---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: multibertfinetuned2209
  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. -->

# multibertfinetuned2209

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: 0.3973
- Precision: 0.7567
- Recall: 0.7607
- F1: 0.7587
- Accuracy: 0.9064

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 118  | 0.4058          | 0.7597    | 0.7343 | 0.7468 | 0.9032   |
| No log        | 2.0   | 236  | 0.3973          | 0.7567    | 0.7607 | 0.7587 | 0.9064   |
| No log        | 3.0   | 354  | 0.4153          | 0.7540    | 0.7677 | 0.7608 | 0.9062   |
| No log        | 4.0   | 472  | 0.4656          | 0.7645    | 0.7466 | 0.7555 | 0.9082   |
| 0.0692        | 5.0   | 590  | 0.4940          | 0.7594    | 0.7554 | 0.7574 | 0.9043   |
| 0.0692        | 6.0   | 708  | 0.5446          | 0.7668    | 0.7484 | 0.7575 | 0.9059   |
| 0.0692        | 7.0   | 826  | 0.5732          | 0.7818    | 0.7420 | 0.7613 | 0.9069   |
| 0.0692        | 8.0   | 944  | 0.5668          | 0.7844    | 0.7431 | 0.7632 | 0.9082   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3