---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- trackio
- trackio:https://huggingface.co/spaces/ciphertext/trackio
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vijil-bias-detector-v3b
  results: []
---

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<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-v3b

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2030
- Accuracy: 0.9194
- F1: 0.9200
- Precision: 0.8977
- Recall: 0.9434

## 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2831        | 0.6242 | 500  | 0.3003          | 0.8776   | 0.8853 | 0.8196    | 0.9625 |
| 0.2473        | 1.2484 | 1000 | 0.2369          | 0.9007   | 0.8993 | 0.8953    | 0.9033 |
| 0.2137        | 1.8727 | 1500 | 0.2166          | 0.9060   | 0.9052 | 0.8964    | 0.9141 |
| 0.1733        | 2.4969 | 2000 | 0.2127          | 0.9191   | 0.9194 | 0.9001    | 0.9396 |
| 0.1660        | 3.0    | 2403 | 0.2030          | 0.9194   | 0.9200 | 0.8977    | 0.9434 |


### Framework versions

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