Openn_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: 0.8569
- Accuracy: 0.6700
- Precision: 0.6977
- Recall: 0.5985
- F1: 0.6443
- Auc: 0.7168
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 | 0.6668 | 0.6127 | 0.7228 | 0.3641 | 0.4842 | 0.7056 |
| No log | 2.0 | 236 | 0.6320 | 0.6364 | 0.5945 | 0.8554 | 0.7014 | 0.7416 |
| No log | 3.0 | 354 | 0.6293 | 0.6550 | 0.6202 | 0.7980 | 0.6979 | 0.7477 |
| No log | 4.0 | 472 | 0.6748 | 0.6663 | 0.6301 | 0.8030 | 0.7061 | 0.7421 |
| 0.5831 | 5.0 | 590 | 0.7168 | 0.6687 | 0.6452 | 0.7481 | 0.6928 | 0.7382 |
| 0.5831 | 6.0 | 708 | 0.8569 | 0.6700 | 0.6977 | 0.5985 | 0.6443 | 0.7168 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1