Instructions to use MayBashendy/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k10_task5_organization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayBashendy/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k10_task5_organization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MayBashendy/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k10_task5_organization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MayBashendy/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k10_task5_organization") model = AutoModelForSequenceClassification.from_pretrained("MayBashendy/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k10_task5_organization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_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: 0.6874
- Qwk: 0.7847
- Mse: 0.6874
- Rmse: 0.8291
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 | 0.0571 | 2 | 2.2972 | 0.0110 | 2.2972 | 1.5156 |
| No log | 0.1143 | 4 | 1.4630 | 0.2304 | 1.4630 | 1.2095 |
| No log | 0.1714 | 6 | 1.3978 | 0.1779 | 1.3978 | 1.1823 |
| No log | 0.2286 | 8 | 1.5397 | 0.2818 | 1.5397 | 1.2409 |
| No log | 0.2857 | 10 | 1.5387 | 0.1958 | 1.5387 | 1.2405 |
| No log | 0.3429 | 12 | 1.5722 | 0.1630 | 1.5722 | 1.2539 |
| No log | 0.4 | 14 | 1.6124 | 0.1450 | 1.6124 | 1.2698 |
| No log | 0.4571 | 16 | 1.6718 | 0.1215 | 1.6718 | 1.2930 |
| No log | 0.5143 | 18 | 1.9206 | 0.2437 | 1.9206 | 1.3858 |
| No log | 0.5714 | 20 | 2.1268 | 0.2464 | 2.1268 | 1.4584 |
| No log | 0.6286 | 22 | 1.9543 | 0.2903 | 1.9543 | 1.3980 |
| No log | 0.6857 | 24 | 1.7201 | 0.3319 | 1.7201 | 1.3115 |
| No log | 0.7429 | 26 | 1.5161 | 0.1445 | 1.5161 | 1.2313 |
| No log | 0.8 | 28 | 1.4194 | 0.1009 | 1.4194 | 1.1914 |
| No log | 0.8571 | 30 | 1.3716 | 0.1009 | 1.3716 | 1.1711 |
| No log | 0.9143 | 32 | 1.3518 | 0.1472 | 1.3518 | 1.1627 |
| No log | 0.9714 | 34 | 1.3477 | 0.2184 | 1.3477 | 1.1609 |
| No log | 1.0286 | 36 | 1.3176 | 0.3354 | 1.3176 | 1.1479 |
| No log | 1.0857 | 38 | 1.3511 | 0.4113 | 1.3511 | 1.1624 |
| No log | 1.1429 | 40 | 1.4065 | 0.4018 | 1.4065 | 1.1859 |
| No log | 1.2 | 42 | 1.4115 | 0.4298 | 1.4115 | 1.1881 |
| No log | 1.2571 | 44 | 1.4555 | 0.4203 | 1.4555 | 1.2064 |
| No log | 1.3143 | 46 | 1.5353 | 0.4022 | 1.5353 | 1.2391 |
| No log | 1.3714 | 48 | 1.6011 | 0.4217 | 1.6011 | 1.2653 |
