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GLiNER2 Regex Validators

Regex validators filter extracted spans to ensure they match expected patterns, improving extraction quality and reducing false positives.

Quick Start

from gliner2 import GLiNER2, RegexValidator

extractor = GLiNER2.from_pretrained("your-model")

# Create validator and apply to field
email_validator = RegexValidator(r"^[\w\.-]+@[\w\.-]+\.\w+$")
schema = (extractor.create_schema()
    .structure("contact")
        .field("email", dtype="str", validators=[email_validator])
)

RegexValidator Parameters

  • pattern: Regex pattern (string or compiled Pattern)
  • mode: "full" (exact match) or "partial" (substring match)
  • exclude: False (keep matches) or True (exclude matches)
  • flags: Regex flags like re.IGNORECASE (for string patterns only)

Examples

Email Validation

email_validator = RegexValidator(r"^[\w\.-]+@[\w\.-]+\.\w+$")

text = "Contact: john@company.com, not-an-email, jane@domain.org"
# Output: ['john@company.com', 'jane@domain.org']

Phone Numbers (US Format)

phone_validator = RegexValidator(r"\(\d{3}\)\s\d{3}-\d{4}", mode="partial")

text = "Call (555) 123-4567 or 5551234567"
# Output: ['(555) 123-4567']  # Second number filtered out

URLs Only

url_validator = RegexValidator(r"^https?://", mode="partial")

text = "Visit https://example.com or www.site.com"
# Output: ['https://example.com']  # www.site.com filtered out

Exclude Test Data

no_test_validator = RegexValidator(r"^(test|demo|sample)", exclude=True, flags=re.IGNORECASE)

text = "Products: iPhone, Test Phone, Samsung Galaxy"
# Output: ['iPhone', 'Samsung Galaxy']  # Test Phone excluded

Length Constraints

length_validator = RegexValidator(r"^.{5,50}$")  # 5-50 characters

text = "Names: Jo, Alexander, A Very Long Name That Exceeds Fifty Characters"
# Output: ['Alexander']  # Others filtered by length

Multiple Validators

# All validators must pass
username_validators = [
    RegexValidator(r"^[a-zA-Z0-9_]+$"),  # Alphanumeric + underscore
    RegexValidator(r"^.{3,20}$"),        # 3-20 characters
    RegexValidator(r"^(?!admin)", exclude=True, flags=re.IGNORECASE)  # No "admin"
]

schema = (extractor.create_schema()
    .structure("user")
        .field("username", dtype="str", validators=username_validators)
)

text = "Users: ab, john_doe, user@domain, admin, valid_user123"
# Output: ['john_doe', 'valid_user123']

Common Patterns

Use Case Pattern Mode
Email r"^[\w\.-]+@[\w\.-]+\.\w+$" full
Phone (US) r"\(\d{3}\)\s\d{3}-\d{4}" partial
URL r"^https?://" partial
Numbers only r"^\d+$" full
No spaces r"^\S+$" full
Min length r"^.{5,}$" full
Alphanumeric r"^[a-zA-Z0-9]+$" full

Best Practices

  1. Use specific patterns - More specific = fewer false positives
  2. Test your regex - Validate patterns before deployment
  3. Combine validators - Chain multiple simple validators
  4. Consider case sensitivity - Use re.IGNORECASE when needed
  5. Start simple - Begin with basic patterns, refine as needed

Performance Notes

  • Validators run after span extraction but before formatting
  • Failed validation simply excludes the span (no errors)
  • Multiple validators use short-circuit evaluation (stops at first failure)
  • Compiled patterns are cached automatically