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

# results

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: 0.4003
- Accuracy: 0.8589
- F1: 0.7308
- Precision: 0.7238
- Recall: 0.7379

## 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: 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: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.2623 | 16   | 0.6597          | 0.7406   | 0.0    | 0.0       | 0.0    |
| No log        | 0.5246 | 32   | 0.5547          | 0.7406   | 0.0    | 0.0       | 0.0    |
| No log        | 0.7869 | 48   | 0.5144          | 0.7406   | 0.0    | 0.0       | 0.0    |
| No log        | 1.0492 | 64   | 0.4658          | 0.8237   | 0.5205 | 0.8837    | 0.3689 |
| No log        | 1.3115 | 80   | 0.4164          | 0.8338   | 0.7    | 0.6581    | 0.7476 |
| No log        | 1.5738 | 96   | 0.3812          | 0.8212   | 0.6872 | 0.6290    | 0.7573 |
| No log        | 1.8361 | 112  | 0.3799          | 0.8564   | 0.6705 | 0.8286    | 0.5631 |
| No log        | 2.0984 | 128  | 0.3736          | 0.8111   | 0.6725 | 0.6111    | 0.7476 |
| No log        | 2.3607 | 144  | 0.3726          | 0.8564   | 0.7047 | 0.7556    | 0.6602 |
| No log        | 2.6230 | 160  | 0.4651          | 0.7456   | 0.6456 | 0.5055    | 0.8932 |
| No log        | 2.8852 | 176  | 0.3592          | 0.8413   | 0.7070 | 0.6786    | 0.7379 |
| No log        | 3.1475 | 192  | 0.3633          | 0.8514   | 0.7035 | 0.7292    | 0.6796 |
| No log        | 3.4098 | 208  | 0.4381          | 0.8086   | 0.6984 | 0.5906    | 0.8544 |
| No log        | 3.6721 | 224  | 0.4114          | 0.8338   | 0.7080 | 0.6504    | 0.7767 |
| No log        | 3.9344 | 240  | 0.4588          | 0.8186   | 0.7025 | 0.6115    | 0.8252 |
| No log        | 4.1967 | 256  | 0.3795          | 0.8615   | 0.7291 | 0.74      | 0.7184 |
| No log        | 4.4590 | 272  | 0.4418          | 0.8262   | 0.7113 | 0.625     | 0.8252 |
| No log        | 4.7213 | 288  | 0.3962          | 0.8489   | 0.7170 | 0.6972    | 0.7379 |
| No log        | 4.9836 | 304  | 0.4003          | 0.8589   | 0.7308 | 0.7238    | 0.7379 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1