---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-tiny-emotion-KD-distilBERT
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.913
---

<!-- 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. -->

# bert-tiny-emotion-KD-distilBERT

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5444
- Accuracy: 0.913

## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.2533        | 1.0   | 1000  | 2.8358          | 0.7675   |
| 2.3274        | 2.0   | 2000  | 1.5893          | 0.8675   |
| 1.3974        | 3.0   | 3000  | 1.0286          | 0.891    |
| 0.9035        | 4.0   | 4000  | 0.7534          | 0.8955   |
| 0.6619        | 5.0   | 5000  | 0.6350          | 0.905    |
| 0.5482        | 6.0   | 6000  | 0.6180          | 0.899    |
| 0.4937        | 7.0   | 7000  | 0.5448          | 0.91     |
| 0.4013        | 8.0   | 8000  | 0.5493          | 0.906    |
| 0.3839        | 9.0   | 9000  | 0.5481          | 0.9095   |
| 0.3281        | 10.0  | 10000 | 0.5528          | 0.9115   |
| 0.3098        | 11.0  | 11000 | 0.5864          | 0.9095   |
| 0.2762        | 12.0  | 12000 | 0.5566          | 0.9095   |
| 0.2467        | 13.0  | 13000 | 0.5444          | 0.913    |
| 0.2286        | 14.0  | 14000 | 0.5306          | 0.912    |
| 0.2215        | 15.0  | 15000 | 0.5312          | 0.9115   |
| 0.2038        | 16.0  | 16000 | 0.5242          | 0.912    |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1