metadata
base_model: gokuls/model_v1_complete_training_wt_init_48_small_freeze_new
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv1-emotion-logit_KD-small
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: 0.9335
hbertv1-emotion-logit_KD-small
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_small_freeze_new on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.2473
- Accuracy: 0.9335
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 |
|---|---|---|---|---|
| 1.4023 | 1.0 | 250 | 0.5204 | 0.8825 |
| 0.3903 | 2.0 | 500 | 0.3014 | 0.91 |
| 0.2438 | 3.0 | 750 | 0.2849 | 0.9185 |
| 0.1778 | 4.0 | 1000 | 0.2489 | 0.9265 |
| 0.1394 | 5.0 | 1250 | 0.2878 | 0.9205 |
| 0.1218 | 6.0 | 1500 | 0.2887 | 0.923 |
| 0.1083 | 7.0 | 1750 | 0.2788 | 0.9285 |
| 0.1019 | 8.0 | 2000 | 0.2373 | 0.928 |
| 0.0898 | 9.0 | 2250 | 0.2473 | 0.9335 |
| 0.0817 | 10.0 | 2500 | 0.2822 | 0.926 |
| 0.0827 | 11.0 | 2750 | 0.2474 | 0.926 |
| 0.0733 | 12.0 | 3000 | 0.2329 | 0.9285 |
| 0.0631 | 13.0 | 3250 | 0.2301 | 0.929 |
| 0.06 | 14.0 | 3500 | 0.2565 | 0.9295 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0