---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: microsoft/deberta-base
model-index:
- name: deberta-base-finetuned-wnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: wnli
      split: train
      args: wnli
    metrics:
    - type: accuracy
      value: 0.5633802816901409
      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. -->

# deberta-base-finetuned-wnli

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6926
- Accuracy: 0.5634

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 0.6926          | 0.5634   |
| No log        | 2.0   | 80   | 0.6911          | 0.5634   |
| No log        | 3.0   | 120  | 0.6903          | 0.5634   |
| No log        | 4.0   | 160  | 0.6905          | 0.5634   |
| No log        | 5.0   | 200  | 0.6904          | 0.5634   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1