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---
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
|