---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-multilingual-uncased-classification
  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. -->

# bert-base-multilingual-uncased-classification

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2025
- Accuracy: 0.9563

## 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
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 95   | 0.2181          | 0.9523   |
| No log        | 2.0   | 190  | 0.2287          | 0.9543   |
| No log        | 3.0   | 285  | 0.2135          | 0.9523   |
| No log        | 4.0   | 380  | 0.1975          | 0.9523   |
| No log        | 5.0   | 475  | 0.2015          | 0.9563   |
| 0.0475        | 6.0   | 570  | 0.1930          | 0.9523   |
| 0.0475        | 7.0   | 665  | 0.1784          | 0.9563   |
| 0.0475        | 8.0   | 760  | 0.2306          | 0.9563   |
| 0.0475        | 9.0   | 855  | 0.2177          | 0.9543   |
| 0.0475        | 10.0  | 950  | 0.1579          | 0.9563   |
| 0.0353        | 11.0  | 1045 | 0.1645          | 0.9563   |
| 0.0353        | 12.0  | 1140 | 0.2233          | 0.9563   |
| 0.0353        | 13.0  | 1235 | 0.1985          | 0.9523   |
| 0.0353        | 14.0  | 1330 | 0.1932          | 0.9543   |
| 0.0353        | 15.0  | 1425 | 0.2461          | 0.9563   |
| 0.0312        | 16.0  | 1520 | 0.1834          | 0.9563   |
| 0.0312        | 17.0  | 1615 | 0.1821          | 0.9543   |
| 0.0312        | 18.0  | 1710 | 0.1985          | 0.9563   |
| 0.0312        | 19.0  | 1805 | 0.1984          | 0.9583   |
| 0.0312        | 20.0  | 1900 | 0.2036          | 0.9583   |
| 0.0312        | 21.0  | 1995 | 0.1957          | 0.9563   |
| 0.0264        | 22.0  | 2090 | 0.1996          | 0.9563   |
| 0.0264        | 23.0  | 2185 | 0.2041          | 0.9583   |
| 0.0264        | 24.0  | 2280 | 0.2022          | 0.9583   |
| 0.0264        | 25.0  | 2375 | 0.2025          | 0.9563   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2