Tag: TG_QUERY_ONLY

The amount of IDF words of the request. The name does not reflect the essence: for example, for the request of 'Gadyach' this factor will be more than for the request of 'Moscow Peter Yekaterinburg Samara'.

The value of the news detector calculated in the Hippo. Always 0 with a detector value less than the threshold.

The popularity of the request

Min (number of words of request/10, 1.f)

1 / quantity_lov_v_

For this request, there is no information about the clickness of 1 - there is no request in the click database, 0 - the request is in the click database.

The musicality of the request. The results of the sorcerer Anton Konygin.

The commercial request for the dictionary of phrases from Direct: 0 - maximum commercial, 1 - minimal.

Are there any words from Yweb/Pornofilter/Porno.query.

The value of the commerce detector calculated in the Hippo.

The degree of centralization of the points from which the request is set

Whether the request of blog vocabulary contains

Binar non -profit request: Querynoncommerciality> 0.965.

Is the request for navigation, on the clicking of the answers

Request is not in Russian

The size of the region of the request

The factor is inversely proportional to the size of the regional region

The request mentions the product category. Not used (depreded)

The request mentions a vendor. Not used (depreded)

Geographical distribution of the request

The request is set mainly at night

The request is set mainly in the morning

The request is given mainly in the afternoon

The request is set mainly in the evening

The severity of the querial tasks at different times of the day

Classifier of Porn Causions, another dictionary than Pornoquery

The request is local-specific. The request is often reformulated with the obvious task of the region. ((https://ml.yandex-team.ru/archive/thread1433892/#Message1433892 more))

Request about the video

Requestful factors - the result of work ((http://wiki.yandex-team.ru/jandekspoisk/kachestvopoiska/obshayaformula/tekushhiekomponenty/georegionality classifier of geolocalization of the request)))))))))))))

R- Georelevan - regional results in the issuance could be useful, but nothing more

V- geovital - regional issuance is of fundamental importance

Does the request have a complete syntactic analysis

The request is the name of the organization (example: Gazprom, Gazprom) ((http://wiki.yandex-team.ru/arsengadzhikurbanov/warees Description))

Requestful factors - the result of the work ((http://wiki.yandex-team.ru/poiskovajaplatforma/lingvistika/zaprosnyjefefactory/localizovannyjezaprosya classifier of the request of the request)) - a new version of factors [328] - [328] - [328]: u - u - u - u - u - u - uceleless sites the request is meaningless;

Запросные факторы - результат работы ((http://wiki.yandex-team.ru/PoiskovajaPlatforma/Lingvistika/ZaprosnyjeFactory/LocalizovannyjeZaprosy классификатора геолокализованности запроса)) - новая версия факторов [328]-[330]: R - георелевантные - региональные результаты в issuing could be useful, but nothing more;

Requestful factors - the result of work ((http://wiki.yandex-team.ru/poiskovajaplatforma/lingvistika/zaprosnyjefefactory/localizovannyjezaprosya classifier of the request of the request)) - a new version of factors [328]: Vegetable fundamental importance.

= 1 - v. Download formula. Class requests: download/watch online/play/photo/listen

The result of the classifier of the request - in the request there are words from the corresponding dictionary. brand

Medication Dictionary

question

Specific request for Moscow

organization

porn

trips

The value of the pirate detector calculated in the hippo.

The popularity of the request within the country

The degree of centralization of the points from which the request is set (inside the country)

Geographical distribution of the request within the country

The hour at which this request is given the most

The degree of severity of the querial tasks at different times of the day (inside the country)

Country classifier of localization - how much the request implies the context of the country

The measure of 'commercial' request. It is a comprehensively calculated Matrixnet factor formula for the procurement vocabulary in direct + for user queries + add. Intensive dictionaries. Requests with intensity to buy a factor seeks to -> 1 commodity requests -> 0.6 with intensity cannot buy, reviews, etc. -> 0 ((http://wiki.yandex-team.ru/Faktorydljanovogokatorazaprosov Factors of the Classifier))) (HTTP : //wiki.yandex-team.ru/jandekspoisk/antispam/antiseo/klassifikATORCHESSKIXZAPROSOV STUNITURE OF HIM))

The value of the chopped commerce detector, calculated in the hippo.

It launches on the basic search under the name ISGEO the maximum weight of the meters of the gelator in the request. A geo-object is understood as an object of the category GEO, Geo1, Geoaddr, Geoaddr1, Landmark, Landmark1 (see ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects kaovsky allocation))))))))))))))))))))))))))))))). wiki.yandex-team.ru/arsengadzhikurbanov/wares Read more))

It launches for the basic search under the name ISMUSIC the maximum weight of the Music or Music1 category of the category of the Category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/warees more)))))))))))))))))))))

When responding to a request, the results are important in close proximity ([pharmacies], [children's clinic])

When answering a request, the results within the city are important (the bulk of localized queries)

When responding to a request, the results from the region of the user ([airport], [dairy]) are important

There is a typo in the request

The value of the health detector calculated in the Hippo.

