update

README.md

@@ -11,9 +11,11 @@ license: apache-2.0
 数据集从网上收集整理如下:
 |    数据     |  语言  |  任务类型  |  原始数据/项目地址  | 样本个数  |  原始数据描述  | 替代数据下载地址 |
 |    :---     | :---: |   :---:   |       :---:        |   :---:  |     :---:     |     :---:     |
-|  sms_spam  |  英语  | 垃圾短信分类 | [SMS Spam Collection](https://archive.ics.uci.edu/dataset/228/sms+spam+collection); [SMS Spam Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) | 5,574 | SMS 垃圾邮件集合是一组公开的 SMS 标记消息，为移动电话垃圾邮件研究而收集。 | [sms_spam](https://huggingface.co/datasets/sms_spam) |
-| spam_assassin  |  英语  |  垃圾邮件分类  |  [Apache SpamAssassin’s public datasets](https://spamassassin.apache.org/old/publiccorpus/); [Spam or Not Spam Dataset](https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset)  | 10.7K |  这是一组邮件消息，适合用于测试垃圾邮件过滤系统。  | [SpamAssassin](https://huggingface.co/datasets/talby/spamassassin) |
-| enron_spam |  英语  |  垃圾邮件分类  | [enron_spam_data](https://github.com/MWiechmann/enron_spam_data); [Enron-Spam](https://www2.aueb.gr/users/ion/data/enron-spam/); [spam-mails-dataset](https://www.kaggle.com/datasets/venky73/spam-mails-dataset)  | 17,171 spam; 16,545 ham  |  Enron-Spam 数据集是 V. Metsis、I. Androutsopoulos 和 G. Paliouras 收集的绝佳资源  | [enron_spam](https://huggingface.co/datasets/SetFit/enron_spam) |
+|  sms_spam  |  英语  | 垃圾短信分类 | [SMS Spam Collection](https://archive.ics.uci.edu/dataset/228/sms+spam+collection); [SMS Spam Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) | ham: 4827; spam: 747 | SMS 垃圾邮件集合是一组公开的 SMS 标记消息，为移动电话垃圾邮件研究而收集。 | [sms_spam](https://huggingface.co/datasets/sms_spam) |
+| spam_assassin  |  英语  |  垃圾邮件分类  | [datasets-spam-assassin](https://github.com/stdlib-js/datasets-spam-assassin); [Apache SpamAssassin’s public datasets](https://spamassassin.apache.org/old/publiccorpus/); [Spam or Not Spam Dataset](https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset)  | ham: 3795; spam: 6954 |  这是一组邮件消息，适合用于测试垃圾邮件过滤系统。备注：text 很乱，不推荐使用。  | [talby/SpamAssassin](https://huggingface.co/datasets/talby/spamassassin) |
+| enron_spam |  英语  |  垃圾邮件分类  | [enron_spam_data](https://github.com/MWiechmann/enron_spam_data); [Enron-Spam](https://www2.aueb.gr/users/ion/data/enron-spam/); [spam-mails-dataset](https://www.kaggle.com/datasets/venky73/spam-mails-dataset)  | ham: 16545; spam: 17171 |  Enron-Spam 数据集是 V. Metsis、I. Androutsopoulos 和 G. Paliouras 收集的绝佳资源  | [SetFit/enron_spam](https://huggingface.co/datasets/SetFit/enron_spam) |
+| spam_detection |  英语  |  垃圾短信分类  |  [Deysi/spam-detection-dataset](https://huggingface.co/datasets/Deysi/spam-detection-dataset)  | ham: 5400; spam: 5500  |  |  |
+| email_spam |  英语  |  垃圾短信分类  |  [NotShrirang/email-spam-filter](https://huggingface.co/datasets/NotShrirang/email-spam-filter)  | ham: 3672; spam: 1499  |  |  |
 
