update

README.md

@@ -11,54 +11,22 @@ 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 |
 |    :---     | :---: |   :---:   |       :---:        |   :---:  |     :---:     |     :---:     |
-| 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  |  |  |
-| 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 |  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  |  |  |
+| ling_spam |  英语  |  垃圾邮件分类  |  [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)  | ham: 2172; spam: 433  |  |  |
 |  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) |
+| 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_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  |  |  |
+| 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_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。  |  |
 
 
-### 样本示例
 
 
-<details>
-<summary>email_spam 样本示例</summary>
-<pre><code>------------
-Subject: photoshop , windows , office . cheap . main trending
-abasements darer prudently fortuitous undergone
-lighthearted charm orinoco taster
-railroad affluent pornographic cuvier
-irvin parkhouse blameworthy chlorophyll
-robed diagrammatic fogarty clears bayda
-inconveniencing managing represented smartness hashish
-academies shareholders unload badness
-danielson pure caffein
-spaniard chargeable levin
-<br>
-spam
-------------
-Subject: re : indian springs
-this deal is to book the teco pvr revenue . it is my understanding that teco
-just sends us a check , i haven ' t received an answer as to whether there is a
-predermined price associated with this deal or if teco just lets us know what
-we are giving . i can continue to chase this deal down if you need .
-ham
-------------
-Subject: report 01405 !
-wffur attion brom est inst siupied 1 pgst our riwe asently rest .
-tont to presyou tew cons of benco 4 . yee : fater 45 y . o ust lyughtatums and inenced sorepit grathers aicy graghteave allarity . oarity wow to yur coons , as were then 60 ve mers of oite .
-ithat yoit ? ! berst thar ! enth excives 2004 . . .
-<br>
-spam
-------------
-Subject: nominations for oct . 21 - 23 , 2000
-( see attached file : hplnl 021 . xls )
-- hplnl 021 . xls
-ham
-------------
-</code></pre>
-</details>
+### 样本示例
 
