transformers pytorch hybridbert text-classification generated_from_trainer en dataset:glue base_model:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48
datasets:
- glue
language:
- en
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hBERTv2_new_pretrain_w_init_48_ver2_qnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QNLI
      type: glue
      config: qnli
      split: validation
      args: qnli
    metrics:
    - type: accuracy
      value: 0.5053999633900788
      name: Accuracy
---

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# hBERTv2_new_pretrain_w_init_48_ver2_qnli

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6931
- Accuracy: 0.5054

## 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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7008        | 1.0   | 1637  | 0.6943          | 0.5054   |
| 0.6946        | 2.0   | 3274  | 0.6931          | 0.5054   |
| 0.6938        | 3.0   | 4911  | 0.6932          | 0.4946   |
| 0.6943        | 4.0   | 6548  | 0.6934          | 0.5054   |
| 0.694         | 5.0   | 8185  | 0.6933          | 0.4946   |
| 0.6932        | 6.0   | 9822  | 0.6931          | 0.5054   |
| 0.6934        | 7.0   | 11459 | 0.6931          | 0.5054   |
| 0.6932        | 8.0   | 13096 | 0.6931          | 0.5054   |
| 0.6932        | 9.0   | 14733 | 0.6932          | 0.4946   |
| 0.6932        | 10.0  | 16370 | 0.6933          | 0.4946   |
| 0.6932        | 11.0  | 18007 | 0.6931          | 0.5054   |
| 0.6932        | 12.0  | 19644 | 0.6931          | 0.5054   |
| 0.6932        | 13.0  | 21281 | 0.6931          | 0.4946   |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
