Update README.md
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README.md
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@@ -41,22 +41,37 @@ These changes have been made in order to apply SimCSE method.
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from datasets import load_dataset
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from tqdm import tqdm
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import pandas as pd
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def create_trios(df, save_path):
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list_of_examples = []
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unique_premises = df.drop_duplicates("premise")["premise"]
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for premise in tqdm(unique_premises):
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examples_df = pd.DataFrame(list_of_examples, columns=["premise", "entailment", "contradiction"])
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examples_df.dropna(inplace=True)
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examples_df.to_csv(save_path)
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```
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**How to download**
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from datasets import load_dataset
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from tqdm import tqdm
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import pandas as pd
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import numpy as np
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def create_trios(df, save_path):
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list_of_examples = []
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unique_premises = df.drop_duplicates("premise")["premise"]
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for premise in tqdm(unique_premises):
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premise_dataset = df[(df["premise"] == premise)]
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positive_examples = premise_dataset[premise_dataset["label"] == "entailment"]["hypothesis"]
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negative_examples = premise_dataset[premise_dataset["label"] == "contradiction"]["hypothesis"]
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if len(positive_examples) == 0 or len(negative_examples) == 0:
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continue
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for positive_example in positive_examples:
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for negative_example in negative_examples:
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list_of_examples.append((premise, positive_example, negative_example))
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examples_df = pd.DataFrame(list_of_examples, columns=["premise", "entailment", "contradiction"])
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examples_df.to_csv(save_path)
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if __name__ == "__main__":
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dataset1 = load_dataset("kor_nli", "snli")["train"]
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dataset2 = load_dataset("kor_nli", "multi_nli")["train"]
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df_1 = pd.DataFrame(dataset1)
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df_2 = pd.DataFrame(dataset2)
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df_full = pd.concat([df_1, df_2])
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df_full.dropna(inplace=True)
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df_full["label"] = ["neutral" if label == 1 else "contradiction" if label == 2 else "entailment" for label in df_full["label"]]
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create_trios(df_full, <insert your path>)
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```
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**How to download**
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