transformers tensorboard safetensors hybridbert text-classification generated_from_trainer dataset:emotion base_model:gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2 base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2
datasets:
- emotion
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-emotion-logit_KD-tiny_ffn_2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9005
      name: Accuracy
---

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# hbertv1-emotion-logit_KD-tiny_ffn_2

This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4740
- Accuracy: 0.9005

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.1189        | 1.0   | 250  | 2.6103          | 0.514    |
| 2.0804        | 2.0   | 500  | 1.4939          | 0.7695   |
| 1.2677        | 3.0   | 750  | 0.8999          | 0.8445   |
| 0.8885        | 4.0   | 1000 | 0.6887          | 0.874    |
| 0.7023        | 5.0   | 1250 | 0.5821          | 0.889    |
| 0.5796        | 6.0   | 1500 | 0.5364          | 0.8875   |
| 0.5106        | 7.0   | 1750 | 0.5043          | 0.89     |
| 0.4603        | 8.0   | 2000 | 0.5055          | 0.889    |
| 0.405         | 9.0   | 2250 | 0.4903          | 0.89     |
| 0.3782        | 10.0  | 2500 | 0.4793          | 0.8965   |
| 0.3488        | 11.0  | 2750 | 0.4832          | 0.8945   |
| 0.3301        | 12.0  | 3000 | 0.4740          | 0.9005   |
| 0.3163        | 13.0  | 3250 | 0.4768          | 0.89     |
| 0.2983        | 14.0  | 3500 | 0.4925          | 0.887    |
| 0.2835        | 15.0  | 3750 | 0.4764          | 0.898    |
| 0.2702        | 16.0  | 4000 | 0.4856          | 0.8905   |
| 0.2522        | 17.0  | 4250 | 0.4829          | 0.897    |


### Framework versions

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