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date

The date when the counts were assigned.

datetime

general

General word count details. This is the information that you would see in the typical Wordbee Translator word count pages. See below for more details.

object

words

Counts in terms of words. This includes all the counts from general that strictly refer to words.

object

chars

Counts in terms of chars. This is the extrapolation of counts in terms of characters. Basically, we take all the numbers from words multiplied with the total characters and divided by the total words.

object

intervals

An array with the lower and upper limits of the fuzzy intervals to apply. Example when a word count profile defines 2 intervals:

Code Block
[
    {
        "min": 110,
        "max": 110
    },
    {
        "min": 100,
        "max": 95
    }
]

object[]

General

The general object has these properties:

total

Total number

int

chars

Total characters

int

pages

Total pages. Null if not supplied.

int?

minutes

Total minutes. Null if not supplied.

int?

wordsExcluded

Words excluded from the counts.

int

charsTranslated

Total translated characters. Null if not calculated.

int?

wordsTranslated

Total translated words. Null if not calculated.

int?

Pretranslated words

wdPretransIdentical

Total pretranslated words, whether pretranslations are 100%, 110% or MT.

int

wdPretransIdenticalCtx

Among wdPretransIdentical, the 110% pretranslated words. Also called perfect or in-context match.

int

wdPretransIdenticalPrevCtx

Among wdPretransIdentical, the 110% pretranslated words, leveraged from the previous document version.

int

wdPretransIdenticalPrev

Among wdPretransIdentical, the 100% pretranslated words, leveraged from the previous document version.

int

wdPretransIdenticalMT

Among wdPretransIdentical, the machine translated words

int

wdPretransFuzzy

<100% pretranslated words

int

wd110

Total of 110% pretranslations. Equals sum of wdPretransIdenticalCtx + wdPretransIdenticalPrevCtx

int

wd100

Total of 100% pretranslations. Equals wdPretransIdentical - wdPretransIdenticalCtx - wdPretransIdenticalPrevCtx

int

Fuzzy matches (not used for pre-translation) and repetitions

wdMatch1

Total words falling into interval #1. The intervals (minimum % to maximum %) are configured in the project word count profile.

int

wdMatch2

Total words falling into interval #2.

int

wdMatch3

Total words falling into interval #3.

int

wdMatch4

Total words falling into interval #4.

int

wdMatch5

Total words falling into interval #5.

int

Advanced

tags

Total amount of markup

int

spaces

Total count of whitespace characters

int

punctuation

Total punctuation characters

int

nonAsianWords

Total non-Asian words

int

asianCharacters

Total Asian characters (Japanese, Chinese, Korean signs)

int

Words & Chars

The words and chars objects contain the counts in terms of words and characters, respectively:

segments

Total segments

int

words

Total words

int

chars

Total characters

int

pages

Total pages. Null if not supplied.

int?

minutes

Total minutes. Null if not supplied.

int?

wordsExcluded

Words isNullCounts

True: The counts are not available.

(This is the case for chars when the word count does not indicate the total characters and the counts can thus not be extrapolated to characters)

bool

total

Total units

decimal

excluded

Units excluded from the counts.

int

charsTranslated

Total translated characters. Null if not calculated.

int?

wordsTranslated

Total translated words. Null if not calculated.

int?

Pretranslated words

wdPretransIdenticaldecimal

pretransIdentical

Total pretranslated wordsunits, whether pretranslations are 100%, 110% or MT.

intdecimal

wdPretransIdenticalCtxpretransIdenticalCtx

Among wdPretransIdentical pretransIdentical, the 110% pretranslated wordsunits. Also called perfect or in-context match.

intdecimal

wdPretransIdenticalPrevCtxpretransIdenticalPrevCtx

Among wdPretransIdentical pretransIdentical, the 110% pretranslated wordsunits, leveraged from the previous document version.

intdecimal

wdPretransIdenticalPrevpretransIdenticalPrev

Among wdPretransIdentical pretransIdentical, the 100% pretranslated wordsunits, leveraged from the previous document version.

