---
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 SST2
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9151376146788991
---

<!-- 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 SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3079
- Accuracy: 0.9151

## 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 |                                                                                                                                                                                                                                                                             |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.1786        | 1.0   | 2105 | 0.3156          | 0.9151   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 256, 3: 192, 4: 256, 5: 192, 6: 128, 7: 320, 8: 256, 9: 192, 10: 128, 11: 64, 12: 1585, 13: 1570, 14: 1775, 15: 1717, 16: 1679, 17: 1580, 18: 1621, 19: 1406, 20: 1188, 21: 930, 22: 828, 23: 654})])     |
| 0.1786        | 1.0   | 2105 | 0.2938          | 0.9220   | 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})]) |
| 0.0868        | 2.0   | 4210 | 0.3035          | 0.9197   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 256, 3: 192, 4: 256, 5: 192, 6: 128, 7: 320, 8: 256, 9: 192, 10: 128, 11: 64, 12: 1585, 13: 1570, 14: 1775, 15: 1717, 16: 1679, 17: 1580, 18: 1621, 19: 1406, 20: 1188, 21: 930, 22: 828, 23: 654})])     |
| 0.0868        | 2.0   | 4210 | 0.3008          | 0.9232   | 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})]) |
| 0.0371        | 3.0   | 6315 | 0.3073          | 0.9151   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 256, 3: 192, 4: 256, 5: 192, 6: 128, 7: 320, 8: 256, 9: 192, 10: 128, 11: 64, 12: 1585, 13: 1570, 14: 1775, 15: 1717, 16: 1679, 17: 1580, 18: 1621, 19: 1406, 20: 1188, 21: 930, 22: 828, 23: 654})])     |
| 0.0371        | 3.0   | 6315 | 0.2674          | 0.9289   | 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})]) |
| 0.0249        | 4.0   | 8420 | 0.3040          | 0.9140   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 320, 1: 256, 2: 256, 3: 192, 4: 256, 5: 192, 6: 128, 7: 320, 8: 256, 9: 192, 10: 128, 11: 64, 12: 1585, 13: 1570, 14: 1775, 15: 1717, 16: 1679, 17: 1580, 18: 1621, 19: 1406, 20: 1188, 21: 930, 22: 828, 23: 654})])     |
| 0.0249        | 4.0   | 8420 | 0.2658          | 0.9312   | 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