---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv2_mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7622549019607843
    - name: F1
      type: f1
      value: 0.8380634390651085
---

<!-- 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_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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9954
- Accuracy: 0.7623
- F1: 0.8381
- Combined Score: 0.8002

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6388        | 1.0   | 15   | 0.6297          | 0.6838   | 0.8122 | 0.7480         |
| 0.612         | 2.0   | 30   | 0.6315          | 0.6887   | 0.8135 | 0.7511         |
| 0.5725        | 3.0   | 45   | 0.5772          | 0.6936   | 0.8086 | 0.7511         |
| 0.512         | 4.0   | 60   | 0.6261          | 0.7010   | 0.8152 | 0.7581         |
| 0.3924        | 5.0   | 75   | 0.6433          | 0.7279   | 0.8195 | 0.7737         |
| 0.2592        | 6.0   | 90   | 0.7531          | 0.6863   | 0.7594 | 0.7228         |
| 0.1689        | 7.0   | 105  | 0.7904          | 0.7377   | 0.8158 | 0.7768         |
| 0.1292        | 8.0   | 120  | 0.9954          | 0.7623   | 0.8381 | 0.8002         |


### Framework versions

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