|
--- |
|
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 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# hbertv1-emotion-logit_KD-tiny |
|
|
|
This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new](https://huggingface.co/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 |
|
|
