---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERT-Router-v1
  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. -->

# BERT-Router-v1

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Accuracy: 0.955
- Auc: 0.992

## 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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Auc   |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|
| 0.134         | 2.1778 | 50   | 0.1322          | 0.949    | 0.991 |
| 0.1212        | 4.3556 | 100  | 0.1267          | 0.951    | 0.991 |
| 0.1199        | 6.5333 | 150  | 0.1223          | 0.953    | 0.992 |
| 0.1193        | 8.7111 | 200  | 0.1199          | 0.955    | 0.992 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu121
- Datasets 3.3.0
- Tokenizers 0.21.0