spaCy
English
Turkish
epistemology
knowledge-base
information-extraction
bias-detection
confidence-scoring
nlp
Instructions to use NextGenC/AEE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use NextGenC/AEE with spaCy:
!pip install https://huggingface.co/NextGenC/AEE/resolve/main/AEE-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("AEE") # Importing as module. import AEE nlp = AEE.load() - Notebooks
- Google Colab
- Kaggle
| # aee_core_classes_era.py | |
| # AEE Projesi için temel veri yapılarını tanımlar. | |
| # Era Sürümü: EpistemicData'ya plausibility eklendi. | |
| import uuid | |
| from dataclasses import dataclass, field | |
| from datetime import datetime | |
| from typing import List, Optional, Dict, Any | |
| class EpistemicData: | |
| """Bir önerme ile ilişkili epistemik (bilgibilimsel) verileri tutar.""" | |
| source_id: str | |
| timestamp: datetime = field(default_factory=datetime.now) | |
| initial_confidence: float = 0.5 | |
| computed_confidence: float = 0.5 | |
| source_type: Optional[str] = None | |
| reliability_score: Optional[float] = None | |
| # v2+ Bağlantılar | |
| supports: List[str] = field(default_factory=list) | |
| contradicts: List[str] = field(default_factory=list) | |
| # v3+ İşaretler | |
| bias_flags: List[str] = field(default_factory=list) | |
| # YENİ ERA Alanları: | |
| plausibility_score: Optional[float] = None # Önermenin genel makullük/olabilirlik skoru (örn: 0.0-1.0) | |
| validation_notes: List[str] = field(default_factory=list) # Makullük kontrolünden gelen notlar (örn: ['Contradicts common sense']) | |
| other_metadata: Dict[str, Any] = field(default_factory=dict) | |
| def __post_init__(self): | |
| self.computed_confidence = self.initial_confidence | |
| class Proposition: | |
| """Metinden çıkarılan bir bilgi birimini (önermeyi) temsil eder.""" | |
| # Non-default fields first | |
| text_span: str | |
| sentence_text: str | |
| epistemic_data: EpistemicData # Artık Era uyumlu EpistemicData içerecek | |
| # Default fields last | |
| prop_id: str = field(default_factory=lambda: str(uuid.uuid4())) | |
| subject_lemma: Optional[str] = None | |
| relation_lemma: Optional[str] = None | |
| value_lemma: Optional[str] = None | |
| is_negated: bool = False | |
| other_analysis: Dict[str, Any] = field(default_factory=dict) | |
| def __str__(self): | |
| # Raporlamada kolaylık için __str__ güncellenebilir, şimdilik aynı. | |
| neg_str = "[NEGATED] " if self.is_negated else "" | |
| return (f"Prop({self.prop_id[:8]}): {neg_str}" | |
| f"{self.subject_lemma} - {self.relation_lemma} - {self.value_lemma} " | |
| f"(Conf: {self.epistemic_data.computed_confidence:.2f}, Src: {self.epistemic_data.source_id})") | |
| # --- Test Bloğu --- | |
| if __name__ == "__main__": | |
| print("Testing AEE Core Classes (Era Version)...") | |
| ed1 = EpistemicData(source_id="src_test", initial_confidence=0.7) | |
| ed1.plausibility_score = 0.9 # Test için manuel atama | |
| ed1.validation_notes.append("Seems plausible based on initial check.") | |
| print(f"Created EpistemicData (Era): {ed1}") | |
| prop1 = Proposition( | |
| text_span="Test span", sentence_text="Test sentence.", epistemic_data=ed1, | |
| subject_lemma="test", relation_lemma="be", value_lemma="ok" | |
| ) | |
| print(f"Created Proposition (Era): {prop1}") | |
| print(f" Plausibility: {prop1.epistemic_data.plausibility_score}") | |
| print(f" Validation Notes: {prop1.epistemic_data.validation_notes}") | |
| print("\nCore classes (Era) seem functional.") |