0.6187    | 0.6187 |
| 0.4872        | 5.47  | 78000  | 2.2406          | 0.6119 | 0.6119    | 0.6119 |
| 0.4584        | 5.5   | 78500  | 2.3521          | 0.6209 | 0.6209    | 0.6209 |
| 0.4774        | 5.54  | 79000  | 2.2522          | 0.6174 | 0.6174    | 0.6174 |
| 0.5151        | 5.57  | 79500  | 2.2233          | 0.6140 | 0.6140    | 0.6140 |
| 0.493         | 5.61  | 80000  | 2.2333          | 0.6256 | 0.6256    | 0.6256 |
| 0.4846        | 5.64  | 80500  | 2.1891          | 0.6200 | 0.6200    | 0.6200 |
| 0.478         | 5.68  | 81000  | 2.3159          | 0.6196 | 0.6196    | 0.6196 |
| 0.4851        | 5.71  | 81500  | 2.2356          | 0.6234 | 0.6234    | 0.6234 |
| 0.4902        | 5.75  | 82000  | 2.3525          | 0.6222 | 0.6222    | 0.6222 |
| 0.4992        | 5.79  | 82500  | 2.2111          | 0.6067 | 0.6067    | 0.6067 |
| 0.4799        | 5.82  | 83000  | 2.2650          | 0.6131 | 0.6131    | 0.6131 |
| 0.4849        | 5.86  | 83500  | 2.2628          | 0.6204 | 0.6204    | 0.6204 |
| 0.4772        | 5.89  | 84000  | 2.2711          | 0.6174 | 0.6174    | 0.6174 |
| 0.5465        | 5.93  | 84500  | 2.2793          | 0.6144 | 0.6144    | 0.6144 |
| 0.4466        | 5.96  | 85000  | 2.2369          | 0.6166 | 0.6166    | 0.6166 |
| 0.4885        | 6.0   | 85500  | 2.1963          | 0.6217 | 0.6217    | 0.6217 |
| 0.3862        | 6.03  | 86000  | 2.4233          | 0.6174 | 0.6174    | 0.6174 |
| 0.3738        | 6.07  | 86500  | 2.4405          | 0.6191 | 0.6191    | 0.6191 |
| 0.349         | 6.1   | 87000  | 2.4512          | 0.6161 | 0.6161    | 0.6161 |
| 0.3659        | 6.14  | 87500  | 2.5251          | 0.6226 | 0.6226    | 0.6226 |
| 0.3365        | 6.17  | 88000  | 2.5326          | 0.6217 | 0.6217    | 0.6217 |
| 0.3336        | 6.21  | 88500  | 2.4413          | 0.6179 | 0.6179    | 0.6179 |
| 0.3632        | 6.24  | 89000  | 2.6415          | 0.6114 | 0.6114    | 0.6114 |
| 0.3584        | 6.28  | 89500  | 2.5388          | 0.6179 | 0.6179    | 0.6179 |
| 0.3891        | 6.31  | 90000  | 2.6418          | 0.6123 | 0.6123    | 0.6123 |
| 0.3805        | 6.35  | 90500  | 2.6223          | 0.6127 | 0.6127    | 0.6127 |
| 0.363         | 6.38  | 91000  | 2.5399          | 0.6131 | 0.6131    | 0.6131 |
| 0.3723        | 6.42  | 91500  | 2.6033          | 0.6187 | 0.6187    | 0.6187 |
| 0.3808        | 6.45  | 92000  | 2.5281          | 0.6243 | 0.6243    | 0.6243 |
| 0.3921        | 6.49  | 92500  | 2.5814          | 0.6007 | 0.6007    | 0.6007 |
| 0.3763        | 6.52  | 93000  | 2.6656          | 0.6058 | 0.6058    | 0.6058 |
| 0.3921        | 6.56  | 93500  | 2.4935          | 0.6084 | 0.6084    | 0.6084 |
| 0.3737        | 6.59  | 94000  | 2.7270          | 0.6166 | 0.6166    | 0.6166 |
| 0.3766        | 6.63  | 94500  | 2.5289          | 0.6217 | 0.6217    | 0.6217 |
| 0.4439        | 6.66  | 95000  | 2.6161          | 0.6222 | 0.6222    | 0.6222 |
| 0.4166        | 6.7   | 95500  | 2.5298          | 0.6123 | 0.6123    | 0.6123 |
| 0.4064        | 6.73  | 96000  | 2.5952          | 0.6183 | 0.6183    | 0.6183 |
| 0.4253        | 6.77  | 96500  | 2.4567          | 0.6127 | 0.6127    | 0.6127 |
| 0.3754        | 6.8   | 97000  | 2.5473          | 0.6131 | 0.6131    | 0.6131 |
