Instructions to use MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_organization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_organization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_organization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_organization") model = AutoModelForSequenceClassification.from_pretrained("MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_organization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_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: 1.0043
- Qwk: 0.5737
- Mse: 1.0043
- Rmse: 1.0022
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 | 0.0556 | 2 | 5.2469 | -0.0111 | 5.2469 | 2.2906 |
| No log | 0.1111 | 4 | 3.2751 | 0.0818 | 3.2751 | 1.8097 |
| No log | 0.1667 | 6 | 2.2291 | -0.0153 | 2.2291 | 1.4930 |
| No log | 0.2222 | 8 | 1.5187 | 0.1576 | 1.5187 | 1.2323 |
| No log | 0.2778 | 10 | 1.2004 | 0.2565 | 1.2004 | 1.0956 |
| No log | 0.3333 | 12 | 1.1149 | 0.2552 | 1.1149 | 1.0559 |
| No log | 0.3889 | 14 | 1.0988 | 0.3050 | 1.0988 | 1.0482 |
| No log | 0.4444 | 16 | 1.0968 | 0.3310 | 1.0968 | 1.0473 |
| No log | 0.5 | 18 | 0.9906 | 0.2665 | 0.9906 | 0.9953 |
| No log | 0.5556 | 20 | 1.0613 | 0.3858 | 1.0613 | 1.0302 |
| No log | 0.6111 | 22 | 1.2054 | 0.2482 | 1.2054 | 1.0979 |
| No log | 0.6667 | 24 | 1.3662 | 0.1668 | 1.3662 | 1.1688 |
| No log | 0.7222 | 26 | 1.6590 | 0.1628 | 1.6590 | 1.2880 |
| No log | 0.7778 | 28 | 1.6178 | 0.2039 | 1.6178 | 1.2719 |
| No log | 0.8333 | 30 | 1.2013 | 0.3057 | 1.2013 | 1.0960 |
| No log | 0.8889 | 32 | 0.9300 | 0.4869 | 0.9300 | 0.9644 |
| No log | 0.9444 | 34 | 0.9305 | 0.4883 | 0.9305 | 0.9646 |
| No log | 1.0 | 36 | 0.8981 | 0.4987 | 0.8981 | 0.9477 |
| No log | 1.0556 | 38 | 0.9113 | 0.5735 | 0.9113 | 0.9546 |
| No log | 1.1111 | 40 | 0.9019 | 0.5676 | 0.9019 | 0.9497 |
| No log | 1.1667 | 42 | 0.8636 | 0.5574 | 0.8636 | 0.9293 |
| No log | 1.2222 | 44 | 0.8134 | 0.5410 | 0.8134 | 0.9019 |
| No log | 1.2778 | 46 | 0.8083 | 0.5458 | 0.8083 | 0.8991 |
| No log | 1.3333 | 48 | 0.7926 | 0.6393 | 0.7926 | 0.8903 |
| No log | 1.3889 | 50 | 0.8683 | 0.5798 | 0.8683 | 0.9318 |
| No log | 1.4444 | 52 | 0.8400 | 0.6097 | 0.8400 | 0.9165 |
| No log | 1.5 | 54 | 0.8212 | 0.6184 | 0.8212 | 0.9062 |
| No log | 1.5556 | 56 | 0.7999 | 0.6265 | 0.7999 | 0.8944 |
| No log | 1.6111 | 58 | 0.7857 | 0.5942 | 0.7857 | 0.8864 |
