| """ |
| inference_engine.py |
| |
| Controlled Implicit Inference using MNLI. |
| Compatible with transformers >=5.x |
| |
| roberta-large-mnli Label-Meaning mapping is usually: |
| LABEL_0 CONTRADICTION |
| LABEL_1 NEUTRAL |
| LABEL_2 ENTAILMENT |
| """ |
|
|
| from transformers import pipeline |
|
|
|
|
| class InferenceEngine: |
| def __init__(self, threshold=0.80): |
| """ |
| threshold: minimum confidence score required |
| """ |
| self.threshold = threshold |
|
|
| self.classifier = pipeline( |
| "text-classification", |
| model="roberta-large-mnli" |
| ) |
|
|
| def validate_hypotheses(self, premise, hypotheses): |
| """ |
| Validate candidate hypotheses using MNLI. |
| Returns: |
| List of inference classifications. |
| """ |
|
|
| validated = [] |
|
|
| label_mapping = { |
| "LABEL_0": "CONTRADICTION", |
| "LABEL_1": "NEUTRAL", |
| "LABEL_2": "ENTAILMENT" |
| } |
|
|
| for hypothesis in hypotheses: |
|
|
| result = self.classifier( |
| f"{premise} </s></s> {hypothesis}" |
| )[0] |
|
|
| raw_label = result["label"] |
| score = result["score"] |
|
|
| label = label_mapping.get(raw_label, raw_label) |
|
|
| validated.append({ |
| "hypothesis": hypothesis, |
| "label": label, |
| "confidence": round(score, 3), |
| "confidence_tier": self._confidence_tier(score) |
| }) |
|
|
| return validated |
| |
| def _confidence_tier(self, score): |
| """ |
| Convert numeric confidence into analyst-friendly tier. |
| """ |
|
|
| if score >= 0.95: |
| return "High" |
|
|
| elif score >= 0.80: |
| return "Moderate" |
|
|
| return "Low" |
