Sympathy_binary
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9482
- Accuracy: 0.6575
- Precision: 0.6302
- Recall: 0.7606
- F1: 0.6893
- Auc: 0.7203
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 118 | 0.6545 | 0.6115 | 0.5712 | 0.8903 | 0.6959 | 0.6869 |
| No log | 2.0 | 236 | 0.6528 | 0.6476 | 0.6093 | 0.8204 | 0.6993 | 0.7143 |
| No log | 3.0 | 354 | 0.7286 | 0.6413 | 0.5972 | 0.8653 | 0.7067 | 0.7130 |
| No log | 4.0 | 472 | 0.8153 | 0.6351 | 0.6534 | 0.5736 | 0.6109 | 0.7151 |
| 0.5008 | 5.0 | 590 | 0.9482 | 0.6575 | 0.6302 | 0.7606 | 0.6893 | 0.7203 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1