| No log | 1.4286 | 50 | 1.4552 | 0.4645 | 1.4552 | 1.2063 |
| No log | 1.4857 | 52 | 1.4912 | 0.4601 | 1.4912 | 1.2212 |
| No log | 1.5429 | 54 | 1.7279 | 0.4578 | 1.7279 | 1.3145 |
| No log | 1.6 | 56 | 2.0942 | 0.4444 | 2.0942 | 1.4471 |
| No log | 1.6571 | 58 | 1.9834 | 0.4573 | 1.9834 | 1.4083 |
| No log | 1.7143 | 60 | 1.3639 | 0.5101 | 1.3639 | 1.1679 |
| No log | 1.7714 | 62 | 1.1420 | 0.5077 | 1.1420 | 1.0686 |
| No log | 1.8286 | 64 | 1.1203 | 0.5111 | 1.1203 | 1.0584 |
| No log | 1.8857 | 66 | 1.1286 | 0.5382 | 1.1286 | 1.0623 |
| No log | 1.9429 | 68 | 0.9497 | 0.5708 | 0.9497 | 0.9745 |
| No log | 2.0 | 70 | 0.8276 | 0.6029 | 0.8276 | 0.9097 |
| No log | 2.0571 | 72 | 0.8756 | 0.6217 | 0.8756 | 0.9357 |
| No log | 2.1143 | 74 | 0.8333 | 0.6259 | 0.8333 | 0.9129 |
| No log | 2.1714 | 76 | 0.8119 | 0.6481 | 0.8119 | 0.9011 |
| No log | 2.2286 | 78 | 0.8326 | 0.6438 | 0.8326 | 0.9125 |
| No log | 2.2857 | 80 | 0.8643 | 0.6157 | 0.8643 | 0.9297 |
| No log | 2.3429 | 82 | 1.0641 | 0.6183 | 1.0641 | 1.0315 |
| No log | 2.4 | 84 | 1.1008 | 0.5882 | 1.1008 | 1.0492 |
| No log | 2.4571 | 86 | 0.8478 | 0.6340 | 0.8478 | 0.9207 |
| No log | 2.5143 | 88 | 0.8033 | 0.6413 | 0.8033 | 0.8963 |
| No log | 2.5714 | 90 | 0.8650 | 0.6554 | 0.8650 | 0.9301 |
| No log | 2.6286 | 92 | 1.1340 | 0.6196 | 1.1340 | 1.0649 |
| No log | 2.6857 | 94 | 1.2782 | 0.5974 | 1.2782 | 1.1306 |
| No log | 2.7429 | 96 | 1.2523 | 0.5967 | 1.2523 | 1.1191 |
| No log | 2.8 | 98 | 1.0197 | 0.6358 | 1.0197 | 1.0098 |
| No log | 2.8571 | 100 | 0.8786 | 0.6832 | 0.8786 | 0.9373 |
| No log | 2.9143 | 102 | 0.8502 | 0.6645 | 0.8502 | 0.9220 |
| No log | 2.9714 | 104 | 0.7691 | 0.6958 | 0.7691 | 0.8770 |
| No log | 3.0286 | 106 | 0.6839 | 0.7037 | 0.6839 | 0.8270 |
| No log | 3.0857 | 108 | 0.7165 | 0.7287 | 0.7165 | 0.8465 |
| No log | 3.1429 | 110 | 0.8380 | 0.6636 | 0.8380 | 0.9154 |
| No log | 3.2 | 112 | 0.8036 | 0.6803 | 0.8036 | 0.8965 |
| No log | 3.2571 | 114 | 0.7184 | 0.7265 | 0.7184 | 0.8476 |
| No log | 3.3143 | 116 | 0.7569 | 0.7216 | 0.7569 | 0.8700 |
| No log | 3.3714 | 118 | 0.8419 | 0.6945 | 0.8419 | 0.9175 |
| No log | 3.4286 | 120 | 0.9811 | 0.6642 | 0.9811 | 0.9905 |
| No log | 3.4857 | 122 | 0.9357 | 0.6887 | 0.9357 | 0.9673 |
| No log | 3.5429 | 124 | 0.9774 | 0.6657 | 0.9774 | 0.9886 |
| No log | 3.6 | 126 | 0.8239 | 0.7190 | 0.8239 | 0.9077 |
