Neuro_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: 0.5617
- Accuracy: 0.7460
- Precision: 0.7184
- Recall: 0.8080
- F1: 0.7606
- Auc: 0.8251
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 | 0.5544 | 0.7136 | 0.7165 | 0.7057 | 0.7111 | 0.8020 |
| No log | 2.0 | 236 | 0.5718 | 0.7447 | 0.7112 | 0.8229 | 0.7630 | 0.8135 |
| No log | 3.0 | 354 | 0.6401 | 0.7061 | 0.6476 | 0.9027 | 0.7542 | 0.8218 |
| No log | 4.0 | 472 | 0.5617 | 0.7460 | 0.7184 | 0.8080 | 0.7606 | 0.8251 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1