---
base_model: gokuls/model_v1_complete_training_wt_init_48_small_freeze_new
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv1-emotion-logit_KD-small
  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: 0.9335
---

<!-- 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-emotion-logit_KD-small

This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small_freeze_new](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small_freeze_new) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2473
- Accuracy: 0.9335

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4023        | 1.0   | 250  | 0.5204          | 0.8825   |
| 0.3903        | 2.0   | 500  | 0.3014          | 0.91     |
| 0.2438        | 3.0   | 750  | 0.2849          | 0.9185   |
| 0.1778        | 4.0   | 1000 | 0.2489          | 0.9265   |
| 0.1394        | 5.0   | 1250 | 0.2878          | 0.9205   |
| 0.1218        | 6.0   | 1500 | 0.2887          | 0.923    |
| 0.1083        | 7.0   | 1750 | 0.2788          | 0.9285   |
| 0.1019        | 8.0   | 2000 | 0.2373          | 0.928    |
| 0.0898        | 9.0   | 2250 | 0.2473          | 0.9335   |
| 0.0817        | 10.0  | 2500 | 0.2822          | 0.926    |
| 0.0827        | 11.0  | 2750 | 0.2474          | 0.926    |
| 0.0733        | 12.0  | 3000 | 0.2329          | 0.9285   |
| 0.0631        | 13.0  | 3250 | 0.2301          | 0.929    |
| 0.06          | 14.0  | 3500 | 0.2565          | 0.9295   |


### Framework versions

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