Instructions to use MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization") model = AutoModelForSequenceClassification.from_pretrained("MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9387
- Qwk: 0.4346
- Mse: 0.9387
- Rmse: 0.9689
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: 2e-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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|---|---|---|---|---|---|---|
| No log | 0.0588 | 2 | 4.0665 | -0.0151 | 4.0665 | 2.0166 |
| No log | 0.1176 | 4 | 2.0305 | 0.0284 | 2.0305 | 1.4250 |
| No log | 0.1765 | 6 | 1.1765 | 0.0592 | 1.1765 | 1.0846 |
| No log | 0.2353 | 8 | 0.8194 | 0.0770 | 0.8194 | 0.9052 |
| No log | 0.2941 | 10 | 0.9559 | 0.1514 | 0.9559 | 0.9777 |
| No log | 0.3529 | 12 | 1.1527 | 0.1040 | 1.1527 | 1.0736 |
| No log | 0.4118 | 14 | 0.8714 | 0.1748 | 0.8714 | 0.9335 |
| No log | 0.4706 | 16 | 0.6749 | 0.2880 | 0.6749 | 0.8215 |
| No log | 0.5294 | 18 | 0.6440 | 0.3611 | 0.6440 | 0.8025 |
| No log | 0.5882 | 20 | 0.8069 | 0.1337 | 0.8069 | 0.8983 |
| No log | 0.6471 | 22 | 0.9382 | 0.1106 | 0.9382 | 0.9686 |
| No log | 0.7059 | 24 | 0.9534 | 0.1590 | 0.9534 | 0.9764 |
| No log | 0.7647 | 26 | 1.1871 | 0.1583 | 1.1871 | 1.0895 |
| No log | 0.8235 | 28 | 1.5034 | 0.1392 | 1.5034 | 1.2261 |
| No log | 0.8824 | 30 | 1.8216 | 0.1717 | 1.8216 | 1.3497 |
| No log | 0.9412 | 32 | 1.7859 | 0.1794 | 1.7859 | 1.3364 |
| No log | 1.0 | 34 | 1.4960 | 0.1639 | 1.4960 | 1.2231 |
| No log | 1.0588 | 36 | 1.7451 | 0.1670 | 1.7451 | 1.3210 |
| No log | 1.1176 | 38 | 1.6910 | 0.1884 | 1.6910 | 1.3004 |
| No log | 1.1765 | 40 | 1.3136 | 0.1579 | 1.3136 | 1.1461 |
| No log | 1.2353 | 42 | 1.0181 | 0.1786 | 1.0181 | 1.0090 |
| No log | 1.2941 | 44 | 0.9802 | 0.2295 | 0.9802 | 0.9900 |
| No log | 1.3529 | 46 | 0.9418 | 0.2887 | 0.9418 | 0.9704 |
| No log | 1.4118 | 48 | 0.8933 | 0.2487 | 0.8933 | 0.9451 |
| No log | 1.4706 | 50 | 0.8008 | 0.2508 | 0.8008 | 0.8949 |
| No log | 1.5294 | 52 | 0.7552 | 0.3298 | 0.7552 | 0.8690 |
| No log | 1.5882 | 54 | 0.9062 | 0.2864 | 0.9062 | 0.9520 |
| No log | 1.6471 | 56 | 1.0425 | 0.1967 | 1.0425 | 1.0210 |
| No log | 1.7059 | 58 | 1.0477 | 0.2196 | 1.0477 | 1.0236 |
| No log | 1.7647 | 60 | 0.9542 | 0.2626 | 0.9542 | 0.9769 |
| No log | 1.8235 | 62 | 0.8922 | 0.2739 | 0.8922 | 0.9446 |
| No log | 1.8824 | 64 | 0.8045 | 0.3342 | 0.8045 | 0.8969 |
| No log | 1.9412 | 66 | 0.7611 | 0.3828 | 0.7611 | 0.8724 |
| No log | 2.0 | 68 | 0.7806 | 0.3835 | 0.7806 | 0.8835 |
| No log | 2.0588 | 70 | 0.8746 | 0.3877 | 0.8746 | 0.9352 |
| No log | 2.1176 | 72 | 1.0062 | 0.3381 | 1.0062 | 1.0031 |
| No log | 2.1765 | 74 | 1.2002 | 0.3087 | 1.2002 | 1.0955 |
| No log | 2.2353 | 76 | 1.1859 | 0.3109 | 1.1859 | 1.0890 |
