arnocandel
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Browse files- README.md +37 -3
- added_tokens.json +7 -0
- config.json +268 -0
- gitattributes +35 -0
- merges.txt +0 -0
- optimizer.pt +3 -0
- pytorch_model.bin +3 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- special_tokens_map.json +9 -0
- tokenizer.json +0 -0
- tokenizer_config.json +60 -0
- trainer_state.json +618 -0
- training_args.bin +3 -0
- vocab.json +0 -0
README.md
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---
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license: mit
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---
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license: mit
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language:
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- en
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pipeline_tag: token-classification
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---
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A finetuned model designed to recognize and classify Personally Identifiable Information (PII) within unstructured text data. This powerful model accurately identifies a wide range of PII categories, such as account names, credit card numbers, emails, phone numbers, and addresses. The model is specifically trained to detect various PII types, including but not limited to:
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```
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| Category | Data |
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|------------------------|----------------------------------------------------------------------------------------|
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| Account-related information | Account name, account number, and transaction amounts |
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| Banking details | BIC, IBAN, and Bitcoin or Ethereum addresses |
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| Personal information | Full name, first name, middle name, last name, gender, and date of birth |
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| Contact information | Email, phone number, and street address (including building number, city, county, state, and zip code) |
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| Job-related data | Job title, job area, job descriptor, and job type |
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| Financial data | Credit card number, issuer, CVV, and currency information (code, name, and symbol) |
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| Digital identifiers | IP addresses (IPv4 and IPv6), MAC addresses, and user agents |
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| Online presence | URL, usernames, and passwords |
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| Other sensitive data | SSN, vehicle VIN and VRM, phone IMEI, and nearby GPS coordinates |
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```
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The PII Identifier Model ensures data privacy and compliance by effectively detecting and categorizing sensitive information within documents, emails, user-generated content, and more. Make your data processing safer and more secure with our state-of-the-art PII detection technology.
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How to do Inference :
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```
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from transformers import pipeline
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gen = pipeline("token-classification", "lakshyakh93/deberta_finetuned_pii", device=-1)
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text = "My name is John and I live in California."
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output = gen(text, aggregation_strategy="first")
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```
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For any more details reach out to lakshaya.khandelwal@gmail.com
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added_tokens.json
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{
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"[CLS]": 1,
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"[MASK]": 50264,
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"[PAD]": 0,
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"[SEP]": 2,
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"[UNK]": 3
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}
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config.json
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{
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"_name_or_path": "microsoft/deberta-base",
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"architectures": [
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"DebertaForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "B-PREFIX",
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"1": "I-PREFIX",
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"2": "B-FIRSTNAME",
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"3": "I-FIRSTNAME",
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"4": "B-MIDDLENAME",
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"5": "B-LASTNAME",
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"6": "I-LASTNAME",
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"7": "O",
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"8": "B-JOBDESCRIPTOR",
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"9": "B-JOBTITLE",
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"10": "I-JOBTITLE",
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"11": "B-COMPANY_NAME",
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"12": "I-COMPANY_NAME",
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"13": "B-JOBAREA",
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"14": "B-EMAIL",
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"15": "I-EMAIL",
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"16": "B-TIME",
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"17": "I-TIME",
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"18": "B-DATE",
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"19": "I-DATE",
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"20": "B-URL",
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"21": "I-URL",
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"22": "B-BITCOINADDRESS",
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"23": "I-BITCOINADDRESS",
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"24": "B-ETHEREUMADDRESS",
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"25": "I-ETHEREUMADDRESS",
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"26": "B-ACCOUNTNAME",
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"27": "I-ACCOUNTNAME",
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"28": "B-IBAN",
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"29": "I-IBAN",
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"30": "B-ACCOUNTNUMBER",
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"31": "I-ACCOUNTNUMBER",
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"32": "B-BIC",
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"33": "I-BIC",
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"34": "B-IPV4",
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"35": "I-IPV4",
