---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MBERT_multilingual-outputs
  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_multilingual-outputs

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: 0.6542
- Accuracy: 0.7265
- F1: 0.7449
- Precision: 0.7226
- Recall: 0.7685

## 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-06
- train_batch_size: 16
- 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_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6934        | 1.2225  | 1000  | 0.6863          | 0.5622   | 0.4867 | 0.6227    | 0.3995 |
| 0.6304        | 2.4450  | 2000  | 0.5690          | 0.6687   | 0.6699 | 0.6946    | 0.6468 |
| 0.5145        | 3.6675  | 3000  | 0.5066          | 0.6962   | 0.7009 | 0.7175    | 0.6852 |
| 0.451         | 4.8900  | 4000  | 0.4770          | 0.7107   | 0.7221 | 0.7207    | 0.7235 |
| 0.4102        | 6.1125  | 5000  | 0.4892          | 0.7182   | 0.7418 | 0.7079    | 0.7791 |
| 0.3832        | 7.3350  | 6000  | 0.4712          | 0.7223   | 0.7314 | 0.7353    | 0.7275 |
| 0.3597        | 8.5575  | 7000  | 0.4848          | 0.7368   | 0.7543 | 0.7323    | 0.7778 |
| 0.3427        | 9.7800  | 8000  | 0.4802          | 0.7409   | 0.7677 | 0.7186    | 0.8241 |
| 0.3242        | 11.0024 | 9000  | 0.5468          | 0.7278   | 0.7494 | 0.7184    | 0.7831 |
| 0.3075        | 12.2249 | 10000 | 0.5706          | 0.7244   | 0.7343 | 0.7357    | 0.7328 |
| 0.2897        | 13.4474 | 11000 | 0.5962          | 0.7182   | 0.7263 | 0.7332    | 0.7196 |
| 0.2883        | 14.6699 | 12000 | 0.5836          | 0.7230   | 0.7343 | 0.7319    | 0.7368 |
| 0.2702        | 15.8924 | 13000 | 0.6106          | 0.7354   | 0.7631 | 0.7135    | 0.8201 |
| 0.2651        | 17.1149 | 14000 | 0.6323          | 0.7278   | 0.7503 | 0.7169    | 0.7870 |
| 0.2605        | 18.3374 | 15000 | 0.6400          | 0.7292   | 0.7451 | 0.7291    | 0.7619 |
| 0.2473        | 19.5599 | 16000 | 0.6542          | 0.7265   | 0.7449 | 0.7226    | 0.7685 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1