metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
base_model: tingtone/jq_emo_distilbert
model-index:
- name: jq_emo_distilbert
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.9385
name: Accuracy
jq_emo_distilbert
This model is a fine-tuned version of tingtone/jq_emo_distilbert on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.3185
- Accuracy: 0.9385
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16000
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1042 | 1.0 | 1000 | 0.1816 | 0.932 |
| 0.0998 | 2.0 | 2000 | 0.1799 | 0.934 |
| 0.0957 | 3.0 | 3000 | 0.2015 | 0.935 |
| 0.0846 | 4.0 | 4000 | 0.2129 | 0.9335 |
| 0.0943 | 5.0 | 5000 | 0.2215 | 0.935 |
| 0.075 | 6.0 | 6000 | 0.2627 | 0.9375 |
| 0.0607 | 7.0 | 7000 | 0.2908 | 0.9345 |
| 0.0636 | 8.0 | 8000 | 0.3207 | 0.935 |
| 0.0953 | 9.0 | 9000 | 0.3165 | 0.936 |
| 0.0748 | 10.0 | 10000 | 0.3185 | 0.9385 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3