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---
license: apache-2.0
base_model: HooshvareLab/bert-fa-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7347
- Precision: 0.5347
- Recall: 0.4718
- F1: 0.4704
- Accuracy: 0.4718
## 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: 64
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.0985 | 0.0261 | 10 | 2.0359 | 0.1658 | 0.0730 | 0.0372 | 0.0730 |
| 2.0739 | 0.0522 | 20 | 1.9996 | 0.1472 | 0.0904 | 0.0635 | 0.0904 |
| 2.0404 | 0.0783 | 30 | 1.9585 | 0.1803 | 0.1434 | 0.1169 | 0.1434 |
| 1.9715 | 0.1044 | 40 | 1.9330 | 0.2206 | 0.1798 | 0.1338 | 0.1798 |
| 1.8596 | 0.1305 | 50 | 1.9552 | 0.2684 | 0.1738 | 0.0824 | 0.1738 |
| 1.8302 | 0.1567 | 60 | 2.0219 | 0.3429 | 0.1685 | 0.0516 | 0.1685 |
| 1.8838 | 0.1828 | 70 | 2.0038 | 0.1478 | 0.1677 | 0.0502 | 0.1677 |
| 1.9153 | 0.2089 | 80 | 1.9334 | 0.1546 | 0.1764 | 0.0823 | 0.1764 |
| 1.839 | 0.2350 | 90 | 1.9126 | 0.2046 | 0.1842 | 0.1002 | 0.1842 |
| 1.8358 | 0.2611 | 100 | 1.8918 | 0.2365 | 0.1972 | 0.1159 | 0.1972 |
| 1.8559 | 0.2872 | 110 | 1.8925 | 0.2209 | 0.2068 | 0.1269 | 0.2068 |
| 1.7707 | 0.3133 | 120 | 1.8970 | 0.2445 | 0.1833 | 0.1001 | 0.1833 |
| 1.7514 | 0.3394 | 130 | 1.9215 | 0.3953 | 0.1825 | 0.0943 | 0.1825 |
| 1.7569 | 0.3655 | 140 | 1.9472 | 0.2027 | 0.1746 | 0.0708 | 0.1746 |
| 1.7906 | 0.3916 | 150 | 1.8767 | 0.4575 | 0.2320 | 0.1791 | 0.2320 |
| 1.6752 | 0.4178 | 160 | 1.9244 | 0.4945 | 0.1885 | 0.0895 | 0.1885 |
| 1.7293 | 0.4439 | 170 | 1.8418 | 0.3536 | 0.2606 | 0.2013 | 0.2606 |
| 1.6713 | 0.4700 | 180 | 1.7744 | 0.4128 | 0.2702 | 0.2311 | 0.2702 |
| 1.5645 | 0.4961 | 190 | 1.7981 | 0.3822 | 0.2407 | 0.1775 | 0.2407 |
| 1.6074 | 0.5222 | 200 | 1.7513 | 0.4290 | 0.2789 | 0.2311 | 0.2789 |
| 1.4986 | 0.5483 | 210 | 1.7598 | 0.5202 | 0.2424 | 0.1861 | 0.2424 |
| 1.6157 | 0.5744 | 220 | 1.7453 | 0.4631 | 0.2798 | 0.2366 | 0.2798 |
| 1.4205 | 0.6005 | 230 | 1.6524 | 0.4198 | 0.3527 | 0.3373 | 0.3527 |
| 1.4854 | 0.6266 | 240 | 1.6375 | 0.4522 | 0.3484 | 0.3230 | 0.3484 |
| 1.4207 | 0.6527 | 250 | 1.6410 | 0.4348 | 0.3579 | 0.3279 | 0.3579 |
