update

README.md

@@ -12,17 +12,18 @@ license: apache-2.0
 |    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 |
 |    :---     | :---: |   :---:   |       :---:        |   :---:  |     :---:     |     :---:     |
 | 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); [enron-spam](https://www.kaggle.com/datasets/wanderfj/enron-spam) |
-| enron_spam_subset |  英语  |  垃圾邮件分类  |  [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)  | ham: 5000; spam: 5000  |  |  |
+| enron_spam_subset |  英语  |  垃圾邮件分类  |  [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)  | ham: 5000; spam: 5000 |  |  |
 | ling_spam |  英语  |  垃圾邮件分类  |  [lingspam-dataset](https://www.kaggle.com/datasets/mandygu/lingspam-dataset); [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)  | ham: 2172; spam: 433  | Ling-Spam 数据集是从语言学家列表中整理的 2,893 条垃圾邮件和非垃圾邮件消息的集合。 |  |
 | 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) |
-| sms_spam_collection |  英语  |  垃圾短信分类  |  [spam-emails](https://www.kaggle.com/datasets/abdallahwagih/spam-emails)  | ham: 4825; spam: 747  | 该数据集包含电子邮件的集合 | [email-spam-detection-dataset-classification](https://www.kaggle.com/datasets/shantanudhakadd/email-spam-detection-dataset-classification); [spam-identification](https://www.kaggle.com/datasets/amirdhavarshinis/spam-identification); [sms-spam-collection](https://www.kaggle.com/datasets/thedevastator/sms-spam-collection-a-more-diverse-dataset); [spam-or-ham](https://www.kaggle.com/datasets/arunasivapragasam/spam-or-ham) |
+| sms_spam_collection |  英语  |  垃圾短信分类  |  [spam-emails](https://www.kaggle.com/datasets/abdallahwagih/spam-emails)  | ham: 4825; spam: 747 | 该数据集包含电子邮件的集合 | [email-spam-detection-dataset-classification](https://www.kaggle.com/datasets/shantanudhakadd/email-spam-detection-dataset-classification); [spam-identification](https://www.kaggle.com/datasets/amirdhavarshinis/spam-identification); [sms-spam-collection](https://www.kaggle.com/datasets/thedevastator/sms-spam-collection-a-more-diverse-dataset); [spam-or-ham](https://www.kaggle.com/datasets/arunasivapragasam/spam-or-ham) |
 | 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: 4150; spam: 1896 |  数据集从[email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)的completeSpamAssassin.csv文件而来。  | [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset); [talby/SpamAssassin](https://huggingface.co/datasets/talby/spamassassin); [spamassassin-2002](https://www.kaggle.com/datasets/cesaber/spam-email-data-spamassassin-2002) |
-| spam_base |  英语  |  垃圾邮件分类  |  [spambase](https://archive.ics.uci.edu/dataset/94/spambase)  | 样本个数  |  将电子邮件分类为垃圾邮件或非垃圾邮件  | [spam-email-data-uci](https://www.kaggle.com/datasets/kaggleprollc/spam-email-data-uci) |
-| 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  |  |  |
+| spam_base |  英语  |  垃圾邮件分类  |  [spambase](https://archive.ics.uci.edu/dataset/94/spambase)  |  |  将电子邮件分类为垃圾邮件或非垃圾邮件  | [spam-email-data-uci](https://www.kaggle.com/datasets/kaggleprollc/spam-email-data-uci) |
+| 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 |  |  |
 | spam_message |  汉语  |  垃圾短信分类  |  [SpamMessage](https://github.com/hrwhisper/SpamMessage)  | ham: 720000; spam: 80000 | 其中spam的数据是正确的数据，但是做了脱敏处理（招生电话：xxxxxxxxxxx），这里的 x 可能会成为显著特征。而ham样本像是从普通文本中截断出来充作样本的，建议不要用这些数据。 |  |
 | spam_message_lr |  汉语  |  垃圾短信分类  |  [SpamMessagesLR](https://github.com/x-hacker/SpamMessagesLR)  | ham: 3983; spam: 6990 |  |  |
-| trec_2007 |  英语  |  垃圾邮件分类  |  [2007 TREC Public Spam Corpus](https://plg.uwaterloo.ca/~gvcormac/treccorpus07/); [Spam Track](https://trec.nist.gov/data/spam.html)  | 样本个数  | 2007 TREC Public Spam Corpus | [trec07p.tar.gz](https://pan.baidu.com/s/1jC9CxVaxwizFCvGtI1JvJA?pwd=g72z) |
-| youtube_spam_collection |  英语  |  垃圾评论分类  |  [youtube+spam+collection](https://archive.ics.uci.edu/dataset/380/youtube+spam+collection); [YouTube Spam Collection Data Set](https://www.kaggle.com/datasets/lakshmi25npathi/images)  | ham: 951; spam: 1005  |  它是为垃圾邮件研究而收集的公共评论集。  |  |
+| trec07p |  英语  |  垃圾邮件分类  |  [2007 TREC Public Spam Corpus](https://plg.uwaterloo.ca/~gvcormac/treccorpus07/); [Spam Track](https://trec.nist.gov/data/spam.html)  | ham: 25220; spam: 50199 | 2007 TREC Public Spam Corpus | [trec07p.tar.gz](https://pan.baidu.com/s/1jC9CxVaxwizFCvGtI1JvJA?pwd=g72z) |
+| trec06c |  汉语  |  垃圾邮件分类  |  [2006 TREC Public Spam Corpora](https://plg.uwaterloo.ca/~gvcormac/treccorpus06/); |  | 2006 TREC Public Spam Corpora |  |
+| youtube_spam_collection |  英语  |  垃圾评论分类  |  [youtube+spam+collection](https://archive.ics.uci.edu/dataset/380/youtube+spam+collection); [YouTube Spam Collection Data Set](https://www.kaggle.com/datasets/lakshmi25npathi/images)  | ham: 951; spam: 1005 |  它是为垃圾邮件研究而收集的公共评论集。  |  |
 
