---
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- google/boolq
metrics:
- accuracy
model-index:
- name: Bert Base Uncased Boolean Question Answer model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: boolq
      type: google/boolq
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7149847094801223
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Bert Base Uncased Boolean Question Answer model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1993
- Accuracy: 0.7150

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2317        | 0.9966 | 147  | 0.2198          | 0.6569   |
| 0.2           | 2.0    | 295  | 0.2002          | 0.6960   |
| 0.1741        | 2.9966 | 442  | 0.1968          | 0.7122   |
| 0.1469        | 3.9864 | 588  | 0.1993          | 0.7150   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1