---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv2_new_pretrain_w_init__qnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      config: qnli
      split: validation
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5958264689730917
---

<!-- 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. -->

# hBERTv2_new_pretrain_w_init__qnli

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6573
- Accuracy: 0.5958

## 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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6809        | 1.0   | 819  | 0.6640          | 0.6021   |
| 0.6539        | 2.0   | 1638 | 0.6573          | 0.5958   |
| 0.6358        | 3.0   | 2457 | 0.6694          | 0.5942   |
| 0.6313        | 4.0   | 3276 | 0.6898          | 0.5790   |
| 0.6293        | 5.0   | 4095 | 0.6801          | 0.5880   |
| 0.6253        | 6.0   | 4914 | 0.6829          | 0.5925   |
| 0.6166        | 7.0   | 5733 | 0.6865          | 0.5916   |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3