 
 <details>
@@ -99,6 +67,74 @@ ham
 </details>
 
 
+<details>
+<summary>enron_spam_subset 样本示例</summary>
+<pre><code>------------
+Subject: edrugs online
+ viagra - proven step to start something all over again .
+ nothing is more useful than silence .
+ teachers open the door . you enter by yourself .
+ how sharper than a serpent ' s tooth it isto have a thankless child !
+spam
+------------
+Subject: start date : 12 / 13 / 01 ; hourahead hour : 5 ;
+ start date : 12 / 13 / 01 ; hourahead hour : 5 ; no ancillary schedules awarded . no variances detected .
+ log messages :
+ parsing file - - > > o : \ portland \ westdesk \ california scheduling \ iso final schedules \ 2001121305 . txt
+ham
+------------
+Subject: cheapestt medss !
+ mediccationns at lowesst pricess everyy !
+ over 80 . % offf , pricess wontt get lowerr
+ we selll vic ' od ( in v , ia . gra x , ana . x
+ http : / / www . pr 3 sdlugs . com / ? refid = 87
+<br>
+spam
+------------
+Subject: fw : picture
+ >
+ >
+ > the following is an aerial photo of the wtc area . it kinda brings on
+ > vertigo , but is a phenomenal shot .
+ >
+ > http : / / userwww . service . emory . edu / ~ rdgarr / wtc . htm
+ham
+------------
+</code></pre>
+</details>
+
+
+<details>
+<summary>ling_spam 样本示例</summary>
+<pre><code>------------
+Subject: internet specialist 007 - the spy
+<br>
+internet specialist 007 - the spy learn everything about your friends , neighbors , enemies , employees or anyone else ! - - even your boss ! - - even yourself ! this mammoth snoop collection of internet sites will provide you the newest and most current addresses available on the net today . . . = 20 * track down an old debt , or anyone else that has done you wrong ! it 's incredible , and so many new data sites have come online in the past 90 days . . . * over 300 giant resources to look up people , credit , social security , current or past employment , mail order purchases , = 20 addresses , phone numbers , maps to city locations . . . * investigate your family history ! check birth , death , adoption or social security records check service records or army , navy , air force or = 20 marine corps . * locate an old friend ( or an enemy who is hiding ) or a lost = 20 love - - find e-mail , telephone or address information on anyone ! = 20 even look up * unlisted * phone numbers ! * find work by searching classified ads all over the world ! * screen prospective employees - - check credit , driving or criminal records verify income or educational accomplishments = 20 * check out your daughter 's new boyfriend ! * find trial transcripts and court orders ! * enjoy the enchantment of finding out a juicy tid-bit about a co-worker . the internet is a powerful megasource of information , = 20 if you only know where to look . i tell you how to find = 20 out nearly anything about anybody , and tell you exactly where to find it ! you will be amazed to find out what personal information = 20 other people can find out about you ! check your credit = 20 report so you can correct wrong information that may be = 20 used to deny you credit . research yourself first ! you ' ll be horrified , as i was , = 20 at how much data has been accumulated about you . any my huge collection is only the beginning ! once you = 20 locate these free private , college and government web sites , you ' ll find even more links to even more = 20 information search engines ! = 20 if you believe ( like i do ) that the information that is stored about each one of us should be freely accessible , you ' ll want to see the snoop collection i ' ve compiled . verify your own records , or find out what you need to = 20 know about others . i ' m telling you , it 's incredible what you can find out using the internet ! we will accept checks by fax at 813-269 - 9651 or > > > send $ 14 . 95 cash , check or money order to : > > > the coldwell group > > > p . o . box 3787 > > > dept 1007 > > > petersburg , va 23805 i will rush back to you my snoop information for fastest service include your * e-mail * address . = 20 * what information is available - - and exact url to get there ! * exactly where to look for - - and the clever way to use - - = 20 the above search engines , and tons more ! * my easy-to - browse categorized megacenter of information has my own description of how to use each site , and what you ' ll find when you get there - - and tricky tips on how to = 20 extract the best data ! you can know everything about everybody with this internet specialist collection ! * * soon to be available - - the most complete international internet spy = 20 sites available on the web today * * don ' t miss this one or you ' ll be sorry = 20 to be removed from our list please fax your address to 813-269 - 9651 . l = e3 = 01 @ u = 0b
+<br>
+spam
+------------
+Subject: usage - based models - symposium
+<br>
+announcing the sixth biennial symposium of the rice university department of linguistics usage-based models of language rice university march 15-18 , 1995 invited speakers : mira ariel tel aviv university joan bybee university of new mexico john du bois university of california , santa barbara michael israel university of california , san diego sydney lamb rice university ronald langacker university of california , san diego tom givon university of oregon brian macwhinney carnegie - mellon university janet pierrehumbert northwestern university john sinclair university of birmingham ( u . k . ) arie verhagen university of utrecht description : the goal of this symposium is to explore approaches to linguistic theory that have in common the aim of accounting for linguistic usage . the empirical data for such theories is not restricted to linguistic intuitions about acceptibility , but comes from usage events of varied types . the focus is on the patterns found in the various sorts of usage data examined , and how those patterns can be extracted , represented , and used by the human mind . research from a variety of traditions will be represented , including corpus-based analyses , discourse studies , experimental studies of language processing and language acquisition , and instrumental phonetics . the approaches taken can be called data-driven , rather than model-driven , in that the fewest possible prior assumptions are made