---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: first_try
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9094978976007915
    - name: F1
      type: f1
      value: 0.8781916841439461
---

<!-- 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 QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2975
- Accuracy: 0.9095
- F1: 0.8782
- Combined Score: 0.8938

## 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 | F1     | Combined Score |                                                                                                                                                                                                                                                                             |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.3347        | 1.0   | 11371 | 0.2781          | 0.8986   | 0.8645 | 0.8816         | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})])     |
| 0.3347        | 1.0   | 11371 | 0.2633          | 0.9022   | 0.8709 | 0.8865         | 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})]) |
| 0.1664        | 2.0   | 22742 | 0.2724          | 0.9048   | 0.8736 | 0.8892         | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})])     |
| 0.1664        | 2.0   | 22742 | 0.2665          | 0.9106   | 0.8809 | 0.8958         | 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})]) |
| 0.092         | 3.0   | 34113 | 0.2872          | 0.9094   | 0.8786 | 0.8940         | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})])     |
| 0.092         | 3.0   | 34113 | 0.2708          | 0.9141   | 0.8846 | 0.8994         | 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})]) |
| 0.0693        | 4.0   | 45484 | 0.2966          | 0.9088   | 0.8771 | 0.8930         | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 320, 3: 192, 4: 256, 5: 256, 6: 192, 7: 256, 8: 64, 9: 192, 10: 192, 11: 512, 12: 1675, 13: 1666, 14: 1787, 15: 1791, 16: 1772, 17: 1751, 18: 1709, 19: 1590, 20: 1320, 21: 762, 22: 348, 23: 115})])     |
| 0.0693        | 4.0   | 45484 | 0.2779          | 0.9144   | 0.8846 | 0.8995         | 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