Instructions to use MayBashendy/ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_organization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayBashendy/ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_organization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MayBashendy/ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_organization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MayBashendy/ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_organization") model = AutoModelForSequenceClassification.from_pretrained("MayBashendy/ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_organization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_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: 0.6689
- Qwk: 0.7573
- Mse: 0.6689
- Rmse: 0.8179
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 | 0.0513 | 2 | 2.2799 | 0.0370 | 2.2799 | 1.5099 |
| No log | 0.1026 | 4 | 1.4895 | 0.2002 | 1.4895 | 1.2205 |
| No log | 0.1538 | 6 | 1.5225 | 0.1085 | 1.5225 | 1.2339 |
| No log | 0.2051 | 8 | 1.4590 | 0.1209 | 1.4590 | 1.2079 |
| No log | 0.2564 | 10 | 1.4373 | 0.1057 | 1.4373 | 1.1989 |
| No log | 0.3077 | 12 | 1.4567 | 0.1009 | 1.4567 | 1.2069 |
| No log | 0.3590 | 14 | 1.5446 | 0.1132 | 1.5446 | 1.2428 |
| No log | 0.4103 | 16 | 1.6118 | 0.1735 | 1.6118 | 1.2696 |
| No log | 0.4615 | 18 | 1.5945 | 0.1563 | 1.5945 | 1.2627 |
| No log | 0.5128 | 20 | 1.5173 | 0.1412 | 1.5173 | 1.2318 |
| No log | 0.5641 | 22 | 1.4742 | 0.1758 | 1.4742 | 1.2142 |
| No log | 0.6154 | 24 | 1.4794 | 0.1911 | 1.4794 | 1.2163 |
| No log | 0.6667 | 26 | 1.4680 | 0.2093 | 1.4680 | 1.2116 |
| No log | 0.7179 | 28 | 1.3946 | 0.2726 | 1.3946 | 1.1809 |
| No log | 0.7692 | 30 | 1.3692 | 0.3595 | 1.3692 | 1.1701 |
| No log | 0.8205 | 32 | 1.2597 | 0.3214 | 1.2597 | 1.1224 |
| No log | 0.8718 | 34 | 1.2928 | 0.3884 | 1.2928 | 1.1370 |
| No log | 0.9231 | 36 | 1.2730 | 0.3441 | 1.2730 | 1.1283 |
| No log | 0.9744 | 38 | 1.2156 | 0.2932 | 1.2156 | 1.1025 |
| No log | 1.0256 | 40 | 1.1690 | 0.2601 | 1.1690 | 1.0812 |
| No log | 1.0769 | 42 | 1.1471 | 0.2601 | 1.1471 | 1.0710 |
| No log | 1.1282 | 44 | 1.1251 | 0.3739 | 1.1251 | 1.0607 |
| No log | 1.1795 | 46 | 1.1438 | 0.4171 | 1.1438 | 1.0695 |
| No log | 1.2308 | 48 | 1.3104 | 0.4199 | 1.3104 | 1.1447 |
| No log | 1.2821 | 50 | 1.2624 | 0.4201 | 1.2624 | 1.1236 |
| No log | 1.3333 | 52 | 1.0784 | 0.4447 | 1.0784 | 1.0385 |
| No log | 1.3846 | 54 | 0.9812 | 0.4718 | 0.9812 | 0.9906 |
| No log | 1.4359 | 56 | 0.9704 | 0.4759 | 0.9704 | 0.9851 |
| No log | 1.4872 | 58 | 0.9502 | 0.5255 | 0.9502 | 0.9748 |
