├── LICENSE.md ├── README.md ├── google-10000-english-usa-no-swears-short.txt ├── google-10000-english-usa-no-swears-long.txt └── google-10000-english-usa-no-swears-medium.txt /LICENSE.md: -------------------------------------------------------------------------------- 1 | Data files are derived from the *Google Web Trillion Word Corpus*, as described by [Thorsten Brants and Alex Franz](http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html), and distributed by the [Linguistic Data Consortium](http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2006T13). Subsets of this corpus distributed by [Peter Novig](http://norvig.com/ngrams/). Corpus editing and cleanup by Josh Kaufman. 2 | 3 | Educational and personal/research use of this data is permitted under the LDC license, Norvig's MIT license for his contributions, and US fair use doctrine. I do not recommend using this data for commercial purposes without licensing it from the Linguistic Data Consortium. 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Not Maintained](https://img.shields.io/badge/Maintenance%20Level-Not%20Maintained-yellow.svg)](https://gist.github.com/cheerfulstoic/d107229326a01ff0f333a1d3476e068d) 2 | 3 | About This Repo 4 | =============== 5 | 6 | This repo contains a list of the 10,000 most common English words in order of frequency, as determined by [n-gram](https://en.wikipedia.org/wiki/N-gram) [frequency analysis](https://en.wikipedia.org/wiki/Frequency_analysis) of the [Google's Trillion Word Corpus](https://books.google.com/ngrams/info). 7 | 8 | According to the [Google Machine Translation Team](https://ai.googleblog.com/2006/08/all-our-n-gram-are-belong-to-you.html): 9 | 10 | >Here at Google Research we have been using word n-gram models for a variety of R&D projects, such as statistical machine translation, speech recognition, spelling correction, entity detection, information extraction, and others. While such models have usually been estimated from training corpora containing at most a few billion words, we have been harnessing the vast power of Google's datacenters and distributed processing infrastructure to process larger and larger training corpora. We found that there's no data like more data, and scaled up the size of our data by one order of magnitude, and then another, and then one more - resulting in a training corpus of one trillion words from public Web pages. 11 | > 12 | >We believe that the entire research community can benefit from access to such massive amounts of data. It will advance the state of the art, it will focus research in the promising direction of large-scale, data-driven approaches, and it will allow all research groups, no matter how large or small their computing resources, to play together. That's why we decided to share this enormous dataset with everyone. We processed 1,024,908,267,229 words of running text and are publishing the counts for all 1,176,470,663 five-word sequences that appear at least 40 times. There are 13,588,391 unique words, after discarding words that appear less than 200 times. 