---
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-mass
  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. -->

[<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/bim0galx)
# bert-fraud-classification-test-mass

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: 0.3963
- F1: 0.8194
- Precision: 0.8445
- Val Accuracy: 0.8375

## 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------------:|
| 0.5197        | 0.1743 | 40   | 0.5468          | 0.7488 | 0.6907    | 0.7459       |
| 0.5208        | 0.3486 | 80   | 0.4667          | 0.7687 | 0.7890    | 0.7911       |
| 0.4235        | 0.5229 | 120  | 0.4351          | 0.7986 | 0.7898    | 0.8113       |
| 0.404         | 0.6972 | 160  | 0.4577          | 0.7972 | 0.7751    | 0.8066       |
| 0.3736        | 0.8715 | 200  | 0.4274          | 0.7914 | 0.8775    | 0.8240       |
| 0.419         | 1.0458 | 240  | 0.4058          | 0.7912 | 0.8737    | 0.8232       |
| 0.2701        | 1.2200 | 280  | 0.4075          | 0.8124 | 0.8393    | 0.8316       |
| 0.4345        | 1.3943 | 320  | 0.4246          | 0.8110 | 0.8088    | 0.8244       |
| 0.3258        | 1.5686 | 360  | 0.4023          | 0.7992 | 0.8788    | 0.8294       |
| 0.3938        | 1.7429 | 400  | 0.3945          | 0.8174 | 0.8447    | 0.8361       |
| 0.2529        | 1.9172 | 440  | 0.3963          | 0.8194 | 0.8445    | 0.8375       |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0