---
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 MNLI
      type: glue
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8417412530512612
---

<!-- 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 MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4506
- Accuracy: 0.8417

## 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 |                                                                                                                                                                                                                                                                             |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.3038        | 1.0   | 12272 | 0.4950          | 0.8238   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})])    |
| 0.3038        | 1.0   | 12272 | 0.4592          | 0.8385   | 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})]) |
| 0.1683        | 2.0   | 24544 | 0.4678          | 0.8326   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})])    |
| 0.1683        | 2.0   | 24544 | 0.4285          | 0.8479   | 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})]) |
| 0.1132        | 3.0   | 36816 | 0.4638          | 0.8381   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})])    |
| 0.1132        | 3.0   | 36816 | 0.4231          | 0.8492   | 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})]) |
| 0.0894        | 4.0   | 49088 | 0.4678          | 0.8383   | OrderedDict([(<ElasticityDim.WIDTH: 'width'>, {0: 256, 1: 256, 2: 192, 3: 320, 4: 192, 5: 384, 6: 128, 7: 256, 8: 256, 9: 256, 10: 192, 11: 256, 12: 1542, 13: 1611, 14: 1891, 15: 1877, 16: 1825, 17: 1790, 18: 1678, 19: 1544, 20: 1223, 21: 628, 22: 345, 23: 213})])    |
| 0.0894        | 4.0   | 49088 | 0.4261          | 0.8497   | 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