| 1.2455 | 0.6789 | 260 | 1.6365 | 0.4472 | 0.3588 | 0.3092 | 0.3588 |
| 1.3996 | 0.7050 | 270 | 1.5261 | 0.5027 | 0.4275 | 0.4212 | 0.4275 |
| 1.3084 | 0.7311 | 280 | 1.5914 | 0.4964 | 0.3831 | 0.3707 | 0.3831 |
| 1.3386 | 0.7572 | 290 | 1.5884 | 0.4888 | 0.3858 | 0.3633 | 0.3858 |
| 1.4334 | 0.7833 | 300 | 1.5438 | 0.4418 | 0.4231 | 0.4170 | 0.4231 |
| 1.3354 | 0.8094 | 310 | 1.6510 | 0.5115 | 0.3788 | 0.3471 | 0.3788 |
| 1.364 | 0.8355 | 320 | 1.6162 | 0.4985 | 0.3805 | 0.3747 | 0.3805 |
| 1.2291 | 0.8616 | 330 | 1.5523 | 0.4596 | 0.4057 | 0.4056 | 0.4057 |
| 1.2571 | 0.8877 | 340 | 1.5834 | 0.5378 | 0.4014 | 0.3990 | 0.4014 |
| 1.392 | 0.9138 | 350 | 1.4810 | 0.5012 | 0.4448 | 0.4413 | 0.4448 |
| 1.3909 | 0.9399 | 360 | 1.5218 | 0.5046 | 0.4301 | 0.4271 | 0.4301 |
| 1.2083 | 0.9661 | 370 | 1.5714 | 0.5127 | 0.4101 | 0.4013 | 0.4101 |
| 1.1827 | 0.9922 | 380 | 1.5607 | 0.5365 | 0.4196 | 0.4181 | 0.4196 |
| 1.2544 | 1.0183 | 390 | 1.4977 | 0.4942 | 0.4440 | 0.4392 | 0.4440 |
| 1.0718 | 1.0444 | 400 | 1.5737 | 0.5124 | 0.4257 | 0.4239 | 0.4257 |
| 1.1034 | 1.0705 | 410 | 1.5629 | 0.5218 | 0.4162 | 0.4128 | 0.4162 |
| 1.1171 | 1.0966 | 420 | 1.5049 | 0.4958 | 0.4718 | 0.4702 | 0.4718 |
| 1.1174 | 1.1227 | 430 | 1.5840 | 0.5175 | 0.4057 | 0.4019 | 0.4057 |
| 1.2966 | 1.1488 | 440 | 1.5740 | 0.5178 | 0.4214 | 0.4214 | 0.4214 |
| 1.0597 | 1.1749 | 450 | 1.7422 | 0.5221 | 0.3944 | 0.3808 | 0.3944 |
| 1.027 | 1.2010 | 460 | 1.5282 | 0.4853 | 0.4509 | 0.4457 | 0.4509 |
| 1.0327 | 1.2272 | 470 | 1.6277 | 0.4810 | 0.4005 | 0.3922 | 0.4005 |
| 1.127 | 1.2533 | 480 | 1.6321 | 0.4847 | 0.4275 | 0.4238 | 0.4275 |
| 1.1265 | 1.2794 | 490 | 1.6081 | 0.4854 | 0.4257 | 0.4148 | 0.4257 |
| 1.0853 | 1.3055 | 500 | 1.7379 | 0.4871 | 0.3884 | 0.3697 | 0.3884 |
| 1.1961 | 1.3316 | 510 | 1.6069 | 0.5028 | 0.4361 | 0.4182 | 0.4361 |
| 1.0534 | 1.3577 | 520 | 1.4849 | 0.5123 | 0.4831 | 0.4745 | 0.4831 |
| 1.1954 | 1.3838 | 530 | 1.6723 | 0.5260 | 0.4205 | 0.4078 | 0.4205 |
| 1.28 | 1.4099 | 540 | 1.8150 | 0.5381 | 0.3614 | 0.3311 | 0.3614 |
| 1.122 | 1.4360 | 550 | 1.4803 | 0.5268 | 0.4761 | 0.4738 | 0.4761 |
