first commit

README.md

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+---
+language:
+- en
+task_categories:
+- text-classification
+
+dataset_info:
+- config_name: default
+  features:
+  - name: stars
+    dtype:
+      class_label:
+        names:
+          '0': 1 star
+          '1': 2 stars
+          '2': 3 stars
+          '3': 4 stars
+          '4': 5 stars
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  - name: useful
+    dtype: int16
+  - name: funny
+    dtype: int16
+  - name: cool
+    dtype: int16
+- config_name: polarity
+  features:
+  - name: stars
+    dtype:
+      class_label:
+        names:
+          '0': negative
+          '1': positive
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  - name: useful
+    dtype: int16
+  - name: funny
+    dtype: int16
+  - name: cool
+    dtype: int16
+
+configs:
+- config_name: default
+  data_files:
+  - split: complete
+    path: "reviews.parquet"
+  default: true
+- config_name: polarity
+  data_files:
+  - split: complete
+    path: "reviews_polarity.parquet"
+---

convert.py

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+import json
+import random
+
+import pandas as pd
+
+
+def parse_review(line):
+    d = json.loads(line)
+    d.pop("review_id")
+    d.pop("user_id")
+    d.pop("business_id")
+    d["stars"] = int(d["stars"]) - 1  # from 0 to 4
+
+    return d
+
+
+def remap2polarity(row):
+    if row["stars"] in [0, 1]:
+        row["stars"] = 0
+    elif row["stars"] in [3, 4]:
+        row["stars"] = 1
+    else:
+        raise ValueError("Invalid value")
+
+    return row
+
+
+def run():
+    rows = []
+
+    with open(
+        "yelp_dataset/yelp_academic_dataset_review.json", "r", encoding="utf8"
+    ) as file:
+        lines = file.readlines()
+
+    for line in lines:
+        obj = parse_review(line)
+        rows.append(obj)
+
+    df = pd.DataFrame(rows)
+    df.to_parquet("reviews.parquet")
+
+    # polarity
+    polarity_rows = [remap2polarity(row) for row in rows if row["stars"] != 2]
+
+    positive = [row for row in polarity_rows if row["stars"] == 1]
+    negative = [row for row in polarity_rows if row["stars"] == 0]
+    min_size = min(len(positive), len(negative))
+
+    polarity_rows = positive[:min_size] + negative[:min_size]
+    random.shuffle(polarity_rows)
+
+    df = pd.DataFrame(polarity_rows)
+    df.to_parquet("reviews_polarity.parquet")
+
+
+if __name__ == "__main__":
+    run()

reviews.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:069f66f46b8b6def36fd346b9ca3c3ad3a81ae573d696865c8fcac47a01bc3fc
+size 2636471449

reviews_polarity.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0db32db7029489aadb11e4d51cf8421727978e03e9a2c6f534e70657787d707
+size 1301672500