Regional and navigation request - in the user region there are one or more navigation results on it

It launches on the basic search under the name ISHUM the maximum weight of the enclosed object of the Hum or Hum1 category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#ishum more)))))

It launches on the basic search under the name ISTEXT the maximum weight of the TEXT or Text1 category of the category of the category met in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#istext more)))

It launches on the basic search under the name Ispicture the maximum weight of the Picture or Picture1 category of the category of the category of the category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#ispicture))))))))))))))))))

Returns the maximum degree of household objects in the request under the name Wmaxone. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#maxone more)))))))

Returns the maximum degree of household objects in the request under the name Wminone. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#minone more)))))

The most likely theme of the request determined ((http://wiki.yandex-team.ru/jandekspoisk/zarubezhnyjjinternet/dmozqueryClassifier1 The rule of the sorcerer DmozTheme)), only the most popular topics are taken into account (but there are more than in the DMOZQUREMES factor). The factor contains the likelihood of a correspondence of the request of the theme, but for each topic, its own interval is taken on the segment [0..1]

The theme of the request determined ((http://wiki.yandex-team.ru/jandekspoisk/zarubezhnyjjinternet/dmozqueryClassifier1 The rule of the sorcerer Dmoztheme)), only a few of the most popular topics are taken into account.

0 or 1, depending on the presence in the request of the clearly expressed intent Need_photo from the variety

0 or 1, depending on the presence in the request of the clearly expressed intent Need_map from the variety

The factor is an analogue of LongQuery (the sum of the IDF words of the request), but with the 'correct' accounting of synonyms. Specifically, a minimum of IDF (i.e. the most frequent) of synonyms and words is selected.

Expected number of found on request

Double logarithm of the probability of a request for a language model of the Erratum typo service

The average length of the logical session in which there was a request

0 or 1 - whether the request is matured by the tickt

0 or 1 - whether the request is matured by the tickt

0 or 1 - whether the request is matured by the tickt

The prediction of the share of “good” (at least two different cities and frequency> = 10) references to the request with geography in Turkey

The result of the work of the lexical classifier of requests predicting the likelihood of click on the theme of 3561

The result of the work of the lexical classifier of requests predicting the likelihood of click on the page 3973 page

Rank 'navigation'

The average number of found on request

The angle in the Depth Nodes space, counted only by words (min for all)

Shows that a request in Ukrainian

The number of requests in the group of frequency requests similar to a given

The result of the work of Adult Rules for the Sorcerer.

The value of the conversion of the request calculated in the Hippo.

The value of PURCHASETOTALPredict, calculated in the Hippo.

The value of serpsummarysurpluspredict, calculated in the hippo.

Requestwith120d3ClickpartPredict value, calculated in the Hippo.

The value of the requester detector of the parasites calculated in the hippo.

The value of Halfecompredict, calculated in the Hippo.

Prediction of a share of requests with geography on a bag of words built for request

The request is a Urle with an accuracy of the points and testing characters - the ISURL sorcerer's rule is used

Linguistic boosting factor. Type of extensions: XFDTSHOW. Factor: The minimum expansion weight.

The value of the classifier of localization for request

Relev_locale == ru

Relev_locale == ua

relev_locale == by

relev_locale == kz

relev_locale == tr

relev_locale == world

Porn query classification result from Wizard (iad_vw flag, based on Vowpal Wabbit)

relev_locale == id

request came from yandsearch (rearr.is_desktop == 1)

request came from touchsearch (rearr.is_mobile == 1)

request came from padsearch (rearr.is_tablet == 1)

request came from device with Android OS (rearr.dd_osfamily == Android)

request came from device with iOS (rearr.dd_osfamily == iOS)

request came from device with Windows OS (rearr.dd_osfamily == Windows)

request does not come from devices with Android, iOS or Windows OS (rearr.dd_osfamily != [Android, iOS, Windows])

The distance from the city from where the request was set to Ankara

The distance from the city from where the request was set to Magadan

The geographical breadth of the city, from where a request was set

Geographical longitude of the city, from where a request was set

Was the request set in a voice

The probability that the VK.com host is popular for this request in accordance with the corresponding DSSM model.

The likelihood that the Onliner.by host is popular for this request according to the corresponding DSSM model.

The probability that the Rambler.ru host is popular for this request in accordance with the corresponding DSSM model.

The likelihood that the ExpertCen.ru host is popular for this request in accordance with the corresponding DSSM model.

The probability that the Sunhome.ru host is popular for this request according to the corresponding DSSM model.

