---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- f1
- precision
model-index:
- name: bert-fraud-classification-test
  results: []
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sandeshrajx/ultron-nlp/runs/dti4tuxz)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sandeshrajx/ultron-nlp/runs/dti4tuxz)
# bert-fraud-classification-test

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3850
- F1: 0.7886
- Precision: 0.8400
- Val Accuracy: 0.8429

## 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: 44
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 88
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     | Precision | Val Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------------:|
| 0.5784        | 0.1386 | 40   | 0.4995          | 0.6845 | 0.8251    | 0.7873       |
| 0.4545        | 0.2773 | 80   | 0.4295          | 0.7430 | 0.8504    | 0.8200       |
| 0.4566        | 0.4159 | 120  | 0.4116          | 0.7564 | 0.8483    | 0.8266       |
| 0.4468        | 0.5546 | 160  | 0.4149          | 0.7366 | 0.8827    | 0.8217       |
| 0.3454        | 0.6932 | 200  | 0.4062          | 0.7442 | 0.8812    | 0.8254       |
| 0.3333        | 0.8319 | 240  | 0.4046          | 0.7475 | 0.8993    | 0.8296       |
| 0.4618        | 0.9705 | 280  | 0.3973          | 0.7797 | 0.8279    | 0.8358       |
| 0.3581        | 1.1092 | 320  | 0.3869          | 0.7843 | 0.8431    | 0.8410       |
| 0.3946        | 1.2478 | 360  | 0.3869          | 0.7823 | 0.8471    | 0.8405       |
| 0.3263        | 1.3865 | 400  | 0.3875          | 0.7850 | 0.8379    | 0.8405       |
| 0.31          | 1.5251 | 440  | 0.3907          | 0.7721 | 0.8835    | 0.8404       |
| 0.2547        | 1.6638 | 480  | 0.3822          | 0.7855 | 0.8561    | 0.8437       |
| 0.2613        | 1.8024 | 520  | 0.3886          | 0.7883 | 0.8346    | 0.8418       |
| 0.3142        | 1.9411 | 560  | 0.3850          | 0.7886 | 0.8400    | 0.8429       |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0