---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv1_data_aug_wnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE WNLI
      type: glue
      args: wnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.323943661971831
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hBERTv1_data_aug_wnli

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8232
- Accuracy: 0.3239

## 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6916        | 1.0   | 218  | 0.8232          | 0.3239   |
| 0.5909        | 2.0   | 436  | 2.9065          | 0.0704   |
| 0.3754        | 3.0   | 654  | 4.7671          | 0.0845   |
| 0.2639        | 4.0   | 872  | 5.6922          | 0.1127   |
| 0.1921        | 5.0   | 1090 | 5.9948          | 0.0845   |
| 0.1317        | 6.0   | 1308 | 6.7444          | 0.0986   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2