rchan26/english-char-roberta
Fill-Mask
•
Updated
•
11
word
stringlengths 2
23
| language
stringclasses 1
value | input_ids
sequencelengths 4
25
| attention_mask
sequencelengths 4
25
| special_tokens_mask
sequencelengths 4
25
| tokens
sequencelengths 2
23
|
---|---|---|---|---|---|
knots | en | [
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stalemating | en | [
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whoops | en | [
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implantation | en | [
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levers | en | [
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statesman | en | [
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13,
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14,
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1
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"a",
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reels | en | [
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19,
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0,
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tweediest | en | [
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19,
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1
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"e",
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"s",
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tilde | en | [
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"d",
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salved | en | [
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22,
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0,
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"s",
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"l",
"v",
"e",
"d"
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patronisers | en | [
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15,
14,
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19,
5,
18,
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"r",
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"n",
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tromping | en | [
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1
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corneas | en | [
53,
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1,
19,
54
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1
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"c",
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"n",
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"a",
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sables | en | [
53,
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1
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"s",
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"e",
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aeronautical | en | [
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"n",
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moonbeam | en | [
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coping | en | [
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doorplate | en | [
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5,
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1
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hemorrhages | en | [
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7,
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"r",
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fowled | en | [
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0,
1
] | [
"f",
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"d"
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tournament | en | [
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5,
14,
20,
54
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1,
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1,
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1,
1,
1
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1
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"n",
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"e",
"n",
"t"
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thingamabob | en | [
53,
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1,
13,
1,
2,
15,
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] | [
1,
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"n",
"g",
"a",
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"a",
"b",
"o",
"b"
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locksmiths | en | [
53,
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19,
13,
9,
20,
8,
19,
54
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1,
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1
] | [
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1
] | [
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"c",
"k",
"s",
"m",
"i",
"t",
"h",
"s"
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tweak | en | [
53,
20,
23,
5,
1,
11,
54
] | [
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"t",
"w",
"e",
"a",
"k"
] |
inertness | en | [
53,
9,
14,
5,
18,
20,
14,
5,
19,
19,
54
] | [
1,
1,
1,
1,
1,
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1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
