metadata
base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_0.5
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv1-emotion-logit_KD-tiny_ffn_0.5
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: 0.8945
hbertv1-emotion-logit_KD-tiny_ffn_0.5
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_0.5 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.5131
- Accuracy: 0.8945
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 |
|---|---|---|---|---|
| 3.1241 | 1.0 | 250 | 2.5267 | 0.5775 |
| 2.0224 | 2.0 | 500 | 1.4869 | 0.748 |
| 1.2988 | 3.0 | 750 | 0.9838 | 0.836 |
| 0.9355 | 4.0 | 1000 | 0.7613 | 0.8535 |
| 0.7507 | 5.0 | 1250 | 0.6392 | 0.8805 |
| 0.6071 | 6.0 | 1500 | 0.5669 | 0.888 |
| 0.5377 | 7.0 | 1750 | 0.5131 | 0.8945 |
| 0.4707 | 8.0 | 2000 | 0.5133 | 0.8935 |
| 0.4223 | 9.0 | 2250 | 0.5078 | 0.8905 |
| 0.3933 | 10.0 | 2500 | 0.5156 | 0.8855 |
| 0.3612 | 11.0 | 2750 | 0.4883 | 0.894 |
| 0.3409 | 12.0 | 3000 | 0.4883 | 0.894 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0