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"36": "B-STREETADDRESS",
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"37": "I-STREETADDRESS",
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"38": "B-CITY",
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"39": "I-CITY",
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"40": "B-ZIPCODE",
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"41": "I-ZIPCODE",
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"42": "B-USERNAME",
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"43": "I-USERNAME",
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"44": "B-IPV6",
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"45": "I-IPV6",
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"46": "B-CREDITCARDNUMBER",
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"47": "I-CREDITCARDNUMBER",
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"48": "B-VEHICLEVIN",
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"49": "I-VEHICLEVIN",
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"50": "B-SUFFIX",
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"51": "I-SUFFIX",
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"52": "B-AMOUNT",
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"53": "I-AMOUNT",
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"54": "B-CURRENCY",
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"55": "I-CURRENCY",
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"56": "B-PASSWORD",
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"57": "I-PASSWORD",
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"58": "B-JOBTYPE",
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"59": "B-STATE",
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"60": "B-BUILDINGNUMBER",
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"61": "I-BUILDINGNUMBER",
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"62": "B-VEHICLEVRM",
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"63": "I-VEHICLEVRM",
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"64": "B-PHONEIMEI",
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"65": "I-PHONEIMEI",
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"66": "I-JOBAREA",
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"67": "I-STATE",
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"68": "B-COUNTY",
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"69": "B-CURRENCYNAME",
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"70": "I-CURRENCYNAME",
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"71": "B-CURRENCYSYMBOL",
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"72": "B-MASKEDNUMBER",
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"73": "I-MASKEDNUMBER",
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"74": "B-PHONE_NUMBER",
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"75": "I-PHONE_NUMBER",
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"76": "B-SECONDARYADDRESS",
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"77": "I-SECONDARYADDRESS",
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"78": "B-SSN",
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"79": "I-SSN",
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"80": "B-CURRENCYCODE",
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"81": "B-LITECOINADDRESS",
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"82": "I-LITECOINADDRESS",
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"83": "B-MAC",
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"84": "I-MAC",
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"85": "B-CREDITCARDISSUER",
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"86": "I-CREDITCARDISSUER",
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"87": "B-CREDITCARDCVV",
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"88": "I-CREDITCARDCVV",
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"89": "B-USERAGENT",
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"90": "I-USERAGENT",
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"91": "B-IP",
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"92": "I-IP",
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"93": "B-SEX",
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"94": "B-STREET",
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"95": "I-STREET",
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"96": "B-PIN",
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"97": "I-PIN",
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"98": "I-JOBTYPE",
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"99": "I-MIDDLENAME",
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"100": "I-CURRENCYCODE",
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"101": "I-CURRENCYSYMBOL",
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"102": "B-FULLNAME",
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"103": "I-FULLNAME",
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"104": "B-NAME",
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"105": "I-NAME",
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"106": "B-GENDER",
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"107": "B-NUMBER",
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"108": "I-NUMBER",
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"109": "I-GENDER",
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"110": "B-NEARBYGPSCOORDINATE",
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"111": "I-NEARBYGPSCOORDINATE",
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"112": "B-DISPLAYNAME",
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"113": "I-DISPLAYNAME",
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"114": "B-SEXTYPE",
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"115": "B-ORDINALDIRECTION"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-ACCOUNTNAME": 26,
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"B-ACCOUNTNUMBER": 30,
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"B-AMOUNT": 52,
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"B-BIC": 32,
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"B-BITCOINADDRESS": 22,
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"B-BUILDINGNUMBER": 60,
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"B-CITY": 38,
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"B-COMPANY_NAME": 11,
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"B-COUNTY": 68,
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"B-CREDITCARDCVV": 87,
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"B-CREDITCARDISSUER": 85,
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"B-CREDITCARDNUMBER": 46,
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"B-CURRENCY": 54,
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"B-CURRENCYCODE": 80,
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"B-CURRENCYNAME": 69,
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"B-CURRENCYSYMBOL": 71,
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"B-DATE": 18,
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"B-DISPLAYNAME": 112,
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"B-EMAIL": 14,
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"B-ETHEREUMADDRESS": 24,
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"B-FIRSTNAME": 2,
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"B-FULLNAME": 102,
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"B-GENDER": 106,