about what types of data are relevant , and that large sets of usage events are observed so that the detailed patterns found in actual usage can emerge . moreover , the various approaches taken show signs of converging toward a view of language as a dynamic system in which linguistic knowledge is not separate from its processing in language use . the linguistic models representing this view are usage-based by virtue of three factors : ( 1 ) the importance placed on usage data for theory construction ; ( 2 ) the direct incorporation of processing ( production and comprehension ) into linguistic theory ; and ( 3 ) the requirement that the models arrived at , whatever the direct source of evidence , must be testable with reference to language use . registration : no charge . symposium attendance on a space-available basis . for further information , contact suzanne kemmer ( kemmer @ ruf . rice . edu ) or michael barlow ( barlow @ ruf . rice . edu ) snailmail : dept . of linguistics , rice university , houston tx 77251-1892 .
+<br>
+ham
+------------
+Subject: domani
+<br>
+new improved with free software , free bulk e mail system , free web site = to do what you wish , ongoing support ( optional ) , and a lot more ! all = included . . . . . . . . . . . this is a one time mailing . . . . . . . . . . . . . . . \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ = \ \ \ \ \ you are about to make at least $ 50 , 000 in less than 90 days read the enclosed program . . . then read it again . . . / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / = / / / / / / / dear friend , the enclosed information is something i almost let slip through my fingers . fortunately , sometime later i re-read everything and gave some thought and study to it . my name is christopher erickson . two years ago , the corporation i worked at for the past twelve years down-sized and my position was eliminated . after unproductive job interviews , i decided to open my own business . over the past year , i incurred many unforeseen financial problems . i owed my family , friends , and creditors over $ 35 , 000 . the economy was taking a toll on my business and i just could n't seem to make ends meet . i had to refinance and borrow against my home to support my family and struggling business . i truly believe it was wrong for me to be in debt like this . at that moment something significant happened in my life and i am writing to share my experience in hopes that this will change your life forever . . . . financially ! ! ! in mid - december , i received this program via email . six months prior to receiving this program i had been sending away for information on various business opportunities . all of the programs i received , in my opinion , were not cost effective . they were either too difficult for me to comprehend or the initial investment was too much for me to risk to see if they worked or not . one claimed i 'd make a million dollars in one year . . . it did n't tell me i 'd have to write a book to make it . but like i was saying , in december of ' 92 i received this program . i did n't send for it , or ask for it , they just got my name off a mailing list . thank goodness for that ! ! ! after reading it several times , to = make sure i was reading it correctly , i could n't believe my eyes . = 20 here was a money-making phenomenon . i could invest as much as i wanted = to start , without putting me further in debt . after i got a pencil and paper and figured it out , i would at least get my money back . after determining that the program is legal and not a chain letter , i decided " why not " . initially i sent out 10 , 000 emails . it only cost me about $ 15 . 00 for my time on-line . the great thing about email is that i did n't need any money for printing to send out the program , only the cost to fulfill my orders . i am telling you like it is , i hope it does n't turn you off , but i promised myself that i would not " rip-off " anyone , no matter how much money it cost me ! . in less than one week , i was starting to receive orders for report # 1 . by january 13th , i had received 26 orders for report # 1 . when you read the guarantee in the program , you will see that " you must receive = 15 to 20 orders for report # 1 within two weeks . if you don ' t , send out = more programs until you do ! " my first step in making $ 50 , 000 in 20 to = 90 days was done . by january 30th , i had received 196 orders for report = # 2 . if you go back to the guarantee , " you must receive 100 or more orders for report # 2 within two weeks . if not , send out more = programs until you do . once you have 100 orders , the rest is easy , = relax , you will make your $ 50 , 000 goal . " well , i had 196 orders for = report # 2 , 96 more than i needed . so i sat back and relaxed . by march = 19th , of my emailing of 10 , 000 , i received $ 58 , 000 with more coming in = every day . i paid off all my debts and bought a much needed new car . please take time to read the attached program , it will change your life forever ! remember , it wont work if you do n't try it . this program does work , but you must follow it exactly ! especially the rules of not trying to place your name in a different place . it does n't work , you ' ll lose out on a lot of money ! report # 2 explains this . = 20 always follow the guarantee , 15 to 20 orders for report # 1 , and 100 or more orders for report # 2 and you will make $ 50 , 000 or more in 20 to 90 days . i am living proof that it works ! ! ! if you choose not to participate in this program , i ' m sorry . it really is a great opportunity with little cost or risk to you . if you choose to participate , follow the program and you will be on your way to financial security . if you are a fellow business owner and you are in financial trouble like i was , or you want to start your own business , consider this a sign . i did ! sincerely , christopher erickson ps do you have any idea what 11 , 700 $ 5 bills ( $ 58 , 000 ) look like piled up on a kitchen table ? it ' s awesome ! " threw it away " " i had received this program before . i threw it away , but later wondered if i should n't have given it a try . of course , i had no idea who to contact to get a copy , so i had to wait until i was emailed