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
tags:
- generated_from_trainer
model-index:
- name: hBERTv2_data_aug_rte
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE RTE
      type: glue
      args: rte
    metrics:
    - type: accuracy
      value: 0.49097472924187724
      name: Accuracy
---

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# hBERTv2_data_aug_rte

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 RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3232
- Accuracy: 0.4910

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6262        | 1.0   | 568  | 1.3232          | 0.4910   |
| 0.0855        | 2.0   | 1136 | 2.3457          | 0.4946   |
| 0.022         | 3.0   | 1704 | 2.9797          | 0.5018   |
| 0.0128        | 4.0   | 2272 | 2.6395          | 0.5271   |
| 0.0085        | 5.0   | 2840 | 3.1634          | 0.5379   |
| 0.0059        | 6.0   | 3408 | 3.5948          | 0.5199   |


### Framework versions

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