Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-classification
+language:
+- en
+---
+
+```
+import pandas as pd
+from datasets import load_dataset
+
+dataset_name_list = [
+    "mteb/sts12-sts",
+    "mteb/sts13-sts",
+    "mteb/sts14-sts",
+    "mteb/sts15-sts",
+    "mteb/sts16-sts",
+    "mteb/stsbenchmark-sts",
+    "mteb/sickr-sts",
+]
+dataset_dict = { _[5:-4]:load_dataset(_) for _ in dataset_name_list}
+
+
+df_list = []
+for dataset_name, datasetDict in dataset_dict.items():
+    for split_name, dataset in datasetDict.items():
+        df = pd.DataFrame(dataset)
+        df = df[['sentence1', 'sentence2', 'score']]
+        df['dataset'] = dataset_name
+        df['split'] = split_name
+
+        df = df[['dataset', 'split', 'sentence1', 'sentence2', 'score']]
+
+        df_list.append(df)
+df = pd.concat(df_list, axis=0)
+
+
+df['text_sim'] = df.apply(lambda row :int(text_sim(row['sentence1'].lower(), row['sentence2'].lower()) * 100 + 0.5) / 100, axis=1)
+df['fuzz_sim'] = df.apply(lambda row :fuzz.ratio(row['sentence1'].lower(), row['sentence2'].lower()) / 100, axis=1)
+df['scaled_score'] = df.apply(lambda row : row['score'] / 5, axis=1)
+```