---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine_tuned_boolq_bert
  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. -->

# fine_tuned_boolq_bert

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: 1.5736
- Accuracy: 0.7222
- F1: 0.7325

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.6443        | 4.1667  | 50   | 0.5606          | 0.7778   | 0.6806 |
| 0.3932        | 8.3333  | 100  | 0.6016          | 0.6111   | 0.6255 |
| 0.126         | 12.5    | 150  | 1.0887          | 0.5      | 0.5418 |
| 0.0166        | 16.6667 | 200  | 1.5543          | 0.5556   | 0.5829 |
| 0.0041        | 20.8333 | 250  | 1.5032          | 0.7222   | 0.7325 |
| 0.0022        | 25.0    | 300  | 1.7354          | 0.6667   | 0.6872 |
| 0.0018        | 29.1667 | 350  | 1.5756          | 0.6667   | 0.6667 |
| 0.0016        | 33.3333 | 400  | 1.5736          | 0.7222   | 0.7325 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1