---
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/ehdpl54g)
[<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/ehdpl54g)
# 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: 0.6431
- F1: 0.7435
- Precision: 0.6605
- Val Accuracy: {'accuracy': 0.706}

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Val Accuracy        |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:-------------------:|
| 0.601         | 0.32  | 40   | 0.6152          | 0.7039 | 0.6597    | {'accuracy': 0.682} |
| 0.6332        | 0.64  | 80   | 0.6143          | 0.7068 | 0.6515    | {'accuracy': 0.679} |
| 0.4862        | 0.96  | 120  | 0.5791          | 0.7151 | 0.7137    | {'accuracy': 0.714} |
| 0.6297        | 1.28  | 160  | 0.6323          | 0.7281 | 0.6495    | {'accuracy': 0.69}  |
| 0.6164        | 1.6   | 200  | 0.5010          | 0.7522 | 0.8345    | {'accuracy': 0.774} |
| 0.6333        | 1.92  | 240  | 0.5824          | 0.7310 | 0.6828    | {'accuracy': 0.71}  |
| 0.4465        | 2.24  | 280  | 0.5335          | 0.7579 | 0.7695    | {'accuracy': 0.761} |
| 0.5342        | 2.56  | 320  | 0.5065          | 0.7644 | 0.8040    | {'accuracy': 0.775} |
| 0.5462        | 2.88  | 360  | 0.6431          | 0.7435 | 0.6605    | {'accuracy': 0.706} |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1