another copy of the program . eleven months passed , then it came . i didn ' t throw this one away . i made $ 41 , 000 on the first try . " dawn w . , evansville , in " no free lunch " " my late father always told me , ' remember , alan , there is no free lunch in life . you get out of life what you put into it . ' through trial and error and a somewhat slow frustrating start , i finally figured it out . the program works very well , i just had to find the right target group of people to email it to . so far this year , i have made over $ 63 , 000 using this program . i know my dad would have been very proud of me . " alan b . , philadelphia , pa a personal note from the originator of this program by the time you have read the enclosed information and looked over the enclosed program and reports , you should have concluded that such a program , and one that is legal , could not have been created by an amateur . let me tell you a little about myself . i had a profitable business for ten years . then in 1979 my business began falling off . i was doing the same things that were previously successful for me , but it was n't working . finally , i figured it out . it was n't me , it was the economy . inflation and recession had replaced the stable economy that had been with us since 1945 . i do n't have to tell you what happened to the unemployment rate . . . because many of you know from first hand experience . there were more failures and bankruptcies than ever before . the middle class was vanishing . those who knew what they were doing = invested wisely and moved up . those who did not , including those who = never had anything to save or invest , were moving down into the ranks of = the poor . as the saying goes , " the rich get richer and the poor get = poorer . " the traditional methods of making money will never allow you = to " move up " or " get rich " , inflation will see to that . you have just received information that can give you financial freedom for the rest of your life , with " no risk " and " just a little bit of effort . " you can make more money in the next few months than you have = ever imagined . i should also point out that i will not see a penny of your money , nor anyone else who has provided a testimonial for this program . i have already made over four million dollars ! i have retired from the program after sending out over 16 , 000 programs . now i have several offices which market this and several other programs here in the us and overseas . by the spring , we wish to market the ' internet ' by a partnership with america on line . follow the program exactly as instructed . do not change it in any way . = it works exceedingly well as it is now . remember to email a copy of = this exciting program to everyone that you can think of . one of the people you send this to may send out 50 , 000 . . . and your name will be on every one of them ! . remember though , the more you send out , the = more potential customers you will reach . so my friend , i have given you the ideas , information , materials and opportunity to become financially independent , it is up to you now ! " think about it " before you delete this program from your mailbox , as i almost did , take a little time to read it and really think about it . get a pencil and figure out what could happen when you participate . figure out the worst possible response and no matter how you calculate it , you will still make a lot of money ! definitely get back what you invested . = 20 any doubts you have will vanish when your first orders come in . it works ! paul johnson , raleigh , nc here ' s how this amazing program will make you $ $ $ $ $ $ let 's say that you decide to start small , just to see how it goes , and we ' ll assume you and all those involved send out 2 , 000 programs each . let 's also assume that the mailing receives a . 5 % response . using a good list the response could be much better . also many people will send out hundreds of thousands of programs instead of 2 , 000 . but continuing with this example , you send out only 2 , 000 programs . with a . 5 % response , that is only 10 orders for report # 1 . those 10 people respond by sending out 2 , 000 programs each for a total of 20 , 000 . out of those . 5 % , 100 people respond and order report # 2 . those 100 mail out 2 , 000 programs each for a total of 200 , 000 . the . 5 % response to that is 1 , 000 orders for report # 3 . those 1 , 000 send out 2 , 000 programs each for a 2 , 000 , 000 total . the . 5 % response to that is 10 , 000 orders for report # 4 . that 's 10 , 000 five dollar bills for you . cash ! ! ! ! your total income in this example is $ 50 + $ 500 + $ 5000 + $ 50 , 000 for a total of $ 55 , 550 ! ! ! ! remember friend , this is assuming 1 , 990 out of 2 , 000 people you mail to = will do absolutely nothing . . . and trash this program ! dare to think for = a moment what would happen if everyone or half sent out 100 , 000 programs instead of only 2 , 000 . believe me , many people will do = that and more ! by the way , your cost to participate in this is = practically nothing . you obviously already have an internet connection and email is free ! ! ! report # 3 will show you the best methods for bulk emailing and purchasing email lists . this is a legitimate , legal , money making opportunity . it does not require you to come in contact with people , do any hard work , and best of all , you never have to leave the house except to get the mail . if you believe that someday you ' ll get that big break that you ' ve been waiting for , this is it ! simply follow the instructions , and your dream will come true . this multi-level email order marketing program works perfectly . . . 