| No log | 2.2941 | 78 | 0.8804 | 0.4553 | 0.8804 | 0.9383 |
| No log | 2.3529 | 80 | 0.7851 | 0.4756 | 0.7851 | 0.8860 |
| No log | 2.4118 | 82 | 0.7514 | 0.4407 | 0.7514 | 0.8668 |
| No log | 2.4706 | 84 | 0.7220 | 0.4470 | 0.7220 | 0.8497 |
| No log | 2.5294 | 86 | 0.6965 | 0.4655 | 0.6965 | 0.8346 |
| No log | 2.5882 | 88 | 0.6990 | 0.4789 | 0.6990 | 0.8360 |
| No log | 2.6471 | 90 | 0.8786 | 0.4517 | 0.8786 | 0.9373 |
| No log | 2.7059 | 92 | 1.2701 | 0.2916 | 1.2701 | 1.1270 |
| No log | 2.7647 | 94 | 1.4689 | 0.2385 | 1.4689 | 1.2120 |
| No log | 2.8235 | 96 | 1.3729 | 0.2553 | 1.3729 | 1.1717 |
| No log | 2.8824 | 98 | 1.0169 | 0.3935 | 1.0169 | 1.0084 |
| No log | 2.9412 | 100 | 0.6982 | 0.5465 | 0.6982 | 0.8356 |
| No log | 3.0 | 102 | 0.7005 | 0.5664 | 0.7005 | 0.8369 |
| No log | 3.0588 | 104 | 0.7189 | 0.5061 | 0.7189 | 0.8479 |
| No log | 3.1176 | 106 | 0.7028 | 0.5024 | 0.7028 | 0.8383 |
| No log | 3.1765 | 108 | 0.6616 | 0.5748 | 0.6616 | 0.8134 |
| No log | 3.2353 | 110 | 0.6902 | 0.5839 | 0.6902 | 0.8308 |
| No log | 3.2941 | 112 | 0.7648 | 0.5466 | 0.7648 | 0.8745 |
| No log | 3.3529 | 114 | 0.7680 | 0.5368 | 0.7680 | 0.8764 |
| No log | 3.4118 | 116 | 0.7914 | 0.5440 | 0.7914 | 0.8896 |
| No log | 3.4706 | 118 | 0.8295 | 0.5099 | 0.8295 | 0.9107 |
| No log | 3.5294 | 120 | 0.8752 | 0.5291 | 0.8752 | 0.9355 |
| No log | 3.5882 | 122 | 0.9250 | 0.5036 | 0.9250 | 0.9617 |
| No log | 3.6471 | 124 | 0.9585 | 0.4668 | 0.9585 | 0.9790 |
| No log | 3.7059 | 126 | 1.0455 | 0.4595 | 1.0455 | 1.0225 |
| No log | 3.7647 | 128 | 1.0704 | 0.4356 | 1.0704 | 1.0346 |
| No log | 3.8235 | 130 | 1.0678 | 0.4522 | 1.0678 | 1.0333 |
| No log | 3.8824 | 132 | 1.0560 | 0.4662 | 1.0560 | 1.0276 |
| No log | 3.9412 | 134 | 1.0661 | 0.4728 | 1.0661 | 1.0325 |
| No log | 4.0 | 136 | 1.1035 | 0.4522 | 1.1035 | 1.0505 |
| No log | 4.0588 | 138 | 1.1184 | 0.4426 | 1.1184 | 1.0576 |
| No log | 4.1176 | 140 | 1.1446 | 0.4759 | 1.1446 | 1.0699 |
| No log | 4.1765 | 142 | 1.1757 | 0.4452 | 1.1757 | 1.0843 |
| No log | 4.2353 | 144 | 1.1946 | 0.4615 | 1.1946 | 1.0930 |
| No log | 4.2941 | 146 | 1.1838 | 0.4350 | 1.1838 | 1.0880 |
| No log | 4.3529 | 148 | 1.2254 | 0.4234 | 1.2254 | 1.1070 |
| No log | 4.4118 | 150 | 1.2148 | 0.3912 | 1.2148 | 1.1022 |
| No log | 4.4706 | 152 | 1.1693 | 0.3925 | 1.1693 | 1.0813 |
| No log | 4.5294 | 154 | 1.1186 | 0.4257 | 1.1186 | 1.0577 |
| No log | 4.5882 | 156 | 1.0553 | 0.4358 | 1.0553 | 1.0273 |
| No log | 4.6471 | 158 | 1.0427 | 0.4691 | 1.0427 | 1.0211 |
| No log | 4.7059 | 160 | 1.0303 | 0.4311 | 1.0303 | 1.0150 |
| No log | 4.7647 | 162 | 0.9643 | 0.4310 | 0.9643 | 0.9820 |
| No log | 4.8235 | 164 | 0.9096 | 0.5018 | 0.9096 | 0.9537 |
| No log | 4.8824 | 166 | 0.8893 | 0.4318 | 0.8893 | 0.9430 |
