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Update README.md

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@@ -7,8 +7,12 @@ language:
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  ---
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  ```
 
 
 
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  import pandas as pd
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  from datasets import load_dataset
 
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  dataset_name_list = [
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  "mteb/sts12-sts",
@@ -35,6 +39,29 @@ for dataset_name, datasetDict in dataset_dict.items():
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  df_list.append(df)
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  df = pd.concat(df_list, axis=0)
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  df['text_sim'] = df.apply(lambda row :int(text_sim(row['sentence1'].lower(), row['sentence2'].lower()) * 100 + 0.5) / 100, axis=1)
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  df['fuzz_sim'] = df.apply(lambda row :fuzz.ratio(row['sentence1'].lower(), row['sentence2'].lower()) / 100, axis=1)
 
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  ---
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  ```
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+ ! pip install python-Levenshtein
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+ ! pip install fuzzywuzzy
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+
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  import pandas as pd
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  from datasets import load_dataset
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+ from fuzzywuzzy import fuzz
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  dataset_name_list = [
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  "mteb/sts12-sts",
 
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  df_list.append(df)
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  df = pd.concat(df_list, axis=0)
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+ def text_sim(sent0, sent1):
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+ is_str = False
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+ if isinstance(sent0, str):
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+ sent0 = [sent0]
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+ sent1 = [sent1]
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+ is_str = True
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+ scores = []
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+ for s1, s2 in zip(sent0, sent1):
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+ set1 = set(s1.split(' '))
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+ # print(set1)
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+ set2 = set(s2.split(' '))
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+ # print(set2)
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+ # 计算交集和并集
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+ intersection = set1.intersection(set2)
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+ union = set1.union(set2)
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+
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+ # 计算雅可比相似度
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+ similarity = len(intersection) / len(union)
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+
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+ scores.append(similarity )
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+ return scores[0] if is_str else scores
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+
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+ print(text_sim('hello', 'hello world'))
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  df['text_sim'] = df.apply(lambda row :int(text_sim(row['sentence1'].lower(), row['sentence2'].lower()) * 100 + 0.5) / 100, axis=1)
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  df['fuzz_sim'] = df.apply(lambda row :fuzz.ratio(row['sentence1'].lower(), row['sentence2'].lower()) / 100, axis=1)