| No log | 3.6571 | 128 | 0.6689 | 0.7277 | 0.6689 | 0.8178 |
| No log | 3.7143 | 130 | 0.6298 | 0.6868 | 0.6298 | 0.7936 |
| No log | 3.7714 | 132 | 0.6457 | 0.6934 | 0.6457 | 0.8036 |
| No log | 3.8286 | 134 | 0.7277 | 0.7453 | 0.7277 | 0.8531 |
| No log | 3.8857 | 136 | 0.8333 | 0.6946 | 0.8333 | 0.9128 |
| No log | 3.9429 | 138 | 0.8492 | 0.6946 | 0.8492 | 0.9215 |
| No log | 4.0 | 140 | 0.8777 | 0.6882 | 0.8777 | 0.9368 |
| No log | 4.0571 | 142 | 0.7604 | 0.7493 | 0.7604 | 0.8720 |
| No log | 4.1143 | 144 | 0.6463 | 0.7260 | 0.6463 | 0.8039 |
| No log | 4.1714 | 146 | 0.6455 | 0.7387 | 0.6455 | 0.8034 |
| No log | 4.2286 | 148 | 0.6502 | 0.7290 | 0.6502 | 0.8063 |
| No log | 4.2857 | 150 | 0.7433 | 0.7555 | 0.7433 | 0.8622 |
| No log | 4.3429 | 152 | 0.9514 | 0.6886 | 0.9514 | 0.9754 |
| No log | 4.4 | 154 | 0.9935 | 0.6729 | 0.9935 | 0.9967 |
| No log | 4.4571 | 156 | 0.9296 | 0.6737 | 0.9296 | 0.9642 |
| No log | 4.5143 | 158 | 0.8901 | 0.6956 | 0.8901 | 0.9435 |
| No log | 4.5714 | 160 | 0.8180 | 0.7006 | 0.8180 | 0.9044 |
| No log | 4.6286 | 162 | 0.7805 | 0.7101 | 0.7805 | 0.8835 |
| No log | 4.6857 | 164 | 0.7792 | 0.7289 | 0.7792 | 0.8827 |
| No log | 4.7429 | 166 | 0.7821 | 0.7127 | 0.7821 | 0.8843 |
| No log | 4.8 | 168 | 0.7871 | 0.7192 | 0.7871 | 0.8872 |
| No log | 4.8571 | 170 | 0.7984 | 0.7199 | 0.7984 | 0.8935 |
| No log | 4.9143 | 172 | 0.7447 | 0.7135 | 0.7447 | 0.8629 |
| No log | 4.9714 | 174 | 0.7028 | 0.7322 | 0.7028 | 0.8384 |
| No log | 5.0286 | 176 | 0.6870 | 0.7583 | 0.6870 | 0.8289 |
| No log | 5.0857 | 178 | 0.6906 | 0.7463 | 0.6906 | 0.8310 |
| No log | 5.1429 | 180 | 0.6520 | 0.7514 | 0.6520 | 0.8075 |
| No log | 5.2 | 182 | 0.6649 | 0.7529 | 0.6649 | 0.8154 |
| No log | 5.2571 | 184 | 0.6349 | 0.7472 | 0.6349 | 0.7968 |
| No log | 5.3143 | 186 | 0.6457 | 0.7472 | 0.6457 | 0.8035 |
| No log | 5.3714 | 188 | 0.6770 | 0.7464 | 0.6770 | 0.8228 |
| No log | 5.4286 | 190 | 0.6979 | 0.7431 | 0.6979 | 0.8354 |
| No log | 5.4857 | 192 | 0.6938 | 0.7605 | 0.6938 | 0.8329 |
| No log | 5.5429 | 194 | 0.6506 | 0.7678 | 0.6506 | 0.8066 |
| No log | 5.6 | 196 | 0.6801 | 0.7692 | 0.6801 | 0.8247 |
| No log | 5.6571 | 198 | 0.7665 | 0.7192 | 0.7665 | 0.8755 |
| No log | 5.7143 | 200 | 0.8992 | 0.6798 | 0.8992 | 0.9483 |
| No log | 5.7714 | 202 | 0.9167 | 0.6538 | 0.9167 | 0.9575 |
