---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv1_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
should probably proofread and complete it, then remove this comment. -->

# hBERTv1_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: 0.6877
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7359        | 1.0   | 3    | 0.7194          | 0.4366   |
| 0.6989        | 2.0   | 6    | 0.6899          | 0.5634   |
| 0.7031        | 3.0   | 9    | 0.7028          | 0.4366   |
| 0.7012        | 4.0   | 12   | 0.6889          | 0.5634   |
| 0.697         | 5.0   | 15   | 0.6894          | 0.5634   |
| 0.6971        | 6.0   | 18   | 0.7015          | 0.4366   |
| 0.7           | 7.0   | 21   | 0.6882          | 0.5634   |
| 0.6928        | 8.0   | 24   | 0.6890          | 0.5634   |
| 0.6932        | 9.0   | 27   | 0.6897          | 0.5634   |
| 0.6954        | 10.0  | 30   | 0.6956          | 0.4366   |
| 0.6962        | 11.0  | 33   | 0.6913          | 0.5634   |
| 0.6956        | 12.0  | 36   | 0.6877          | 0.5634   |
| 0.6973        | 13.0  | 39   | 0.6926          | 0.5070   |
| 0.6978        | 14.0  | 42   | 0.6933          | 0.4930   |
| 0.6945        | 15.0  | 45   | 0.6883          | 0.5634   |
| 0.6974        | 16.0  | 48   | 0.6881          | 0.5634   |
| 0.6936        | 17.0  | 51   | 0.6925          | 0.5211   |


### Framework versions

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