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"B-IBAN": 28,
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"B-IP": 91,
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"B-IPV4": 34,
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"B-IPV6": 44,
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"B-JOBAREA": 13,
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"B-JOBDESCRIPTOR": 8,
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"B-JOBTITLE": 9,
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+
"B-JOBTYPE": 58,
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+
"B-LASTNAME": 5,
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+
"B-LITECOINADDRESS": 81,
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"B-MAC": 83,
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+
"B-MASKEDNUMBER": 72,
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+
"B-MIDDLENAME": 4,
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"B-NAME": 104,
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+
"B-NEARBYGPSCOORDINATE": 110,
|
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"B-NUMBER": 107,
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"B-ORDINALDIRECTION": 115,
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+
"B-PASSWORD": 56,
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"B-PHONEIMEI": 64,
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+
"B-PHONE_NUMBER": 74,
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"B-PIN": 96,
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"B-PREFIX": 0,
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176 |
+
"B-SECONDARYADDRESS": 76,
|
177 |
+
"B-SEX": 93,
|
178 |
+
"B-SEXTYPE": 114,
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+
"B-SSN": 78,
|
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+
"B-STATE": 59,
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+
"B-STREET": 94,
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182 |
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"B-STREETADDRESS": 36,
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"B-SUFFIX": 50,
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184 |
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"B-TIME": 16,
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185 |
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"B-URL": 20,
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186 |
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"B-USERAGENT": 89,
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187 |
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"B-USERNAME": 42,
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+
"B-VEHICLEVIN": 48,
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"B-VEHICLEVRM": 62,
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"B-ZIPCODE": 40,
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"I-ACCOUNTNAME": 27,
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"I-ACCOUNTNUMBER": 31,
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"I-AMOUNT": 53,
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"I-BIC": 33,
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"I-BITCOINADDRESS": 23,
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"I-BUILDINGNUMBER": 61,
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"I-CITY": 39,
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198 |
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"I-COMPANY_NAME": 12,
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"I-CREDITCARDCVV": 88,
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"I-CREDITCARDISSUER": 86,
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"I-CREDITCARDNUMBER": 47,
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"I-CURRENCY": 55,
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"I-CURRENCYCODE": 100,
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"I-CURRENCYNAME": 70,
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"I-CURRENCYSYMBOL": 101,
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"I-DATE": 19,
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"I-DISPLAYNAME": 113,
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"I-EMAIL": 15,
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"I-ETHEREUMADDRESS": 25,
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"I-FIRSTNAME": 3,
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"I-FULLNAME": 103,
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"I-GENDER": 109,
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"I-IBAN": 29,
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"I-IP": 92,
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"I-IPV4": 35,
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"I-IPV6": 45,
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"I-JOBAREA": 66,
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"I-JOBTITLE": 10,
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"I-JOBTYPE": 98,
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"I-LASTNAME": 6,
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"I-LITECOINADDRESS": 82,
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"I-MAC": 84,
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"I-MASKEDNUMBER": 73,
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"I-MIDDLENAME": 99,
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"I-NAME": 105,
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"I-NEARBYGPSCOORDINATE": 111,
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"I-NUMBER": 108,
|
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"I-PASSWORD": 57,
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"I-PHONEIMEI": 65,
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"I-PHONE_NUMBER": 75,
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"I-PIN": 97,
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232 |
+
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"eval_ZIPCODE_f1": 1.0,
|
600 |
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"eval_loss": 0.008737307973206043,
|
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|
602 |
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"eval_overall_f1": 0.9932909729864046,
|
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"eval_overall_precision": 0.9905834041348061,
|
604 |
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"eval_overall_recall": 0.996013383640635,
|
605 |
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"eval_runtime": 85.9696,
|
606 |
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"eval_samples_per_second": 124.381,
|
607 |
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|
608 |
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"step": 9359
|
609 |
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}
|
610 |
+
],
|
611 |
+
"logging_steps": 500,
|
612 |
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"max_steps": 9359,
|
613 |
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"num_train_epochs": 7,
|
614 |
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"save_steps": 500,
|
615 |
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|
616 |
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"trial_name": null,
|
617 |
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"trial_params": null
|
618 |
+
}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:ed8e8c274d68a9ab2c8cd8253471b18e6035136643f0483c06a7e2df27f9fd3b
|
3 |
+
size 4155
|
vocab.json
ADDED
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|
|