 
 
@@ -297,6 +298,7 @@ ham
 <details>
 <summary>参考的数据来源,展开查看</summary>
 <pre><code>
+https://huggingface.co/datasets/dbarbedillo/SMS_Spam_Multilingual_Collection_Dataset
 
 https://huggingface.co/datasets/FredZhang7/all-scam-spam
 https://huggingface.co/datasets/Deysi/spam-detection-dataset

data/trec07p.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:157d6a981f70a3789282ffb79c300a19a7332aa635ba3c479757a970aa9ba4b3
+size 609190484

examples/preprocess/process_trec07p.py

@@ -0,0 +1,89 @@
+#!/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("--data_dir", default="data/trec07p/full", type=str)
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/trec07p.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    data_dir = Path(args.data_dir)
+
+    full_index = data_dir / "index"
+
+    with open(args.output_file, "w", encoding="utf-8") as fout:
+        with open(full_index.as_posix(), "r", encoding="utf-8") as fin:
+            for row in fin:
+                row = str(row).strip()
+                row = row.split(" ", maxsplit=1)
+
+                if len(row) != 2:
+                    print(row)
+                    raise AssertionError
+
+                label = row[0]
+                fn = row[1]
+                filename = data_dir / fn
+
+                for encoding in ("utf-8", "gbk", "ANSI"):
+                    try:
+                        with open(filename.as_posix(), "r", encoding=encoding) as finmail:
+                            text = finmail.read()
+                    except UnicodeDecodeError:
+                        # print(filename.as_posix())
+                        # print("UnicodeDecodeError")
+                        continue
+
+                if label not in ("spam", "ham"):
+                    raise AssertionError
+
+                num = random.random()
+                if num < 0.9:
+                    split = "train"
+                elif num < 0.95:
+                    split = "validation"
+                else:
+                    split = "test"
+
+                row = {
+                    "text": text,
+                    "label": label,
+                    "category": None,
+                    "data_source": "trec07p",
+                    "split": split
+                }
+                row = json.dumps(row, ensure_ascii=False)
+                fout.write("{}\n".format(row))
+
+    return
+
+
+if __name__ == '__main__':
+    main()

examples/preprocess/samples_count.py

@@ -15,7 +15,8 @@ dataset_dict = load_dataset(
     # name="sms_spam_collection",
     # name="spam_message",
     # name="spam_message_lr",
-    name="youtube_spam_collection",
+    name="trec07p",
+    # name="youtube_spam_collection",
     split=None,
     cache_dir=None,
     download_mode=DownloadMode.FORCE_REDOWNLOAD

spam_detect.py

@@ -20,6 +20,7 @@ _urls = {
     "spam_emails": "data/spam_emails.jsonl",
     "spam_message": "data/spam_message.jsonl",
     "spam_message_lr": "data/spam_message_lr.jsonl",
+    "trec07p": "data/trec07p.jsonl",
     "youtube_spam_collection": "data/youtube_spam_collection.jsonl",
 
 }