---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv2_data_aug_mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6838235294117647
    - name: F1
      type: f1
      value: 0.8122270742358079
---

<!-- 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. -->

# hBERTv2_data_aug_mrpc

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6240
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6319        | 1.0   | 980  | 0.6245          | 0.6838   | 0.8122 | 0.7480         |
| 0.6305        | 2.0   | 1960 | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
| 0.6303        | 3.0   | 2940 | 0.6259          | 0.6838   | 0.8122 | 0.7480         |
| 0.6302        | 4.0   | 3920 | 0.6252          | 0.6838   | 0.8122 | 0.7480         |
| 0.6302        | 5.0   | 4900 | 0.6241          | 0.6838   | 0.8122 | 0.7480         |
| 0.6302        | 6.0   | 5880 | 0.6241          | 0.6838   | 0.8122 | 0.7480         |
| 0.6301        | 7.0   | 6860 | 0.6242          | 0.6838   | 0.8122 | 0.7480         |


### Framework versions

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