100 % every time . email is the sales tool of the future . take advantage of this non-commercialized method of advertising now ! ! the longer you wait , the more people will be doing business using email . get your piece of this action ! ! multi-level marketing ( mlm ) has finally gained respectability . it is = being taught in the harvard business school , and both stanford research and the wall street journal have stated that between 50 % and = 65 % of all goods and services will be sold throughout multi - level methods by the mid to late 1990 's . this is a multi - billion dollar industry and of the 500 , 000 millionaires in the us , 20 % ( 100 , 000 ) made their fortune in the last several years in mlm . moreover , statistics show 45 people become millionaires everyday through multi - level marketing . instructions we at erris mail order marketing business , have a method of raising capital that really works 100 % every time . i am sure that you could use = $ 50 , 000 to $ 125 , 000 in the next 20 to 90 days . before you say " bull " , please read the program carefully . this is not a chain letter , but a perfectly legal money making opportunity . basically , this is what we do : as with all multi-level business , we build our business by recruiting new partners and selling our products . every state in the usa allows you to recruit new multi - level business partners , and we offer a product for every dollar sent . your orders come and are filled through the mail , so you are not = involved in personal selling . you do it privately in your own home , = store or office . this is the greatest multi - level mail order marketing anywhere : step ( 1 ) order all four 4 reports listed by name and number . dothis by ordering the report from each of the four 4 names listed on the next page . for each report , send $ 5 cash and a self - addressed , stamped envelope ( business size # 10 ) = to the person listed for the specific report . international = = 20 orders should also include $ 2 extra for postage . it is essential that you specify the name and number of the report requested to the person you are ordering from . you will need all four 4 reports because you will be reprinting and reselling them . do not alter the names or sequence other than what the instructions say . important : always provide same-day service on all orders . step ( 2 ) replace the name and address under report # 1 with yours , moving the one that was there down to report # 2 . drop the name and address under report # 2 to report # 3 , moving the one that was there to report # 4 . the name and address that was under report # 4 is dropped from the list and this party is no doubt on the way to the bank . when doing this , make certain you type the names and addresses accurately ! do not mix up moving product / report positions ! ! ! step ( 3 ) having made the required changes in the name list , save it as a text ( . txt ) file in it 's own directory to be used with whatever email program you like . again , report # 3 will tell you the best methods of bulk emailing and acquiring email lists . step ( 4 ) email a copy of the entire program ( all of this is very important ) to everyone whose address you can get your hands on . start with friends and relatives since you can encourage them to take advantage of this fabulous = 20 money-making opportunity . that 's what i did . and they love me now , more than ever . then , email to anyone and everyone ! use your imagination ! you can get email addresses from companies on the internet who specialize in email mailing lists . these are very cheap , 100 , 000 addresses for around $ 35 . 00 . important : you won't get a good response if you use an old list , so always request a fresh , new list . you will find out where to purchase these lists when you order the four 4 reports . always provide same-day service on all orders ! ! ! required reports * * * order each report by number and name * * * always send a self-addressed , stamped envelope and $ 5 usd cash for each order requesting the specific report by name and number ( international orders should also include $ 2 usd extra for postage ) = 20 add you e amil address when sending in for your report this is for = updated information and continueing support ( optional ) that will be = handed down by you sponcers . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 1 " how to make $ 250 , 000 through multi-level sales " order report # 1 from : a . siegmund # 57 trakehnenstr . 13 53332 bornheim , germany _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 2 " major corporations and multi-level sales " j . maz 15774 s . lagrange rd suite # 312 orland pk , il 60462 usa _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ order report # 2 from : a . siegmund # 57 trakehnenstr . 13 53332 bornheim , germany _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 3 " sources for the best mailing lists " order report # 3 from : b . thompson 13504 greencaslte ridge tr . 404 burtonsville md . 20866 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 4 " evaluating multi-level sales plans " order report # 4 from : muw # 2 po box 71442 salt lake city , ut 84171-0442 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ conclusion . i am enjoying my fortune that i made by sending out this program . you too , will be making money in 20 to 90 days , if you follow the simple steps outlined in this mailing . to be financially independent is to be free . free to make financial decisions as never before . go into business , get into investments , retire or take a vacation . = 20 = = = = = = 20
+<br>
+spam
+------------
+Subject: linguistic datasources
+<br>
+at the request of subscribers , we ' ve been collecting the addresses of linguistic datasources which can be reached through world wide web . these addresses are now available to any of you who have web access on the linguist web server at the following url : http : / / engserve . tamu . edu / files / linguistics / linguist / datasources . html this file is also available , to those of you who read web - linguist , through the " linguistic datasources " link . we 'd be delighted to hear any comments anyone would care to make . and if there ' re any addresses we need to add , please let us know what they are . we 'd like to emphasize that we 'd be happy to include sites where individual linguists keep data they would like to make available to their colleagues . since the web allows us to share not merely text , but pictures and sound-recordings , we can now begin an interchange of linguistic information that is of a very different nature from that which was possible in the past . anthony & helen
+<br>
+ham
+------------
+</code></pre>
+</details>
+
+
 <details>
 <summary>sms_spam 样本示例</summary>
 <pre><code>------------
@@ -224,6 +260,37 @@ ham
 </details>
 
