---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_dataaugmentation
  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. -->

# multibert_dataaugmentation

This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co./bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7216
- Precisions: 0.8745
- Recall: 0.8029
- F-measure: 0.8291
- Accuracy: 0.9015

## 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: 7.5e-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: 14

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5776        | 1.0   | 284  | 0.5209          | 0.7928     | 0.6866 | 0.7216    | 0.8385   |
| 0.2774        | 2.0   | 568  | 0.4278          | 0.8274     | 0.7457 | 0.7626    | 0.8802   |
| 0.1471        | 3.0   | 852  | 0.5814          | 0.8455     | 0.7114 | 0.7399    | 0.8627   |
| 0.0933        | 4.0   | 1136 | 0.5386          | 0.8006     | 0.7300 | 0.7556    | 0.8835   |
| 0.0631        | 5.0   | 1420 | 0.6197          | 0.8122     | 0.7445 | 0.7682    | 0.8846   |
| 0.0437        | 6.0   | 1704 | 0.6973          | 0.8675     | 0.7423 | 0.7802    | 0.8815   |
| 0.0274        | 7.0   | 1988 | 0.6409          | 0.7960     | 0.7586 | 0.7729    | 0.8895   |
| 0.0202        | 8.0   | 2272 | 0.6477          | 0.8550     | 0.7712 | 0.8003    | 0.8922   |
| 0.0136        | 9.0   | 2556 | 0.7231          | 0.8722     | 0.7765 | 0.8088    | 0.8919   |
| 0.0094        | 10.0  | 2840 | 0.6787          | 0.8585     | 0.7916 | 0.8106    | 0.8955   |
| 0.0064        | 11.0  | 3124 | 0.7215          | 0.8750     | 0.7819 | 0.8107    | 0.8974   |
| 0.0052        | 12.0  | 3408 | 0.7295          | 0.8811     | 0.7840 | 0.8154    | 0.8985   |
| 0.0038        | 13.0  | 3692 | 0.7216          | 0.8745     | 0.8029 | 0.8291    | 0.9015   |
| 0.003         | 14.0  | 3976 | 0.7180          | 0.8745     | 0.8023 | 0.8289    | 0.9001   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1