| No log | 1.5385 | 60 | 1.0676 | 0.4847 | 1.0676 | 1.0333 |
| No log | 1.5897 | 62 | 1.1469 | 0.4599 | 1.1469 | 1.0709 |
| No log | 1.6410 | 64 | 1.2069 | 0.4422 | 1.2069 | 1.0986 |
| No log | 1.6923 | 66 | 1.2879 | 0.4642 | 1.2879 | 1.1349 |
| No log | 1.7436 | 68 | 1.2587 | 0.4511 | 1.2587 | 1.1219 |
| No log | 1.7949 | 70 | 1.0725 | 0.4613 | 1.0725 | 1.0356 |
| No log | 1.8462 | 72 | 0.9539 | 0.5418 | 0.9539 | 0.9767 |
| No log | 1.8974 | 74 | 0.9228 | 0.5217 | 0.9228 | 0.9606 |
| No log | 1.9487 | 76 | 0.9497 | 0.5601 | 0.9497 | 0.9745 |
| No log | 2.0 | 78 | 1.0161 | 0.4783 | 1.0161 | 1.0080 |
| No log | 2.0513 | 80 | 1.0208 | 0.4903 | 1.0208 | 1.0104 |
| No log | 2.1026 | 82 | 1.1312 | 0.4628 | 1.1312 | 1.0636 |
| No log | 2.1538 | 84 | 1.0307 | 0.4892 | 1.0307 | 1.0152 |
| No log | 2.2051 | 86 | 0.9093 | 0.5610 | 0.9093 | 0.9535 |
| No log | 2.2564 | 88 | 0.8910 | 0.5733 | 0.8910 | 0.9439 |
| No log | 2.3077 | 90 | 1.1036 | 0.4816 | 1.1036 | 1.0505 |
| No log | 2.3590 | 92 | 1.1993 | 0.4906 | 1.1993 | 1.0951 |
| No log | 2.4103 | 94 | 1.2661 | 0.4774 | 1.2661 | 1.1252 |
| No log | 2.4615 | 96 | 1.0730 | 0.5635 | 1.0730 | 1.0359 |
| No log | 2.5128 | 98 | 0.7651 | 0.6987 | 0.7651 | 0.8747 |
| No log | 2.5641 | 100 | 0.7106 | 0.7232 | 0.7106 | 0.8430 |
| No log | 2.6154 | 102 | 0.7028 | 0.7278 | 0.7028 | 0.8383 |
| No log | 2.6667 | 104 | 0.6842 | 0.7309 | 0.6842 | 0.8272 |
| No log | 2.7179 | 106 | 0.7962 | 0.7195 | 0.7962 | 0.8923 |
| No log | 2.7692 | 108 | 0.8277 | 0.7318 | 0.8277 | 0.9098 |
| No log | 2.8205 | 110 | 0.7043 | 0.7364 | 0.7043 | 0.8392 |
| No log | 2.8718 | 112 | 0.7109 | 0.7266 | 0.7109 | 0.8432 |
| No log | 2.9231 | 114 | 0.7663 | 0.7342 | 0.7663 | 0.8754 |
| No log | 2.9744 | 116 | 0.8543 | 0.6765 | 0.8543 | 0.9243 |
| No log | 3.0256 | 118 | 0.9221 | 0.6328 | 0.9221 | 0.9603 |
| No log | 3.0769 | 120 | 0.7695 | 0.6982 | 0.7695 | 0.8772 |
| No log | 3.1282 | 122 | 0.6604 | 0.7278 | 0.6604 | 0.8127 |
| No log | 3.1795 | 124 | 0.6612 | 0.7429 | 0.6612 | 0.8132 |
| No log | 3.2308 | 126 | 0.6359 | 0.7562 | 0.6359 | 0.7975 |
| No log | 3.2821 | 128 | 0.6677 | 0.7530 | 0.6677 | 0.8171 |
| No log | 3.3333 | 130 | 0.8117 | 0.6792 | 0.8117 | 0.9009 |
| No log | 3.3846 | 132 | 0.8038 | 0.6876 | 0.8038 | 0.8965 |
| No log | 3.4359 | 134 | 0.7688 | 0.7218 | 0.7687 | 0.8768 |
| No log | 3.4872 | 136 | 0.7998 | 0.6995 | 0.7998 | 0.8943 |
| No log | 3.5385 | 138 | 0.7958 | 0.6902 | 0.7958 | 0.8921 |
| No log | 3.5897 | 140 | 0.7521 | 0.7273 | 0.7521 | 0.8672 |
| No log | 3.6410 | 142 | 0.6782 | 0.7671 | 0.6782 | 0.8235 |
