distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of
distilbert-base-uncased on the privy dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0016
- Precision: 0.9984
- Recall: 0.9986
- F1: 0.9985
- Accuracy: 0.9996
Model description
Output indices map to following labels:
['O',
'B-O',
'I-O',
'L-O',
'U-O',
'B-PER',
'I-PER',
'L-PER',
'U-PER',
'B-LOC',
'I-LOC',
'L-LOC',
'U-LOC',
'B-ORG',
'I-ORG',
'L-ORG',
'U-ORG',
'B-NRP',
'I-NRP',
'L-NRP',
'U-NRP',
'B-DATE_TIME',
'I-DATE_TIME',
'L-DATE_TIME',
'U-DATE_TIME',
'B-CREDIT_CARD',
'I-CREDIT_CARD',
'L-CREDIT_CARD',
'U-CREDIT_CARD',
'B-URL',
'I-URL',
'L-URL',
'U-URL',
'B-IBAN_CODE',
'I-IBAN_CODE',
'L-IBAN_CODE',
'U-IBAN_CODE',
'B-US_BANK_NUMBER',
'I-US_BANK_NUMBER',
'L-US_BANK_NUMBER',
'U-US_BANK_NUMBER',
'B-PHONE_NUMBER',
'I-PHONE_NUMBER',
'L-PHONE_NUMBER',
'U-PHONE_NUMBER',
'B-US_SSN',
'I-US_SSN',
'L-US_SSN',
'U-US_SSN',
'B-US_PASSPORT',
'I-US_PASSPORT',
'L-US_PASSPORT',
'U-US_PASSPORT',
'B-US_DRIVER_LICENSE',
'I-US_DRIVER_LICENSE',
'L-US_DRIVER_LICENSE',
'U-US_DRIVER_LICENSE',
'B-US_LICENSE_PLATE',
'I-US_LICENSE_PLATE',
'L-US_LICENSE_PLATE',
'U-US_LICENSE_PLATE',
'B-IP_ADDRESS',
'I-IP_ADDRESS',
'L-IP_ADDRESS',
'U-IP_ADDRESS',
'B-US_ITIN',
'I-US_ITIN',
'L-US_ITIN',
'U-US_ITIN',
'B-EMAIL_ADDRESS',
'I-EMAIL_ADDRESS',
'L-EMAIL_ADDRESS',
'U-EMAIL_ADDRESS',
'B-TITLE',
'I-TITLE',
'L-TITLE',
'U-TITLE',
'B-COORDINATE',
'I-COORDINATE',
'L-COORDINATE',
'U-COORDINATE',
'B-IMEI',
'I-IMEI',
'L-IMEI',
'U-IMEI',
'B-PASSWORD',
'I-PASSWORD',
'L-PASSWORD',
'U-PASSWORD',
'B-LICENSE_PLATE',
'I-LICENSE_PLATE',
'L-LICENSE_PLATE',
'U-LICENSE_PLATE',
'B-CURRENCY',
'I-CURRENCY',
'L-CURRENCY',
'U-CURRENCY',
'B-FINANCIAL',
'I-FINANCIAL',
'L-FINANCIAL',
'U-FINANCIAL',
'B-ROUTING_NUMBER',
'I-ROUTING_NUMBER',
'L-ROUTING_NUMBER',
'U-ROUTING_NUMBER',
'B-SWIFT_CODE',
'I-SWIFT_CODE',
'L-SWIFT_CODE',
'U-SWIFT_CODE',
'B-MAC_ADDRESS',
'I-MAC_ADDRESS',
'L-MAC_ADDRESS',
'U-MAC_ADDRESS',
'B-AGE',
'I-AGE',
'L-AGE',
'U-AGE']
Intended uses & limitations
NER detection for PII anonymization
Training and evaluation data
beki/privy dataset
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|
| 0.0028 | 1.0 | 6310 | 0.0025 | 0.9977 | 0.9977 | 0.9977 | 0.9995 |
| 0.0015 | 2.0 | 12620 | 0.0017 | 0.9983 | 0.9985 | 0.9984 | 0.9996 |
| 0.001 | 3.0 | 18930 | 0.0016 | 0.9984 | 0.9986 | 0.9985 | 0.9996 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
