---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mbert-Nepali-NER
  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. -->

# mbert-Nepali-NER

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2462
- Precision: 0.3727
- Recall: 0.3154
- F1: 0.3417
- Accuracy: 0.9555

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3162        | 0.29  | 500  | 0.2577          | 0.2647    | 0.0692 | 0.1098 | 0.9303   |
| 0.205         | 0.58  | 1000 | 0.2505          | 0.5312    | 0.1308 | 0.2099 | 0.9378   |
| 0.176         | 0.87  | 1500 | 0.2241          | 0.3372    | 0.2231 | 0.2685 | 0.9434   |
| 0.1364        | 1.16  | 2000 | 0.2304          | 0.3125    | 0.1923 | 0.2381 | 0.9473   |
| 0.1188        | 1.44  | 2500 | 0.2136          | 0.25      | 0.3385 | 0.2876 | 0.9413   |
| 0.1056        | 1.73  | 3000 | 0.2134          | 0.3462    | 0.2769 | 0.3077 | 0.9517   |
| 0.1002        | 2.02  | 3500 | 0.2207          | 0.2632    | 0.3077 | 0.2837 | 0.9528   |
| 0.0695        | 2.31  | 4000 | 0.2153          | 0.3041    | 0.3462 | 0.3237 | 0.9524   |
| 0.0701        | 2.6   | 4500 | 0.2038          | 0.2674    | 0.3538 | 0.3046 | 0.9545   |
| 0.0649        | 2.89  | 5000 | 0.2090          | 0.2848    | 0.3462 | 0.3125 | 0.9536   |
| 0.0533        | 3.18  | 5500 | 0.2341          | 0.3913    | 0.2769 | 0.3243 | 0.9546   |
| 0.0422        | 3.47  | 6000 | 0.2459          | 0.4545    | 0.3077 | 0.3670 | 0.9537   |
| 0.0396        | 3.76  | 6500 | 0.2389          | 0.3846    | 0.3077 | 0.3419 | 0.9540   |
| 0.0376        | 4.04  | 7000 | 0.2296          | 0.4091    | 0.2769 | 0.3303 | 0.9550   |
| 0.0221        | 4.33  | 7500 | 0.2533          | 0.3962    | 0.3231 | 0.3559 | 0.9555   |
| 0.021         | 4.62  | 8000 | 0.2609          | 0.3922    | 0.3077 | 0.3448 | 0.9548   |
| 0.0251        | 4.91  | 8500 | 0.2462          | 0.3727    | 0.3154 | 0.3417 | 0.9555   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2