---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-multi-class-classification-cs
  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. -->

# bert-base-multi-class-classification-cs

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: 0.0385
- Accuracy: 0.9942
- F1: 0.9942
- Precision: 0.9942
- Recall: 0.9942

## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 3.3053        | 0.0595 | 50   | 3.1767          | 0.0871   | 0.0306 | 0.0333    | 0.0869 |
| 2.8293        | 0.1190 | 100  | 2.1807          | 0.4647   | 0.3675 | 0.5055    | 0.4651 |
| 1.6887        | 0.1786 | 150  | 1.1300          | 0.7705   | 0.7362 | 0.7621    | 0.7722 |
| 0.9012        | 0.2381 | 200  | 0.6245          | 0.8321   | 0.8086 | 0.8549    | 0.8356 |
| 0.5237        | 0.2976 | 250  | 0.3510          | 0.9129   | 0.9029 | 0.9007    | 0.9147 |
| 0.3115        | 0.3571 | 300  | 0.2218          | 0.9512   | 0.9517 | 0.9545    | 0.9518 |
| 0.2217        | 0.4167 | 350  | 0.1746          | 0.9382   | 0.9284 | 0.9596    | 0.9388 |
| 0.1464        | 0.4762 | 400  | 0.1210          | 0.9729   | 0.9731 | 0.9749    | 0.9730 |
| 0.1201        | 0.5357 | 450  | 0.0977          | 0.9804   | 0.9805 | 0.9810    | 0.9805 |
| 0.0921        | 0.5952 | 500  | 0.1212          | 0.9722   | 0.9721 | 0.9741    | 0.9718 |
| 0.1061        | 0.6548 | 550  | 0.1224          | 0.9726   | 0.9728 | 0.9750    | 0.9728 |
| 0.0996        | 0.7143 | 600  | 0.0812          | 0.9817   | 0.9815 | 0.9819    | 0.9816 |
| 0.1196        | 0.7738 | 650  | 0.0726          | 0.9859   | 0.9858 | 0.9862    | 0.9857 |
| 0.101         | 0.8333 | 700  | 0.0711          | 0.9853   | 0.9854 | 0.9856    | 0.9853 |
| 0.1159        | 0.8929 | 750  | 0.1012          | 0.9792   | 0.9795 | 0.9803    | 0.9796 |
| 0.086         | 0.9524 | 800  | 0.0693          | 0.9870   | 0.9871 | 0.9874    | 0.9870 |
| 0.0742        | 1.0119 | 850  | 0.0619          | 0.9885   | 0.9886 | 0.9888    | 0.9885 |
| 0.0713        | 1.0714 | 900  | 0.0517          | 0.9896   | 0.9896 | 0.9897    | 0.9896 |
| 0.02          | 1.1310 | 950  | 0.0707          | 0.9869   | 0.9870 | 0.9874    | 0.9870 |
| 0.038         | 1.1905 | 1000 | 0.0455          | 0.9920   | 0.9920 | 0.9920    | 0.9919 |
| 0.0378        | 1.25   | 1050 | 0.0485          | 0.9906   | 0.9906 | 0.9906    | 0.9906 |
| 0.0257        | 1.3095 | 1100 | 0.0452          | 0.9921   | 0.9921 | 0.9922    | 0.9921 |
| 0.0454        | 1.3690 | 1150 | 0.0494          | 0.9905   | 0.9905 | 0.9906    | 0.9905 |
| 0.0174        | 1.4286 | 1200 | 0.0404          | 0.9923   | 0.9922 | 0.9922    | 0.9922 |
| 0.0425        | 1.4881 | 1250 | 0.0627          | 0.9879   | 0.9877 | 0.9879    | 0.9877 |
| 0.0489        | 1.5476 | 1300 | 0.0525          | 0.9908   | 0.9907 | 0.9907    | 0.9907 |
| 0.0816        | 1.6071 | 1350 | 0.0439          | 0.9918   | 0.9918 | 0.9919    | 0.9917 |
| 0.0375        | 1.6667 | 1400 | 0.0434          | 0.9921   | 0.9920 | 0.9920    | 0.9921 |
| 0.0435        | 1.7262 | 1450 | 0.0368          | 0.9929   | 0.9928 | 0.9929    | 0.9929 |
| 0.0285        | 1.7857 | 1500 | 0.0364          | 0.9935   | 0.9934 | 0.9935    | 0.9934 |
| 0.0222        | 1.8452 | 1550 | 0.0332          | 0.9942   | 0.9942 | 0.9943    | 0.9941 |
| 0.0311        | 1.9048 | 1600 | 0.0394          | 0.9929   | 0.9929 | 0.9930    | 0.9929 |
| 0.0269        | 1.9643 | 1650 | 0.0359          | 0.9935   | 0.9934 | 0.9935    | 0.9934 |
| 0.0258        | 2.0238 | 1700 | 0.0326          | 0.9937   | 0.9937 | 0.9938    | 0.9937 |
| 0.0046        | 2.0833 | 1750 | 0.0324          | 0.9945   | 0.9944 | 0.9945    | 0.9944 |
| 0.0152        | 2.1429 | 1800 | 0.0329          | 0.9946   | 0.9946 | 0.9948    | 0.9946 |
| 0.024         | 2.2024 | 1850 | 0.0305          | 0.9948   | 0.9947 | 0.9948    | 0.9947 |
| 0.0212        | 2.2619 | 1900 | 0.0333          | 0.9943   | 0.9943 | 0.9943    | 0.9943 |
| 0.0029        | 2.3214 | 1950 | 0.0322          | 0.9937   | 0.9937 | 0.9938    | 0.9937 |
| 0.0114        | 2.3810 | 2000 | 0.0342          | 0.9940   | 0.9940 | 0.9941    | 0.9940 |
| 0.0115        | 2.4405 | 2050 | 0.0328          | 0.9942   | 0.9941 | 0.9942    | 0.9941 |
| 0.0182        | 2.5    | 2100 | 0.0333          | 0.9937   | 0.9937 | 0.9938    | 0.9936 |
| 0.01          | 2.5595 | 2150 | 0.0314          | 0.9940   | 0.9940 | 0.9941    | 0.9940 |
| 0.0205        | 2.6190 | 2200 | 0.0325          | 0.9937   | 0.9937 | 0.9938    | 0.9937 |
| 0.0173        | 2.6786 | 2250 | 0.0335          | 0.9940   | 0.9940 | 0.9941    | 0.9940 |
| 0.0093        | 2.7381 | 2300 | 0.0340          | 0.9942   | 0.9942 | 0.9943    | 0.9941 |
| 0.0151        | 2.7976 | 2350 | 0.0327          | 0.9946   | 0.9947 | 0.9947    | 0.9946 |
| 0.0077        | 2.8571 | 2400 | 0.0321          | 0.9948   | 0.9948 | 0.9949    | 0.9948 |
| 0.0074        | 2.9167 | 2450 | 0.0311          | 0.9946   | 0.9946 | 0.9947    | 0.9946 |
| 0.0021        | 2.9762 | 2500 | 0.0310          | 0.9946   | 0.9946 | 0.9947    | 0.9946 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1