Instructions to use jefftherover/pii-layout-synth-distil-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jefftherover/pii-layout-synth-distil-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jefftherover/pii-layout-synth-distil-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jefftherover/pii-layout-synth-distil-v1") model = AutoModelForTokenClassification.from_pretrained("jefftherover/pii-layout-synth-distil-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
pii-layout-synth-distil-v1
This model is a fine-tuned version of jefftherover/pii-layout-synth-distil-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0047
- Precision: 0.9911
- Recall: 0.9949
- F1: 0.9930
- Accuracy: 0.9987
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 8.3007 | 0.9818 | 2000 | 0.0061 | 0.9878 | 0.9924 | 0.9901 | 0.9983 |
| 7.8973 | 1.9637 | 4000 | 0.0059 | 0.9910 | 0.9951 | 0.9931 | 0.9985 |
| 7.6048 | 2.9455 | 6000 | 0.0048 | 0.9912 | 0.9949 | 0.9931 | 0.9987 |
| 7.4293 | 3.9273 | 8000 | 0.0047 | 0.9912 | 0.9944 | 0.9928 | 0.9987 |
| 7.6729 | 4.9092 | 10000 | 0.0047 | 0.9911 | 0.9949 | 0.9930 | 0.9987 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
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