| No log | 4.9412 | 168 | 0.9167 | 0.3801 | 0.9167 | 0.9574 |
| No log | 5.0 | 170 | 0.9512 | 0.4179 | 0.9512 | 0.9753 |
| No log | 5.0588 | 172 | 0.9828 | 0.4340 | 0.9828 | 0.9914 |
| No log | 5.1176 | 174 | 1.0007 | 0.5042 | 1.0007 | 1.0003 |
| No log | 5.1765 | 176 | 1.0242 | 0.4682 | 1.0242 | 1.0120 |
| No log | 5.2353 | 178 | 1.0493 | 0.4440 | 1.0493 | 1.0244 |
| No log | 5.2941 | 180 | 1.0712 | 0.4496 | 1.0712 | 1.0350 |
| No log | 5.3529 | 182 | 1.0768 | 0.4695 | 1.0768 | 1.0377 |
| No log | 5.4118 | 184 | 1.1345 | 0.4464 | 1.1345 | 1.0651 |
| No log | 5.4706 | 186 | 1.2182 | 0.4129 | 1.2182 | 1.1037 |
| No log | 5.5294 | 188 | 1.2162 | 0.4129 | 1.2162 | 1.1028 |
| No log | 5.5882 | 190 | 1.1601 | 0.4531 | 1.1601 | 1.0771 |
| No log | 5.6471 | 192 | 1.0941 | 0.4381 | 1.0941 | 1.0460 |
| No log | 5.7059 | 194 | 1.0678 | 0.4671 | 1.0678 | 1.0334 |
| No log | 5.7647 | 196 | 1.0822 | 0.4452 | 1.0822 | 1.0403 |
| No log | 5.8235 | 198 | 1.1089 | 0.4654 | 1.1089 | 1.0530 |
| No log | 5.8824 | 200 | 1.1277 | 0.4701 | 1.1277 | 1.0619 |
| No log | 5.9412 | 202 | 1.1231 | 0.3850 | 1.1231 | 1.0598 |
| No log | 6.0 | 204 | 1.1071 | 0.3996 | 1.1071 | 1.0522 |
| No log | 6.0588 | 206 | 1.0830 | 0.4187 | 1.0830 | 1.0407 |
| No log | 6.1176 | 208 | 1.0420 | 0.4279 | 1.0420 | 1.0208 |
| No log | 6.1765 | 210 | 1.0035 | 0.4641 | 1.0035 | 1.0017 |
| No log | 6.2353 | 212 | 0.9808 | 0.4752 | 0.9808 | 0.9903 |
| No log | 6.2941 | 214 | 0.9835 | 0.4471 | 0.9835 | 0.9917 |
| No log | 6.3529 | 216 | 0.9981 | 0.4305 | 0.9981 | 0.9991 |
| No log | 6.4118 | 218 | 0.9881 | 0.4448 | 0.9881 | 0.9940 |
| No log | 6.4706 | 220 | 0.9810 | 0.4476 | 0.9810 | 0.9905 |
| No log | 6.5294 | 222 | 0.9927 | 0.4613 | 0.9927 | 0.9964 |
| No log | 6.5882 | 224 | 1.0113 | 0.4613 | 1.0113 | 1.0056 |
| No log | 6.6471 | 226 | 1.0283 | 0.4534 | 1.0283 | 1.0141 |
| No log | 6.7059 | 228 | 1.0610 | 0.4410 | 1.0610 | 1.0301 |
| No log | 6.7647 | 230 | 1.0927 | 0.4420 | 1.0927 | 1.0453 |
| No log | 6.8235 | 232 | 1.1058 | 0.4420 | 1.1058 | 1.0516 |
| No log | 6.8824 | 234 | 1.0999 | 0.4582 | 1.0999 | 1.0488 |
| No log | 6.9412 | 236 | 1.0817 | 0.4517 | 1.0817 | 1.0400 |
| No log | 7.0 | 238 | 1.0623 | 0.4353 | 1.0623 | 1.0307 |
| No log | 7.0588 | 240 | 1.0303 | 0.4384 | 1.0303 | 1.0150 |
| No log | 7.1176 | 242 | 1.0032 | 0.4506 | 1.0032 | 1.0016 |
| No log | 7.1765 | 244 | 0.9903 | 0.4777 | 0.9903 | 0.9952 |
| No log | 7.2353 | 246 | 0.9897 | 0.4814 | 0.9897 | 0.9948 |
| No log | 7.2941 | 248 | 0.9998 | 0.4681 | 0.9998 | 0.9999 |
| No log | 7.3529 | 250 | 1.0065 | 0.4719 | 1.0065 | 1.0032 |
| No log | 7.4118 | 252 | 1.0130 | 0.4759 | 1.0130 | 1.0065 |
| No log | 7.4706 | 254 | 1.0126 | 0.4692 | 1.0126 | 1.0063 |
