---
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: 0.6354
- Precisions: 0.8725
- Recall: 0.8025
- F-measure: 0.8295
- Accuracy: 0.8996

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5999        | 1.0   | 284  | 0.4542          | 0.8507     | 0.7167 | 0.7422    | 0.8708   |
| 0.2808        | 2.0   | 568  | 0.3886          | 0.8552     | 0.7964 | 0.8192    | 0.8864   |
| 0.1596        | 3.0   | 852  | 0.4749          | 0.8786     | 0.7893 | 0.8208    | 0.8905   |
| 0.1035        | 4.0   | 1136 | 0.5060          | 0.8568     | 0.8034 | 0.8208    | 0.8954   |
| 0.0719        | 5.0   | 1420 | 0.5716          | 0.8498     | 0.8102 | 0.8255    | 0.8919   |
| 0.0456        | 6.0   | 1704 | 0.6255          | 0.8701     | 0.8060 | 0.8279    | 0.8960   |
| 0.0284        | 7.0   | 1988 | 0.6354          | 0.8725     | 0.8025 | 0.8295    | 0.8996   |
| 0.0205        | 8.0   | 2272 | 0.7146          | 0.8518     | 0.8105 | 0.8266    | 0.8988   |
| 0.011         | 9.0   | 2556 | 0.7307          | 0.8614     | 0.8045 | 0.8279    | 0.9004   |
| 0.0082        | 10.0  | 2840 | 0.7403          | 0.8785     | 0.7988 | 0.8255    | 0.9009   |
| 0.0064        | 11.0  | 3124 | 0.7756          | 0.8809     | 0.7913 | 0.8217    | 0.8989   |
| 0.0039        | 12.0  | 3408 | 0.8036          | 0.8650     | 0.7877 | 0.8130    | 0.8966   |
| 0.0038        | 13.0  | 3692 | 0.7660          | 0.8781     | 0.7950 | 0.8222    | 0.8997   |
| 0.0025        | 14.0  | 3976 | 0.7640          | 0.8829     | 0.7961 | 0.8249    | 0.9012   |


### Framework versions

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