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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_style_task1_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_style_task1_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: 0.4983 |
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- Qwk: 0.5052 |
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- Mse: 0.4984 |
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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 | 5.8113 | -0.0102 | 5.7669 | |
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| No log | 0.6667 | 4 | 2.3576 | 0.0342 | 2.3554 | |
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| No log | 1.0 | 6 | 1.4911 | 0.0652 | 1.4969 | |
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| No log | 1.3333 | 8 | 0.7580 | 0.1021 | 0.7628 | |
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| No log | 1.6667 | 10 | 0.5619 | 0.2851 | 0.5662 | |
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| No log | 2.0 | 12 | 0.6485 | 0.0870 | 0.6536 | |
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| No log | 2.3333 | 14 | 1.0404 | 0.1138 | 1.0530 | |
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| No log | 2.6667 | 16 | 0.6654 | 0.2646 | 0.6722 | |
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| No log | 3.0 | 18 | 0.4343 | 0.3425 | 0.4348 | |
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| No log | 3.3333 | 20 | 0.4318 | 0.4057 | 0.4340 | |
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| No log | 3.6667 | 22 | 0.4542 | 0.4057 | 0.4579 | |
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| No log | 4.0 | 24 | 0.5421 | 0.3191 | 0.5486 | |
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| No log | 4.3333 | 26 | 0.4470 | 0.3780 | 0.4507 | |
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| No log | 4.6667 | 28 | 0.4369 | 0.5130 | 0.4397 | |
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| No log | 5.0 | 30 | 0.4435 | 0.5130 | 0.4458 | |
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| No log | 5.3333 | 32 | 0.4758 | 0.6488 | 0.4772 | |
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| No log | 5.6667 | 34 | 0.5072 | 0.5130 | 0.5092 | |
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| No log | 6.0 | 36 | 0.5982 | 0.4160 | 0.6024 | |
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| No log | 6.3333 | 38 | 0.6201 | 0.3368 | 0.6243 | |
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| No log | 6.6667 | 40 | 0.5336 | 0.4731 | 0.5349 | |
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| No log | 7.0 | 42 | 0.5080 | 0.6613 | 0.5072 | |
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| No log | 7.3333 | 44 | 0.4953 | 0.6613 | 0.4944 | |
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| No log | 7.6667 | 46 | 0.4884 | 0.5689 | 0.4879 | |
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| No log | 8.0 | 48 | 0.4997 | 0.5052 | 0.4999 | |
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| No log | 8.3333 | 50 | 0.5187 | 0.4731 | 0.5194 | |
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| No log | 8.6667 | 52 | 0.5092 | 0.4731 | 0.5095 | |
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| No log | 9.0 | 54 | 0.4978 | 0.5052 | 0.4977 | |
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| No log | 9.3333 | 56 | 0.4999 | 0.5052 | 0.5000 | |
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| No log | 9.6667 | 58 | 0.5004 | 0.5052 | 0.5006 | |
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| No log | 10.0 | 60 | 0.4983 | 0.5052 | 0.4984 | |
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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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