| No log | 7.5294 | 256 | 1.0047 | 0.4784 | 1.0047 | 1.0023 |
| No log | 7.5882 | 258 | 0.9931 | 0.4863 | 0.9931 | 0.9965 |
| No log | 7.6471 | 260 | 0.9850 | 0.4880 | 0.9850 | 0.9925 |
| No log | 7.7059 | 262 | 0.9809 | 0.4973 | 0.9809 | 0.9904 |
| No log | 7.7647 | 264 | 0.9903 | 0.4628 | 0.9903 | 0.9952 |
| No log | 7.8235 | 266 | 1.0041 | 0.4702 | 1.0041 | 1.0020 |
| No log | 7.8824 | 268 | 1.0068 | 0.4642 | 1.0068 | 1.0034 |
| No log | 7.9412 | 270 | 1.0026 | 0.4718 | 1.0026 | 1.0013 |
| No log | 8.0 | 272 | 0.9946 | 0.4987 | 0.9946 | 0.9973 |
| No log | 8.0588 | 274 | 0.9798 | 0.4897 | 0.9798 | 0.9898 |
| No log | 8.1176 | 276 | 0.9671 | 0.4889 | 0.9671 | 0.9834 |
| No log | 8.1765 | 278 | 0.9706 | 0.4912 | 0.9706 | 0.9852 |
| No log | 8.2353 | 280 | 0.9742 | 0.4941 | 0.9742 | 0.9870 |
| No log | 8.2941 | 282 | 0.9839 | 0.4859 | 0.9839 | 0.9919 |
| No log | 8.3529 | 284 | 0.9976 | 0.4685 | 0.9976 | 0.9988 |
| No log | 8.4118 | 286 | 1.0005 | 0.4685 | 1.0005 | 1.0003 |
| No log | 8.4706 | 288 | 0.9985 | 0.4625 | 0.9985 | 0.9992 |
| No log | 8.5294 | 290 | 0.9951 | 0.4625 | 0.9951 | 0.9975 |
| No log | 8.5882 | 292 | 0.9873 | 0.4625 | 0.9873 | 0.9936 |
| No log | 8.6471 | 294 | 0.9776 | 0.4625 | 0.9776 | 0.9887 |
| No log | 8.7059 | 296 | 0.9681 | 0.4718 | 0.9681 | 0.9839 |
| No log | 8.7647 | 298 | 0.9612 | 0.4575 | 0.9612 | 0.9804 |
| No log | 8.8235 | 300 | 0.9530 | 0.4471 | 0.9530 | 0.9762 |
| No log | 8.8824 | 302 | 0.9526 | 0.4469 | 0.9526 | 0.9760 |
| No log | 8.9412 | 304 | 0.9533 | 0.4469 | 0.9533 | 0.9764 |
| No log | 9.0 | 306 | 0.9515 | 0.4471 | 0.9515 | 0.9755 |
| No log | 9.0588 | 308 | 0.9477 | 0.4649 | 0.9477 | 0.9735 |
| No log | 9.1176 | 310 | 0.9411 | 0.4649 | 0.9411 | 0.9701 |
| No log | 9.1765 | 312 | 0.9368 | 0.4585 | 0.9368 | 0.9679 |
| No log | 9.2353 | 314 | 0.9372 | 0.4585 | 0.9372 | 0.9681 |
| No log | 9.2941 | 316 | 0.9377 | 0.4475 | 0.9377 | 0.9684 |
| No log | 9.3529 | 318 | 0.9402 | 0.4572 | 0.9402 | 0.9696 |
| No log | 9.4118 | 320 | 0.9394 | 0.4457 | 0.9394 | 0.9692 |
| No log | 9.4706 | 322 | 0.9368 | 0.4457 | 0.9368 | 0.9679 |
| No log | 9.5294 | 324 | 0.9354 | 0.4329 | 0.9354 | 0.9672 |
| No log | 9.5882 | 326 | 0.9356 | 0.4329 | 0.9356 | 0.9672 |
| No log | 9.6471 | 328 | 0.9374 | 0.4329 | 0.9374 | 0.9682 |
| No log | 9.7059 | 330 | 0.9376 | 0.4329 | 0.9376 | 0.9683 |
| No log | 9.7647 | 332 | 0.9379 | 0.4457 | 0.9379 | 0.9684 |
| No log | 9.8235 | 334 | 0.9381 | 0.4457 | 0.9381 | 0.9686 |
| No log | 9.8824 | 336 | 0.9384 | 0.4346 | 0.9384 | 0.9687 |
| No log | 9.9412 | 338 | 0.9386 | 0.4346 | 0.9386 | 0.9688 |
| No log | 10.0 | 340 | 0.9387 | 0.4346 | 0.9387 | 0.9689 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k5_task2_organization
Base model
aubmindlab/bert-base-arabertv02