transformers tensorboard safetensors hybridbert text-classification generated_from_trainer dataset:emotion base_model:gokuls/bert_12_layer_model_v1_complete_training_new_48 base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_48 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48
datasets:
- emotion
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: hbertv1-emotion-logit_KD_new
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.884
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hbertv1-emotion-logit_KD_new

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6758
- Accuracy: 0.884

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3717        | 1.0   | 250  | 1.7665          | 0.6635   |
| 1.3315        | 2.0   | 500  | 1.1797          | 0.7825   |
| 0.9074        | 3.0   | 750  | 0.7837          | 0.864    |
| 0.636         | 4.0   | 1000 | 0.7591          | 0.861    |
| 0.5276        | 5.0   | 1250 | 0.7692          | 0.85     |
| 0.4694        | 6.0   | 1500 | 0.7129          | 0.871    |
| 0.4317        | 7.0   | 1750 | 0.6381          | 0.8765   |
| 0.3791        | 8.0   | 2000 | 0.6646          | 0.879    |
| 0.3551        | 9.0   | 2250 | 0.6414          | 0.882    |
| 0.3236        | 10.0  | 2500 | 0.6436          | 0.8795   |
| 0.301         | 11.0  | 2750 | 0.6758          | 0.884    |
| 0.2854        | 12.0  | 3000 | 0.6706          | 0.8695   |
| 0.2698        | 13.0  | 3250 | 0.6216          | 0.8825   |
| 0.2554        | 14.0  | 3500 | 0.6592          | 0.879    |
| 0.2311        | 15.0  | 3750 | 0.7509          | 0.877    |
| 0.2275        | 16.0  | 4000 | 0.7177          | 0.8775   |


### Framework versions

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