| No log | 1.6667 | 60 | 0.7837 | 0.5903 | 0.7837 | 0.8852 |
| No log | 1.7222 | 62 | 0.7773 | 0.6192 | 0.7773 | 0.8816 |
| No log | 1.7778 | 64 | 0.7523 | 0.6314 | 0.7523 | 0.8673 |
| No log | 1.8333 | 66 | 0.7616 | 0.6348 | 0.7616 | 0.8727 |
| No log | 1.8889 | 68 | 0.7639 | 0.6649 | 0.7639 | 0.8740 |
| No log | 1.9444 | 70 | 0.7323 | 0.6311 | 0.7323 | 0.8558 |
| No log | 2.0 | 72 | 0.7811 | 0.6431 | 0.7811 | 0.8838 |
| No log | 2.0556 | 74 | 0.8533 | 0.6401 | 0.8533 | 0.9237 |
| No log | 2.1111 | 76 | 0.8097 | 0.6561 | 0.8097 | 0.8998 |
| No log | 2.1667 | 78 | 0.8021 | 0.6418 | 0.8021 | 0.8956 |
| No log | 2.2222 | 80 | 0.8554 | 0.6551 | 0.8554 | 0.9249 |
| No log | 2.2778 | 82 | 0.9214 | 0.6069 | 0.9214 | 0.9599 |
| No log | 2.3333 | 84 | 0.9266 | 0.6100 | 0.9266 | 0.9626 |
| No log | 2.3889 | 86 | 0.8842 | 0.5963 | 0.8842 | 0.9403 |
| No log | 2.4444 | 88 | 0.7912 | 0.6342 | 0.7912 | 0.8895 |
| No log | 2.5 | 90 | 0.7644 | 0.6433 | 0.7644 | 0.8743 |
| No log | 2.5556 | 92 | 0.7595 | 0.6692 | 0.7595 | 0.8715 |
| No log | 2.6111 | 94 | 0.8079 | 0.6400 | 0.8079 | 0.8989 |
| No log | 2.6667 | 96 | 0.7911 | 0.6269 | 0.7911 | 0.8895 |
| No log | 2.7222 | 98 | 0.7595 | 0.6779 | 0.7595 | 0.8715 |
| No log | 2.7778 | 100 | 0.7216 | 0.6815 | 0.7216 | 0.8494 |
| No log | 2.8333 | 102 | 0.7200 | 0.6570 | 0.7200 | 0.8485 |
| No log | 2.8889 | 104 | 0.7542 | 0.6686 | 0.7542 | 0.8684 |
| No log | 2.9444 | 106 | 0.8376 | 0.6200 | 0.8376 | 0.9152 |
| No log | 3.0 | 108 | 0.8923 | 0.5742 | 0.8923 | 0.9446 |
| No log | 3.0556 | 110 | 0.9077 | 0.5642 | 0.9077 | 0.9527 |
| No log | 3.1111 | 112 | 0.8862 | 0.5823 | 0.8862 | 0.9414 |
| No log | 3.1667 | 114 | 0.8448 | 0.5871 | 0.8448 | 0.9192 |
| No log | 3.2222 | 116 | 0.8024 | 0.6154 | 0.8024 | 0.8958 |
| No log | 3.2778 | 118 | 0.8022 | 0.6062 | 0.8022 | 0.8957 |
| No log | 3.3333 | 120 | 0.8041 | 0.6159 | 0.8041 | 0.8967 |
| No log | 3.3889 | 122 | 0.8069 | 0.6420 | 0.8069 | 0.8983 |
| No log | 3.4444 | 124 | 0.8712 | 0.6102 | 0.8712 | 0.9334 |
| No log | 3.5 | 126 | 0.9100 | 0.6027 | 0.9100 | 0.9539 |
| No log | 3.5556 | 128 | 0.9323 | 0.6158 | 0.9323 | 0.9655 |
| No log | 3.6111 | 130 | 0.9729 | 0.5799 | 0.9729 | 0.9863 |
| No log | 3.6667 | 132 | 0.9956 | 0.5877 | 0.9956 | 0.9978 |
| No log | 3.7222 | 134 | 0.9196 | 0.6478 | 0.9196 | 0.9589 |
| No log | 3.7778 | 136 | 0.8526 | 0.6414 | 0.8526 | 0.9233 |
