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import random |
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from difflib import Differ |
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from textattack.attack_recipes import BAEGarg2019 |
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from textattack.datasets import Dataset |
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from textattack.models.wrappers import HuggingFaceModelWrapper |
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from findfile import find_files |
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from flask import Flask |
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from textattack import Attacker |
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class ModelWrapper(HuggingFaceModelWrapper): |
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def __init__(self, model): |
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self.model = model |
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def __call__(self, text_inputs, **kwargs): |
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outputs = [] |
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for text_input in text_inputs: |
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raw_outputs = self.model.infer(text_input, print_result=False, **kwargs) |
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outputs.append(raw_outputs["probs"]) |
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return outputs |
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class SentAttacker: |
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def __init__(self, model, recipe_class=BAEGarg2019): |
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model = model |
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model_wrapper = ModelWrapper(model) |
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recipe = recipe_class.build(model_wrapper) |
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_dataset = [("", 0)] |
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_dataset = Dataset(_dataset) |
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self.attacker = Attacker(recipe, _dataset) |
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def diff_texts(text1, text2): |
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d = Differ() |
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return [ |
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(token[2:], token[0] if token[0] != " " else None) |
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for token in d.compare(text1, text2) |
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] |
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def get_ensembled_tad_results(results): |
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target_dict = {} |
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for r in results: |
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target_dict[r["label"]] = ( |
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target_dict.get(r["label"]) + 1 if r["label"] in target_dict else 1 |
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) |
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return dict(zip(target_dict.values(), target_dict.keys()))[ |
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max(target_dict.values()) |
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] |
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def get_sst2_example(): |
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filter_key_words = [ |
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".py", |
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".md", |
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"readme", |
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"log", |
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"result", |
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"zip", |
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".state_dict", |
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".model", |
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".png", |
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"acc_", |
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"f1_", |
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".origin", |
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".adv", |
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".csv", |
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] |
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dataset_file = {"train": [], "test": [], "valid": []} |
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dataset = "sst2" |
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search_path = "./" |
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task = "text_defense" |
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dataset_file["test"] += find_files( |
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search_path, |
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[dataset, "test", task], |
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exclude_key=[".adv", ".org", ".defense", ".inference", "train."] |
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+ filter_key_words, |
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) |
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for dat_type in ["test"]: |
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data = [] |
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label_set = set() |
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for data_file in dataset_file[dat_type]: |
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with open(data_file, mode="r", encoding="utf8") as fin: |
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lines = fin.readlines() |
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for line in lines: |
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text, label = line.split("$LABEL$") |
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text = text.strip() |
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label = int(label.strip()) |
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data.append((text, label)) |
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label_set.add(label) |
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return random.choice(data) |
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def get_agnews_example(): |
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filter_key_words = [ |
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".py", |
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".md", |
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"readme", |
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"log", |
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"result", |
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"zip", |
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".state_dict", |
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".model", |
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".png", |
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"acc_", |
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"f1_", |
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".origin", |
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".adv", |
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".csv", |
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] |
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dataset_file = {"train": [], "test": [], "valid": []} |
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dataset = "agnews" |
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search_path = "./" |
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task = "text_defense" |
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dataset_file["test"] += find_files( |
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search_path, |
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[dataset, "test", task], |
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exclude_key=[".adv", ".org", ".defense", ".inference", "train."] |
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+ filter_key_words, |
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) |
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for dat_type in ["test"]: |
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data = [] |
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label_set = set() |
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for data_file in dataset_file[dat_type]: |
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with open(data_file, mode="r", encoding="utf8") as fin: |
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lines = fin.readlines() |
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for line in lines: |
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text, label = line.split("$LABEL$") |
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text = text.strip() |
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label = int(label.strip()) |
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data.append((text, label)) |
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label_set.add(label) |
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return random.choice(data) |
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def get_amazon_example(): |
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filter_key_words = [ |
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".py", |
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".md", |
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"readme", |
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"log", |
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"result", |
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"zip", |
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".state_dict", |
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".model", |
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".png", |
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"acc_", |
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"f1_", |
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".origin", |
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".adv", |
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".csv", |
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] |
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dataset_file = {"train": [], "test": [], "valid": []} |
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dataset = "amazon" |
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search_path = "./" |
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task = "text_defense" |
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dataset_file["test"] += find_files( |
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search_path, |
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[dataset, "test", task], |
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exclude_key=[".adv", ".org", ".defense", ".inference", "train."] |
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+ filter_key_words, |
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) |
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for dat_type in ["test"]: |
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data = [] |
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label_set = set() |
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for data_file in dataset_file[dat_type]: |
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with open(data_file, mode="r", encoding="utf8") as fin: |
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lines = fin.readlines() |
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for line in lines: |
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text, label = line.split("$LABEL$") |
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text = text.strip() |
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label = int(label.strip()) |
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data.append((text, label)) |
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label_set.add(label) |
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return random.choice(data) |
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def get_imdb_example(): |
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filter_key_words = [ |
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".py", |
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".md", |
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"readme", |
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"log", |
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"result", |
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"zip", |
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".state_dict", |
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".model", |
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".png", |
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"acc_", |
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"f1_", |
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".origin", |
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".adv", |
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".csv", |
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] |
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dataset_file = {"train": [], "test": [], "valid": []} |
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dataset = "imdb" |
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search_path = "./" |
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task = "text_defense" |
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dataset_file["test"] += find_files( |
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search_path, |
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[dataset, "test", task], |
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exclude_key=[".adv", ".org", ".defense", ".inference", "train."] |
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+ filter_key_words, |
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) |
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for dat_type in ["test"]: |
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data = [] |
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label_set = set() |
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for data_file in dataset_file[dat_type]: |
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with open(data_file, mode="r", encoding="utf8") as fin: |
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lines = fin.readlines() |
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for line in lines: |
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text, label = line.split("$LABEL$") |
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text = text.strip() |
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label = int(label.strip()) |
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data.append((text, label)) |
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label_set.add(label) |
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return random.choice(data) |
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