Gregariousness_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: 0.8553
- Accuracy: 0.6289
- Precision: 0.6859
- Recall: 0.4738
- F1: 0.5605
- Auc: 0.6918
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 | 0.6298 | 0.6675 | 0.6588 | 0.6933 | 0.6756 | 0.7052 |
| No log | 2.0 | 236 | 0.6307 | 0.6513 | 0.6417 | 0.6833 | 0.6618 | 0.7148 |
| No log | 3.0 | 354 | 0.6795 | 0.6426 | 0.6122 | 0.7756 | 0.6843 | 0.7006 |
| No log | 4.0 | 472 | 0.7995 | 0.6339 | 0.6348 | 0.6284 | 0.6316 | 0.6901 |
| 0.5698 | 5.0 | 590 | 0.8553 | 0.6289 | 0.6859 | 0.4738 | 0.5605 | 0.6918 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1