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---
base_model: Anwaarma/Improved-mBERT-attempt2
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: robust-mbert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust-mbert
This model is a fine-tuned version of [Anwaarma/Improved-mBERT-attempt2](https://huggingface.co/Anwaarma/Improved-mBERT-attempt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2410
- Accuracy: 0.92
## 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: 16
- eval_batch_size: 16
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.0546 | 50 | 0.3348 | 0.88 |
| No log | 0.1092 | 100 | 0.3140 | 0.89 |
| No log | 0.1638 | 150 | 0.4226 | 0.84 |
| No log | 0.2183 | 200 | 0.2552 | 0.92 |
| No log | 0.2729 | 250 | 0.3494 | 0.85 |
| No log | 0.3275 | 300 | 0.2387 | 0.94 |
| No log | 0.3821 | 350 | 0.3383 | 0.87 |
| No log | 0.4367 | 400 | 0.3088 | 0.9 |
| No log | 0.4913 | 450 | 0.3561 | 0.89 |
| 0.3057 | 0.5459 | 500 | 0.3598 | 0.85 |
| 0.3057 | 0.6004 | 550 | 0.2880 | 0.89 |
| 0.3057 | 0.6550 | 600 | 0.2306 | 0.92 |
| 0.3057 | 0.7096 | 650 | 0.3648 | 0.88 |
| 0.3057 | 0.7642 | 700 | 0.2796 | 0.9 |
| 0.3057 | 0.8188 | 750 | 0.3100 | 0.88 |
| 0.3057 | 0.8734 | 800 | 0.2689 | 0.91 |
| 0.3057 | 0.9279 | 850 | 0.2707 | 0.89 |
| 0.3057 | 0.9825 | 900 | 0.2684 | 0.87 |
| 0.3057 | 1.0371 | 950 | 0.4417 | 0.86 |
| 0.2777 | 1.0917 | 1000 | 0.3980 | 0.88 |
| 0.2777 | 1.1463 | 1050 | 0.3233 | 0.9 |
| 0.2777 | 1.2009 | 1100 | 0.2857 | 0.9 |
| 0.2777 | 1.2555 | 1150 | 0.3229 | 0.89 |
| 0.2777 | 1.3100 | 1200 | 0.2364 | 0.92 |
| 0.2777 | 1.3646 | 1250 | 0.3015 | 0.87 |
| 0.2777 | 1.4192 | 1300 | 0.2713 | 0.89 |
| 0.2777 | 1.4738 | 1350 | 0.3839 | 0.87 |
| 0.2777 | 1.5284 | 1400 | 0.3173 | 0.9 |
| 0.2777 | 1.5830 | 1450 | 0.2690 | 0.91 |
| 0.2138 | 1.6376 | 1500 | 0.3804 | 0.89 |
| 0.2138 | 1.6921 | 1550 | 0.3020 | 0.88 |
| 0.2138 | 1.7467 | 1600 | 0.2702 | 0.89 |
| 0.2138 | 1.8013 | 1650 | 0.2815 | 0.9 |
| 0.2138 | 1.8559 | 1700 | 0.2867 | 0.89 |
| 0.2138 | 1.9105 | 1750 | 0.2861 | 0.87 |
| 0.2138 | 1.9651 | 1800 | 0.2585 | 0.89 |
| 0.2138 | 2.0197 | 1850 | 0.3170 | 0.9 |
| 0.2138 | 2.0742 | 1900 | 0.2928 | 0.9 |
| 0.2138 | 2.1288 | 1950 | 0.2635 | 0.93 |
| 0.1966 | 2.1834 | 2000 | 0.2695 | 0.93 |
| 0.1966 | 2.2380 | 2050 | 0.3348 | 0.9 |
| 0.1966 | 2.2926 | 2100 | 0.3577 | 0.91 |
| 0.1966 | 2.3472 | 2150 | 0.3360 | 0.92 |
| 0.1966 | 2.4017 | 2200 | 0.3721 | 0.91 |
| 0.1966 | 2.4563 | 2250 | 0.2410 | 0.92 |
### Framework versions
- Transformers 4.42.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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