--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_organization_task2_fold3 results: [] --- # arabert_cross_organization_task2_fold3 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5676 - Qwk: 0.8167 - Mse: 0.5676 ## 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: 64 - eval_batch_size: 64 - 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 | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | No log | 0.1333 | 2 | 1.8692 | 0.1353 | 1.8692 | | No log | 0.2667 | 4 | 1.4213 | 0.1397 | 1.4213 | | No log | 0.4 | 6 | 1.4691 | 0.3731 | 1.4691 | | No log | 0.5333 | 8 | 1.1350 | 0.5085 | 1.1350 | | No log | 0.6667 | 10 | 1.0589 | 0.4468 | 1.0589 | | No log | 0.8 | 12 | 0.6836 | 0.7114 | 0.6836 | | No log | 0.9333 | 14 | 0.7934 | 0.7564 | 0.7934 | | No log | 1.0667 | 16 | 0.6366 | 0.7754 | 0.6366 | | No log | 1.2 | 18 | 0.6294 | 0.6090 | 0.6294 | | No log | 1.3333 | 20 | 0.5984 | 0.6582 | 0.5984 | | No log | 1.4667 | 22 | 0.6612 | 0.7573 | 0.6612 | | No log | 1.6 | 24 | 0.6149 | 0.7707 | 0.6149 | | No log | 1.7333 | 26 | 0.5768 | 0.7921 | 0.5768 | | No log | 1.8667 | 28 | 0.5478 | 0.7836 | 0.5478 | | No log | 2.0 | 30 | 0.6323 | 0.7873 | 0.6323 | | No log | 2.1333 | 32 | 0.7541 | 0.7799 | 0.7541 | | No log | 2.2667 | 34 | 0.5899 | 0.7730 | 0.5899 | | No log | 2.4 | 36 | 0.5646 | 0.7204 | 0.5646 | | No log | 2.5333 | 38 | 0.5812 | 0.7665 | 0.5812 | | No log | 2.6667 | 40 | 0.7559 | 0.7804 | 0.7559 | | No log | 2.8 | 42 | 0.6782 | 0.7852 | 0.6782 | | No log | 2.9333 | 44 | 0.5336 | 0.7721 | 0.5336 | | No log | 3.0667 | 46 | 0.5156 | 0.7461 | 0.5156 | | No log | 3.2 | 48 | 0.5374 | 0.7842 | 0.5374 | | No log | 3.3333 | 50 | 0.6264 | 0.7999 | 0.6264 | | No log | 3.4667 | 52 | 0.8571 | 0.7933 | 0.8571 | | No log | 3.6 | 54 | 0.7545 | 0.8124 | 0.7545 | | No log | 3.7333 | 56 | 0.5238 | 0.7922 | 0.5238 | | No log | 3.8667 | 58 | 0.4796 | 0.7536 | 0.4796 | | No log | 4.0 | 60 | 0.4953 | 0.7964 | 0.4953 | | No log | 4.1333 | 62 | 0.6345 | 0.7733 | 0.6345 | | No log | 4.2667 | 64 | 0.7544 | 0.7901 | 0.7544 | | No log | 4.4 | 66 | 0.6943 | 0.7949 | 0.6943 | | No log | 4.5333 | 68 | 0.5528 | 0.8088 | 0.5528 | | No log | 4.6667 | 70 | 0.4774 | 0.7978 | 0.4774 | | No log | 4.8 | 72 | 0.5067 | 0.7997 | 0.5067 | | No log | 4.9333 | 74 | 0.6779 | 0.8166 | 0.6779 | | No log | 5.0667 | 76 | 0.7490 | 0.8085 | 0.7490 | | No log | 5.2 | 78 | 0.6112 | 0.8038 | 0.6112 | | No log | 5.3333 | 80 | 0.5181 | 0.7860 | 0.5181 | | No log | 5.4667 | 82 | 0.5173 | 0.7861 | 0.5173 | | No log | 5.6 | 84 | 0.5810 | 0.8108 | 0.5810 | | No log | 5.7333 | 86 | 0.6378 | 0.8013 | 0.6378 | | No log | 5.8667 | 88 | 0.7117 | 0.8099 | 0.7117 | | No log | 6.0 | 90 | 0.6295 | 0.8118 | 0.6295 | | No log | 6.1333 | 92 | 0.5548 | 0.8052 | 0.5548 | | No log | 6.2667 | 94 | 0.5066 | 0.7699 | 0.5066 | | No log | 6.4 | 96 | 0.5174 | 0.7849 | 0.5174 | | No log | 6.5333 | 98 | 0.6036 | 0.8042 | 0.6036 | | No log | 6.6667 | 100 | 0.7393 | 0.8146 | 0.7393 | | No log | 6.8 | 102 | 0.7344 | 0.8098 | 0.7344 | | No log | 6.9333 | 104 | 0.6184 | 0.8121 | 0.6184 | | No log | 7.0667 | 106 | 0.5416 | 0.7974 | 0.5416 | | No log | 7.2 | 108 | 0.5343 | 0.7929 | 0.5343 | | No log | 7.3333 | 110 | 0.5632 | 0.8094 | 0.5632 | | No log | 7.4667 | 112 | 0.5781 | 0.8097 | 0.5781 | | No log | 7.6 | 114 | 0.6404 | 0.8068 | 0.6404 | | No log | 7.7333 | 116 | 0.6828 | 0.8187 | 0.6828 | | No log | 7.8667 | 118 | 0.6504 | 0.8045 | 0.6504 | | No log | 8.0 | 120 | 0.5838 | 0.8057 | 0.5838 | | No log | 8.1333 | 122 | 0.5378 | 0.8062 | 0.5378 | | No log | 8.2667 | 124 | 0.5466 | 0.8079 | 0.5466 | | No log | 8.4 | 126 | 0.5939 | 0.8052 | 0.5939 | | No log | 8.5333 | 128 | 0.6680 | 0.8023 | 0.6680 | | No log | 8.6667 | 130 | 0.6894 | 0.8051 | 0.6894 | | No log | 8.8 | 132 | 0.6689 | 0.8044 | 0.6689 | | No log | 8.9333 | 134 | 0.6450 | 0.8040 | 0.6450 | | No log | 9.0667 | 136 | 0.5946 | 0.8173 | 0.5946 | | No log | 9.2 | 138 | 0.5617 | 0.8132 | 0.5617 | | No log | 9.3333 | 140 | 0.5493 | 0.8151 | 0.5493 | | No log | 9.4667 | 142 | 0.5410 | 0.8105 | 0.5410 | | No log | 9.6 | 144 | 0.5422 | 0.8142 | 0.5422 | | No log | 9.7333 | 146 | 0.5512 | 0.8086 | 0.5512 | | No log | 9.8667 | 148 | 0.5620 | 0.8148 | 0.5620 | | No log | 10.0 | 150 | 0.5676 | 0.8167 | 0.5676 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1