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0,
0,
0,
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1
] | [
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"e",
"r",
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"n",
"e",
"s",
"s"
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frostbitten | en | [
53,
6,
18,
15,
19,
20,
2,
9,
20,
20,
5,
14,
54
] | [
1,
1,
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1
] | [
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"s",
"t",
"b",
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"t",
"t",
"e",
"n"
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interlope | en | [
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15,
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5,
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] | [
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1
] | [
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abnormally | en | [
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12,
12,
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1,
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1,
0,
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0,
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0,
0,
0,
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0,
1
] | [
"a",
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"n",
"o",
"r",
"m",
"a",
"l",
"l",
"y"
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crammed | en | [
53,
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18,
1,
13,
13,
5,
4,
54
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1,
1,
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1,
1
] | [
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0,
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0,
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1
] | [
"c",
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"a",
"m",
"m",
"e",
"d"
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drools | en | [
53,
4,
18,
15,
15,
12,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1
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1,
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0,
0,
0,
0,
1
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"d",
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"o",
"o",
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"s"
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mispronounce | en | [
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21,
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1,
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"r",
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"n",
"o",
"u",
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"c",
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outdated | en | [
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20,
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20,
5,
4,
54
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1,
1,
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1
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0,
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1
] | [
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"a",
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"e",
"d"
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gillion | en | [
53,
7,
9,
12,
12,
9,
15,
14,
54
] | [
1,
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1,
1,
1,
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1
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1
] | [
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"l",
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"o",
"n"
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declawing | en | [
53,
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3,
12,
1,
23,
9,
14,
7,
54
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1,
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1
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1
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"c",
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"a",
"w",
"i",
"n",
"g"
] |
goose | en | [
53,
7,
15,
15,
19,
5,
54
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1,
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1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"g",
"o",
"o",
"s",
"e"
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shortsightedly | en | [
53,
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8,
15,
18,
20,
19,
9,
7,
8,
20,
5,
4,
12,
25,
54
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1
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1
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"r",
"t",
"s",
"i",
"g",
"h",
"t",
"e",
"d",
"l",
"y"
] |
eddies | en | [
53,
5,
4,
4,
9,
5,
19,
54
] | [
1,
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1,
1,
1,
1,
1,
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1,
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0,
0,
0,
0,
1
] | [
"e",
"d",
"d",
"i",
"e",
"s"
] |
tear | en | [
53,
20,
5,
1,
18,
54
] | [
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
1
] | [
"t",
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"a",
"r"
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ensigns | en | [
