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---
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: Rajan/NepaliBERT
model-index:
- name: nepali_complaints_classification_nepbert3
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. -->
# nepali_complaints_classification_nepbert3
This model is a fine-tuned version of [Rajan/NepaliBERT](https://huggingface.co/Rajan/NepaliBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2687
- Accuracy: 0.9494
- F1-score: 0.9483
## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.4921 | 0.22 | 500 | 0.8642 | 0.7235 | 0.7143 |
| 0.7781 | 0.45 | 1000 | 0.6241 | 0.7974 | 0.7923 |
| 0.5865 | 0.67 | 1500 | 0.5342 | 0.8243 | 0.8125 |
| 0.4625 | 0.89 | 2000 | 0.4250 | 0.8576 | 0.8553 |
| 0.3648 | 1.11 | 2500 | 0.3856 | 0.8759 | 0.8725 |
| 0.3001 | 1.34 | 3000 | 0.3424 | 0.8899 | 0.8891 |
| 0.2723 | 1.56 | 3500 | 0.3199 | 0.9007 | 0.8981 |
| 0.2538 | 1.78 | 4000 | 0.2898 | 0.9085 | 0.9066 |
| 0.231 | 2.01 | 4500 | 0.2676 | 0.9203 | 0.9189 |
| 0.1478 | 2.23 | 5000 | 0.3029 | 0.9210 | 0.9187 |
| 0.1666 | 2.45 | 5500 | 0.2580 | 0.9283 | 0.9271 |
| 0.1519 | 2.67 | 6000 | 0.2573 | 0.9308 | 0.9292 |
| 0.1498 | 2.9 | 6500 | 0.2746 | 0.9328 | 0.9306 |
| 0.1112 | 3.12 | 7000 | 0.2564 | 0.9398 | 0.9389 |
| 0.0903 | 3.34 | 7500 | 0.2726 | 0.9403 | 0.9393 |
| 0.1036 | 3.57 | 8000 | 0.2664 | 0.9398 | 0.9385 |
| 0.1043 | 3.79 | 8500 | 0.2614 | 0.9459 | 0.9447 |
| 0.0972 | 4.01 | 9000 | 0.2499 | 0.9453 | 0.9443 |
| 0.0663 | 4.23 | 9500 | 0.2643 | 0.9469 | 0.9458 |
| 0.0683 | 4.46 | 10000 | 0.2688 | 0.9474 | 0.9462 |
| 0.0671 | 4.68 | 10500 | 0.2657 | 0.9491 | 0.9481 |
| 0.0605 | 4.9 | 11000 | 0.2687 | 0.9494 | 0.9483 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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