| No log | 5.8286 | 204 | 0.7982 | 0.7075 | 0.7982 | 0.8934 |
| No log | 5.8857 | 206 | 0.6725 | 0.7249 | 0.6725 | 0.8201 |
| No log | 5.9429 | 208 | 0.6532 | 0.7194 | 0.6532 | 0.8082 |
| No log | 6.0 | 210 | 0.6952 | 0.7280 | 0.6952 | 0.8338 |
| No log | 6.0571 | 212 | 0.7490 | 0.7310 | 0.7490 | 0.8655 |
| No log | 6.1143 | 214 | 0.8049 | 0.6819 | 0.8049 | 0.8971 |
| No log | 6.1714 | 216 | 0.8365 | 0.6688 | 0.8365 | 0.9146 |
| No log | 6.2286 | 218 | 0.8717 | 0.6639 | 0.8717 | 0.9337 |
| No log | 6.2857 | 220 | 0.8018 | 0.6754 | 0.8018 | 0.8954 |
| No log | 6.3429 | 222 | 0.7529 | 0.7488 | 0.7529 | 0.8677 |
| No log | 6.4 | 224 | 0.6724 | 0.7548 | 0.6724 | 0.8200 |
| No log | 6.4571 | 226 | 0.6570 | 0.7698 | 0.6570 | 0.8106 |
| No log | 6.5143 | 228 | 0.7123 | 0.7735 | 0.7123 | 0.8440 |
| No log | 6.5714 | 230 | 0.8270 | 0.6794 | 0.8270 | 0.9094 |
| No log | 6.6286 | 232 | 0.9534 | 0.6750 | 0.9534 | 0.9764 |
| No log | 6.6857 | 234 | 0.9587 | 0.6750 | 0.9587 | 0.9791 |
| No log | 6.7429 | 236 | 0.9023 | 0.6911 | 0.9023 | 0.9499 |
| No log | 6.8 | 238 | 0.7901 | 0.7353 | 0.7901 | 0.8889 |
| No log | 6.8571 | 240 | 0.7562 | 0.7282 | 0.7562 | 0.8696 |
| No log | 6.9143 | 242 | 0.7579 | 0.7281 | 0.7579 | 0.8706 |
| No log | 6.9714 | 244 | 0.7135 | 0.7341 | 0.7135 | 0.8447 |
| No log | 7.0286 | 246 | 0.6600 | 0.7729 | 0.6600 | 0.8124 |
| No log | 7.0857 | 248 | 0.6107 | 0.7623 | 0.6107 | 0.7815 |
| No log | 7.1429 | 250 | 0.6013 | 0.7623 | 0.6013 | 0.7754 |
| No log | 7.2 | 252 | 0.6295 | 0.7679 | 0.6295 | 0.7934 |
| No log | 7.2571 | 254 | 0.6579 | 0.7801 | 0.6579 | 0.8111 |
| No log | 7.3143 | 256 | 0.6767 | 0.7847 | 0.6767 | 0.8226 |
| No log | 7.3714 | 258 | 0.6633 | 0.7855 | 0.6633 | 0.8144 |
| No log | 7.4286 | 260 | 0.6407 | 0.7855 | 0.6407 | 0.8004 |
| No log | 7.4857 | 262 | 0.6082 | 0.7795 | 0.6082 | 0.7798 |
| No log | 7.5429 | 264 | 0.6037 | 0.7758 | 0.6037 | 0.7770 |
| No log | 7.6 | 266 | 0.6332 | 0.7824 | 0.6332 | 0.7957 |
| No log | 7.6571 | 268 | 0.6837 | 0.7687 | 0.6837 | 0.8268 |
| No log | 7.7143 | 270 | 0.7349 | 0.7563 | 0.7349 | 0.8573 |
| No log | 7.7714 | 272 | 0.7617 | 0.7301 | 0.7617 | 0.8728 |
| No log | 7.8286 | 274 | 0.7883 | 0.7282 | 0.7883 | 0.8879 |
| No log | 7.8857 | 276 | 0.7669 | 0.7282 | 0.7669 | 0.8757 |
| No log | 7.9429 | 278 | 0.7230 | 0.7492 | 0.7230 | 0.8503 |
| No log | 8.0 | 280 | 0.7165 | 0.7492 | 0.7165 | 0.8465 |
