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--- |
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base_model: Anwaarma/Improved-Arabert-twitter-sentiment2 |
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metrics: |
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- accuracy |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: robust-arabert |
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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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# robust-arabert |
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This model is a fine-tuned version of [Anwaarma/Improved-Arabert-twitter-sentiment2](https://huggingface.co/Anwaarma/Improved-Arabert-twitter-sentiment2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2072 |
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- Accuracy: 0.94 |
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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 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| No log | 0.0546 | 50 | 0.2677 | 0.91 | |
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| No log | 0.1092 | 100 | 0.2432 | 0.93 | |
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| No log | 0.1638 | 150 | 0.3328 | 0.91 | |
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| No log | 0.2183 | 200 | 0.2894 | 0.92 | |
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| No log | 0.2729 | 250 | 0.3008 | 0.91 | |
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| No log | 0.3275 | 300 | 0.1951 | 0.93 | |
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| No log | 0.3821 | 350 | 0.2176 | 0.94 | |
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| No log | 0.4367 | 400 | 0.2291 | 0.95 | |
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| No log | 0.4913 | 450 | 0.2678 | 0.93 | |
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| 0.2068 | 0.5459 | 500 | 0.2975 | 0.9 | |
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| 0.2068 | 0.6004 | 550 | 0.2245 | 0.92 | |
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| 0.2068 | 0.6550 | 600 | 0.2014 | 0.94 | |
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| 0.2068 | 0.7096 | 650 | 0.2189 | 0.93 | |
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| 0.2068 | 0.7642 | 700 | 0.2272 | 0.94 | |
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| 0.2068 | 0.8188 | 750 | 0.2148 | 0.92 | |
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| 0.2068 | 0.8734 | 800 | 0.2082 | 0.94 | |
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| 0.2068 | 0.9279 | 850 | 0.1869 | 0.93 | |
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| 0.2068 | 0.9825 | 900 | 0.2166 | 0.95 | |
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| 0.2068 | 1.0371 | 950 | 0.2095 | 0.94 | |
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| 0.1852 | 1.0917 | 1000 | 0.2948 | 0.92 | |
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| 0.1852 | 1.1463 | 1050 | 0.2586 | 0.93 | |
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| 0.1852 | 1.2009 | 1100 | 0.2669 | 0.93 | |
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| 0.1852 | 1.2555 | 1150 | 0.2635 | 0.94 | |
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| 0.1852 | 1.3100 | 1200 | 0.2600 | 0.94 | |
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| 0.1852 | 1.3646 | 1250 | 0.2290 | 0.95 | |
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| 0.1852 | 1.4192 | 1300 | 0.2248 | 0.95 | |
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| 0.1852 | 1.4738 | 1350 | 0.1840 | 0.96 | |
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| 0.1852 | 1.5284 | 1400 | 0.2366 | 0.95 | |
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| 0.1852 | 1.5830 | 1450 | 0.2322 | 0.95 | |
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| 0.1137 | 1.6376 | 1500 | 0.1797 | 0.96 | |
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| 0.1137 | 1.6921 | 1550 | 0.1662 | 0.96 | |
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| 0.1137 | 1.7467 | 1600 | 0.1762 | 0.96 | |
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| 0.1137 | 1.8013 | 1650 | 0.1508 | 0.96 | |
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| 0.1137 | 1.8559 | 1700 | 0.1996 | 0.95 | |
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| 0.1137 | 1.9105 | 1750 | 0.2072 | 0.94 | |
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### Framework versions |
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- Transformers 4.42.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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