---
license: mit
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-boolq
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: boolq
      type: boolq
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7825688073394496
---

<!-- 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. -->

# roberta-base-finetuned-boolq

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4811
- Accuracy: 0.7826

## 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: 3.719487849449238e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5966        | 1.0   | 590  | 0.5603          | 0.7269   |
| 0.4151        | 2.0   | 1180 | 0.4811          | 0.7826   |


### Framework versions

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