| No log | 3.8333 | 138 | 0.8700 | 0.6261 | 0.8700 | 0.9327 |
| No log | 3.8889 | 140 | 0.9301 | 0.6183 | 0.9301 | 0.9644 |
| No log | 3.9444 | 142 | 1.0484 | 0.5803 | 1.0484 | 1.0239 |
| No log | 4.0 | 144 | 1.1091 | 0.5741 | 1.1091 | 1.0532 |
| No log | 4.0556 | 146 | 1.1110 | 0.5659 | 1.1110 | 1.0541 |
| No log | 4.1111 | 148 | 0.9923 | 0.6155 | 0.9923 | 0.9961 |
| No log | 4.1667 | 150 | 0.8453 | 0.6297 | 0.8453 | 0.9194 |
| No log | 4.2222 | 152 | 0.7781 | 0.6229 | 0.7781 | 0.8821 |
| No log | 4.2778 | 154 | 0.7775 | 0.6135 | 0.7775 | 0.8817 |
| No log | 4.3333 | 156 | 0.8172 | 0.6073 | 0.8172 | 0.9040 |
| No log | 4.3889 | 158 | 0.8941 | 0.5990 | 0.8941 | 0.9455 |
| No log | 4.4444 | 160 | 0.9801 | 0.6226 | 0.9801 | 0.9900 |
| No log | 4.5 | 162 | 1.0383 | 0.6121 | 1.0383 | 1.0190 |
| No log | 4.5556 | 164 | 0.9879 | 0.6045 | 0.9879 | 0.9939 |
| No log | 4.6111 | 166 | 0.9332 | 0.6031 | 0.9332 | 0.9660 |
| No log | 4.6667 | 168 | 0.9445 | 0.6193 | 0.9445 | 0.9719 |
| No log | 4.7222 | 170 | 0.9444 | 0.6196 | 0.9444 | 0.9718 |
| No log | 4.7778 | 172 | 0.9084 | 0.6231 | 0.9084 | 0.9531 |
| No log | 4.8333 | 174 | 0.9431 | 0.6223 | 0.9431 | 0.9711 |
| No log | 4.8889 | 176 | 1.0156 | 0.5974 | 1.0156 | 1.0078 |
| No log | 4.9444 | 178 | 1.0278 | 0.5863 | 1.0278 | 1.0138 |
| No log | 5.0 | 180 | 0.9856 | 0.6179 | 0.9856 | 0.9928 |
| No log | 5.0556 | 182 | 0.9552 | 0.6171 | 0.9552 | 0.9774 |
| No log | 5.1111 | 184 | 0.9237 | 0.6175 | 0.9237 | 0.9611 |
| No log | 5.1667 | 186 | 0.8798 | 0.6397 | 0.8798 | 0.9380 |
| No log | 5.2222 | 188 | 0.8816 | 0.6368 | 0.8816 | 0.9389 |
| No log | 5.2778 | 190 | 0.9744 | 0.6141 | 0.9744 | 0.9871 |
| No log | 5.3333 | 192 | 1.1355 | 0.5899 | 1.1355 | 1.0656 |
| No log | 5.3889 | 194 | 1.1864 | 0.5829 | 1.1864 | 1.0892 |
| No log | 5.4444 | 196 | 1.1050 | 0.5883 | 1.1050 | 1.0512 |
| No log | 5.5 | 198 | 0.9617 | 0.6084 | 0.9617 | 0.9807 |
| No log | 5.5556 | 200 | 0.8779 | 0.5689 | 0.8779 | 0.9369 |
| No log | 5.6111 | 202 | 0.8490 | 0.5888 | 0.8490 | 0.9214 |
| No log | 5.6667 | 204 | 0.8715 | 0.6091 | 0.8715 | 0.9336 |
| No log | 5.7222 | 206 | 0.8456 | 0.6266 | 0.8456 | 0.9196 |
| No log | 5.7778 | 208 | 0.8197 | 0.6309 | 0.8197 | 0.9054 |
| No log | 5.8333 | 210 | 0.8443 | 0.6472 | 0.8443 | 0.9189 |
| No log | 5.8889 | 212 | 0.9337 | 0.5952 | 0.9337 | 0.9663 |
