metadata
base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv1-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: 0.884
hbertv1-emotion-logit_KD_new
This model is a fine-tuned version of 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