---
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv2-emotion-logit_KD_new
  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: 0.8855
---

<!-- 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. -->

# hbertv2-emotion-logit_KD_new

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_48) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6082
- Accuracy: 0.8855

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1324        | 1.0   | 250  | 1.0855          | 0.801    |
| 0.8121        | 2.0   | 500  | 0.7251          | 0.872    |
| 0.5982        | 3.0   | 750  | 0.6929          | 0.8695   |
| 0.4694        | 4.0   | 1000 | 0.6529          | 0.8775   |
| 0.3873        | 5.0   | 1250 | 0.7370          | 0.873    |
| 0.3477        | 6.0   | 1500 | 0.6082          | 0.8855   |
| 0.3169        | 7.0   | 1750 | 0.6202          | 0.885    |
| 0.2855        | 8.0   | 2000 | 0.5843          | 0.88     |
| 0.2669        | 9.0   | 2250 | 0.6290          | 0.8825   |
| 0.2493        | 10.0  | 2500 | 0.7612          | 0.8785   |
| 0.2326        | 11.0  | 2750 | 0.6896          | 0.883    |


### Framework versions

- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0