---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_seed33_1311
  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_seed33_1311

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: 0.4996
- Precisions: 0.8590
- Recall: 0.8170
- F-measure: 0.8353
- Accuracy: 0.9359

## 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: 33
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.4674        | 1.0   | 236  | 0.2914          | 0.8891     | 0.6852 | 0.7119    | 0.9125   |
| 0.2266        | 2.0   | 472  | 0.2489          | 0.8410     | 0.7811 | 0.8044    | 0.9294   |
| 0.1394        | 3.0   | 708  | 0.2650          | 0.8611     | 0.7777 | 0.7929    | 0.9296   |
| 0.0878        | 4.0   | 944  | 0.2721          | 0.8608     | 0.8165 | 0.8324    | 0.9373   |
| 0.06          | 5.0   | 1180 | 0.3164          | 0.8457     | 0.7877 | 0.8105    | 0.9342   |
| 0.0378        | 6.0   | 1416 | 0.3793          | 0.8788     | 0.7972 | 0.8309    | 0.9335   |
| 0.0285        | 7.0   | 1652 | 0.3807          | 0.8665     | 0.7905 | 0.8233    | 0.9299   |
| 0.0153        | 8.0   | 1888 | 0.4636          | 0.8555     | 0.7855 | 0.8152    | 0.9303   |
| 0.0115        | 9.0   | 2124 | 0.4649          | 0.8336     | 0.8135 | 0.8190    | 0.9337   |
| 0.0064        | 10.0  | 2360 | 0.5120          | 0.8522     | 0.8010 | 0.8219    | 0.9325   |
| 0.0052        | 11.0  | 2596 | 0.5008          | 0.8616     | 0.8034 | 0.8288    | 0.9337   |
| 0.0038        | 12.0  | 2832 | 0.4807          | 0.8616     | 0.8133 | 0.8346    | 0.9354   |
| 0.0016        | 13.0  | 3068 | 0.4995          | 0.8514     | 0.8186 | 0.8322    | 0.9359   |
| 0.0012        | 14.0  | 3304 | 0.4996          | 0.8590     | 0.8170 | 0.8353    | 0.9359   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1