metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: bert-tiny-emotion-KD-BERT
    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: 0.9175

bert-tiny-emotion-KD-BERT

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the emotion dataset. It achieves the following results on the evaluation set:

Loss: 0.4810
Accuracy: 0.9175

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
3.8247	1.0	1000	2.5170	0.7745
1.9864	2.0	2000	1.3436	0.874
1.1126	3.0	3000	0.8299	0.894
0.6924	4.0	4000	0.6500	0.9025
0.5272	5.0	5000	0.6097	0.908
0.4298	6.0	6000	0.5913	0.904
0.3936	7.0	7000	0.5165	0.9135
0.3238	8.0	8000	0.5120	0.9075
0.3018	9.0	9000	0.4989	0.916
0.2605	10.0	10000	0.4810	0.9175
0.2512	11.0	11000	0.4757	0.9135
0.219	12.0	12000	0.4676	0.914
0.2046	13.0	13000	0.4794	0.911

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1