| 1.1675 | 1.4621 | 560 | 1.6255 | 0.5431 | 0.4170 | 0.4105 | 0.4170 |
| 1.1381 | 1.4883 | 570 | 1.5229 | 0.5410 | 0.4500 | 0.4285 | 0.4500 |
| 1.1103 | 1.5144 | 580 | 1.5931 | 0.5449 | 0.4526 | 0.4387 | 0.4526 |
| 1.0581 | 1.5405 | 590 | 1.5439 | 0.5312 | 0.4596 | 0.4504 | 0.4596 |
| 0.9962 | 1.5666 | 600 | 1.5441 | 0.5339 | 0.4579 | 0.4452 | 0.4579 |
| 1.0863 | 1.5927 | 610 | 1.5504 | 0.5364 | 0.4761 | 0.4578 | 0.4761 |
| 1.0893 | 1.6188 | 620 | 1.5631 | 0.5224 | 0.4770 | 0.4606 | 0.4770 |
| 1.1396 | 1.6449 | 630 | 1.5557 | 0.5045 | 0.4500 | 0.4469 | 0.4500 |
| 1.0648 | 1.6710 | 640 | 1.6417 | 0.5462 | 0.4431 | 0.4336 | 0.4431 |
| 1.2972 | 1.6971 | 650 | 1.6543 | 0.5509 | 0.4431 | 0.4206 | 0.4431 |
| 1.1413 | 1.7232 | 660 | 1.5779 | 0.5438 | 0.4440 | 0.4400 | 0.4440 |
| 1.076 | 1.7493 | 670 | 1.4805 | 0.5208 | 0.4666 | 0.4682 | 0.4666 |
| 1.1984 | 1.7755 | 680 | 1.5434 | 0.5126 | 0.4518 | 0.4482 | 0.4518 |
| 0.9841 | 1.8016 | 690 | 1.4483 | 0.5229 | 0.4865 | 0.4869 | 0.4865 |
| 1.235 | 1.8277 | 700 | 1.4452 | 0.5239 | 0.4935 | 0.4935 | 0.4935 |
| 1.0239 | 1.8538 | 710 | 1.5506 | 0.5414 | 0.4466 | 0.4395 | 0.4466 |
| 0.9993 | 1.8799 | 720 | 1.5191 | 0.5388 | 0.4579 | 0.4521 | 0.4579 |
| 0.8789 | 1.9060 | 730 | 1.5620 | 0.5662 | 0.4509 | 0.4497 | 0.4509 |
| 0.9412 | 1.9321 | 740 | 1.4985 | 0.5489 | 0.4726 | 0.4623 | 0.4726 |
| 1.0592 | 1.9582 | 750 | 1.5027 | 0.5366 | 0.4700 | 0.4609 | 0.4700 |
| 0.9971 | 1.9843 | 760 | 1.4782 | 0.5427 | 0.4726 | 0.4591 | 0.4726 |
| 0.9067 | 2.0104 | 770 | 1.4520 | 0.5386 | 0.4831 | 0.4790 | 0.4831 |
| 0.7288 | 2.0366 | 780 | 1.6074 | 0.5414 | 0.4474 | 0.4518 | 0.4474 |
| 0.7942 | 2.0627 | 790 | 1.4652 | 0.5256 | 0.4961 | 0.4964 | 0.4961 |
| 0.56 | 2.0888 | 800 | 1.4838 | 0.5312 | 0.4996 | 0.5013 | 0.4996 |
| 0.6195 | 2.1149 | 810 | 1.6563 | 0.5676 | 0.4692 | 0.4506 | 0.4692 |
| 0.6324 | 2.1410 | 820 | 1.7346 | 0.5614 | 0.4657 | 0.4666 | 0.4657 |
| 0.5347 | 2.1671 | 830 | 1.5751 | 0.5405 | 0.5065 | 0.5045 | 0.5065 |
| 0.5954 | 2.1932 | 840 | 1.6409 | 0.5521 | 0.4900 | 0.4878 | 0.4900 |
| 0.5179 | 2.2193 | 850 | 1.6171 | 0.5450 | 0.5004 | 0.4995 | 0.5004 |
| 0.5723 | 2.2454 | 860 | 1.6798 | 0.5494 | 0.4874 | 0.4861 | 0.4874 |