| No log | 3.6923 | 144 | 0.6821 | 0.7595 | 0.6821 | 0.8259 |
| No log | 3.7436 | 146 | 0.6914 | 0.7321 | 0.6914 | 0.8315 |
| No log | 3.7949 | 148 | 0.7213 | 0.7406 | 0.7213 | 0.8493 |
| No log | 3.8462 | 150 | 0.6743 | 0.7406 | 0.6743 | 0.8212 |
| No log | 3.8974 | 152 | 0.7139 | 0.7655 | 0.7139 | 0.8449 |
| No log | 3.9487 | 154 | 0.7102 | 0.7593 | 0.7102 | 0.8427 |
| No log | 4.0 | 156 | 0.7015 | 0.7544 | 0.7015 | 0.8375 |
| No log | 4.0513 | 158 | 0.6312 | 0.7774 | 0.6312 | 0.7945 |
| No log | 4.1026 | 160 | 0.6329 | 0.7849 | 0.6329 | 0.7955 |
| No log | 4.1538 | 162 | 0.6959 | 0.7581 | 0.6959 | 0.8342 |
| No log | 4.2051 | 164 | 0.8545 | 0.6770 | 0.8545 | 0.9244 |
| No log | 4.2564 | 166 | 0.9277 | 0.6770 | 0.9277 | 0.9632 |
| No log | 4.3077 | 168 | 0.8439 | 0.6763 | 0.8439 | 0.9186 |
| No log | 4.3590 | 170 | 0.7414 | 0.7389 | 0.7414 | 0.8610 |
| No log | 4.4103 | 172 | 0.7329 | 0.7311 | 0.7329 | 0.8561 |
| No log | 4.4615 | 174 | 0.6783 | 0.7232 | 0.6783 | 0.8236 |
| No log | 4.5128 | 176 | 0.6715 | 0.7309 | 0.6715 | 0.8195 |
| No log | 4.5641 | 178 | 0.7279 | 0.7453 | 0.7279 | 0.8532 |
| No log | 4.6154 | 180 | 0.7431 | 0.7345 | 0.7431 | 0.8620 |
| No log | 4.6667 | 182 | 0.7532 | 0.7345 | 0.7532 | 0.8679 |
| No log | 4.7179 | 184 | 0.6682 | 0.7551 | 0.6682 | 0.8174 |
| No log | 4.7692 | 186 | 0.6579 | 0.7646 | 0.6579 | 0.8111 |
| No log | 4.8205 | 188 | 0.6565 | 0.7646 | 0.6565 | 0.8102 |
| No log | 4.8718 | 190 | 0.6519 | 0.7545 | 0.6519 | 0.8074 |
| No log | 4.9231 | 192 | 0.7060 | 0.7449 | 0.7060 | 0.8402 |
| No log | 4.9744 | 194 | 0.7410 | 0.7399 | 0.7410 | 0.8608 |
| No log | 5.0256 | 196 | 0.7194 | 0.7235 | 0.7194 | 0.8482 |
| No log | 5.0769 | 198 | 0.6661 | 0.7350 | 0.6661 | 0.8161 |
| No log | 5.1282 | 200 | 0.6484 | 0.7425 | 0.6484 | 0.8052 |
| No log | 5.1795 | 202 | 0.6883 | 0.7485 | 0.6883 | 0.8297 |
| No log | 5.2308 | 204 | 0.7731 | 0.7168 | 0.7731 | 0.8793 |
| No log | 5.2821 | 206 | 0.7678 | 0.7210 | 0.7678 | 0.8763 |
| No log | 5.3333 | 208 | 0.7239 | 0.7583 | 0.7239 | 0.8508 |
| No log | 5.3846 | 210 | 0.6454 | 0.7613 | 0.6454 | 0.8034 |
| No log | 5.4359 | 212 | 0.6274 | 0.7805 | 0.6274 | 0.7921 |
| No log | 5.4872 | 214 | 0.6359 | 0.7685 | 0.6359 | 0.7975 |
| No log | 5.5385 | 216 | 0.6706 | 0.7613 | 0.6706 | 0.8189 |
| No log | 5.5897 | 218 | 0.7187 | 0.7317 | 0.7187 | 0.8478 |
| No log | 5.6410 | 220 | 0.7908 | 0.7112 | 0.7908 | 0.8893 |
| No log | 5.6923 | 222 | 0.7463 | 0.7317 | 0.7463 | 0.8639 |
| No log | 5.7436 | 224 | 0.6947 | 0.7243 | 0.6947 | 0.8335 |
| No log | 5.7949 | 226 | 0.6357 | 0.7512 | 0.6357 | 0.7973 |
| No log | 5.8462 | 228 | 0.6208 | 0.7627 | 0.6208 | 0.7879 |
