adding the main file for the correction process

main_correction.py

@@ -0,0 +1,89 @@
+import textattack
+import transformers
+from FlowCorrector import Flow_Corrector
+import torch
+import torch.nn.functional as F
+
+def count_matching_classes(original, corrected):
+    if len(original) != len(corrected):
+        raise ValueError("Arrays must have the same length")
+
+    matching_count = 0
+
+    for i in range(len(corrected)):
+        if original[i] == corrected[i]:
+            matching_count += 1
+
+    return matching_count
+
+if __name__ == "main" :
+
+    # Load model, tokenizer, and model_wrapper
+    model = transformers.AutoModelForSequenceClassification.from_pretrained(
+        "textattack/bert-base-uncased-ag-news"
+    )
+    tokenizer = transformers.AutoTokenizer.from_pretrained(
+        "textattack/bert-base-uncased-ag-news"
+    )
+    model_wrapper = textattack.models.wrappers.HuggingFaceModelWrapper(model, tokenizer)
+
+    # Construct our four components for `Attack`
+    from textattack.constraints.pre_transformation import (
+        RepeatModification,
+        StopwordModification,
+    )
+    from textattack.constraints.semantics import WordEmbeddingDistance
+    from textattack.transformations import WordSwapEmbedding
+    from textattack.search_methods import GreedyWordSwapWIR
+
+    goal_function = textattack.goal_functions.UntargetedClassification(model_wrapper)
+    constraints = [
+        RepeatModification(),
+        StopwordModification(),
+        WordEmbeddingDistance(min_cos_sim=0.9),
+    ]
+    transformation = WordSwapEmbedding(max_candidates=50)
+    search_method = GreedyWordSwapWIR(wir_method="weighted-saliency")
+
+    # Construct the actual attack
+    attack = textattack.Attack(goal_function, constraints, transformation, search_method)
+    attack.cuda_()
+
+    # intialisation de coreecteur
+    corrector = Flow_Corrector(
+        attack,
+        word_rank_file="en_full_ranked.json",
+        word_freq_file="en_full_freq.json",
+    )
+
+    # All these texts are adverserial ones
+
+    with open('perturbed_texts_ag_news.txt', 'r') as f:
+        detected_texts = [line.strip() for line in f]
+
+
+    #These are orginal texts in same order of adverserial ones 
+
+    with open("original_texts_ag_news.txt", "r") as f:
+        original_texts = [line.strip() for line in f]
+
+    victim_model = attack.goal_function.model
+
+    # getting original labels for benchmarking later
+    original_classes = [
+        torch.argmax(F.softmax(victim_model(original_text), dim=1)).item()
+        for original_text in original_texts
+    ]
+
+    """ 0 :World
+        1 : Sports
+        2 : Business
+        3 : Sci/Tech"""
+
+    corrected_classes = corrector.correct(original_texts)
+    print(f"match {count_matching_classes()}")
+
+
+
+
+