---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv1_qqp
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8679940638139995
    - name: F1
      type: f1
      value: 0.8221652060910999
---

<!-- 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_qqp

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3039
- Accuracy: 0.8680
- F1: 0.8222
- Combined Score: 0.8451

## 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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4011        | 1.0   | 1422  | 0.3665          | 0.8286   | 0.7947 | 0.8116         |
| 0.3026        | 2.0   | 2844  | 0.3111          | 0.8625   | 0.8171 | 0.8398         |
| 0.2472        | 3.0   | 4266  | 0.3039          | 0.8680   | 0.8222 | 0.8451         |
| 0.1983        | 4.0   | 5688  | 0.3232          | 0.8737   | 0.8327 | 0.8532         |
| 0.157         | 5.0   | 7110  | 0.3742          | 0.8717   | 0.8194 | 0.8456         |
| 0.1251        | 6.0   | 8532  | 0.4009          | 0.8716   | 0.8146 | 0.8431         |
| 0.1009        | 7.0   | 9954  | 0.4471          | 0.8699   | 0.8300 | 0.8500         |
| 0.0828        | 8.0   | 11376 | 0.4176          | 0.8781   | 0.8354 | 0.8568         |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2