transformers pytorch tensorboard hybridbert text-classification generated_from_trainer dataset:emotion base_model:gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp_frz base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp_frz model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp_frz
datasets:
- emotion
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-tiny-wt-frz-48-emotion-emb-comp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.894
      name: Accuracy
---

<!-- 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-tiny-wt-frz-48-emotion-emb-comp

This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp_frz](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp_frz) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3126
- Accuracy: 0.894

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3096        | 1.0   | 250  | 0.8485          | 0.725    |
| 0.617         | 2.0   | 500  | 0.4853          | 0.836    |
| 0.3993        | 3.0   | 750  | 0.3664          | 0.8735   |
| 0.3085        | 4.0   | 1000 | 0.3248          | 0.881    |
| 0.2593        | 5.0   | 1250 | 0.3289          | 0.8845   |
| 0.2245        | 6.0   | 1500 | 0.3139          | 0.89     |
| 0.2019        | 7.0   | 1750 | 0.3107          | 0.8885   |
| 0.1797        | 8.0   | 2000 | 0.3081          | 0.8925   |
| 0.167         | 9.0   | 2250 | 0.3126          | 0.894    |
| 0.1537        | 10.0  | 2500 | 0.3133          | 0.8925   |


### Framework versions

- Transformers 4.31.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.1
- Tokenizers 0.13.3
