metadata
base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv1-emotion-logit_KD-tiny
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: 0.8995
hbertv1-emotion-logit_KD-tiny
This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.4386
- Accuracy: 0.8995
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 |
|---|---|---|---|---|
| 2.9341 | 1.0 | 250 | 2.0281 | 0.6225 |
| 1.5579 | 2.0 | 500 | 1.0162 | 0.812 |
| 0.9088 | 3.0 | 750 | 0.6563 | 0.8705 |
| 0.6557 | 4.0 | 1000 | 0.5484 | 0.879 |
| 0.538 | 5.0 | 1250 | 0.4913 | 0.8865 |
| 0.4524 | 6.0 | 1500 | 0.4836 | 0.888 |
| 0.4072 | 7.0 | 1750 | 0.4416 | 0.896 |
| 0.3797 | 8.0 | 2000 | 0.4346 | 0.8905 |
| 0.3426 | 9.0 | 2250 | 0.4386 | 0.8995 |
| 0.3183 | 10.0 | 2500 | 0.4602 | 0.896 |
| 0.2911 | 11.0 | 2750 | 0.4296 | 0.8945 |
| 0.2807 | 12.0 | 3000 | 0.4442 | 0.896 |
| 0.2609 | 13.0 | 3250 | 0.4513 | 0.894 |
| 0.249 | 14.0 | 3500 | 0.4612 | 0.8975 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0