---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv1_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: 0.598572213069742
---

<!-- 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_w_init__qnli

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6672
- Accuracy: 0.5986

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6909        | 1.0   | 819  | 0.6783          | 0.5653   |
| 0.684         | 2.0   | 1638 | 0.6904          | 0.5100   |
| 0.6765        | 3.0   | 2457 | 0.6709          | 0.5881   |
| 0.6696        | 4.0   | 3276 | 0.6774          | 0.5695   |
| 0.6676        | 5.0   | 4095 | 0.6704          | 0.5903   |
| 0.6626        | 6.0   | 4914 | 0.6672          | 0.5986   |
| 0.6661        | 7.0   | 5733 | 0.6703          | 0.5907   |
| 0.6642        | 8.0   | 6552 | 0.6693          | 0.5960   |
| 0.6698        | 9.0   | 7371 | 0.6733          | 0.5799   |
| 0.6724        | 10.0  | 8190 | 0.6815          | 0.5636   |
| 0.68          | 11.0  | 9009 | 0.6908          | 0.5427   |


### Framework versions

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