Upload entailment.py

entailment.py

@@ -0,0 +1,31 @@
+from transformers import pipeline
+import numpy as np
+
+def analyze_entailment(original_sentence, paraphrased_sentences, threshold):
+    # Load the entailment model using pipeline
+    entailment_pipe = pipeline("text-classification", model="ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli")
+
+    # Function to perform entailment
+    def check_entailment(premise, hypothesis):
+        results = entailment_pipe(f"{premise} [SEP] {hypothesis}", return_all_scores=True)
+        return results[0]
+
+    all_sentences = {}
+    selected_sentences = {}
+    discarded_sentences = {}
+
+    # Check entailment for each paraphrased sentence
+    for paraphrased_sentence in paraphrased_sentences:
+        entailment_results = check_entailment(original_sentence, paraphrased_sentence)
+        entailment_score = next(result['score'] for result in entailment_results if result['label'] == 'entailment')
+        
+        all_sentences[paraphrased_sentence] = entailment_score
+        
+        if entailment_score >= threshold:
+            selected_sentences[paraphrased_sentence] = entailment_score
+        else:
+            discarded_sentences[paraphrased_sentence] = entailment_score
+
+    return all_sentences, selected_sentences, discarded_sentences
+
+print(analyze_entailment("I love you", ["You're being loved by me"], 0.7))