---
library_name: transformers
license: apache-2.0
base_model: sandeshrajx/bert-fraud-classification-test-mass
tags:
- generated_from_trainer
metrics:
- f1
- precision
model-index:
- name: bert-fraud-classification-test-mass-4
  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/xnpaqmt6)
# bert-fraud-classification-test-mass-4

This model is a fine-tuned version of [sandeshrajx/bert-fraud-classification-test-mass](https://huggingface.co/sandeshrajx/bert-fraud-classification-test-mass) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3408
- F1: 0.8508
- Precision: 0.8627
- Val Accuracy: 0.8663

## 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------------:|
| 0.3874        | 0.1743 | 40   | 0.3197          | 0.8579 | 0.8936    | 0.8758       |
| 0.3614        | 0.3486 | 80   | 0.3427          | 0.8382 | 0.8846    | 0.8603       |
| 0.3563        | 0.5229 | 120  | 0.3505          | 0.8435 | 0.8468    | 0.8584       |
| 0.4263        | 0.6972 | 160  | 0.3407          | 0.8454 | 0.8589    | 0.8617       |
| 0.3514        | 0.8715 | 200  | 0.3473          | 0.8413 | 0.8421    | 0.8560       |
| 0.259         | 1.0458 | 240  | 0.3378          | 0.8417 | 0.9106    | 0.8663       |
| 0.3148        | 1.2200 | 280  | 0.3543          | 0.8479 | 0.8889    | 0.8679       |
| 0.2685        | 1.3943 | 320  | 0.3507          | 0.8501 | 0.9040    | 0.8715       |
| 0.2271        | 1.5686 | 360  | 0.3773          | 0.8406 | 0.8262    | 0.8526       |
| 0.376         | 1.7429 | 400  | 0.3412          | 0.8520 | 0.8731    | 0.8687       |
| 0.2739        | 1.9172 | 440  | 0.3408          | 0.8508 | 0.8627    | 0.8663       |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1