pytorch tensorboard bert multiple-choice dataset:piqa transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- piqa
metrics:
- accuracy
model-index:
- name: finetuned-bert-piqa
  results: []
---

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# finetuned-bert-piqa

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the piqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6603
- Accuracy: 0.6518

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 251  | 0.6751          | 0.6115   |
| 0.6628        | 2.0   | 502  | 0.6556          | 0.6534   |
| 0.6628        | 3.0   | 753  | 0.6603          | 0.6518   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