13 | 14 | This repo is derived from [Peter Norvig's](https://norvig.com/ngrams/) compilation of the [1/3 million most frequent English words](https://norvig.com/ngrams/count_1w.txt). I limited this file to the 10,000 most common words, then removed the appended frequency counts by running this sed command in my text editor: 15 | 16 | sed 's/[0-9]*//g' 17 | 18 | Special thanks to [koseki](https://github.com/koseki) for [de-duplicating the list](https://github.com/first20hours/google-10000-english/issues/6). 19 | 20 | Swear-free lists 21 | ----- 22 | 23 | There are two additional lists which are identical to the original 10,000 word list, but with swear words removed. Swear words were removed based on these lists: 24 | 25 | * [reimertz/curse-words](https://github.com/reimertz/curse-words) 26 | * [MauriceButler/badwords](https://github.com/MauriceButler/badwords) 27 | * [LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) 28 | 29 | Word length lists 30 | ----- 31 | 32 | Three of the lists (all based on the US english list) are based on word length: 33 | 34 | * **Short**: 1-4 characters 35 | * **Medium**: 5-8 characters 36 | * **Long**: 9+ characters 37 | 38 | Each list retains the original list sorting (by frequency, decending). 39 | 40 | Usage 41 | ----- 42 | 43 | This repo is useful as a corpus for typing training programs. According to analysis of the [Oxford English Corpus](https://en.oxforddictionaries.com/explore/what-can-corpus-tell-us-about-language/), the 7,000 most common English lemmas account for approximately 90% of usage, so a 10,000 word training corpus is more than sufficient for practical training applications. 44 | 45 | To use this list as a training corpus in [Amphetype](https://code.google.com/archive/p/amphetype/), paste the contents into the "Lesson Generator" tab with the following settings: 46 | 47 | Make **3** copies of the list 48 | 49 | Divide into sublists of size **3** 50 | 51 | Add to sources as **google-10000-english** 52 | 53 | In the "Sources" tab, you should see **google-10000-english** available for training. Set WPM at 10 more than your current average, set accuracy to 98%, and you're set to train. 54 | 55 | Enjoy! 56 | -------------------------------------------------------------------------------- /google-10000-english-usa-no-swears-short.txt: -------------------------------------------------------------------------------- 1 | the 2 | of 3 | and 4 | to 5 | a 6 | in 7 | for 8 | is 9 | on 10 | that 11 | by 12 | this 13 | with 14 | i 15 | you 16 | it 17 | not 18 | or 19 | be 20 | are 21 | from 22 | at 23 | as 24 | your 25 | all 26 | have 27 | new 28 | more 29 | an 30 | was 31 | we 32 | will 33 | home 34 | can 35 | us 36 | if 37 | page 38 | my 39 | has 40 | free 41 | but 42 | our 43 | one 44 | do 45 | no 46 | time 47 | they 48 | site 49 | he 50 | up 51 | may 52 | what 53 | news 54 | out 55 | use 56 | any 57 | see 58 | only 59 | so 60 | his 61 | when 62 | here 63 | who 64 | web 65 | also 66 | now 67 | help 68 | get 69 | pm 70 | view 71 | c 72 | e 73 | am 74 | been 75 | how 76 | were 77 | me 78 | s 79 | some 