 
 ### 样本示例
@@ -62,6 +64,7 @@ spam
 
 
 ### 参考来源
+
 <details>
 <summary>参考的数据来源,展开查看</summary>
 <pre><code>

data/email_spam.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b530e530e6251b7755b2329e15482a95332d71caad0b8c2fb85a7433a335fd7
+size 5919435

data/enron_spam.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:831a68669823136108e9e2a90c86220ccd3afe8b7c46a7d9df4a8667c21db477
+size 54956602

data/sms_spam.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:65b1bb054a8b310bbf949cb2ce527a93f9e51401275e06f0e4b43d422edb46d5
-size 868244
+oid sha256:2f2c3c37b23bcbc97d37e86be652b70d6bbc4c69626ceeef6b3118eb9d3f2bc1
+size 968576

data/spam_assassin.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48753da2e31d5d65efcacf31f12e2f3b3f68b02a1a625222173a7bc8fb86435c
+size 41867894

data/spam_detection.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8645dd871427da0d5cad715fedfb89ded865fa8cba27fe05b7331bb4bd9c1068
+size 5239829

examples/preprocess/process_email_spam.py

@@ -1,6 +1,72 @@
 #!/usr/bin/python3
 # -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+import json
+import os
+from pathlib import Path
+import random
+import re
+import sys
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, '../../'))
+
+from datasets import load_dataset
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--dataset_path", default="NotShrirang/email-spam-filter", type=str)
+    parser.add_argument(
+        "--dataset_cache_dir",
+        default=(project_path / "hub_datasets").as_posix(),
+        type=str
+    )
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/email_spam.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    dataset_dict = load_dataset(
+        path=args.dataset_path,
+        cache_dir=args.dataset_cache_dir,
+    )
+    print(dataset_dict)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for split, dataset in dataset_dict.items():
+            for sample in tqdm(dataset):
+                # print(sample)
+                text = sample["text"]
+                label = sample["label"]
+
+                if label not in ("spam", "ham"):
+                    raise AssertionError
+
+                row = {
+                    "text": text,
+                    "label": label,
+                    "category": None,
+                    "data_source": "email_spam",
+                    "split": split
+                }
+                row = json.dumps(row, ensure_ascii=False)
+                f.write("{}\n".format(row))
+
+    return
 
 
 if __name__ == '__main__':
-    pass
+    main()

examples/preprocess/process_enron_spam.py

@@ -1,6 +1,76 @@
 #!/usr/bin/python3
 # -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+import json
+import os
+from pathlib import Path
+import random
+import re
+import sys
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, '../../'))
+
+from datasets import load_dataset
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--dataset_path", default="SetFit/enron_spam", type=str)
+    parser.add_argument(
+        "--dataset_cache_dir",
+        default=(project_path / "hub_datasets").as_posix(),
+        type=str
+    )
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/enron_spam.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    dataset_dict = load_dataset(
+        path=args.dataset_path,
+        cache_dir=args.dataset_cache_dir,
+    )
+    print(dataset_dict)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for split, dataset in dataset_dict.items():
+            for sample in tqdm(dataset):
+                # print(sample)
+                # text = sample["text"]
+                subject = sample["subject"]
+                message = sample["message"]
+                label = sample["label_text"]
+
+                text = "{}\n\n{}".format(subject, message)
+
+                if label not in ("spam", "ham"):
+                    raise AssertionError
+
+                row = {
+                    "text": text,
+                    "label": label,
+                    "category": None,
+                    "data_source": "enron_spam",
+                    "split": split
+                }
+                row = json.dumps(row, ensure_ascii=False)
+                f.write("{}\n".format(row))
+
+    return
 
 
 if __name__ == '__main__':
-    pass
+    main()

examples/preprocess/process_sms_spam.py

@@ -54,9 +54,13 @@ def main():
             text = text.strip()
             label = "spam" if label == 1 else "ham"
 
+            if label not in ("spam", "ham"):
+                raise AssertionError
+
             row = {
                 "text": text,
                 "label": label,
+                "category": None,
                 "data_source": "sms_spam",
                 "split": "train"
             }

examples/preprocess/process_spam_assassin.py

@@ -49,17 +49,20 @@ def main():
         for split, dataset in dataset_dict.items():
             for sample in tqdm(dataset):
                 # print(sample)
-                # text = sample["text"]
-                subject = sample["subject"]
-                message = sample["message"]
-                label = sample["label_text"]
+                text = sample["text"]
+                group = sample["group"]
+                label = sample["label"]
 
