---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv1_new_pretrain_48_KD_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: 0.6009518579535054
---

<!-- 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_new_pretrain_48_KD_qnli

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48_KD](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48_KD) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6648
- Accuracy: 0.6010

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6818        | 1.0   | 819  | 0.6669          | 0.5966   |
| 0.6689        | 2.0   | 1638 | 0.6732          | 0.5858   |
| 0.6675        | 3.0   | 2457 | 0.6721          | 0.5810   |
| 0.663         | 4.0   | 3276 | 0.6793          | 0.5832   |
| 0.66          | 5.0   | 4095 | 0.6663          | 0.5999   |
| 0.6574        | 6.0   | 4914 | 0.6648          | 0.6010   |
| 0.6591        | 7.0   | 5733 | 0.6781          | 0.5731   |
| 0.659         | 8.0   | 6552 | 0.6685          | 0.5951   |
| 0.6697        | 9.0   | 7371 | 0.6793          | 0.5792   |
| 0.6755        | 10.0  | 8190 | 0.6829          | 0.5698   |
| 0.6794        | 11.0  | 9009 | 0.6780          | 0.5773   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3