| No log | 5.8974 | 230 | 0.6447 | 0.7372 | 0.6447 | 0.8030 |
| No log | 5.9487 | 232 | 0.7232 | 0.7361 | 0.7232 | 0.8504 |
| No log | 6.0 | 234 | 0.7511 | 0.7281 | 0.7511 | 0.8666 |
| No log | 6.0513 | 236 | 0.7188 | 0.7361 | 0.7188 | 0.8478 |
| No log | 6.1026 | 238 | 0.6529 | 0.7506 | 0.6529 | 0.8080 |
| No log | 6.1538 | 240 | 0.6140 | 0.7458 | 0.6140 | 0.7836 |
| No log | 6.2051 | 242 | 0.5961 | 0.7525 | 0.5961 | 0.7721 |
| No log | 6.2564 | 244 | 0.5989 | 0.7496 | 0.5989 | 0.7739 |
| No log | 6.3077 | 246 | 0.6258 | 0.7416 | 0.6258 | 0.7911 |
| No log | 6.3590 | 248 | 0.6635 | 0.7287 | 0.6635 | 0.8145 |
| No log | 6.4103 | 250 | 0.7121 | 0.7009 | 0.7121 | 0.8439 |
| No log | 6.4615 | 252 | 0.7600 | 0.7022 | 0.7600 | 0.8718 |
| No log | 6.5128 | 254 | 0.7260 | 0.7101 | 0.7260 | 0.8520 |
| No log | 6.5641 | 256 | 0.6508 | 0.7357 | 0.6508 | 0.8067 |
| No log | 6.6154 | 258 | 0.5958 | 0.7591 | 0.5958 | 0.7719 |
| No log | 6.6667 | 260 | 0.5892 | 0.7597 | 0.5892 | 0.7676 |
| No log | 6.7179 | 262 | 0.5999 | 0.7561 | 0.5999 | 0.7745 |
| No log | 6.7692 | 264 | 0.6156 | 0.7506 | 0.6156 | 0.7846 |
| No log | 6.8205 | 266 | 0.6550 | 0.7510 | 0.6550 | 0.8093 |
| No log | 6.8718 | 268 | 0.7131 | 0.7492 | 0.7131 | 0.8445 |
| No log | 6.9231 | 270 | 0.7242 | 0.7492 | 0.7242 | 0.8510 |
| No log | 6.9744 | 272 | 0.6972 | 0.7492 | 0.6972 | 0.8350 |
| No log | 7.0256 | 274 | 0.6328 | 0.7710 | 0.6328 | 0.7955 |
| No log | 7.0769 | 276 | 0.6176 | 0.7795 | 0.6176 | 0.7859 |
| No log | 7.1282 | 278 | 0.6203 | 0.7795 | 0.6203 | 0.7876 |
| No log | 7.1795 | 280 | 0.6586 | 0.7668 | 0.6586 | 0.8115 |
| No log | 7.2308 | 282 | 0.7264 | 0.7492 | 0.7264 | 0.8523 |
| No log | 7.2821 | 284 | 0.7600 | 0.7345 | 0.7600 | 0.8718 |
| No log | 7.3333 | 286 | 0.7324 | 0.7472 | 0.7324 | 0.8558 |
| No log | 7.3846 | 288 | 0.6707 | 0.7638 | 0.6707 | 0.8190 |
| No log | 7.4359 | 290 | 0.6391 | 0.7517 | 0.6391 | 0.7994 |
| No log | 7.4872 | 292 | 0.6316 | 0.7517 | 0.6316 | 0.7948 |
| No log | 7.5385 | 294 | 0.6188 | 0.7503 | 0.6188 | 0.7866 |
| No log | 7.5897 | 296 | 0.6341 | 0.7710 | 0.6341 | 0.7963 |
| No log | 7.6410 | 298 | 0.6589 | 0.7723 | 0.6589 | 0.8117 |
| No log | 7.6923 | 300 | 0.6841 | 0.7492 | 0.6841 | 0.8271 |
| No log | 7.7436 | 302 | 0.7084 | 0.7492 | 0.7084 | 0.8417 |
| No log | 7.7949 | 304 | 0.7096 | 0.7393 | 0.7096 | 0.8424 |
| No log | 7.8462 | 306 | 0.6752 | 0.7615 | 0.6752 | 0.8217 |
| No log | 7.8974 | 308 | 0.6409 | 0.7723 | 0.6409 | 0.8005 |
| No log | 7.9487 | 310 | 0.6132 | 0.7740 | 0.6132 | 0.7831 |
| No log | 8.0 | 312 | 0.5947 | 0.7591 | 0.5947 | 0.7712 |
