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

# prompt_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: 0.6468
- Accuracy: 0.7778
- F1: 0.7481

## 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
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 12   | 0.6797          | 0.6667   | 0.6872 |
| No log        | 2.0   | 24   | 0.6667          | 0.6667   | 0.6667 |
| No log        | 3.0   | 36   | 0.6563          | 0.6667   | 0.6667 |
| No log        | 4.0   | 48   | 0.6507          | 0.7222   | 0.7072 |
| No log        | 5.0   | 60   | 0.6478          | 0.7222   | 0.7072 |
| No log        | 6.0   | 72   | 0.6468          | 0.7778   | 0.7481 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1