---
base_model: gokuls/HBERTv1_48_L4_H64_A2
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L4_H64_A2_emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7725
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# HBERTv1_48_L4_H64_A2_emotion

This model is a fine-tuned version of [gokuls/HBERTv1_48_L4_H64_A2](https://huggingface.co/gokuls/HBERTv1_48_L4_H64_A2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7101
- Accuracy: 0.7725

## 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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6141        | 1.0   | 250  | 1.5167          | 0.4625   |
| 1.4469        | 2.0   | 500  | 1.3688          | 0.492    |
| 1.2843        | 3.0   | 750  | 1.1952          | 0.5785   |
| 1.1128        | 4.0   | 1000 | 1.0349          | 0.6375   |
| 0.9601        | 5.0   | 1250 | 0.9099          | 0.6835   |
| 0.8528        | 6.0   | 1500 | 0.8403          | 0.726    |
| 0.7793        | 7.0   | 1750 | 0.7835          | 0.74     |
| 0.7291        | 8.0   | 2000 | 0.7433          | 0.7635   |
| 0.6851        | 9.0   | 2250 | 0.7165          | 0.77     |
| 0.6717        | 10.0  | 2500 | 0.7101          | 0.7725   |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.0