| No log | 8.0513 | 314 | 0.6031 | 0.7512 | 0.6031 | 0.7766 |
| No log | 8.1026 | 316 | 0.6245 | 0.7437 | 0.6245 | 0.7903 |
| No log | 8.1538 | 318 | 0.6265 | 0.7437 | 0.6265 | 0.7915 |
| No log | 8.2051 | 320 | 0.6373 | 0.7574 | 0.6373 | 0.7983 |
| No log | 8.2564 | 322 | 0.6531 | 0.7657 | 0.6531 | 0.8081 |
| No log | 8.3077 | 324 | 0.6711 | 0.7573 | 0.6711 | 0.8192 |
| No log | 8.3590 | 326 | 0.6809 | 0.7472 | 0.6809 | 0.8252 |
| No log | 8.4103 | 328 | 0.6705 | 0.7573 | 0.6705 | 0.8188 |
| No log | 8.4615 | 330 | 0.6557 | 0.7699 | 0.6557 | 0.8097 |
| No log | 8.5128 | 332 | 0.6329 | 0.7692 | 0.6329 | 0.7955 |
| No log | 8.5641 | 334 | 0.5981 | 0.7691 | 0.5981 | 0.7734 |
| No log | 8.6154 | 336 | 0.5723 | 0.7525 | 0.5723 | 0.7565 |
| No log | 8.6667 | 338 | 0.5584 | 0.7695 | 0.5584 | 0.7473 |
| No log | 8.7179 | 340 | 0.5581 | 0.7695 | 0.5581 | 0.7470 |
| No log | 8.7692 | 342 | 0.5665 | 0.7574 | 0.5665 | 0.7527 |
| No log | 8.8205 | 344 | 0.5864 | 0.7678 | 0.5864 | 0.7658 |
| No log | 8.8718 | 346 | 0.6122 | 0.7704 | 0.6122 | 0.7824 |
| No log | 8.9231 | 348 | 0.6418 | 0.7574 | 0.6418 | 0.8011 |
| No log | 8.9744 | 350 | 0.6640 | 0.7573 | 0.6640 | 0.8149 |
| No log | 9.0256 | 352 | 0.6885 | 0.7573 | 0.6885 | 0.8298 |
| No log | 9.0769 | 354 | 0.7033 | 0.7472 | 0.7033 | 0.8386 |
| No log | 9.1282 | 356 | 0.7073 | 0.7508 | 0.7073 | 0.8410 |
| No log | 9.1795 | 358 | 0.6957 | 0.7573 | 0.6957 | 0.8341 |
| No log | 9.2308 | 360 | 0.6739 | 0.7420 | 0.6739 | 0.8209 |
| No log | 9.2821 | 362 | 0.6508 | 0.7443 | 0.6508 | 0.8067 |
| No log | 9.3333 | 364 | 0.6411 | 0.7443 | 0.6411 | 0.8007 |
| No log | 9.3846 | 366 | 0.6419 | 0.7487 | 0.6419 | 0.8012 |
| No log | 9.4359 | 368 | 0.6458 | 0.7564 | 0.6458 | 0.8036 |
| No log | 9.4872 | 370 | 0.6513 | 0.7521 | 0.6513 | 0.8070 |
| No log | 9.5385 | 372 | 0.6585 | 0.7596 | 0.6585 | 0.8115 |
| No log | 9.5897 | 374 | 0.6710 | 0.7573 | 0.6710 | 0.8191 |
| No log | 9.6410 | 376 | 0.6775 | 0.7573 | 0.6775 | 0.8231 |
| No log | 9.6923 | 378 | 0.6777 | 0.7573 | 0.6777 | 0.8232 |
| No log | 9.7436 | 380 | 0.6770 | 0.7573 | 0.6770 | 0.8228 |
| No log | 9.7949 | 382 | 0.6746 | 0.7573 | 0.6746 | 0.8214 |
| No log | 9.8462 | 384 | 0.6704 | 0.7573 | 0.6704 | 0.8188 |
| No log | 9.8974 | 386 | 0.6692 | 0.7573 | 0.6692 | 0.8180 |
| No log | 9.9487 | 388 | 0.6689 | 0.7573 | 0.6689 | 0.8179 |
| No log | 10.0 | 390 | 0.6689 | 0.7573 | 0.6689 | 0.8179 |
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/ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k10_task5_organization
Base model
aubmindlab/bert-base-arabertv02