intdecimal

wdPretransIdenticalMTpretransIdenticalMT

Among wdPretransIdentical pretransIdentical, the machine translated wordsunits

intdecimal

wdPretransFuzzypretransFuzzy

<100% pretranslated wordsunits

intdecimal

wd110pretrans110

Total of 110% pretranslations. Equals sum of wdPretransIdenticalCtx pretransIdenticalCtx + wdPretransIdenticalPrevCtx pretransIdenticalPrevCtx

intdecimal

wd100pretrans100

Total of 100% pretranslations. Equals wdPretransIdentical pretransIdentical - wdPretransIdenticalCtx pretransIdenticalCtx - wdPretransIdenticalPrevCtx pretransIdenticalPrevCtx

int

Fuzzy matches (not used for pre-translation) and repetitions

wdMatch1

Total words decimal

match1

Total units falling into interval #1. The intervals (minimum % to maximum %) are configured in the project word count profile.

intdecimal

wdMatch2match2

Total words units falling into interval #2.

intdecimal

wdMatch3match3

Total words units falling into interval #3.

intdecimal

wdMatch4match4

Total words units falling into interval #4.

intdecimal

wdMatch5match5

Total words units falling into interval #5.

intdecimal

AdvancednoMatch

tags

Total amount of markup

int

spaces

Total count of whitespace characters

int

punctuation

Total punctuation characters

int

nonAsianWords

Total non-Asian words

int

asianCharacters

Total Asian characters (Japanese, Chinese, Korean signs)

int

EXAMPLES

To clarify how reductions, fees and coverage are applied on the base cost, see this screenshot and the corresponding payload below:

...

Total untranslated units which do not fall into any of the fuzzy intervals above.

decimal

EXAMPLES

A typical payload may look like:

Code Block
{
    "date": "2023-12-19T12:44:28.28Z",
    "general": {
        "segments": 1,
        "words": 2,
        "chars": 5,
        "pages": null,
        "minutes": null,
        "wordsExcluded": 0,
        "charsTranslated": null,
        "wordsTranslated": null,
        "wdPretransIdentical": 0,
        "wdPretransIdenticalCtx": 0,
        "wdPretransIdenticalPrevCtx": 0,
        "wdPretransIdenticalPrev": 0,
        "wdPretransIdenticalMT": 0,
        "wdPretransFuzzy": 0,
        "wd110": 0,
        "wd100": 0,
        "wdMatch1": 0,
        "wdMatch2": 0,
        "wdMatch3": 0,
        "wdMatch4": 0,
        "wdMatch5": 0,
        "tags": 0,
        "spaces": 1,
        "punctuation": 0,
        "nonAsianWords": 0,
        "asianCharacters": 0
    },
    "words": {
        "isNullCounts": false,
        "total": 495.002.0,
        "excluded": 0,
        "pretransIdentical": 0,
        "pretransIdenticalCtx": 0,
        "pretransIdenticalPrevCtx": 0,
        "pretransIdenticalPrev": 0,
        "pretransIdenticalMT": 0,
        "pretransIdenticalOther": 0,
        "pretrans110": 0,
        "pretrans100": 0,
        "pretransFuzzy": 0,
        "match1": 0,
        "totalBasematch2": 1000.000,
        "reductionmatch3": {0,
        "amountmatch4": 100.000,
        "percentmatch5": 10.0,
        "subTotalnoMatch": 9002.000
    },
    "feechars": {
        "isNullCounts": false,
        "amounttotal": 90.005.0,
        "excluded": 0,
        "pretransIdentical": 0,
        "pretransIdenticalCtx": 0,
        "pretransIdenticalPrevCtx": 0,
        "pretransIdenticalPrev": 0,
        "pretransIdenticalMT": 0,
        "pretransIdenticalOther": 0,
        "pretrans110": 0,
        "pretrans100": 0,
        "pretransFuzzy": 0,
        "match1": 0,
        "match2": 0,
        "match3": 0,
        "match4": 0,
        "percentmatch5": 10.0,
        "subTotalnoMatch": 9905.000
    },
    "coveredintervals": [
        {
            "percentmin": 50.0,110,
            "subTotalmax": 495.00110
        },
        {
       "currency": "USD",     "min": 100,
            "max": 109
        },
        {
            "min": 75,
            "decimalsmax": 2 99
        }
    ]
}