Instructions to use Kashish-jain/pii-protection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kashish-jain/pii-protection-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Kashish-jain/pii-protection-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Kashish-jain/pii-protection-model") model = AutoModelForTokenClassification.from_pretrained("Kashish-jain/pii-protection-model") - Notebooks
- Google Colab
- Kaggle
pii-protection-model
This model is a fine-tuned version of Kashish-jain/pii-protection-model on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0211
- Precision: 0.9160
- Recall: 0.9729
- F1: 0.9436
- Accuracy: 0.9851
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0213 | 1.0 | 2500 | 0.0217 | 0.9542 | 0.9992 | 0.9762 | 0.9851 |
| 0.0211 | 2.0 | 5000 | 0.0214 | 0.9377 | 0.9819 | 0.9593 | 0.9851 |
| 0.0219 | 3.0 | 7500 | 0.0211 | 0.9160 | 0.9729 | 0.9436 | 0.9851 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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