transformers pytorch tensorboard hybridbert text-classification generated_from_trainer dataset:emotion base_model:gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu
datasets:
- emotion
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv2-emotion-48-emb-comp-gelu
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.8125
      name: Accuracy
---

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# hbertv2-emotion-48-emb-comp-gelu

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6500
- Accuracy: 0.8125

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.583         | 1.0   | 250  | 1.5406          | 0.3875   |
| 1.3735        | 2.0   | 500  | 1.2460          | 0.5365   |
| 1.1696        | 3.0   | 750  | 1.1673          | 0.556    |
| 1.0567        | 4.0   | 1000 | 1.0862          | 0.574    |
| 0.8667        | 5.0   | 1250 | 0.8843          | 0.686    |
| 0.6994        | 6.0   | 1500 | 0.8536          | 0.698    |
| 0.5608        | 7.0   | 1750 | 0.7322          | 0.773    |
| 0.4448        | 8.0   | 2000 | 0.6712          | 0.8045   |
| 0.3793        | 9.0   | 2250 | 0.6298          | 0.8095   |
| 0.335         | 10.0  | 2500 | 0.6500          | 0.8125   |


### Framework versions

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