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_new_pretrain_w_init_48_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: 0.6838235294117647
      name: Accuracy
    - type: f1
      value: 0.8122270742358079
      name: F1
---

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# hBERTv1_new_pretrain_w_init_48_mrpc

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6229
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6607        | 1.0   | 29   | 0.6262          | 0.6838   | 0.8122 | 0.7480         |
| 0.6421        | 2.0   | 58   | 0.6368          | 0.6838   | 0.8122 | 0.7480         |
| 0.6411        | 3.0   | 87   | 0.6258          | 0.6838   | 0.8122 | 0.7480         |
| 0.6406        | 4.0   | 116  | 0.6422          | 0.6838   | 0.8122 | 0.7480         |
| 0.6364        | 5.0   | 145  | 0.6263          | 0.6838   | 0.8122 | 0.7480         |
| 0.6322        | 6.0   | 174  | 0.6253          | 0.6838   | 0.8122 | 0.7480         |
| 0.6398        | 7.0   | 203  | 0.6289          | 0.6838   | 0.8122 | 0.7480         |
| 0.6363        | 8.0   | 232  | 0.6267          | 0.6838   | 0.8122 | 0.7480         |
| 0.6374        | 9.0   | 261  | 0.6375          | 0.6838   | 0.8122 | 0.7480         |
| 0.6374        | 10.0  | 290  | 0.6248          | 0.6838   | 0.8122 | 0.7480         |
| 0.638         | 11.0  | 319  | 0.6262          | 0.6838   | 0.8122 | 0.7480         |
| 0.6353        | 12.0  | 348  | 0.6236          | 0.6838   | 0.8122 | 0.7480         |
| 0.6338        | 13.0  | 377  | 0.6263          | 0.6838   | 0.8122 | 0.7480         |
| 0.637         | 14.0  | 406  | 0.6250          | 0.6838   | 0.8122 | 0.7480         |
| 0.6375        | 15.0  | 435  | 0.6229          | 0.6838   | 0.8122 | 0.7480         |
| 0.7037        | 16.0  | 464  | 0.6438          | 0.6838   | 0.8122 | 0.7480         |
| 0.6198        | 17.0  | 493  | 0.6242          | 0.6961   | 0.8038 | 0.7499         |
| 0.5847        | 18.0  | 522  | 0.6260          | 0.6740   | 0.7742 | 0.7241         |
| 0.4983        | 19.0  | 551  | 0.7174          | 0.7034   | 0.8158 | 0.7596         |
| 0.4245        | 20.0  | 580  | 0.7737          | 0.6789   | 0.7828 | 0.7308         |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
