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MayBashendy/Arabic_FineTuningAraBERT_noAug_task1_organization_fold1
Arabic_FineTuningAraBERT_noAug_task1_organization_fold1 is a text classification model from MayBashendy. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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From the Hugging Face model README
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:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|---|---|---|---|---|---|---|
| No log | 0.1818 | 2 | 3.5265 | -0.0430 | 3.5265 | 1.8779 |
| No log | 0.3636 | 4 | 2.1957 | 0.0 | 2.1957 | 1.4818 |
| No log | 0.5455 | 6 | 1.4276 | 0.0680 | 1.4276 | 1.1948 |
| No log | 0.7273 | 8 | 1.0890 | 0.1355 | 1.0890 | 1.0436 |
| No log | 0.9091 | 10 | 1.1657 | -0.0161 | 1.1657 | 1.0797 |
| No log | 1.0909 | 12 | 1.0771 | -0.0302 | 1.0771 | 1.0378 |
| No log | 1.2727 | 14 | 0.8451 | -0.0284 | 0.8451 | 0.9193 |
| No log | 1.4545 | 16 | 0.7807 | 0.0606 | 0.7807 | 0.8835 |
| No log | 1.6364 | 18 | 0.7112 | -0.0467 | 0.7112 | 0.8433 |
| No log | 1.8182 | 20 | 0.6604 | -0.0096 | 0.6604 | 0.8126 |
| No log | 2.0 | 22 | 0.6288 | 0.4928 | 0.6288 | 0.7930 |
| No log | 2.1818 | 24 | 0.6238 | 0.4510 | 0.6238 | 0.7898 |
| No log | 2.3636 | 26 | 0.6134 | 0.4481 | 0.6134 | 0.7832 |
| No log | 2.5455 | 28 | 0.6019 | 0.5377 | 0.6019 | 0.7758 |
| No log | 2.7273 | 30 | 0.5898 | 0.5377 | 0.5898 | 0.7680 |
| No log | 2.9091 | 32 | 0.6678 | 0.6602 | 0.6678 | 0.8172 |
| No log | 3.0909 | 34 | 0.6491 | 0.6316 | 0.6491 | 0.8057 |
| No log | 3.2727 | 36 | 0.5608 | 0.6142 | 0.5608 | 0.7489 |
| No log | 3.4545 | 38 | 0.5107 | 0.5522 | 0.5107 | 0.7146 |
| No log | 3.6364 | 40 | 0.5347 | 0.5522 | 0.5347 | 0.7312 |
| No log | 3.8182 | 42 | 0.5437 | 0.6715 | 0.5437 | 0.7374 |
| No log | 4.0 | 44 | 0.6201 | 0.6343 | 0.6201 | 0.7875 |
| No log | 4.1818 | 46 | 0.8219 | 0.7055 | 0.8219 | 0.9066 |
| No log | 4.3636 | 48 | 0.8218 | 0.7055 | 0.8218 | 0.9065 |
| No log | 4.5455 | 50 | 0.7538 | 0.7055 | 0.7538 | 0.8682 |
| No log | 4.7273 | 52 | 0.6928 | 0.6859 | 0.6928 | 0.8323 |
| No log | 4.9091 | 54 | 0.6231 | 0.6729 | 0.6231 | 0.7894 |
| No log | 5.0909 | 56 | 0.6595 | 0.7358 | 0.6595 | 0.8121 |
| No log | 5.2727 | 58 | 0.8629 | 0.7336 | 0.8629 | 0.9289 |
| No log | 5.4545 | 60 | 0.8994 | 0.6667 | 0.8994 | 0.9483 |
| No log | 5.6364 | 62 | 0.7006 | 0.6529 | 0.7006 | 0.8370 |
| No log | 5.8182 | 64 | 0.5718 | 0.6142 | 0.5718 | 0.7562 |
| No log | 6.0 | 66 | 0.5318 | 0.5789 | 0.5318 | 0.7293 |
| No log | 6.1818 | 68 | 0.5621 | 0.6597 | 0.5621 | 0.7498 |
| No log | 6.3636 | 70 | 0.6267 | 0.6488 | 0.6267 | 0.7916 |
| No log | 6.5455 | 72 | 0.7690 | 0.6602 | 0.7690 | 0.8769 |
| No log | 6.7273 | 74 | 0.9500 | 0.7391 | 0.9500 | 0.9747 |
| No log | 6.9091 | 76 | 0.9727 | 0.7391 | 0.9727 | 0.9862 |
| No log | 7.0909 | 78 | 0.8586 | 0.7012 | 0.8586 | 0.9266 |
| No log | 7.2727 | 80 | 0.7628 | 0.7255 | 0.7628 | 0.8734 |
| No log | 7.4545 | 82 | 0.6330 | 0.6729 | 0.6330 | 0.7956 |
| No log | 7.6364 | 84 | 0.5902 | 0.7217 | 0.5902 | 0.7682 |
| No log | 7.8182 | 86 | 0.5896 | 0.7217 | 0.5896 | 0.7679 |
| No log | 8.0 | 88 | 0.6291 | 0.7358 | 0.6291 | 0.7931 |
| No log | 8.1818 | 90 | 0.6963 | 0.7308 | 0.6963 | 0.8345 |
| No log | 8.3636 | 92 | 0.7636 | 0.6784 | 0.7636 | 0.8739 |
| No log | 8.5455 | 94 | 0.8187 | 0.6784 | 0.8187 | 0.9048 |
| No log | 8.7273 | 96 | 0.8350 | 0.6784 | 0.8350 | 0.9138 |
| No log | 8.9091 | 98 | 0.8607 | 0.6784 | 0.8607 | 0.9277 |
| No log | 9.0909 | 100 | 0.8221 | 0.6784 | 0.8221 | 0.9067 |
| No log | 9.2727 | 102 | 0.7783 | 0.6784 | 0.7783 | 0.8822 |
| No log | 9.4545 | 104 | 0.7455 | 0.7390 | 0.7455 | 0.8634 |
| No log | 9.6364 | 106 | 0.7289 | 0.7390 | 0.7289 | 0.8538 |
| No log | 9.8182 | 108 | 0.7152 | 0.7390 | 0.7152 | 0.8457 |
| No log | 10.0 | 110 | 0.7133 | 0.7390 | 0.7133 | 0.8446 |