80 | its 81 | like 82 | x 83 | than 84 | find 85 | date 86 | back 87 | top 88 | had 89 | list 90 | name 91 | just 92 | over 93 | year 94 | day 95 | into 96 | two 97 | n 98 | re 99 | next 100 | used 101 | go 102 | b 103 | work 104 | last 105 | most 106 | buy 107 | data 108 | make 109 | them 110 | post 111 | her 112 | city 113 | t 114 | add 115 | such 116 | best 117 | then 118 | jan 119 | good 120 | well 121 | d 122 | info 123 | high 124 | m 125 | each 126 | she 127 | very 128 | book 129 | r 130 | read 131 | need 132 | many 133 | user 134 | said 135 | de 136 | does 137 | set 138 | mail 139 | full 140 | map 141 | life 142 | know 143 | way 144 | days 145 | p 146 | part 147 | real 148 | f 149 | item 150 | ebay 151 | must 152 | made 153 | off 154 | line 155 | did 156 | send 157 | type 158 | car 159 | take 160 | area 161 | want 162 | dvd 163 | l 164 | long 165 | w 166 | code 167 | show 168 | o 169 | even 170 | much 171 | sign 172 | file 173 | link 174 | open 175 | case 176 | same 177 | uk 178 | own 179 | both 180 | g 181 | game 182 | care 183 | down 184 | end 185 | h 186 | him 187 | per 188 | big 189 | law 190 | size 191 | art 192 | shop 193 | text 194 | rate 195 | usa 196 | v 197 | form 198 | love 199 | old 200 | john 201 | main 202 | call 203 | non 204 | k 205 | y 206 | why 207 | cd 208 | save 209 | low 210 | york 211 | man 212 | card 213 | jobs 214 | j 215 | food 216 | u 217 | sale 218 | job 219 | teen 220 | room 221 | too 222 | join 223 | men 224 | west 225 | look 226 | left 227 | team 228 | box 229 | gay 230 | week 231 | note 232 | live 233 | june 234 | air 235 | plan 236 | tv 237 | yes 238 | hot 239 | cost 240 | la 241 | say 242 | july 243 | test 244 | come 245 | dec 246 | pc 247 | cart 248 | san 249 | play 250 | tax 251 | less 252 | got 253 | blog 254 | let 255 | park 256 | side 257 | act 258 | red 259 | give 260 | q 261 | sell 262 | key 263 | body 264 | few 265 | east 266 | ii 267 | age 268 | club 269 | z 270 | road 271 | gift 272 | ca 273 | hard 274 | oct 275 | pay 276 | four 277 | war 278 | nov 279 | blue 280 | al 281 | easy 282 | fax 283 | yet 284 | star 285 | hand 286 | sun 287 | rss 288 | id 289 | keep 290 | baby 291 | run 292 | net 293 | term 294 | film 295 | put 296 | co 297 | try 298 | head 299 | cell 300 | self 301 | away 302 | once 303 | log 304 | sure 305 | faq 306 | cars 307 | tell 308 | able 309 | fun 310 | gold 311 | feb 312 | sep 313 | arts 314 | lot 315 | ask 316 | past 317 | due 318 | et 319 | five 320 | upon 321 | says 322 | mar 323 | land 324 | done 325 | pro 326 | st 327 | url 328 | aug 329 | ever 330 | ago 331 | word 332 | bill 333 | apr 334 | talk 335 | via 336 | kids 337 | true 338 | else 339 | mark 340 | rock 341 | bad 342 | tips 343 | plus 344 | auto 345 | edit 346 | fast 347 | fact 348 | unit 349 | tech 350 | meet 351 | far 352 | en 353 | feel 354 | bank 355 | risk 356 | jul 357 | town 358 | jun 359 | girl 360 | toys 361 | golf 362 | loan 363 | wide 364 | sort 365 | half 366 | step 367 | none 368 | paul 369 | lake 370 | sony 371 | fire 372 | chat 373 | html 374 | loss 375 | face 376 | oil 377 | bit 378 | base 379 | near 380 | oh 381 | stay 382 | turn 383 | mean 384 | king 385 | copy 386 | drug 387 | pics 388 | cash 389 | bay 390 | ad 391 | seen 392 | port 393 | stop 394 | bar 395 | dog 396 | soon 397 | held 398 | ny 399 | eur 400 | mind 401 | pdf 402 | lost 403 | tour 404 | menu 405 | hope 406 | wish 407 | role 408 | came 409 | usr 410 | dc 411 | mon 412 | com 413 | fine 414 | hour 415 | gas 416 | six 417 | bush 418 | pre 419 | huge 420 | sat 421 | zip 422 | bid 423 | kind 424 | move 425 | logo 426 | nice 427 | ok 428 | sent 429 | band 430 | ms 431 | lead 432 | went 433 | fri 434 | hi 435 | mode 436 | fund 437 | wed 438 | male 439 | took 440 | inn 441 | song 442 | cnet 443 | ltd 444 | los 445 | hp 446 | late 447 | fall 448 | idea 449 | inc 450 | win 451 | tool 452 | eg 453 | bed 454 | ip 455 | hill 456 | maps 457 | deal 458 | hold 459 | tue 460 | safe 461 | feed 462 | pa 463 | 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560 | farm 561 | tom 562 | lord 563 | sub 564 | hear 565 | goes 566 | led 567 | fan 568 | wife 569 | ten 570 | hits 571 | zone 572 | th 573 | cat 574 | die 575 | jack 576 | flat 577 | flow 578 | dr 579 | path 580 | kb 581 | laws 582 | pet 583 | guy 584 | dev 585 | cup 586 | vol 587 | pp 588 | na 589 | skip 590 | diet 591 | army 592 | gear 593 | lee 594 | os 595 | lots 596 | firm 597 | jump 598 | dvds 599 | ball 600 | goal 601 | sold 602 | wind 603 | palm 604 | bob 605 | fit 606 | ex 607 | met 608 | pain 609 | xbox 610 | www 611 | oral 612 | ford 613 | edge 614 | root 615 | au 616 | fi 617 | ice 618 | pink 619 | shot 620 | nc 621 | llc 622 | sec 623 | bus 624 | cold 625 | bag 626 | po 627 | va 628 | foot 629 | mass 630 | ibm 631 | rd 632 | sc 633 | heat 634 | wild 635 | miss 636 | task 637 | nor 638 | bug 639 | mid 640 | se 641 | soft 642 | fuel 643 | walk 644 | wait 645 | rose 646 | jim 647 | di 648 | km 649 | pick 650 | del 651 | ga 652 | ac 653 | ft 654 | load 655 | tags 656 | joe 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851 | tn 852 | salt 853 | bell 854 | pin 855 | raw 856 | gnu 857 | jeff 858 | ben 859 | lane 860 | kill 861 | aol 862 | ce 863 | ages 864 | plug 865 | cook 866 | hat 867 | perl 868 | lib 869 | bike 870 | ab 871 | utc 872 | der 873 | lose 874 | seek 875 | tony 876 | kits 877 | cam 878 | soil 879 | wet 880 | ram 881 | matt 882 | fox 883 | exit 884 | iran 885 | arm 886 | keys 887 | wave 888 | holy 889 | acts 890 | mesh 891 | dean 892 | poll 893 | unix 894 | bond 895 | pub 896 | tm 897 | sp 898 | jean 899 | hop 900 | visa 901 | nh 902 | gun 903 | pure 904 | lens 905 | draw 906 | fm 907 | warm 908 | babe 909 | crew 910 | legs 911 | sam 912 | pdt 913 | rear 914 | node 915 | lock 916 | mile 917 | mens 918 | bowl 919 | ref 920 | tank 921 | navy 922 | kid 923 | db 924 | pan 925 | ph 926 | dish 927 | ia 928 | pt 929 | adam 930 | slot 931 | psp 932 | ha 933 | ds 934 | gray 935 | ea 936 | und 937 | demo 938 | lg 939 | hate 940 | rice 941 | loop 942 | nfl 943 | gary 944 | vary 945 | rome 946 | arab 947 | milk 948 | nw 949 | boot 950 | ff 951 | push 952 | iso 953 | sum 954 | misc 955 | alan 956 | dear 957 | oak 958 | vat 959 | beer 960 | jose 961 | jane 962 | ps 963 | sir 964 | earn 965 | kim 966 | twin 967 | ky 968 | dont 969 | spy 970 | br 971 | bits 972 | lo 973 | suit 974 | ml 975 | chip 976 | res 977 | sit 978 | wow 979 | char 980 | cs 981 | echo 982 | que 983 | grid 984 | voip 985 | fig 986 | sf 987 | kg 988 | pull 989 | ut 990 | nasa 991 | tab 992 | si 993 | css 994 | mc 995 | nick 996 | plot 997 | qty 998 | pump 999 | lp 1000 | anne 1001 | bio 1002 | exam 1003 | ryan 1004 | beds 1005 | pcs 1006 | grey 1007 | bold 1008 | von 1009 | ag 1010 | scan 1011 | vi 1012 | aged 1013 | bulk 1014 | sci 1015 | edt 1016 | pmid 1017 | sin 1018 | cute 1019 | ba 1020 | para 1021 | cr 1022 | pg 1023 | seed 1024 | ee 1025 | peer 1026 | meat 1027 | ing 1028 | ks 1029 | alex 1030 | bang 1031 | bone 1032 | bugs 1033 | ftp 1034 | med 1035 | gate 1036 | sw 1037 | tone 1038 | busy 1039 | leg 1040 | 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1218 | marc 1219 | sons 1220 | leaf 1221 | pad 1222 | ja 1223 | bs 1224 | rod 1225 | cuba 1226 | hrs 1227 | silk 1228 | kate 1229 | bi 1230 | sad 1231 | wolf 1232 | cal 1233 | fits 1234 | kick 1235 | meal 1236 | ta 1237 | hurt 1238 | pot 1239 | img 1240 | slip 1241 | rpm 1242 | cuts 1243 | pee 1244 | mars 1245 | tvs 1246 | egg 1247 | mhz 1248 | caps 1249 | pill 1250 | lat 1251 | meta 1252 | mint 1253 | gi 1254 | spin 1255 | sur 1256 | wash 1257 | rev 1258 | ll 1259 | aims 1260 | cl 1261 | ieee 1262 | ho 1263 | corp 1264 | gt 1265 | sh 1266 | soap 1267 | ae 1268 | nyc 1269 | jam 1270 | axis 1271 | guns 1272 | rio 1273 | hs 1274 | hero 1275 | rv 1276 | punk 1277 | pi 1278 | duke 1279 | ai 1280 | pace 1281 | wage 1282 | ot 1283 | arc 1284 | dawn 1285 | carl 1286 | coat 1287 | mrs 1288 | rica 1289 | yr 1290 | app 1291 | roy 1292 | ion 1293 | doll 1294 | ic 1295 | peru 1296 | nike 1297 | fed 1298 | reed 1299 | mice 1300 | ban 1301 | temp 1302 | zus 1303 | vast 1304 | ent 1305 | odd 1306 | 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| brad 1397 | jury 1398 | dos 1399 | tail 1400 | lawn 1401 | soup 1402 | byte 1403 | nose 1404 | oclc 1405 | plc 1406 | juan 1407 | msg 1408 | cod 1409 | thru 1410 | jews 1411 | trim 1412 | cv 1413 | cb 1414 | gen 1415 | espn 1416 | nhl 1417 | quit 1418 | lung 1419 | ti 1420 | fc 1421 | gel 1422 | todd 1423 | fw 1424 | doug 1425 | sees 1426 | gs 1427 | aaa 1428 | bull 1429 | cole 1430 | mart 1431 | tale 1432 | lynn 1433 | bp 1434 | std 1435 | docs 1436 | vid 1437 | oo 1438 | coin 1439 | fake 1440 | fda 1441 | cure 1442 | arch 1443 | ni 1444 | hdtv 1445 | asin 1446 | bomb 1447 | harm 1448 | thy 1449 | deer 1450 | tri 1451 | pal 1452 | um 1453 | ye 1454 | fs 1455 | nn 1456 | mat 1457 | oven 1458 | ted 1459 | noon 1460 | gym 1461 | kde 1462 | vb 1463 | cams 1464 | joel 1465 | yo 1466 | proc 1467 | tan 1468 | fx 1469 | mate 1470 | dl 1471 | chef 1472 | isle 1473 | slim 1474 | luke 1475 | comp 1476 | alt 1477 | pie 1478 | ls 1479 | cbs 1480 | pete 1481 | spec 1482 | bow 1483 | penn 1484 | midi 1485 | tied 1486 | hon 1487 | dale 1488 | oils 1489 | sept 1490 | unto 1491 | lt 1492 | atm 1493 | eq 1494 | pays 1495 | je 1496 | lang 1497 | stud 1498 | fold 1499 | uv 1500 | cms 1501 | sg 1502 | vic 1503 | pos 1504 | phys 1505 | pole 1506 | mega 1507 | bend 1508 | moms 1509 | glen 1510 | nav 1511 | cab 1512 | fa 1513 | ist 1514 | lips 1515 | pond 1516 | lc 1517 | dam 1518 | cnn 1519 | lil 1520 | das 1521 | tire 1522 | chad 1523 | sys 1524 | josh 1525 | drag 1526 | icq 1527 | ripe 1528 | rely 1529 | scsi 1530 | cu 1531 | dns 1532 | pty 1533 | ws 1534 | nuts 1535 | nail 1536 | span 1537 | sox 1538 | joke 1539 | univ 1540 | tub 1541 | pads 1542 | inns 1543 | cups 1544 | ash 1545 | ali 1546 | np 1547 | foam 1548 | tft 1549 | jvc 1550 | poem 1551 | dt 1552 | cgi 1553 | asks 1554 | bean 1555 | bias 1556 | por 1557 | mem 1558 | gc 1559 | tap 1560 | ci 1561 | swim 1562 | nano 1563 | yn 1564 | vii 1565 | bee 1566 | loud 1567 | rats 1568 | cfr 1569 | stat 1570 | cruz 1571 | bios 1572 | 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walt 1927 | acne 1928 | libs 1929 | undo 1930 | wm 1931 | pk 1932 | dana 1933 | halo 1934 | ppm 1935 | ant 1936 | gays 1937 | apt 1938 | exec 1939 | inf 1940 | eos 1941 | vcr 1942 | uri 1943 | gem 1944 | maui 1945 | psi 1946 | pct 1947 | wb 1948 | vids 1949 | yale 1950 | sn 1951 | qld 1952 | pas 1953 | dk 1954 | doom 1955 | owen 1956 | bite 1957 | issn 1958 | myth 1959 | gig 1960 | sas 1961 | fu 1962 | weed 1963 | oecd 1964 | dice 1965 | quad 1966 | dock 1967 | mods 1968 | hint 1969 | msie 1970 | wn 1971 | liz 1972 | ccd 1973 | sv 1974 | buys 1975 | pork 1976 | zu 1977 | barn 1978 | llp 1979 | boc 1980 | fare 1981 | dg 1982 | asus 1983 | vg 1984 | bald 1985 | fuji 1986 | leon 1987 | mold 1988 | dame 1989 | fo 1990 | herb 1991 | tmp 1992 | alot 1993 | ate 1994 | idle 1995 | fin 1996 | io 1997 | mud 1998 | uni 1999 | ul 2000 | ol 2001 | js 2002 | pn 2003 | cove 2004 | casa 2005 | mu 2006 | eden 2007 | incl 2008 | ala 2009 | hq 2010 | dip 2011 | nbc 2012 | reid 2013 | wt 2014 | flex 2015 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2105 | wx 2106 | mae 2107 | mel 2108 | feat 2109 | ntsc 2110 | sic 2111 | usps 2112 | bg 2113 | seq 2114 | conf 2115 | glow 2116 | wma 2117 | cir 2118 | oaks 2119 | erik 2120 | hu 2121 | acm 2122 | kw 2123 | paso 2124 | norm 2125 | ips 2126 | dsc 2127 | ware 2128 | mia 2129 | wan 2130 | jade 2131 | foul 2132 | keno 2133 | gtk 2134 | seas 2135 | ru 2136 | pose 2137 | mrna 2138 | goat 2139 | ira 2140 | sen 2141 | sail 2142 | dts 2143 | qt 2144 | sega 2145 | cdna 2146 | pod 2147 | wu 2148 | bolt 2149 | gage 2150 | lu 2151 | dat 2152 | soa 2153 | urge 2154 | smtp 2155 | kurt 2156 | neon 2157 | ours 2158 | lone 2159 | cope 2160 | lm 2161 | lime 2162 | kirk 2163 | bool 2164 | cho 2165 | wit 2166 | bbs 2167 | spas 2168 | ind 2169 | jets 2170 | qui 2171 | intl 2172 | cz 2173 | yarn 2174 | knit 2175 | mug 2176 | hl 2177 | ob 2178 | pike 2179 | ids 2180 | hugo 2181 | gzip 2182 | ctrl 2183 | bent 2184 | laos 2185 | -------------------------------------------------------------------------------- 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