---
library_name: transformers
tags:
- trackio
- trackio:https://huggingface.co/spaces/ciphertext/trackio
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vijil-bias-detector-v3
  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. -->

<a href="https://huggingface.co/spaces/ciphertext/trackio" target="_blank"><img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" style="height: 40px;"/></a>
# vijil-bias-detector-v3

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1666
- Accuracy: 0.9157
- F1: 0.9160
- Precision: 0.8965
- Recall: 0.9364

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6340        | 0.2497 | 200  | 0.5716          | 0.7364   | 0.7100 | 0.7720    | 0.6571 |
| 0.4299        | 0.4994 | 400  | 0.4346          | 0.7464   | 0.7019 | 0.8299    | 0.6081 |
| 0.3864        | 0.7491 | 600  | 0.3375          | 0.7864   | 0.7992 | 0.7421    | 0.8658 |
| 0.3235        | 0.9988 | 800  | 0.3435          | 0.7701   | 0.7260 | 0.8752    | 0.6202 |
| 0.3131        | 1.2484 | 1000 | 0.2941          | 0.8235   | 0.8265 | 0.7988    | 0.8562 |
| 0.2746        | 1.4981 | 1200 | 0.3141          | 0.8032   | 0.7764 | 0.8780    | 0.6959 |
| 0.1990        | 1.7478 | 1400 | 0.2109          | 0.8894   | 0.8913 | 0.8612    | 0.9237 |
| 0.1862        | 1.9975 | 1600 | 0.1868          | 0.8994   | 0.9021 | 0.8642    | 0.9434 |
| 0.1882        | 2.2472 | 1800 | 0.1825          | 0.9069   | 0.9065 | 0.8947    | 0.9186 |
| 0.1755        | 2.4969 | 2000 | 0.1748          | 0.9101   | 0.9118 | 0.8790    | 0.9472 |
| 0.1558        | 2.7466 | 2200 | 0.1721          | 0.9129   | 0.9131 | 0.8949    | 0.9319 |
| 0.1583        | 2.9963 | 2400 | 0.1773          | 0.9041   | 0.9069 | 0.8667    | 0.9510 |
| 0.1349        | 3.2459 | 2600 | 0.1795          | 0.9082   | 0.9061 | 0.9101    | 0.9020 |
| 0.1323        | 3.4956 | 2800 | 0.1652          | 0.9132   | 0.9121 | 0.9069    | 0.9173 |
| 0.1352        | 3.7453 | 3000 | 0.1664          | 0.9151   | 0.9154 | 0.8954    | 0.9364 |
| 0.1388        | 3.9950 | 3200 | 0.1623          | 0.9185   | 0.9191 | 0.8961    | 0.9434 |
| 0.1134        | 4.2447 | 3400 | 0.1713          | 0.9138   | 0.9130 | 0.905     | 0.9211 |
| 0.1056        | 4.4944 | 3600 | 0.1678          | 0.9182   | 0.9184 | 0.8999    | 0.9377 |
| 0.1123        | 4.7441 | 3800 | 0.1665          | 0.9157   | 0.9156 | 0.8999    | 0.9319 |
| 0.0985        | 4.9938 | 4000 | 0.1666          | 0.9157   | 0.9160 | 0.8965    | 0.9364 |
| 0.0985        | 5.0    | 4005 | 0.1666          | 0.9157   | 0.9160 | 0.8965    | 0.9364 |


### Framework versions

- Transformers 5.5.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2