---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: bert-base-multilingual-cased-som
  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-multilingual-cased-som

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1536
- Accuracy: 0.6696
- F1 Binary: 0.2916
- Precision: 0.1917
- Recall: 0.6088

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:---------:|:------:|
| No log        | 1.0   | 255  | 0.1615          | 0.6841   | 0.2548    | 0.1730    | 0.4835 |
| 0.1677        | 2.0   | 510  | 0.1626          | 0.4153   | 0.2399    | 0.1404    | 0.8264 |
| 0.1677        | 3.0   | 765  | 0.1736          | 0.7246   | 0.2826    | 0.1993    | 0.4857 |
| 0.1349        | 4.0   | 1020 | 0.1536          | 0.6696   | 0.2916    | 0.1917    | 0.6088 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0