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--- |
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base_model: aubmindlab/bert-base-arabertv02 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: arabert_cross_organization_task7_fold6 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# arabert_cross_organization_task7_fold6 |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6600 |
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- Qwk: 0.5581 |
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- Mse: 0.6587 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| No log | 0.125 | 2 | 2.0463 | 0.0838 | 2.0448 | |
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| No log | 0.25 | 4 | 1.0746 | 0.1939 | 1.0725 | |
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| No log | 0.375 | 6 | 1.0643 | 0.3449 | 1.0639 | |
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| No log | 0.5 | 8 | 0.8689 | 0.5214 | 0.8684 | |
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| No log | 0.625 | 10 | 0.7524 | 0.3263 | 0.7518 | |
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| No log | 0.75 | 12 | 0.6423 | 0.3754 | 0.6420 | |
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| No log | 0.875 | 14 | 0.5644 | 0.5844 | 0.5643 | |
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| No log | 1.0 | 16 | 0.5224 | 0.6557 | 0.5223 | |
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| No log | 1.125 | 18 | 0.4855 | 0.6292 | 0.4850 | |
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| No log | 1.25 | 20 | 0.5779 | 0.5418 | 0.5767 | |
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| No log | 1.375 | 22 | 0.5208 | 0.6043 | 0.5197 | |
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| No log | 1.5 | 24 | 0.5175 | 0.7174 | 0.5174 | |
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| No log | 1.625 | 26 | 0.4998 | 0.7107 | 0.4998 | |
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| No log | 1.75 | 28 | 0.4818 | 0.6457 | 0.4809 | |
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| No log | 1.875 | 30 | 0.4990 | 0.6364 | 0.4979 | |
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| No log | 2.0 | 32 | 0.5085 | 0.6403 | 0.5073 | |
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| No log | 2.125 | 34 | 0.4978 | 0.6611 | 0.4969 | |
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| No log | 2.25 | 36 | 0.4811 | 0.6848 | 0.4805 | |
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| No log | 2.375 | 38 | 0.4675 | 0.6672 | 0.4669 | |
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| No log | 2.5 | 40 | 0.4889 | 0.6232 | 0.4881 | |
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| No log | 2.625 | 42 | 0.5071 | 0.6102 | 0.5062 | |
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| No log | 2.75 | 44 | 0.5162 | 0.6263 | 0.5151 | |
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| No log | 2.875 | 46 | 0.5184 | 0.6317 | 0.5172 | |
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| No log | 3.0 | 48 | 0.5229 | 0.6543 | 0.5219 | |
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| No log | 3.125 | 50 | 0.5389 | 0.6233 | 0.5377 | |
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| No log | 3.25 | 52 | 0.5879 | 0.5675 | 0.5861 | |
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| No log | 3.375 | 54 | 0.6183 | 0.5488 | 0.6164 | |
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| No log | 3.5 | 56 | 0.5578 | 0.5898 | 0.5563 | |
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| No log | 3.625 | 58 | 0.5612 | 0.6909 | 0.5607 | |
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| No log | 3.75 | 60 | 0.5964 | 0.7100 | 0.5962 | |
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| No log | 3.875 | 62 | 0.5615 | 0.6815 | 0.5609 | |
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| No log | 4.0 | 64 | 0.5730 | 0.5963 | 0.5716 | |
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| No log | 4.125 | 66 | 0.6867 | 0.5243 | 0.6849 | |
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| No log | 4.25 | 68 | 0.6700 | 0.5276 | 0.6682 | |
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| No log | 4.375 | 70 | 0.5889 | 0.5659 | 0.5873 | |
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| No log | 4.5 | 72 | 0.5446 | 0.6149 | 0.5434 | |
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| No log | 4.625 | 74 | 0.5556 | 0.6355 | 0.5547 | |
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| No log | 4.75 | 76 | 0.5886 | 0.6034 | 0.5871 | |
