metadata
base_model: gokuls/bert_12_layer_model_v2_complete_training_new_48
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv2-emotion-logit_KD_new
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.8855
hbertv2-emotion-logit_KD_new
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_48 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.6082
- Accuracy: 0.8855
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 |
|---|---|---|---|---|
| 2.1324 | 1.0 | 250 | 1.0855 | 0.801 |
| 0.8121 | 2.0 | 500 | 0.7251 | 0.872 |
| 0.5982 | 3.0 | 750 | 0.6929 | 0.8695 |
| 0.4694 | 4.0 | 1000 | 0.6529 | 0.8775 |
| 0.3873 | 5.0 | 1250 | 0.7370 | 0.873 |
| 0.3477 | 6.0 | 1500 | 0.6082 | 0.8855 |
| 0.3169 | 7.0 | 1750 | 0.6202 | 0.885 |
| 0.2855 | 8.0 | 2000 | 0.5843 | 0.88 |
| 0.2669 | 9.0 | 2250 | 0.6290 | 0.8825 |
| 0.2493 | 10.0 | 2500 | 0.7612 | 0.8785 |
| 0.2326 | 11.0 | 2750 | 0.6896 | 0.883 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0