---
library_name: transformers
language:
- np
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Nepali-BERT-sentiment
  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-BERT-sentiment

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the Custom Devangari Datasets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6887
- Accuracy: 0.8660
- F1: 0.4658
- Precision: 0.4343
- Recall: 0.5021

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5999        | 1.0   | 595  | 0.5313          | 0.7274   | 0.3965 | 0.2670    | 0.7700 |
| 0.5114        | 2.0   | 1190 | 0.4717          | 0.7745   | 0.4427 | 0.3106    | 0.7700 |
| 0.4005        | 3.0   | 1785 | 0.4986          | 0.7907   | 0.4556 | 0.3266    | 0.7532 |
| 0.3087        | 4.0   | 2380 | 0.6887          | 0.8660   | 0.4658 | 0.4343    | 0.5021 |
| 0.2292        | 5.0   | 2975 | 0.8148          | 0.8626   | 0.4615 | 0.4240    | 0.5063 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1