-                text = "{}\n\n{}".format(subject, message)
+                label = "spam" if label == 1 else "ham"
+
+                if label not in ("spam", "ham"):
+                    raise AssertionError
 
                 row = {
                     "text": text,
                     "label": label,
-                    "data_source": "enron_spam",
+                    "category": group,
+                    "data_source": "spam_assassin",
                     "split": split
                 }
                 row = json.dumps(row, ensure_ascii=False)

examples/preprocess/process_spam_detection.py

@@ -1,6 +1,74 @@
 #!/usr/bin/python3
 # -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+import json
+import os
+from pathlib import Path
+import random
+import re
+import sys
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, '../../'))
+
+from datasets import load_dataset
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--dataset_path", default="Deysi/spam-detection-dataset", type=str)
+    parser.add_argument(
+        "--dataset_cache_dir",
+        default=(project_path / "hub_datasets").as_posix(),
+        type=str
+    )
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/spam_detection.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    dataset_dict = load_dataset(
+        path=args.dataset_path,
+        cache_dir=args.dataset_cache_dir,
+    )
+    print(dataset_dict)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for split, dataset in dataset_dict.items():
+            for sample in tqdm(dataset):
+                # print(sample)
+                text = sample["text"]
+                label = sample["label"]
+
+                label = "spam" if label == "spam" else "ham"
+
+                if label not in ("spam", "ham"):
+                    raise AssertionError
+
+                row = {
+                    "text": text,
+                    "label": label,
+                    "category": None,
+                    "data_source": "spam_detection",
+                    "split": split
+                }
+                row = json.dumps(row, ensure_ascii=False)
+                f.write("{}\n".format(row))
+
+    return
 
 
 if __name__ == '__main__':
-    pass
+    main()

examples/preprocess/samples_count.py

@@ -1,5 +1,31 @@
 #!/usr/bin/python3
 # -*- coding: utf-8 -*-
+from collections import defaultdict
+from datasets import load_dataset, DownloadMode
+
+
+dataset_dict = load_dataset(
+    "../../spam_detect.py",
+    name="email_spam",
+    # name="enron_spam",
+    # name="sms_spam",
+    # name="spam_assassin",
+    # name="spam_detection",
+    split=None,
+    cache_dir=None,
+    download_mode=DownloadMode.FORCE_REDOWNLOAD
+)
+
+
+counter = defaultdict(int)
+for split, dataset in dataset_dict.items():
+    for sample in dataset:
+        text = sample["text"]
+        label = sample["label"]
+        counter[label] += 1
+
+count = "; ".join(sorted(["{}: {}".format(k, v) for k, v in counter.items()]))
+print(count)
 
 
 if __name__ == '__main__':

main.py

@@ -5,7 +5,10 @@ from datasets import load_dataset, DownloadMode
 
 dataset = load_dataset(
     "spam_detect.py",
-    name="sms_spam",
+    name="email_spam",
+    # name="sms_spam",
+    # name="spam_assassin",
+    # name="spam_detection",
     split="train",
     cache_dir=None,
     download_mode=DownloadMode.FORCE_REDOWNLOAD

requirements.txt

@@ -1,3 +1,6 @@
 datasets==2.10.1
 fsspec==2023.9.2
 tqdm==4.66.1
+pandas==1.5.2
+xlrd==1.2.0
+openpyxl==3.0.9

spam_detect.py

@@ -11,7 +11,12 @@ import datasets
 
 
 _urls = {
-    "sms_spam": "data/sms_spam.jsonl"
+    "email_spam": "data/email_spam.jsonl",
+    "enron_spam": "data/enron_spam.jsonl",
+    "sms_spam": "data/sms_spam.jsonl",
+    "spam_assassin": "data/spam_assassin.jsonl",
+    "spam_detection": "data/spam_detection.jsonl",
+
 }
 
 
@@ -43,6 +48,7 @@ class SpamDetect(datasets.GeneratorBasedBuilder):
         features = datasets.Features({
             "text": datasets.Value("string"),
             "label": datasets.Value("string"),
+            "category": datasets.Value("string"),
             "data_source": datasets.Value("string"),
         })
 
@@ -91,6 +97,7 @@ class SpamDetect(datasets.GeneratorBasedBuilder):
                 yield idx, {
                     "text": sample["text"],
                     "label": sample["label"],
+                    "category": sample["category"],
                     "data_source": sample["data_source"],
                 }
                 idx += 1