0 1
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- augmented_glue_sst2
metrics:
- accuracy
model-index:
- name: miny-bert-aug-sst2-distilled
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: augmented_glue_sst2
      type: augmented_glue_sst2
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9128440366972477
---

<!-- 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. -->

# miny-bert-aug-sst2-distilled

This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the augmented_glue_sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2643
- Accuracy: 0.9128

## 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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.602         | 1.0   | 6227  | 0.3389          | 0.9186   |
| 0.4195        | 2.0   | 12454 | 0.2989          | 0.9151   |
| 0.3644        | 3.0   | 18681 | 0.2794          | 0.9117   |
| 0.3304        | 4.0   | 24908 | 0.2793          | 0.9106   |
| 0.3066        | 5.0   | 31135 | 0.2659          | 0.9186   |
| 0.2881        | 6.0   | 37362 | 0.2668          | 0.9140   |
| 0.2754        | 7.0   | 43589 | 0.2643          | 0.9128   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
