---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-binary-clf
  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-binary-clf

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1279
- Accuracy: 0.9748
- Precision: 0.9825
- Recall: 0.9655
- F1: 0.9739

## 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: 16
- eval_batch_size: 16
- 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
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4681        | 1.0   | 30   | 0.3535          | 0.8824   | 0.8143    | 0.9828 | 0.8906 |
| 0.2283        | 2.0   | 60   | 0.1883          | 0.9412   | 0.9322    | 0.9483 | 0.9402 |
| 0.1144        | 3.0   | 90   | 0.1279          | 0.9748   | 0.9825    | 0.9655 | 0.9739 |
| 0.0695        | 4.0   | 120  | 0.1230          | 0.9580   | 0.9818    | 0.9310 | 0.9558 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1