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: hBERTv1_mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MRPC
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.6862745098039216
      name: Accuracy
    - type: f1
      value: 0.7999999999999999
      name: F1
---

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# hBERTv1_mrpc

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 MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6051
- Accuracy: 0.6863
- F1: 0.8000
- Combined Score: 0.7431

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6536        | 1.0   | 15   | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
| 0.6275        | 2.0   | 30   | 0.6174          | 0.7010   | 0.8117 | 0.7564         |
| 0.6129        | 3.0   | 45   | 0.6089          | 0.6961   | 0.8182 | 0.7571         |
| 0.6087        | 4.0   | 60   | 0.6062          | 0.6887   | 0.8130 | 0.7508         |
| 0.5939        | 5.0   | 75   | 0.6104          | 0.6863   | 0.7935 | 0.7399         |
| 0.5707        | 6.0   | 90   | 0.6184          | 0.7083   | 0.8183 | 0.7633         |
| 0.5426        | 7.0   | 105  | 0.6051          | 0.6863   | 0.8000 | 0.7431         |
| 0.4819        | 8.0   | 120  | 0.6560          | 0.6936   | 0.8019 | 0.7478         |
| 0.4279        | 9.0   | 135  | 0.6673          | 0.6887   | 0.7678 | 0.7283         |
| 0.3374        | 10.0  | 150  | 0.8092          | 0.6863   | 0.7902 | 0.7382         |
| 0.2789        | 11.0  | 165  | 0.9342          | 0.6887   | 0.7935 | 0.7411         |
| 0.2216        | 12.0  | 180  | 0.9708          | 0.6838   | 0.7810 | 0.7324         |


### Framework versions

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