transformers pytorch hybridbert text-classification generated_from_trainer dataset:emotion base_model:gokuls/HBERTv1_48_L8_H256_A4 base_model:finetune:gokuls/HBERTv1_48_L8_H256_A4 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/HBERTv1_48_L8_H256_A4
datasets:
- emotion
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: HBERTv1_48_L8_H256_A4_emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.878
      name: Accuracy
---

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# HBERTv1_48_L8_H256_A4_emotion

This model is a fine-tuned version of [gokuls/HBERTv1_48_L8_H256_A4](https://huggingface.co/gokuls/HBERTv1_48_L8_H256_A4) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4048
- Accuracy: 0.878

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.393         | 1.0   | 250  | 1.1681          | 0.561    |
| 1.004         | 2.0   | 500  | 0.8514          | 0.6875   |
| 0.6992        | 3.0   | 750  | 0.6116          | 0.806    |
| 0.4951        | 4.0   | 1000 | 0.5253          | 0.841    |
| 0.3991        | 5.0   | 1250 | 0.4786          | 0.8585   |
| 0.3368        | 6.0   | 1500 | 0.4240          | 0.868    |
| 0.2891        | 7.0   | 1750 | 0.4324          | 0.8715   |
| 0.2554        | 8.0   | 2000 | 0.4005          | 0.875    |
| 0.2302        | 9.0   | 2250 | 0.4080          | 0.874    |
| 0.2121        | 10.0  | 2500 | 0.4048          | 0.878    |


### Framework versions

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