 
+<details>
+<summary>spam_message_lr 样本示例</summary>
+<pre><code>------------
+3G小贴士提醒您可不要让您的流量白白浪费了哦，快来唤醒吧！与您分享杨子黎2013全新单曲《爱人好累》MV 详情点击：http://yuny.com.cn:3042/tpo/SU/NjiYby
+spam
+------------
+巫倩云:来周总办公室现在
+ham
+------------
+结婚娶亲本是一件高兴事，新郎却因一辆加长林肯车而当场落泪！这是为什么？详情请点击 http://10006.co/lbJ5
+spam
+------------
+PP提醒你来认证啦！在电脑上登录PP租车官方网站（www.ppzuche.com）或下载PP租车手机客户端（www.ppzuche.com/get-app）上传身份证和驾驶证照片，即可完成租客身份认证。600余款车型，低于市场价30%，随时随地取车，开启便捷用车新时代！【PP租车】
+ham
+------------
+【联通飞影】对美女自作多情的后果… http://fql.cc/pub/view/iid-48305
+spam
+------------
+您已成功添加王然(13811083077)为好友，可以接收对方的飞信消息。回复本短信可直接与对方聊天。
+ham
+------------
+棕盛商业广场一期5月18号火爆认筹，孟庭苇邀您亲见世界500强签约蕲春，VIP卡全城免费热办中。0713-7300000【棕盛商业地产】
+spam
+------------
+信用卡1707于10月23日16:19消费人民币360.07,记账金额以账单显示为准。加“浦发银行信用卡中心”官方微信查询/待续【浦发银行】
+ham
+------------
+</code></pre>
+</details>
+
+
 ### 参考来源
 
