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

# content

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: 0.4643
- Accuracy: 0.7959
- F1-score: 0.7686
- Recall: 0.8062
- Precision: 0.7343

## 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: 2.5e-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
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.5842        | 0.3814 | 500  | 0.5475          | 0.7275   | 0.7439   | 0.8704 | 0.6496    |
| 0.5066        | 0.7628 | 1000 | 0.5066          | 0.7527   | 0.7544   | 0.8351 | 0.6879    |
| 0.4702        | 1.1442 | 1500 | 0.5164          | 0.7524   | 0.7611   | 0.8672 | 0.6781    |
| 0.4287        | 1.5256 | 2000 | 0.4908          | 0.7902   | 0.7760   | 0.7992 | 0.7542    |
| 0.428         | 1.9069 | 2500 | 0.5179          | 0.7553   | 0.7643   | 0.8722 | 0.6801    |
| 0.368         | 2.2883 | 3000 | 0.5774          | 0.7476   | 0.7377   | 0.7804 | 0.6994    |
| 0.3507        | 2.6697 | 3500 | 0.5190          | 0.7770   | 0.7784   | 0.8609 | 0.7103    |
| 0.3285        | 3.0511 | 4000 | 0.6028          | 0.7745   | 0.7684   | 0.8225 | 0.7209    |
| 0.2697        | 3.4325 | 4500 | 0.5910          | 0.7725   | 0.7745   | 0.8590 | 0.7051    |
| 0.2697        | 3.8139 | 5000 | 0.5870          | 0.7679   | 0.7554   | 0.7879 | 0.7254    |
| 0.2274        | 4.1953 | 5500 | 0.7693          | 0.7690   | 0.7558   | 0.7860 | 0.7279    |
| 0.2076        | 4.5767 | 6000 | 0.7267          | 0.7676   | 0.7535   | 0.7810 | 0.7279    |
| 0.2057        | 4.9580 | 6500 | 0.7228          | 0.7653   | 0.7494   | 0.7716 | 0.7285    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1