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README.md

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+This is a version of the [Quora Insincere Questions Classification](https://www.kaggle.com/c/quora-insincere-questions-classification). 
+
+An insincere question is defined as a question intended to make a statement rather than look for helpful answers. About 6% of questions are labeled as insincere.

original.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b8549942872d29c9eeb9778762a51d122e12a4e61d1e657cd9cd275a403b415
+size 124206772

prepare.py

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+import pandas as pd
+from collections import Counter
+import json
+import random
+
+
+df = pd.read_csv("original.csv")
+
+print(df)
+
+
+rows = [{'qid': row['qid'],
+        'text': row['question_text'].strip(),
+        'label': row['target'],
+        'label_text': "insincere question" if row['target'] else "valid question",
+         } for idx, row in df.iterrows()]
+
+random.seed(42)
+random.shuffle(rows)
+
+num_test = 50000
+splits = {'test': rows[0:num_test], 'train': rows[num_test:]}
+
+print("Train:", len(splits['train']))
+print("Test:", len(splits['test']))
+
+num_labels = Counter()
+
+for row in splits['test']:
+    num_labels[row['label']] += 1
+print(num_labels)
+
+for split in ['train', 'test']:
+    with open(f'{split}.jsonl', 'w') as fOut:
+        for row in splits[split]:
+            fOut.write(json.dumps(row)+"\n")

test.jsonl

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+size 7957420

train.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e0955e0af5e194f28119b9dd170bb7a599385be7f07e29934c13fdf3b665043
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