---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: first_try
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      config: qnli
      split: validation
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8973091707852828
---

<!-- 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. -->

# first_try

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5902
- Accuracy: 0.8973

## 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: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |                                                                                                                                                                                                                                                                             |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.8032        | 1.0   | 3274  | 0.3192          | 0.8891   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])     |
| 0.8032        | 1.0   | 3274  | 0.2594          | 0.9059   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.5165        | 2.0   | 6548  | 0.3693          | 0.8925   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])     |
| 0.5165        | 2.0   | 6548  | 0.2860          | 0.9200   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.2972        | 3.0   | 9822  | 0.4699          | 0.8949   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])     |
| 0.2972        | 3.0   | 9822  | 0.3910          | 0.9162   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.1611        | 4.0   | 13096 | 0.5542          | 0.8964   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])     |
| 0.1611        | 4.0   | 13096 | 0.4473          | 0.9160   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.1155        | 5.0   | 16370 | 0.5926          | 0.8969   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])     |
| 0.1155        | 5.0   | 16370 | 0.4788          | 0.9180   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |
| 0.0867        | 6.0   | 19644 | 0.6002          | 0.8958   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 512, 1: 320, 2: 320, 3: 576, 4: 576, 5: 512, 6: 448, 7: 448, 8: 448, 9: 320, 10: 384, 11: 448, 12: 1104, 13: 1066, 14: 1126, 15: 1102, 16: 1067, 17: 1023, 18: 1048, 19: 1061, 20: 984, 21: 772, 22: 609, 23: 205})])     |
| 0.0867        | 6.0   | 19644 | 0.4831          | 0.9176   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) |


### Framework versions

- Transformers 4.29.1
- Pytorch 1.12.1
- Datasets 2.13.1
- Tokenizers 0.13.3