---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-fine-tuned-boolq
  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-fine-tuned-boolq

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: 2.0432
- Accuracy: 0.7489

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2975        | 1.0   | 2357  | 1.3526          | 0.7401   |
| 0.224         | 2.0   | 4714  | 1.5869          | 0.7312   |
| 0.2462        | 3.0   | 7071  | 1.6905          | 0.7419   |
| 0.1075        | 4.0   | 9428  | 1.8846          | 0.7529   |
| 0.0262        | 5.0   | 11785 | 2.0432          | 0.7489   |


### Framework versions

- Transformers 4.39.3
- Pytorch 1.13.0
- Datasets 2.18.0
- Tokenizers 0.15.2