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piiranha-v1-detect-personal-information

Token Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
pii
privacy
personaldata
redaction
piidetection
Inference Endpoints
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piiranha-v1-detect-personal-information / README.md
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metadata
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: piiranha-1
    results: []

piiranha-1

This model is a fine-tuned version of microsoft/mdeberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0173
  • Precision: 0.9316
  • Recall: 0.9308
  • F1: 0.9312
  • Accuracy: 0.9944

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: 5e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 5
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2984 0.0983 250 0.1005 0.5446 0.6111 0.5759 0.9702
0.0568 0.1965 500 0.0464 0.7895 0.8459 0.8167 0.9849
0.0441 0.2948 750 0.0400 0.8346 0.8669 0.8504 0.9869
0.0368 0.3931 1000 0.0320 0.8531 0.8784 0.8656 0.9891
0.0323 0.4914 1250 0.0293 0.8779 0.8889 0.8834 0.9903
0.0287 0.5896 1500 0.0269 0.8919 0.8836 0.8877 0.9907
0.0282 0.6879 1750 0.0276 0.8724 0.9012 0.8866 0.9903
0.0268 0.7862 2000 0.0254 0.8890 0.9041 0.8965 0.9914
0.0264 0.8844 2250 0.0236 0.8886 0.9040 0.8962 0.9915
0.0243 0.9827 2500 0.0232 0.8998 0.9033 0.9015 0.9917
0.0213 1.0810 2750 0.0237 0.9115 0.9040 0.9077 0.9923
0.0213 1.1792 3000 0.0222 0.9123 0.9143 0.9133 0.9925
0.0217 1.2775 3250 0.0222 0.8999 0.9169 0.9083 0.9924
0.0209 1.3758 3500 0.0212 0.9111 0.9133 0.9122 0.9928
0.0204 1.4741 3750 0.0206 0.9054 0.9203 0.9128 0.9926
0.0183 1.5723 4000 0.0212 0.9126 0.9160 0.9143 0.9927
0.0191 1.6706 4250 0.0192 0.9122 0.9192 0.9157 0.9929
0.0185 1.7689 4500 0.0195 0.9200 0.9191 0.9196 0.9932
0.018 1.8671 4750 0.0188 0.9136 0.9215 0.9176 0.9933
0.0183 1.9654 5000 0.0191 0.9179 0.9212 0.9196 0.9934
0.0147 2.0637 5250 0.0188 0.9246 0.9242 0.9244 0.9937
0.0149 2.1619 5500 0.0184 0.9188 0.9254 0.9221 0.9937
0.0143 2.2602 5750 0.0193 0.9187 0.9224 0.9205 0.9932
0.014 2.3585 6000 0.0190 0.9246 0.9280 0.9263 0.9936
0.0146 2.4568 6250 0.0190 0.9225 0.9277 0.9251 0.9936
0.0148 2.5550 6500 0.0175 0.9297 0.9306 0.9301 0.9942
0.0136 2.6533 6750 0.0172 0.9191 0.9329 0.9259 0.9938
0.0137 2.7516 7000 0.0166 0.9299 0.9312 0.9306 0.9942
0.014 2.8498 7250 0.0167 0.9285 0.9313 0.9299 0.9942
0.0128 2.9481 7500 0.0166 0.9271 0.9326 0.9298 0.9943
0.0113 3.0464 7750 0.0171 0.9286 0.9347 0.9316 0.9946
0.0103 3.1447 8000 0.0172 0.9284 0.9383 0.9334 0.9945
0.0104 3.2429 8250 0.0169 0.9312 0.9406 0.9359 0.9947
0.0094 3.3412 8500 0.0166 0.9368 0.9359 0.9364 0.9948
0.01 3.4395 8750 0.0166 0.9289 0.9387 0.9337 0.9944
0.0099 3.5377 9000 0.0162 0.9335 0.9332 0.9334 0.9947
0.0099 3.6360 9250 0.0160 0.9321 0.9380 0.9350 0.9947
0.01 3.7343 9500 0.0168 0.9306 0.9389 0.9347 0.9947
0.0101 3.8325 9750 0.0159 0.9339 0.9350 0.9344 0.9947

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1