---
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