metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: BERT-tiny-emotion-intent
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.91
name: Accuracy
BERT-tiny-emotion-intent
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.3620
- Accuracy: 0.91
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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2603 | 1.0 | 1000 | 0.7766 | 0.7815 |
| 0.5919 | 2.0 | 2000 | 0.4117 | 0.884 |
| 0.367 | 3.0 | 3000 | 0.3188 | 0.8995 |
| 0.2848 | 4.0 | 4000 | 0.2928 | 0.8985 |
| 0.2395 | 5.0 | 5000 | 0.2906 | 0.898 |
| 0.2094 | 6.0 | 6000 | 0.2887 | 0.907 |
| 0.1884 | 7.0 | 7000 | 0.2831 | 0.9065 |
| 0.1603 | 8.0 | 8000 | 0.3044 | 0.9065 |
| 0.1519 | 9.0 | 9000 | 0.3124 | 0.9095 |
| 0.1291 | 10.0 | 10000 | 0.3256 | 0.9065 |
| 0.1179 | 11.0 | 11000 | 0.3651 | 0.9035 |
| 0.1091 | 12.0 | 12000 | 0.3620 | 0.91 |
| 0.0977 | 13.0 | 13000 | 0.3992 | 0.907 |
| 0.0914 | 14.0 | 14000 | 0.4285 | 0.908 |
| 0.0876 | 15.0 | 15000 | 0.4268 | 0.9055 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1