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--- |
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base_model: Anwaarma/Improved-mBERT-attempt2 |
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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-mbert |
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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-mbert |
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This model is a fine-tuned version of [Anwaarma/Improved-mBERT-attempt2](https://huggingface.co/Anwaarma/Improved-mBERT-attempt2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2410 |
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- Accuracy: 0.92 |
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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 | 0.3348 | 0.88 | |
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| No log | 0.1092 | 100 | 0.3140 | 0.89 | |
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| No log | 0.1638 | 150 | 0.4226 | 0.84 | |
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| No log | 0.2183 | 200 | 0.2552 | 0.92 | |
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| No log | 0.2729 | 250 | 0.3494 | 0.85 | |
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| No log | 0.3275 | 300 | 0.2387 | 0.94 | |
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| No log | 0.3821 | 350 | 0.3383 | 0.87 | |
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| No log | 0.4367 | 400 | 0.3088 | 0.9 | |
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| No log | 0.4913 | 450 | 0.3561 | 0.89 | |
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| 0.3057 | 0.5459 | 500 | 0.3598 | 0.85 | |
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| 0.3057 | 0.6004 | 550 | 0.2880 | 0.89 | |
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| 0.3057 | 0.6550 | 600 | 0.2306 | 0.92 | |
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| 0.3057 | 0.7096 | 650 | 0.3648 | 0.88 | |
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| 0.3057 | 0.7642 | 700 | 0.2796 | 0.9 | |
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| 0.3057 | 0.8188 | 750 | 0.3100 | 0.88 | |
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| 0.3057 | 0.8734 | 800 | 0.2689 | 0.91 | |
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| 0.3057 | 0.9279 | 850 | 0.2707 | 0.89 | |
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| 0.3057 | 0.9825 | 900 | 0.2684 | 0.87 | |
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| 0.3057 | 1.0371 | 950 | 0.4417 | 0.86 | |
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| 0.2777 | 1.0917 | 1000 | 0.3980 | 0.88 | |
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| 0.2777 | 1.1463 | 1050 | 0.3233 | 0.9 | |
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| 0.2777 | 1.2009 | 1100 | 0.2857 | 0.9 | |
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| 0.2777 | 1.2555 | 1150 | 0.3229 | 0.89 | |
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| 0.2777 | 1.3100 | 1200 | 0.2364 | 0.92 | |
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| 0.2777 | 1.3646 | 1250 | 0.3015 | 0.87 | |
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| 0.2777 | 1.4192 | 1300 | 0.2713 | 0.89 | |
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| 0.2777 | 1.4738 | 1350 | 0.3839 | 0.87 | |
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| 0.2777 | 1.5284 | 1400 | 0.3173 | 0.9 | |
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| 0.2777 | 1.5830 | 1450 | 0.2690 | 0.91 | |
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| 0.2138 | 1.6376 | 1500 | 0.3804 | 0.89 | |
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| 0.2138 | 1.6921 | 1550 | 0.3020 | 0.88 | |
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| 0.2138 | 1.7467 | 1600 | 0.2702 | 0.89 | |
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| 0.2138 | 1.8013 | 1650 | 0.2815 | 0.9 | |
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| 0.2138 | 1.8559 | 1700 | 0.2867 | 0.89 | |
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| 0.2138 | 1.9105 | 1750 | 0.2861 | 0.87 | |
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| 0.2138 | 1.9651 | 1800 | 0.2585 | 0.89 | |
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| 0.2138 | 2.0197 | 1850 | 0.3170 | 0.9 | |
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| 0.2138 | 2.0742 | 1900 | 0.2928 | 0.9 | |
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| 0.2138 | 2.1288 | 1950 | 0.2635 | 0.93 | |
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| 0.1966 | 2.1834 | 2000 | 0.2695 | 0.93 | |
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| 0.1966 | 2.2380 | 2050 | 0.3348 | 0.9 | |
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| 0.1966 | 2.2926 | 2100 | 0.3577 | 0.91 | |
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| 0.1966 | 2.3472 | 2150 | 0.3360 | 0.92 | |
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| 0.1966 | 2.4017 | 2200 | 0.3721 | 0.91 | |
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| 0.1966 | 2.4563 | 2250 | 0.2410 | 0.92 | |
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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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