metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: deberta-pii-finetuned
results: []
deberta-pii-finetuned
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: 0.0065
- F Beta: 0.9611
- Precision: 0.9932
- Recall: 0.9598
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: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F Beta | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.0291 | 0.46 | 300 | 0.0104 | 0.9756 | 0.9854 | 0.9752 |
| 0.0062 | 0.93 | 600 | 0.0041 | 0.9830 | 0.9901 | 0.9827 |
| 0.0044 | 1.39 | 900 | 0.0057 | 0.9713 | 0.9895 | 0.9706 |
| 0.0258 | 1.85 | 1200 | 0.0040 | 0.9799 | 0.9920 | 0.9794 |
| 0.0135 | 2.32 | 1500 | 0.0050 | 0.9845 | 0.9943 | 0.9841 |
| 0.0023 | 2.78 | 1800 | 0.0065 | 0.9611 | 0.9932 | 0.9598 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0