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011bd7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | from __future__ import annotations
import os
import pandas as pd
df_news = pd.read_csv(
"scripts/mind/data/MINDsmall_train/news.tsv", sep="\t", header=None
)
df_news = df_news[[0, 3]]
df_news.columns = ["id", "text"]
df_news.index = df_news["id"]
df_behaviours = pd.read_csv(
"scripts/mind/data/MINDsmall_train/behaviors.tsv", sep="\t", header=None
)
df_behaviours = df_behaviours[[0, 3, 4]]
df_behaviours.columns = ["id", "query", "data"]
df_behaviours.dropna(inplace=True)
# df_behaviours = df_behaviours.iloc[:10]
def proc_row(row):
docs = row["data"].split()
positives, negatives = [], []
for doc in docs:
idx, label = doc.split("-")
if label == "1":
positives.append(idx)
elif label == "0":
negatives.append(idx)
else:
raise Exception("Unknown label: {}".format(label))
row["positive"] = [df_news.loc[idx]["text"] for idx in positives]
row["negative"] = [df_news.loc[idx]["text"] for idx in negatives]
queries = row["query"].split()
row["query"] = [df_news.loc[idx]["text"] for idx in queries]
return row
df_behaviours = df_behaviours.apply(proc_row, axis=1)
df_behaviours.drop(columns=["data"], inplace=True)
print(df_behaviours)
path = "mind"
os.makedirs(path, exist_ok=True)
df_behaviours.to_json(f"{path}/train.jsonl", orient="records", lines=True)
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