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| No log | 4.875 | 78 | 0.6730 | 0.5568 | 0.6709 | |
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| No log | 5.0 | 80 | 0.6892 | 0.5344 | 0.6871 | |
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| No log | 5.125 | 82 | 0.6046 | 0.5665 | 0.6029 | |
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| No log | 5.25 | 84 | 0.5605 | 0.6134 | 0.5591 | |
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| No log | 5.375 | 86 | 0.5415 | 0.6417 | 0.5404 | |
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| No log | 5.5 | 88 | 0.5515 | 0.6247 | 0.5504 | |
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| No log | 5.625 | 90 | 0.5964 | 0.5762 | 0.5948 | |
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| No log | 5.75 | 92 | 0.6466 | 0.5489 | 0.6449 | |
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| No log | 5.875 | 94 | 0.6325 | 0.5648 | 0.6310 | |
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| No log | 6.0 | 96 | 0.6036 | 0.6097 | 0.6022 | |
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| No log | 6.125 | 98 | 0.5955 | 0.6483 | 0.5944 | |
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| No log | 6.25 | 100 | 0.6017 | 0.6168 | 0.6005 | |
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| No log | 6.375 | 102 | 0.6349 | 0.5846 | 0.6335 | |
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| No log | 6.5 | 104 | 0.6941 | 0.5277 | 0.6925 | |
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| No log | 6.625 | 106 | 0.6740 | 0.5262 | 0.6724 | |
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| No log | 6.75 | 108 | 0.6043 | 0.5829 | 0.6030 | |
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| No log | 6.875 | 110 | 0.5813 | 0.6039 | 0.5802 | |
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| No log | 7.0 | 112 | 0.5847 | 0.6056 | 0.5836 | |
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| No log | 7.125 | 114 | 0.6031 | 0.5987 | 0.6019 | |
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| No log | 7.25 | 116 | 0.6490 | 0.5645 | 0.6474 | |
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| No log | 7.375 | 118 | 0.6772 | 0.5326 | 0.6756 | |
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| No log | 7.5 | 120 | 0.6849 | 0.5311 | 0.6833 | |
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| No log | 7.625 | 122 | 0.6620 | 0.5393 | 0.6606 | |
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| No log | 7.75 | 124 | 0.6230 | 0.5696 | 0.6217 | |
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| No log | 7.875 | 126 | 0.5912 | 0.5983 | 0.5901 | |
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| No log | 8.0 | 128 | 0.5924 | 0.5983 | 0.5913 | |
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| No log | 8.125 | 130 | 0.6124 | 0.5864 | 0.6112 | |
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| No log | 8.25 | 132 | 0.6364 | 0.5615 | 0.6351 | |
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| No log | 8.375 | 134 | 0.6650 | 0.5476 | 0.6635 | |
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| No log | 8.5 | 136 | 0.6693 | 0.5397 | 0.6678 | |
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| No log | 8.625 | 138 | 0.6639 | 0.5516 | 0.6624 | |
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| No log | 8.75 | 140 | 0.6658 | 0.5467 | 0.6643 | |
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| No log | 8.875 | 142 | 0.6772 | 0.5437 | 0.6757 | |
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| No log | 9.0 | 144 | 0.6778 | 0.5489 | 0.6763 | |
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| No log | 9.125 | 146 | 0.6641 | 0.5504 | 0.6627 | |
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| No log | 9.25 | 148 | 0.6614 | 0.5557 | 0.6600 | |
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| No log | 9.375 | 150 | 0.6564 | 0.5609 | 0.6551 | |
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| No log | 9.5 | 152 | 0.6530 | 0.5618 | 0.6517 | |
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| No log | 9.625 | 154 | 0.6533 | 0.5618 | 0.6520 | |
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| No log | 9.75 | 156 | 0.6546 | 0.5581 | 0.6533 | |
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| No log | 9.875 | 158 | 0.6579 | 0.5581 | 0.6566 | |
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| No log | 10.0 | 160 | 0.6600 | 0.5581 | 0.6587 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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