transformers pytorch tensorboard hybridbert text-classification generated_from_trainer en dataset:glue model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: hBERTv2_new_pretrain_48_KD_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8244867672520405
      name: Accuracy
    - type: f1
      value: 0.7649085608269283
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hBERTv2_new_pretrain_48_KD_qqp

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48_KD](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48_KD) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4160
- Accuracy: 0.8245
- F1: 0.7649
- Combined Score: 0.7947

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5305        | 1.0   | 2843  | 0.4755          | 0.7733   | 0.6749 | 0.7241         |
| 0.4434        | 2.0   | 5686  | 0.4351          | 0.7950   | 0.7260 | 0.7605         |
| 0.3858        | 3.0   | 8529  | 0.4173          | 0.8063   | 0.7210 | 0.7637         |
| 0.3409        | 4.0   | 11372 | 0.4201          | 0.7998   | 0.7579 | 0.7788         |
| 0.303         | 5.0   | 14215 | 0.4274          | 0.8169   | 0.7577 | 0.7873         |
| 0.2727        | 6.0   | 17058 | 0.4403          | 0.8186   | 0.7638 | 0.7912         |
| 0.2449        | 7.0   | 19901 | 0.4160          | 0.8245   | 0.7649 | 0.7947         |
| 0.2241        | 8.0   | 22744 | 0.4429          | 0.8271   | 0.7518 | 0.7895         |
| 0.2054        | 9.0   | 25587 | 0.4941          | 0.8287   | 0.7659 | 0.7973         |
| 0.1905        | 10.0  | 28430 | 0.4992          | 0.8264   | 0.7744 | 0.8004         |
| 0.1768        | 11.0  | 31273 | 0.4901          | 0.8303   | 0.7686 | 0.7995         |
| 0.1655        | 12.0  | 34116 | 0.5513          | 0.8276   | 0.7446 | 0.7861         |


### Framework versions

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