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README.md

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+This is a version of the [Enron Spam Email Dataset](https://github.com/MWiechmann/enron_spam_data), containing emails (subject + message) and a label whether it is spam or ham.

enron_spam_data.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:009c86359b5bd6ec142a9b9ca85075ec864fd8c7c1c378c9a430e9427d0f7d57
+size 51690056

prepare.py

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+import pandas as pd
+from collections import Counter
+import json
+import random
+
+df = pd.read_csv("enron_spam_data.csv")
+df.fillna('', inplace=True)
+print(df)
+label2id = {'ham': 0, 'spam': 1}
+
+rows = [{'message_id': row['Message ID'], 
+         'text': (row['Subject']+" "+row['Message']).strip(),
+         'label': label2id[row['Spam/Ham']],
+         'label_text': row['Spam/Ham'],
+         'subject': row['Subject'],
+         'message': row['Message'],
+         'date': row['Date']
+         } for idx, row in df.iterrows()]
+
+random.seed(42)
+random.shuffle(rows)
+
+num_test = 2000
+splits = {'test': rows[0:num_test], 'train': rows[num_test:]}
+
+print("Train:", len(splits['train']))
+print("Test:", len(splits['test']))
+
+num_spam = Counter()
+
+for row in splits['test']:
+    num_spam[row['label']] += 1
+print(num_spam)
+
+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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+version https://git-lfs.github.com/spec/v1
+oid sha256:f07a4cca7bfc1d845d4ff59650e69a0ad31d9b5c0409ddad088219f7fc32bb52
+size 6273613

train.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b846cf15bcad7676c37ea3535622e39e6bff5c9c004f634513e4f9c2951e5f02
+size 101069043