53,
5,
14,
19,
9,
7,
14,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
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0,
0,
0,
0,
0,
1
] | [
"e",
"n",
"s",
"i",
"g",
"n",
"s"
] |
perennials | en | [
53,
16,
5,
18,
5,
14,
14,
9,
1,
12,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
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0,
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1
] | [
"p",
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"e",
"n",
"n",
"i",
"a",
"l",
"s"
] |
culmination | en | [
53,
3,
21,
12,
13,
9,
14,
1,
20,
9,
15,
14,
54
] | [
1,
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1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
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0,
0,
0,
0,
0,
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0,
1
] | [
"c",
"u",
"l",
"m",
"i",
"n",
"a",
"t",
"i",
"o",
"n"
] |
unravel | en | [
53,
21,
14,
18,
1,
22,
5,
12,
54
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1,
1,
1,
1,
1,
1,
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1
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1,
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1
] | [
"u",
"n",
"r",
"a",
"v",
"e",
"l"
] |
flair | en | [
53,
6,
12,
1,
9,
18,
54
] | [
1,
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1,
1,
1
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1,
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"i",
"r"
] |
hunts | en | [
53,
8,
21,
14,
20,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"h",
"u",
"n",
"t",
"s"
] |
beau | en | [
53,
2,
5,
1,
21,
54
] | [
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
1
] | [
"b",
"e",
"a",
"u"
] |
alderwomen | en | [
53,
1,
12,
4,
5,
18,
23,
15,
13,
5,
14,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"a",
"l",
"d",
"e",
"r",
"w",
"o",
"m",
"e",
"n"
] |
beheaded | en | [
53,
2,
5,
8,
5,
1,
4,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"b",
"e",
"h",
"e",
"a",
"d",
"e",
"d"
] |
candle | en | [
53,
3,
1,
14,
4,
12,
5,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
1
] | [
"c",
"a",
"n",
"d",
"l",
"e"
] |
typical | en | [
53,
20,
25,
16,
9,
3,
1,
12,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"t",
"y",
"p",
"i",
"c",
"a",
"l"
] |
laundromat | en | [
53,
12,
1,
21,
14,
4,
18,
15,
13,
1,
20,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"l",
"a",
"u",
"n",
"d",
"r",
"o",
"m",
"a",
"t"
] |
terrine | en | [
53,
20,
5,
18,
18,
9,
14,
5,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"t",
"e",
"r",
"r",
"i",
"n",
"e"
] |
detoured | en | [
53,
4,
5,
20,
15,
21,
18,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"d",
"e",
"t",
"o",
"u",
"r",
"e",
"d"
] |
escapology | en | [
53,
5,
19,
3,
1,
16,
15,
12,
15,
7,
25,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"e",
"s",
"c",
"a",
"p",
"o",
"l",
"o",
"g",
"y"
] |
surfed | en | [
53,
19,
21,
18,
6,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"u",
"r",
"f",
"e",
"d"
] |
refolds | en | [
53,
18,
5,
6,
15,
12,
4,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"r",
"e",
"f",
"o",
"l",
"d",
"s"
] |
forensic | en | [
53,
6,
15,
18,
5,
14,
19,
9,
3,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"f",
"o",
"r",
"e",
"n",
"s",
"i",
"c"
] |
article | en | [
53,
1,
18,
20,
9,
3,
12,
5,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"a",
"r",
"t",
"i",
"c",
"l",
"e"
] |
subprogram | en | [
53,
19,
21,
2,
16,
18,
15,
7,
18,
1,
13,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"u",
"b",
"p",
"r",
"o",
"g",
"r",
"a",
"m"
] |
camcorder | en | [
53,
3,
1,
13,
3,
15,
18,
4,
5,
18,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"c",
"a",
"m",
"c",
"o",
"r",
"d",
"e",
"r"
] |
lulus | en | [
53,
12,
21,
12,
21,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"l",
"u",
"l",
"u",
"s"
] |
palimpsest | en | [
53,
16,
1,
12,
9,
13,
16,
19,
5,
19,
20,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"p",
"a",
"l",
"i",
"m",
"p",
"s",
"e",
"s",
"t"
] |
teaspoon | en | [
53,
20,
5,
1,
19,
16,
15,
15,
14,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"t",
"e",
"a",
"s",
"p",
"o",
"o",
"n"
] |
sophomoric | en | [
53,
19,
15,
16,
8,
15,
13,
15,
18,
9,
3,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"o",
"p",
"h",
"o",
"m",
"o",
"r",
"i",
"c"
] |
quarrelsomeness | en | [
53,
17,
21,
1,
18,
18,
5,
12,
19,
15,
13,
5,
14,
5,
19,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"q",
"u",
"a",
"r",
"r",
"e",
"l",
"s",
"o",
"m",
"e",
"n",
"e",
"s",
"s"
] |
gaucheness | en | [
53,
7,
1,
21,
3,
8,
5,
14,
5,
19,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"g",
"a",
"u",
"c",
"h",
"e",
"n",
"e",
"s",
"s"
] |
desperately | en | [
53,
4,
5,
19,
16,
5,
18,
1,
20,
5,
12,
25,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"d",
"e",
"s",
"p",
"e",
"r",
"a",
"t",
"e",
"l",
"y"
] |
nearsighted | en | [
53,
14,
5,
1,
18,
19,
9,
7,
8,
20,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"n",
"e",
"a",
"r",
"s",
"i",
"g",
"h",
"t",
"e",
"d"
] |
venturousness | en | [
53,
22,
5,
14,
20,
21,
18,
15,
21,
19,
14,
5,
19,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"v",
"e",
"n",
"t",
"u",
"r",
"o",
"u",
"s",
"n",
"e",
"s",
"s"
] |
vindicator | en | [
53,
22,
9,
14,
4,
9,
3,
1,
20,
15,
18,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"v",
