---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_wnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE WNLI
      type: glue
      config: wnli
      split: validation
      args: wnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5633802816901409
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hBERTv2_wnli

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6833
- Accuracy: 0.5634

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7351        | 1.0   | 3    | 0.7260          | 0.5211   |
| 0.7223        | 2.0   | 6    | 0.6833          | 0.5634   |
| 0.7189        | 3.0   | 9    | 0.7110          | 0.4507   |
| 0.708         | 4.0   | 12   | 0.7059          | 0.5352   |
| 0.7032        | 5.0   | 15   | 0.6925          | 0.5352   |
| 0.6987        | 6.0   | 18   | 0.7121          | 0.4225   |
| 0.7109        | 7.0   | 21   | 0.6928          | 0.5352   |


### Framework versions

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