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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_baseline_grammar_task2_fold1 |
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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_baseline_grammar_task2_fold1 |
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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: 0.5805 |
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- Qwk: 0.4573 |
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- Mse: 0.5856 |
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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: 16 |
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- eval_batch_size: 16 |
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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 | 0.3333 | 2 | 4.5766 | -0.0114 | 4.6757 | |
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| No log | 0.6667 | 4 | 1.8451 | 0.0840 | 1.9011 | |
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| No log | 1.0 | 6 | 1.0385 | 0.0 | 1.0614 | |
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| No log | 1.3333 | 8 | 0.8509 | 0.0 | 0.8474 | |
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| No log | 1.6667 | 10 | 0.8477 | 0.0 | 0.8402 | |
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| No log | 2.0 | 12 | 0.7153 | 0.2184 | 0.7165 | |
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| No log | 2.3333 | 14 | 0.6748 | 0.2687 | 0.6856 | |
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| No log | 2.6667 | 16 | 0.7113 | 0.1912 | 0.7272 | |
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| No log | 3.0 | 18 | 0.7379 | 0.1902 | 0.7546 | |
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| No log | 3.3333 | 20 | 0.6880 | 0.1923 | 0.7014 | |
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| No log | 3.6667 | 22 | 0.6478 | 0.3544 | 0.6544 | |
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| No log | 4.0 | 24 | 0.6652 | 0.3849 | 0.6682 | |
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| No log | 4.3333 | 26 | 0.6717 | 0.4573 | 0.6748 | |
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| No log | 4.6667 | 28 | 0.6419 | 0.4304 | 0.6475 | |
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| No log | 5.0 | 30 | 0.6314 | 0.4573 | 0.6355 | |
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| No log | 5.3333 | 32 | 0.6776 | 0.3849 | 0.6787 | |
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| No log | 5.6667 | 34 | 0.6654 | 0.3849 | 0.6671 | |
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| No log | 6.0 | 36 | 0.6226 | 0.4573 | 0.6260 | |
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| No log | 6.3333 | 38 | 0.5977 | 0.3544 | 0.6024 | |
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| No log | 6.6667 | 40 | 0.5953 | 0.1306 | 0.6010 | |
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| No log | 7.0 | 42 | 0.5909 | 0.1306 | 0.5966 | |
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| No log | 7.3333 | 44 | 0.5860 | 0.1306 | 0.5916 | |
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| No log | 7.6667 | 46 | 0.5891 | 0.3544 | 0.5944 | |
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| No log | 8.0 | 48 | 0.6067 | 0.4573 | 0.6111 | |
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| No log | 8.3333 | 50 | 0.6190 | 0.4573 | 0.6228 | |
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| No log | 8.6667 | 52 | 0.6206 | 0.4573 | 0.6243 | |
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| No log | 9.0 | 54 | 0.6083 | 0.4573 | 0.6124 | |
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| No log | 9.3333 | 56 | 0.5947 | 0.4573 | 0.5993 | |
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| No log | 9.6667 | 58 | 0.5850 | 0.4573 | 0.5899 | |
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| No log | 10.0 | 60 | 0.5805 | 0.4573 | 0.5856 | |
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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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