metadata
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bnb-sentiment-model-saagie
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9444444444444444
bnb-sentiment-model-saagie
This model is a fine-tuned version of j-hartmann/emotion-english-distilroberta-base on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.3581
- Accuracy: 0.9444
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3724 | 1.0 | 1875 | 0.1799 | 0.9367 |
| 0.2118 | 2.0 | 3750 | 0.1918 | 0.9456 |
| 0.1792 | 3.0 | 5625 | 0.1791 | 0.95 |
| 0.1489 | 4.0 | 7500 | 0.1479 | 0.9489 |
| 0.1168 | 5.0 | 9375 | 0.2561 | 0.9444 |
| 0.081 | 6.0 | 11250 | 0.2863 | 0.9411 |
| 0.0521 | 7.0 | 13125 | 0.3168 | 0.9467 |
| 0.0345 | 8.0 | 15000 | 0.3581 | 0.9444 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.8.1
- Datasets 2.12.0
- Tokenizers 0.12.1