---
license: mit
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
base_model: microsoft/deberta-v3-base
model-index:
- name: yes_no_qna_deberta_model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: super_glue
      type: super_glue
      config: boolq
      split: train
      args: boolq
    metrics:
    - type: accuracy
      value: 0.8507645259938837
      name: Accuracy
---

<!-- 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. -->

# yes_no_qna_deberta_model

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5570
- Accuracy: 0.8508

## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.583         | 1.0   | 590  | 0.4086          | 0.8251   |
| 0.348         | 2.0   | 1180 | 0.4170          | 0.8465   |
| 0.2183        | 3.0   | 1770 | 0.5570          | 0.8508   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2