"i",
"n",
"d",
"i",
"c",
"a",
"t",
"o",
"r"
] |
squaw | en | [
53,
19,
17,
21,
1,
23,
54
] | [
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"s",
"q",
"u",
"a",
"w"
] |
hideaways | en | [
53,
8,
9,
4,
5,
1,
23,
1,
25,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"h",
"i",
"d",
"e",
"a",
"w",
"a",
"y",
"s"
] |
plasterer | en | [
53,
16,
12,
1,
19,
20,
5,
18,
5,
18,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"p",
"l",
"a",
"s",
"t",
"e",
"r",
"e",
"r"
] |
carefullest | en | [
53,
3,
1,
18,
5,
6,
21,
12,
12,
5,
19,
20,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"c",
"a",
"r",
"e",
"f",
"u",
"l",
"l",
"e",
"s",
"t"
] |
baste | en | [
53,
2,
1,
19,
20,
5,
54
] | [
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"b",
"a",
"s",
"t",
"e"
] |
fragmented | en | [
53,
6,
18,
1,
7,
13,
5,
14,
20,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"f",
"r",
"a",
"g",
"m",
"e",
"n",
"t",
"e",
"d"
] |
hypoglycemia | en | [
53,
8,
25,
16,
15,
7,
12,
25,
3,
5,
13,
9,
1,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"h",
"y",
"p",
"o",
"g",
"l",
"y",
"c",
"e",
"m",
"i",
"a"
] |
denouncements | en | [
53,
4,
5,
14,
15,
21,
14,
3,
5,
13,
5,
14,
20,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"d",
"e",
"n",
"o",
"u",
"n",
"c",
"e",
"m",
"e",
"n",
"t",
"s"
] |
bathmat | en | [
53,
2,
1,
20,
8,
13,
1,
20,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"b",
"a",
"t",
"h",
"m",
"a",
"t"
] |
jocoseness | en | [
53,
10,
15,
3,
15,
19,
5,
14,
5,
19,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"j",
"o",
"c",
"o",
"s",
"e",
"n",
"e",
"s",
"s"
] |
elaborations | en | [
53,
5,
12,
1,
2,
15,
18,
1,
20,
9,
15,
14,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"e",
"l",
"a",
"b",
"o",
"r",
"a",
"t",
"i",
"o",
"n",
"s"
] |
stupefied | en | [
53,
19,
20,
21,
16,
5,
6,
9,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"t",
"u",
"p",
"e",
"f",
"i",
"e",
"d"
] |
hindquarter | en | [
53,
8,
9,
14,
4,
17,
21,
1,
18,
20,
5,
18,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"h",
"i",
"n",
"d",
"q",
"u",
"a",
"r",
"t",
"e",
"r"
] |
attaining | en | [
53,
1,
20,
20,
1,
9,
14,
9,
14,
7,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"a",
"t",
"t",
"a",
"i",
"n",
"i",
"n",
"g"
] |
disinter | en | [
53,
4,
9,
19,
9,
14,
20,
5,
18,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"d",
"i",
"s",
"i",
"n",
"t",
"e",
"r"
] |
skittishness | en | [
53,
19,
11,
9,
20,
20,
9,
19,
8,
14,
5,
19,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"k",
"i",
"t",
"t",
"i",
"s",
"h",
"n",
"e",
"s",
"s"
] |
mopiest | en | [
53,
13,
15,
16,
9,
5,
19,
20,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"m",
"o",
"p",
"i",
"e",
"s",
"t"
] |
binoculars | en | [
53,
2,
9,
14,
15,
3,
21,
12,
1,
18,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"b",
"i",
"n",
"o",
"c",
"u",
"l",
"a",
"r",
"s"
] |
aggravatingly | en | [
53,
1,
7,
7,
18,
1,
22,
1,
20,
9,
14,
7,
12,
25,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"a",
"g",
"g",
"r",
"a",
"v",
"a",
"t",
"i",
"n",
"g",
"l",
"y"
] |
bucolics | en | [
53,
2,
21,
3,
15,
12,
9,
3,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"b",
"u",
"c",
"o",
"l",
"i",
"c",
"s"
] |
minnesingers | en | [
53,
13,
9,
14,
14,
5,
19,
9,
14,
7,
5,
18,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"m",
"i",
"n",
"n",
"e",
"s",
"i",
"n",
"g",
"e",
"r",
"s"
] |
surmounted | en | [
53,
19,
21,
18,
13,
15,
21,
14,
20,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"u",
"r",
"m",
"o",
"u",
"n",
"t",
"e",
"d"
] |
spuriously | en | [
53,
19,
16,
21,
18,
9,
15,
21,
19,
12,
25,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"s",
"p",
"u",
"r",
"i",
"o",
"u",
"s",
"l",
"y"
] |
rustiest | en | [
53,
18,
21,
19,
20,
9,
5,
19,
20,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"r",
"u",
"s",
"t",
"i",
"e",
"s",
"t"
] |
franchiser | en | [
53,
6,
18,
1,
14,
3,
8,
9,
19,
5,
18,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"f",
"r",
"a",
"n",
"c",
"h",
"i",
"s",
"e",
"r"
] |
adios | en | [
53,
1,
4,
9,
15,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
1
] | [
"a",
"d",
"i",
"o",
"s"
] |
reagents | en | [
53,
18,
5,
1,
7,
5,
14,
20,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"r",
"e",
"a",
"g",
"e",
"n",
"t",
"s"
] |
plodded | en | [
53,
16,
12,
15,
4,
4,
5,
4,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"p",
"l",
"o",
"d",
"d",
"e",
"d"
] |
biracial | en | [
53,
2,
9,
18,
1,
3,
9,
1,
12,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"b",
"i",
"r",
"a",
"c",
"i",
"a",
"l"
] |
brigadiers | en | [
53,
2,
18,
9,
7,
1,
4,
9,
5,
18,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"b",
"r",
"i",
"g",
"a",
"d",
"i",
"e",
"r",
"s"
] |
ferments | en | [
53,
6,
5,
18,
13,
5,
14,
20,
19,
54
] | [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
] | [
1,
0,
0,
0,
0,
0,
0,
0,
0,
1
] | [
"f",
"e",
"r",
"m",
"e",
"n",
"t",
"s"
] |
This is a dataset of English words which have been tokenised by character.
It was originally used to train a RoBERTa model from scratch on the masked language modelling task where during training, characters were randomly masked.
This was ultimately used in an anomaly detection task where the embeddings from the trained model were used to detect non-English words - see full example here.