metadata
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: hbertv2-emotion_48_w_in
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: 0.931
hbertv2-emotion_48_w_in
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1958
- Accuracy: 0.931
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6416 | 1.0 | 250 | 0.2852 | 0.8945 |
| 0.2565 | 2.0 | 500 | 0.1956 | 0.9205 |
| 0.1769 | 3.0 | 750 | 0.2171 | 0.9225 |
| 0.1447 | 4.0 | 1000 | 0.1815 | 0.9225 |
| 0.1192 | 5.0 | 1250 | 0.1960 | 0.9235 |
| 0.1023 | 6.0 | 1500 | 0.1958 | 0.931 |
| 0.0811 | 7.0 | 1750 | 0.2533 | 0.928 |
| 0.0698 | 8.0 | 2000 | 0.3094 | 0.9305 |
| 0.0547 | 9.0 | 2250 | 0.2719 | 0.93 |
| 0.0442 | 10.0 | 2500 | 0.3100 | 0.926 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3