| No log | 5.9444 | 214 | 1.0544 | 0.5808 | 1.0544 | 1.0268 |
| No log | 6.0 | 216 | 1.1363 | 0.5744 | 1.1363 | 1.0660 |
| No log | 6.0556 | 218 | 1.1875 | 0.5405 | 1.1875 | 1.0897 |
| No log | 6.1111 | 220 | 1.1396 | 0.5661 | 1.1396 | 1.0675 |
| No log | 6.1667 | 222 | 1.0506 | 0.5820 | 1.0506 | 1.0250 |
| No log | 6.2222 | 224 | 0.9929 | 0.5718 | 0.9929 | 0.9964 |
| No log | 6.2778 | 226 | 0.9012 | 0.5872 | 0.9012 | 0.9493 |
| No log | 6.3333 | 228 | 0.8361 | 0.5865 | 0.8361 | 0.9144 |
| No log | 6.3889 | 230 | 0.8236 | 0.6002 | 0.8236 | 0.9075 |
| No log | 6.4444 | 232 | 0.8635 | 0.6005 | 0.8635 | 0.9292 |
| No log | 6.5 | 234 | 0.9298 | 0.5891 | 0.9298 | 0.9643 |
| No log | 6.5556 | 236 | 0.9828 | 0.5762 | 0.9828 | 0.9913 |
| No log | 6.6111 | 238 | 0.9979 | 0.5873 | 0.9979 | 0.9989 |
| No log | 6.6667 | 240 | 0.9551 | 0.5884 | 0.9551 | 0.9773 |
| No log | 6.7222 | 242 | 0.9131 | 0.5950 | 0.9131 | 0.9556 |
| No log | 6.7778 | 244 | 0.8860 | 0.6069 | 0.8860 | 0.9413 |
| No log | 6.8333 | 246 | 0.8772 | 0.6081 | 0.8772 | 0.9366 |
| No log | 6.8889 | 248 | 0.8534 | 0.6444 | 0.8534 | 0.9238 |
| No log | 6.9444 | 250 | 0.8256 | 0.6150 | 0.8256 | 0.9086 |
| No log | 7.0 | 252 | 0.8100 | 0.6219 | 0.8100 | 0.9000 |
| No log | 7.0556 | 254 | 0.8225 | 0.6219 | 0.8225 | 0.9069 |
| No log | 7.1111 | 256 | 0.8724 | 0.6146 | 0.8724 | 0.9340 |
| No log | 7.1667 | 258 | 0.9208 | 0.5671 | 0.9208 | 0.9596 |
| No log | 7.2222 | 260 | 0.9941 | 0.5620 | 0.9941 | 0.9970 |
| No log | 7.2778 | 262 | 1.0648 | 0.5572 | 1.0648 | 1.0319 |
| No log | 7.3333 | 264 | 1.1314 | 0.5324 | 1.1314 | 1.0637 |
| No log | 7.3889 | 266 | 1.1461 | 0.5329 | 1.1461 | 1.0706 |
| No log | 7.4444 | 268 | 1.1316 | 0.5386 | 1.1316 | 1.0638 |
| No log | 7.5 | 270 | 1.1303 | 0.5168 | 1.1303 | 1.0632 |
| No log | 7.5556 | 272 | 1.0894 | 0.5474 | 1.0894 | 1.0438 |
| No log | 7.6111 | 274 | 1.0301 | 0.5529 | 1.0301 | 1.0149 |
| No log | 7.6667 | 276 | 0.9999 | 0.5620 | 0.9999 | 0.9999 |
| No log | 7.7222 | 278 | 0.9828 | 0.5723 | 0.9828 | 0.9914 |
| No log | 7.7778 | 280 | 0.9487 | 0.5755 | 0.9487 | 0.9740 |
| No log | 7.8333 | 282 | 0.9431 | 0.5851 | 0.9431 | 0.9712 |
| No log | 7.8889 | 284 | 0.9402 | 0.5862 | 0.9402 | 0.9696 |
| No log | 7.9444 | 286 | 0.9524 | 0.5839 | 0.9524 | 0.9759 |
| No log | 8.0 | 288 | 0.9425 | 0.6255 | 0.9425 | 0.9708 |
