metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: deberta-v3-base-prompt-injection-v2-2024-04-20-16-52
results: []
deberta-v3-base-prompt-injection-v2-2024-04-20-16-52
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0036
- Accuracy: 0.9993
- Recall: 0.9994
- Precision: 0.9992
- F1: 0.9993
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: 64
- seed: 49994
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 0.0079 | 1.0 | 7711 | 0.0052 | 0.9988 | 0.9982 | 0.9994 | 0.9988 |
| 0.0026 | 2.0 | 15422 | 0.0052 | 0.9987 | 0.9988 | 0.9987 | 0.9988 |
| 0.0004 | 3.0 | 23133 | 0.0063 | 0.9990 | 0.9989 | 0.9992 | 0.9990 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2