| 0.6294 | 2.2715 | 870 | 1.6615 | 0.5341 | 0.4857 | 0.4872 | 0.4857 |
| 0.6877 | 2.2977 | 880 | 1.6713 | 0.5305 | 0.4839 | 0.4837 | 0.4839 |
| 0.6666 | 2.3238 | 890 | 1.7254 | 0.5381 | 0.4744 | 0.4715 | 0.4744 |
| 0.6233 | 2.3499 | 900 | 1.6712 | 0.5264 | 0.4831 | 0.4805 | 0.4831 |
| 0.545 | 2.3760 | 910 | 1.6675 | 0.5309 | 0.4839 | 0.4808 | 0.4839 |
| 0.6514 | 2.4021 | 920 | 1.7287 | 0.5382 | 0.4692 | 0.4695 | 0.4692 |
| 0.6389 | 2.4282 | 930 | 1.6598 | 0.5237 | 0.4761 | 0.4724 | 0.4761 |
| 0.6108 | 2.4543 | 940 | 1.6726 | 0.5232 | 0.4761 | 0.4678 | 0.4761 |
| 0.6409 | 2.4804 | 950 | 1.6736 | 0.5368 | 0.4848 | 0.4782 | 0.4848 |
| 0.4708 | 2.5065 | 960 | 1.7309 | 0.5504 | 0.4787 | 0.4760 | 0.4787 |
| 0.6782 | 2.5326 | 970 | 1.6217 | 0.5280 | 0.4805 | 0.4760 | 0.4805 |
| 0.514 | 2.5587 | 980 | 1.6088 | 0.5196 | 0.4839 | 0.4825 | 0.4839 |
| 0.5716 | 2.5849 | 990 | 1.6967 | 0.5361 | 0.4787 | 0.4780 | 0.4787 |
| 0.5028 | 2.6110 | 1000 | 1.7347 | 0.5347 | 0.4718 | 0.4704 | 0.4718 |
| 0.487 | 2.6371 | 1010 | 1.7448 | 0.5275 | 0.4666 | 0.4562 | 0.4666 |
| 0.5283 | 2.6632 | 1020 | 1.7680 | 0.5380 | 0.4709 | 0.4567 | 0.4709 |
| 0.467 | 2.6893 | 1030 | 1.7712 | 0.5476 | 0.4735 | 0.4638 | 0.4735 |
| 0.6161 | 2.7154 | 1040 | 1.6711 | 0.5423 | 0.4952 | 0.4901 | 0.4952 |
| 0.5924 | 2.7415 | 1050 | 1.5968 | 0.5343 | 0.5056 | 0.5035 | 0.5056 |
| 0.5925 | 2.7676 | 1060 | 1.6077 | 0.5273 | 0.4909 | 0.4867 | 0.4909 |
| 0.5044 | 2.7937 | 1070 | 1.6327 | 0.5390 | 0.4917 | 0.4889 | 0.4917 |
| 0.5258 | 2.8198 | 1080 | 1.6310 | 0.5353 | 0.4909 | 0.4882 | 0.4909 |
| 0.6329 | 2.8460 | 1090 | 1.6199 | 0.5271 | 0.4865 | 0.4837 | 0.4865 |
| 0.5266 | 2.8721 | 1100 | 1.6065 | 0.5215 | 0.4865 | 0.4848 | 0.4865 |
| 0.5093 | 2.8982 | 1110 | 1.6174 | 0.5232 | 0.4874 | 0.4854 | 0.4874 |
| 0.6284 | 2.9243 | 1120 | 1.6325 | 0.5271 | 0.4874 | 0.4851 | 0.4874 |
| 0.4167 | 2.9504 | 1130 | 1.6336 | 0.5274 | 0.4865 | 0.4846 | 0.4865 |
| 0.4789 | 2.9765 | 1140 | 1.6295 | 0.5266 | 0.4857 | 0.4836 | 0.4857 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
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