 <details>

data/enron_spam_subset.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d81b6d1b3a46df6b832e57960292a8d755f528f7668aeedcc81c8a111b0d159c
+size 16895439

data/ling_spam.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0dbdea28dd55b2aba08fa12a02d95c57c47849997b11c3cd00c23de02d1834db
+size 25950867

data/{email_spam.jsonl → sms_spam_collection.jsonl}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4b530e530e6251b7755b2329e15482a95332d71caad0b8c2fb85a7433a335fd7
-size 5919435
+oid sha256:3fae437ae98e46a252af5293bcac84b467c5474c5f7f307d72a717a4b55265fe
+size 985500

data/spam_assassin.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:48753da2e31d5d65efcacf31f12e2f3b3f68b02a1a625222173a7bc8fb86435c
-size 41867894
+oid sha256:0133bf8ef2505d9ffc34cddb8cea67efc64376b6bc91b33ca27de7c10d0485e2
+size 11549868

data/spam_message_lr.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ade197753c9d391530c019dc1ccf39add26fea13c9119fa13f9600d4d93ef863
+size 2977972

data/youtube_spam_collection.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17264121e0c8a12e33db75c781019998273f6a922a42907d17e1347d1edac66d
+size 427008

examples/preprocess/process_enron_spam_subset.py

@@ -0,0 +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
+import pandas as pd
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--data_file", default="data/email_spam/enronSpamSubset.csv", type=str)
+
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/enron_spam_subset.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    df = pd.read_csv(args.data_file)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for i, row in tqdm(df.iterrows(), total=len(df)):
+            # print(row)
+            text = row["Body"]
+            label = row["Label"]
+
+            label = "spam" if label == 1 else "ham"
+
+            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": "enron_spam_subset",
+                "split": split
+            }
+            row = json.dumps(row, ensure_ascii=False)
+            f.write("{}\n".format(row))
+
+    return
+
+
+if __name__ == '__main__':
+    main()

examples/preprocess/process_ling_spam.py

@@ -0,0 +1,73 @@
+#!/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, '../../'))
+
+import pandas as pd
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--data_file", default="data/email_spam/lingSpam.csv", type=str)
+
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/ling_spam.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    df = pd.read_csv(args.data_file)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for i, row in tqdm(df.iterrows(), total=len(df)):
+            # print(row)
+            text = row["Body"]
+            label = row["Label"]
+
+            label = "spam" if label == 1 else "ham"
+
+            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": "ling_spam",
+                "split": split
+            }
+            row = json.dumps(row, ensure_ascii=False)
+            f.write("{}\n".format(row))
+
+    return
+
+
+if __name__ == '__main__':
+    main()

examples/preprocess/process_sms_spam_collection.py

@@ -0,0 +1,71 @@
+#!/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, '../../'))
+
+import pandas as pd
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--data_file", default="data/sms_spam_collection/spam.csv", type=str)
+
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/sms_spam_collection.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    df = pd.read_csv(args.data_file)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for i, row in tqdm(df.iterrows(), total=len(df)):
+            # print(row)
+            text = row["Message"]
+            label = row["Category"]
+
+            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": "sms_spam_collection",
+                "split": split
+            }
+            row = json.dumps(row, ensure_ascii=False)
+            f.write("{}\n".format(row))
+
+    return
+
+
+if __name__ == '__main__':
+    main()

examples/preprocess/process_spam_assassin.py

@@ -13,6 +13,7 @@ pwd = os.path.abspath(os.path.dirname(__file__))
 sys.path.append(os.path.join(pwd, '../../'))
 
 from datasets import load_dataset
+import pandas as pd
 from tqdm import tqdm
 
 from project_settings import project_path
@@ -21,12 +22,8 @@ from project_settings import project_path
 def get_args():
     parser = argparse.ArgumentParser()
 
-    parser.add_argument("--dataset_path", default="talby/spamassassin", type=str)
-    parser.add_argument(
-        "--dataset_cache_dir",
-        default=(project_path / "hub_datasets").as_posix(),
-        type=str
-    )
+    parser.add_argument("--data_file", default="data/email_spam/completeSpamAssassin.csv", type=str)
+
     parser.add_argument(
         "--output_file",
         default=(project_path / "data/spam_assassin.jsonl"),
@@ -39,34 +36,36 @@ def get_args():
 def main():
     args = get_args()
 
-    dataset_dict = load_dataset(
-        path=args.dataset_path,
-        cache_dir=args.dataset_cache_dir,
-    )
-    print(dataset_dict)
+    df = pd.read_csv(args.data_file)
 
     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"]
-                group = sample["group"]
-                label = sample["label"]
-
-                label = "spam" if label == 1 else "ham"
-
-                if label not in ("spam", "ham"):
-                    raise AssertionError
-
-                row = {
-                    "text": text,
-                    "label": label,
-                    "category": group,
-                    "data_source": "spam_assassin",
-                    "split": split
-                }
-                row = json.dumps(row, ensure_ascii=False)
-                f.write("{}\n".format(row))
+        for i, row in tqdm(df.iterrows(), total=len(df)):
+            # print(row)
+            text = row["Body"]
+            label = row["Label"]
+
+            label = "spam" if label == 1 else "ham"
+
+            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": "spam_assassin",
+                "split": split
+            }
+            row = json.dumps(row, ensure_ascii=False)
+            f.write("{}\n".format(row))
 
