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---
base_model: Anwaarma/Improved-MARBERT-attempt2
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: robust-marbert
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-marbert
This model is a fine-tuned version of [Anwaarma/Improved-MARBERT-attempt2](https://huggingface.co/Anwaarma/Improved-MARBERT-attempt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2362
- Accuracy: 0.94
## 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 | 0.2510 | 0.92 |
| No log | 0.1092 | 100 | 0.1780 | 0.94 |
| No log | 0.1638 | 150 | 0.3531 | 0.88 |
| No log | 0.2183 | 200 | 0.2775 | 0.94 |
| No log | 0.2729 | 250 | 0.2566 | 0.94 |
| No log | 0.3275 | 300 | 0.2247 | 0.94 |
| No log | 0.3821 | 350 | 0.1856 | 0.94 |
| No log | 0.4367 | 400 | 0.1221 | 0.96 |
| No log | 0.4913 | 450 | 0.3179 | 0.92 |
| 0.2513 | 0.5459 | 500 | 0.3608 | 0.9 |
| 0.2513 | 0.6004 | 550 | 0.1665 | 0.95 |
| 0.2513 | 0.6550 | 600 | 0.2186 | 0.93 |
| 0.2513 | 0.7096 | 650 | 0.2184 | 0.93 |
| 0.2513 | 0.7642 | 700 | 0.2175 | 0.93 |
| 0.2513 | 0.8188 | 750 | 0.2251 | 0.93 |
| 0.2513 | 0.8734 | 800 | 0.3068 | 0.92 |
| 0.2513 | 0.9279 | 850 | 0.1925 | 0.94 |
| 0.2513 | 0.9825 | 900 | 0.2141 | 0.93 |
| 0.2513 | 1.0371 | 950 | 0.2388 | 0.92 |
| 0.2118 | 1.0917 | 1000 | 0.3367 | 0.93 |
| 0.2118 | 1.1463 | 1050 | 0.2358 | 0.92 |
| 0.2118 | 1.2009 | 1100 | 0.3329 | 0.93 |
| 0.2118 | 1.2555 | 1150 | 0.2384 | 0.92 |
| 0.2118 | 1.3100 | 1200 | 0.3006 | 0.95 |
| 0.2118 | 1.3646 | 1250 | 0.2859 | 0.94 |
| 0.2118 | 1.4192 | 1300 | 0.2504 | 0.93 |
| 0.2118 | 1.4738 | 1350 | 0.2760 | 0.92 |
| 0.2118 | 1.5284 | 1400 | 0.2783 | 0.94 |
| 0.2118 | 1.5830 | 1450 | 0.2242 | 0.94 |
| 0.1485 | 1.6376 | 1500 | 0.2759 | 0.94 |
| 0.1485 | 1.6921 | 1550 | 0.2582 | 0.94 |
| 0.1485 | 1.7467 | 1600 | 0.3341 | 0.91 |
| 0.1485 | 1.8013 | 1650 | 0.3070 | 0.91 |
| 0.1485 | 1.8559 | 1700 | 0.1960 | 0.92 |
| 0.1485 | 1.9105 | 1750 | 0.2362 | 0.94 |
### Framework versions
- Transformers 4.42.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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