| No log | 8.0571 | 282 | 0.6916 | 0.7652 | 0.6916 | 0.8316 |
| No log | 8.1143 | 284 | 0.6894 | 0.7675 | 0.6894 | 0.8303 |
| No log | 8.1714 | 286 | 0.7148 | 0.7492 | 0.7148 | 0.8455 |
| No log | 8.2286 | 288 | 0.7701 | 0.7466 | 0.7701 | 0.8775 |
| No log | 8.2857 | 290 | 0.8042 | 0.7346 | 0.8042 | 0.8968 |
| No log | 8.3429 | 292 | 0.8034 | 0.7346 | 0.8034 | 0.8963 |
| No log | 8.4 | 294 | 0.7726 | 0.7468 | 0.7726 | 0.8790 |
| No log | 8.4571 | 296 | 0.7322 | 0.7757 | 0.7322 | 0.8557 |
| No log | 8.5143 | 298 | 0.7103 | 0.7847 | 0.7103 | 0.8428 |
| No log | 8.5714 | 300 | 0.6919 | 0.7847 | 0.6919 | 0.8318 |
| No log | 8.6286 | 302 | 0.6822 | 0.7847 | 0.6822 | 0.8259 |
| No log | 8.6857 | 304 | 0.6705 | 0.7847 | 0.6705 | 0.8188 |
| No log | 8.7429 | 306 | 0.6597 | 0.7847 | 0.6597 | 0.8122 |
| No log | 8.8 | 308 | 0.6584 | 0.7847 | 0.6584 | 0.8114 |
| No log | 8.8571 | 310 | 0.6696 | 0.7847 | 0.6696 | 0.8183 |
| No log | 8.9143 | 312 | 0.6791 | 0.7847 | 0.6791 | 0.8241 |
| No log | 8.9714 | 314 | 0.6871 | 0.7847 | 0.6871 | 0.8289 |
| No log | 9.0286 | 316 | 0.6749 | 0.7847 | 0.6749 | 0.8215 |
| No log | 9.0857 | 318 | 0.6711 | 0.7847 | 0.6711 | 0.8192 |
| No log | 9.1429 | 320 | 0.6732 | 0.7847 | 0.6732 | 0.8205 |
| No log | 9.2 | 322 | 0.6693 | 0.7847 | 0.6693 | 0.8181 |
| No log | 9.2571 | 324 | 0.6706 | 0.7847 | 0.6706 | 0.8189 |
| No log | 9.3143 | 326 | 0.6662 | 0.7847 | 0.6662 | 0.8162 |
| No log | 9.3714 | 328 | 0.6690 | 0.7847 | 0.6690 | 0.8179 |
| No log | 9.4286 | 330 | 0.6714 | 0.7847 | 0.6714 | 0.8194 |
| No log | 9.4857 | 332 | 0.6693 | 0.7847 | 0.6693 | 0.8181 |
| No log | 9.5429 | 334 | 0.6660 | 0.7847 | 0.6660 | 0.8161 |
| No log | 9.6 | 336 | 0.6668 | 0.7847 | 0.6668 | 0.8166 |
| No log | 9.6571 | 338 | 0.6719 | 0.7847 | 0.6719 | 0.8197 |
| No log | 9.7143 | 340 | 0.6747 | 0.7847 | 0.6747 | 0.8214 |
| No log | 9.7714 | 342 | 0.6805 | 0.7847 | 0.6805 | 0.8250 |
| No log | 9.8286 | 344 | 0.6843 | 0.7847 | 0.6843 | 0.8272 |
| No log | 9.8857 | 346 | 0.6863 | 0.7847 | 0.6863 | 0.8284 |
| No log | 9.9429 | 348 | 0.6872 | 0.7847 | 0.6872 | 0.8290 |
| No log | 10.0 | 350 | 0.6874 | 0.7847 | 0.6874 | 0.8291 |
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/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k10_task5_organization
Base model
aubmindlab/bert-base-arabertv02