| No log | 8.0556 | 290 | 0.9177 | 0.6255 | 0.9177 | 0.9580 |
| No log | 8.1111 | 292 | 0.9184 | 0.6255 | 0.9184 | 0.9583 |
| No log | 8.1667 | 294 | 0.9070 | 0.6247 | 0.9070 | 0.9524 |
| No log | 8.2222 | 296 | 0.8750 | 0.6256 | 0.8750 | 0.9354 |
| No log | 8.2778 | 298 | 0.8442 | 0.6236 | 0.8442 | 0.9188 |
| No log | 8.3333 | 300 | 0.8357 | 0.6355 | 0.8357 | 0.9142 |
| No log | 8.3889 | 302 | 0.8367 | 0.6340 | 0.8367 | 0.9147 |
| No log | 8.4444 | 304 | 0.8532 | 0.6370 | 0.8532 | 0.9237 |
| No log | 8.5 | 306 | 0.8848 | 0.6233 | 0.8848 | 0.9406 |
| No log | 8.5556 | 308 | 0.9282 | 0.6207 | 0.9282 | 0.9635 |
| No log | 8.6111 | 310 | 0.9798 | 0.5757 | 0.9798 | 0.9899 |
| No log | 8.6667 | 312 | 1.0149 | 0.5757 | 1.0149 | 1.0074 |
| No log | 8.7222 | 314 | 1.0286 | 0.5759 | 1.0286 | 1.0142 |
| No log | 8.7778 | 316 | 1.0399 | 0.5738 | 1.0399 | 1.0198 |
| No log | 8.8333 | 318 | 1.0238 | 0.5803 | 1.0238 | 1.0118 |
| No log | 8.8889 | 320 | 0.9937 | 0.5791 | 0.9937 | 0.9969 |
| No log | 8.9444 | 322 | 0.9652 | 0.5721 | 0.9652 | 0.9824 |
| No log | 9.0 | 324 | 0.9555 | 0.5721 | 0.9555 | 0.9775 |
| No log | 9.0556 | 326 | 0.9555 | 0.5721 | 0.9555 | 0.9775 |
| No log | 9.1111 | 328 | 0.9538 | 0.5825 | 0.9538 | 0.9766 |
| No log | 9.1667 | 330 | 0.9529 | 0.5825 | 0.9529 | 0.9762 |
| No log | 9.2222 | 332 | 0.9576 | 0.5814 | 0.9576 | 0.9786 |
| No log | 9.2778 | 334 | 0.9650 | 0.5675 | 0.9650 | 0.9823 |
| No log | 9.3333 | 336 | 0.9702 | 0.5675 | 0.9702 | 0.9850 |
| No log | 9.3889 | 338 | 0.9834 | 0.5746 | 0.9834 | 0.9917 |
| No log | 9.4444 | 340 | 0.9920 | 0.5737 | 0.9920 | 0.9960 |
| No log | 9.5 | 342 | 0.9997 | 0.5737 | 0.9997 | 0.9998 |
| No log | 9.5556 | 344 | 1.0025 | 0.5737 | 1.0025 | 1.0013 |
| No log | 9.6111 | 346 | 1.0051 | 0.5737 | 1.0051 | 1.0025 |
| No log | 9.6667 | 348 | 1.0062 | 0.5737 | 1.0062 | 1.0031 |
| No log | 9.7222 | 350 | 1.0081 | 0.5737 | 1.0081 | 1.0041 |
| No log | 9.7778 | 352 | 1.0077 | 0.5737 | 1.0077 | 1.0038 |
| No log | 9.8333 | 354 | 1.0051 | 0.5737 | 1.0051 | 1.0026 |
| No log | 9.8889 | 356 | 1.0047 | 0.5737 | 1.0047 | 1.0024 |
| No log | 9.9444 | 358 | 1.0047 | 0.5737 | 1.0047 | 1.0023 |
| No log | 10.0 | 360 | 1.0043 | 0.5737 | 1.0043 | 1.0022 |
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_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k6_task1_organization
Base model
aubmindlab/bert-base-arabertv02