---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv1_sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE SST2
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7901376146788991
---

<!-- 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. -->

# hBERTv1_sst2

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 SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4525
- Accuracy: 0.7901

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6905        | 1.0   | 264  | 0.6919          | 0.5252   |
| 0.6609        | 2.0   | 528  | 0.6088          | 0.6915   |
| 0.4152        | 3.0   | 792  | 0.4525          | 0.7901   |
| 0.2611        | 4.0   | 1056 | 0.4627          | 0.8096   |
| 0.1953        | 5.0   | 1320 | 0.4894          | 0.8073   |
| 0.1588        | 6.0   | 1584 | 0.6002          | 0.8016   |
| 0.1336        | 7.0   | 1848 | 0.6467          | 0.8062   |
| 0.1117        | 8.0   | 2112 | 0.6409          | 0.8062   |


### Framework versions

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