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README.md
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model-index:
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- name: deberta-v3-base_finetuned_ai4privacy_v2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# deberta-v3-base_finetuned_ai4privacy_v2
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.0211
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- Overall Precision: 0.9722
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- Overall Recall: 0.9792
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- Overall F1: 0.9757
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- Overall Accuracy: 0.9915
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- Accountname F1: 0.9993
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- Accountnumber F1: 0.9986
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- Age F1: 0.9884
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- Vehiclevrm F1: 0.9870
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- Zipcode F1: 0.9966
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine_with_restarts
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- lr_scheduler_warmup_ratio: 0.2
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Age F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Dob F1 | Email F1 | Ethereumaddress F1 | Eyecolor F1 | Firstname F1 | Gender F1 | Height F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Nearbygpscoordinate F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phonenumber F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Ssn F1 | State F1 | Street F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 |
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu118
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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model-index:
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- name: deberta-v3-base_finetuned_ai4privacy_v2
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results: []
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datasets:
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- ai4privacy/pii-masking-200k
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language:
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- en
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metrics:
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- seqeval
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pipeline_tag: token-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# deberta-v3-base_finetuned_ai4privacy_v2
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [ai4privacy/pii-masking-200k](https://huggingface.co/ai4privacy/pii-masking-200k) dataset.
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## Useage
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GitHub Implementation: [Ai4Privacy](https://github.com/Sripaad/ai4privacy)
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## Model description
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This model has been finetuned on the World's largest open source privacy dataset.
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The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
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The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
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Take a look at the Github implementation for specific reasearch.
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 6e-04
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.96,0.996) and epsilon=1e-08
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- lr_scheduler_type: cosine_with_restarts
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- lr_scheduler_warmup_ratio: 0.2
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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## Class wise metrics
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It achieves the following results on the evaluation set:
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- Loss: 0.0211
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- Overall Precision: 0.9722
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- Overall Recall: 0.9792
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- Overall F1: 0.9757
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- Overall Accuracy: 0.9915
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- Accountname F1: 0.9993
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- Accountnumber F1: 0.9986
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- Age F1: 0.9884
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- Vehiclevrm F1: 0.9870
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- Zipcode F1: 0.9966
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Age F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Dob F1 | Email F1 | Ethereumaddress F1 | Eyecolor F1 | Firstname F1 | Gender F1 | Height F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Nearbygpscoordinate F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phonenumber F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Ssn F1 | State F1 | Street F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 |
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu118
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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