     return
 

examples/preprocess/process_spam_message_lr.py

@@ -0,0 +1,77 @@
+#!/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_file", default="data/spam_message_lr/train.txt", type=str)
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/spam_message_lr.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    with open(args.output_file, "w", encoding="utf-8") as fout:
+        with open(args.data_file, "r", encoding="utf-8") as fin:
+            for row in fin:
+                row = str(row).rstrip("\n")
+                row = row.split("\t", maxsplit=1)
+
+                if len(row) != 2:
+                    print(row)
+                    raise AssertionError
+
+                label = row[0]
+                text = row[1]
+
+                label = "spam" if label == "1" else "ham"
+
+                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": "spam_message_lr",
+                    "split": split
+                }
+                row = json.dumps(row, ensure_ascii=False)
+                fout.write("{}\n".format(row))
+
+    return
+
+
+if __name__ == '__main__':
+    main()

examples/preprocess/{process_email_spam.py → process_youtube_spam_collection.py}

@@ -12,7 +12,7 @@ import sys
 pwd = os.path.abspath(os.path.dirname(__file__))
 sys.path.append(os.path.join(pwd, '../../'))
 
-from datasets import load_dataset
+import pandas as pd
 from tqdm import tqdm
 
 from project_settings import project_path
@@ -21,15 +21,11 @@ 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("--data_dir", default="data/youtube_spam_collection", type=str)
+
     parser.add_argument(
         "--output_file",
-        default=(project_path / "data/email_spam.jsonl"),
+        default=(project_path / "data/youtube_spam_collection.jsonl"),
         type=str
     )
     args = parser.parse_args()
@@ -39,27 +35,37 @@ def get_args():
 def main():
     args = get_args()
 
-    dataset_dict = load_dataset(
-        path=args.dataset_path,
-        cache_dir=args.dataset_cache_dir,
-    )
-    print(dataset_dict)
+    data_dir = Path(args.data_dir)
 
     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"]
+        for filename in data_dir.glob("*.csv"):
+            df = pd.read_csv(filename.as_posix())
+
+            for i, row in tqdm(df.iterrows(), total=len(df)):
+                # print(row)
+                text = row["CONTENT"]
+                label = row["CLASS"]
+
+                text = text.replace("", "")
+
+                label = "spam" if label == 1 else "ham"
 
                 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": "email_spam",
+                    "category": filename.stem,
+                    "data_source": "youtube_spam_collection",
                     "split": split
                 }
                 row = json.dumps(row, ensure_ascii=False)

examples/preprocess/samples_count.py

@@ -6,12 +6,16 @@ from datasets import load_dataset, DownloadMode
 
 dataset_dict = load_dataset(
     "../../spam_detect.py",
-    # name="email_spam",
     # name="enron_spam",
+    # name="enron_spam_subset",
+    # name="ling_spam",
     # name="sms_spam",
     # name="spam_assassin",
     # name="spam_detection",
-    name="spam_message",
+    # name="sms_spam_collection",
+    # name="spam_message",
+    # name="spam_message_lr",
+    name="youtube_spam_collection",
     split=None,
     cache_dir=None,
     download_mode=DownloadMode.FORCE_REDOWNLOAD

main.py

@@ -5,12 +5,16 @@ from datasets import load_dataset, DownloadMode
 
 dataset = load_dataset(
     "spam_detect.py",
-    # name="email_spam",
     # name="enron_spam",
+    # name="enron_spam_subset",
+    # name="ling_spam",
     # name="sms_spam",
     # name="spam_assassin",
     # name="spam_detection",
-    name="spam_message",
+    # name="sms_spam_collection",
+    # name="spam_message",
+    # name="spam_message_lr",
+    name="youtube_spam_collection",
     split="train",
     cache_dir=None,
     download_mode=DownloadMode.FORCE_REDOWNLOAD

spam_detect.py

@@ -11,12 +11,16 @@ import datasets
 
 
 _urls = {
-    "email_spam": "data/email_spam.jsonl",
     "enron_spam": "data/enron_spam.jsonl",
+    "enron_spam_subset": "data/enron_spam_subset.jsonl",
+    "ling_spam": "data/ling_spam.jsonl",
     "sms_spam": "data/sms_spam.jsonl",
     "spam_assassin": "data/spam_assassin.jsonl",
     "spam_detection": "data/spam_detection.jsonl",
+    "spam_emails": "data/spam_emails.jsonl",
     "spam_message": "data/spam_message.jsonl",
+    "spam_message_lr": "data/spam_message_lr.jsonl",
+    "youtube_spam_collection": "data/youtube_spam_collection.jsonl",
 
 }