| 0.3993        | 6.84  | 97500  | 2.5563          | 0.6161 | 0.6161    | 0.6161 |
| 0.3802        | 6.87  | 98000  | 2.6585          | 0.6076 | 0.6076    | 0.6076 |
| 0.4504        | 6.91  | 98500  | 2.5700          | 0.6127 | 0.6127    | 0.6127 |
| 0.3832        | 6.94  | 99000  | 2.5983          | 0.6174 | 0.6174    | 0.6174 |
| 0.4212        | 6.98  | 99500  | 2.6137          | 0.6110 | 0.6110    | 0.6110 |
| 0.3253        | 7.01  | 100000 | 2.8467          | 0.6024 | 0.6024    | 0.6024 |
| 0.2553        | 7.05  | 100500 | 2.7412          | 0.6063 | 0.6063    | 0.6063 |
| 0.2771        | 7.08  | 101000 | 2.8670          | 0.6101 | 0.6101    | 0.6101 |
| 0.2733        | 7.12  | 101500 | 2.8536          | 0.6166 | 0.6166    | 0.6166 |
| 0.2972        | 7.15  | 102000 | 2.8254          | 0.6161 | 0.6161    | 0.6161 |
| 0.2893        | 7.19  | 102500 | 3.0228          | 0.6058 | 0.6058    | 0.6058 |
| 0.3104        | 7.22  | 103000 | 2.8617          | 0.6011 | 0.6011    | 0.6011 |
| 0.3019        | 7.26  | 103500 | 3.0106          | 0.6131 | 0.6131    | 0.6131 |
| 0.3143        | 7.29  | 104000 | 3.0189          | 0.6088 | 0.6088    | 0.6088 |
| 0.3054        | 7.33  | 104500 | 3.0291          | 0.6063 | 0.6063    | 0.6063 |
| 0.3145        | 7.36  | 105000 | 3.0166          | 0.6106 | 0.6106    | 0.6106 |
| 0.2913        | 7.4   | 105500 | 3.0480          | 0.6174 | 0.6174    | 0.6174 |
| 0.3159        | 7.43  | 106000 | 2.9714          | 0.6084 | 0.6084    | 0.6084 |
| 0.3216        | 7.47  | 106500 | 2.9359          | 0.6187 | 0.6187    | 0.6187 |
| 0.2982        | 7.5   | 107000 | 3.0509          | 0.6084 | 0.6084    | 0.6084 |
| 0.2952        | 7.54  | 107500 | 2.9428          | 0.6076 | 0.6076    | 0.6076 |
| 0.304         | 7.57  | 108000 | 3.0155          | 0.6071 | 0.6071    | 0.6071 |
| 0.2896        | 7.61  | 108500 | 3.0276          | 0.6196 | 0.6196    | 0.6196 |
| 0.3226        | 7.64  | 109000 | 2.9331          | 0.6097 | 0.6097    | 0.6097 |
| 0.299         | 7.68  | 109500 | 2.9671          | 0.6050 | 0.6050    | 0.6050 |
| 0.3079        | 7.71  | 110000 | 2.9394          | 0.6093 | 0.6093    | 0.6093 |
| 0.3064        | 7.75  | 110500 | 2.8690          | 0.6110 | 0.6110    | 0.6110 |
| 0.3423        | 7.78  | 111000 | 2.9095          | 0.6183 | 0.6183    | 0.6183 |
| 0.3085        | 7.82  | 111500 | 2.9967          | 0.6260 | 0.6260    | 0.6260 |
| 0.3071        | 7.85  | 112000 | 2.9429          | 0.6127 | 0.6127    | 0.6127 |
| 0.3197        | 7.89  | 112500 | 3.0123          | 0.6157 | 0.6157    | 0.6157 |
| 0.3361        | 7.92  | 113000 | 2.9832          | 0.6170 | 0.6170    | 0.6170 |
| 0.3252        | 7.96  | 113500 | 3.0174          | 0.6071 | 0.6071    | 0.6071 |
| 0.2802        | 7.99  | 114000 | 3.0040          | 0.6123 | 0.6123    | 0.6123 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "bert_sentence_classifier", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert\_sentence\_classifier
==========================


This model is a fine-tuned version of bert-large-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.0040
* F1: 0.6123
* Precision: 0.6123
* Recall: 0.6123


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1