distilbert-base-uncased-finetuned-ner

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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

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3