Summary Skorov words of a request for a language model 3GRAMS-YANDEX-DIRECT.

Summary of the Skorov words of the request by the Web-Mt language model.

Linguistic boosting factor. The average weight of extensions type Querytotext.

Linguistic boosting factor. The average weight of the QFUF type extensions.

Linguistic boosting factor. Type of extensions: QFUF. Transferred the total weight of the extensions.

Linguistic boosting factor. Type of extensions: XFDTSHOWKNN (closest to the DSSM models trained to predict XFDTSHOW of extension). Aggregation on all extensions. The average weight of extensions.

The average value of News for the year. It is calculated in offline.

ADDTIME average value for the year. It is calculated in offline.

The average value of TXTHIRELSY for the year. It is calculated in offline.

The average TEXTLIKE value is for the year. It is calculated in offline.

The average Hasnoallwordstersy value for the year. It is calculated in offline.

The average value of ISFORUM for the year. It is calculated in offline.

The average value of Haspayments for the year. It is calculated in offline.

The average value is Yabarhostavgtime2 for the year. It is calculated in offline.

The average value of Yabarurlvisitors for the year. It is calculated in offline.

The average value of QueryDowneronlyClickRate for the year. It is calculated in offline.

The average value of Dateraage for the year. It is calculated in offline.

The average value of the LonGestText for the year. It is calculated in offline.

The average value is DifferentinTernallinks for the year. It is calculated in offline.

The average value of QueryDowneronlyClickRate_Rreg is for a year. It is calculated in offline.

The average ISHUB value is for the year. It is calculated in offline.

The average value is BM25_0 on request per year. It is calculated in offline.

The average value of BOCM for the year. It is calculated in offline.

The average ISindexpage is for the year. It is calculated in offline.

The average value of queriesavgcm2 for the year. It is calculated in offline.

The average value of BrowserhostDownloadProbabolyity for the year. It is calculated in offline.

The average value of Regbrowseruserhub for the year. It is calculated in offline.

Auxtitlebm25 average value for the year. It is calculated in offline.

The average value of QuryurlCorrectedctrxFactor for the year. It is calculated in offline.

The average value is QueryTodocallsumfcountTextbM11Norm16384 for the year. It is calculated in offline.

The average value of the XFDTSHOWALLSUMWFSUMWBODYMINWIDESIZE for the year. It is calculated in offline.

Maintened by clicks ISMainPage value for the year. It is calculated in offline.

Main -heated clicks of the Yabarurlavgtime value for the year. It is calculated in offline.

Maintened by clicks DifferentinTernallinks for the year. It is calculated in offline.

Malpanized Dwelltime-AMI Value of Urldomainfraction for the year. It is calculated in offline.

Dssm Boosting query self similarity for XfWeight model.

Dssm Boosting query self similarity for XfOne model.

Dssm Boosting query self similarity for Ctr model.

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

Predicted by demand and country, using a DSSM model, the length of the click from this country.

The average for the year for the year predicted using the neural network.

ADDTIME ADDTIME is predicted using a neural network for a year.

The average Txthirelesy value predicted using a neural network for the year.

The average Textlike is predicted using a neural network for the year.

The average HasnoallwordStrsy is predicted using a neural network for a year.

The average ISFORUM is predicted using a neural network for the year.

The average Haspayments is predicted using a neural network for the year.

The average value of Yabarhostavgtime2 for the year for the year.

The average yabarurlvisitors is predicted using a neural network for the year.

The average value of QueryDowneronlyClickRate for the year for the year.

The average Dateraage value for the year for a year predicted using a neural network.

The average LonGestText is predicted using a neural network for the year.

The average DifferentinTernallinks for the year for the year.

The average value of QueryDowneronlyClickRate_Rreg is predicted using a neural network for a year.

The average BOCM value predicted using a neural network for the year.

The average ISindEXPAGE is predicted using a neural network for the year.

The average value of QueriesavGCM2 for the year for the year predicted using a neural network.

The average BrowserhostdowLoadProbabolyity for the year for the year.

The average value of Regbrowseruserhub for the year for a year predicted using a neural network.

The average AuxtitlebM25 average value for the year for the year.

The average value of QuryurlCorrededCTRXFACTOR for the year for the year.

The average value of QueryTodoCallsumfcountTextbM11Norm16384 for the year for the year.

The average value of the XFDTSHOWALSUMWFSUMWBODYMINWINDOWSIZE for the year for the year.

The value of the ISMAINPAGE with clicks predicted using the neural network with clicks on request for the year.

A mid Yabarurlavgtime value predicted using a neural network with clicks for a year.

DiffferentinTernallinks, which is predicted using a neural network, is a weighted net with clicks for a year.

The Malue Network DwellTime-AMI predicted using the neural network is the value of Urldomainfraction for the year.

The prediction of the probability that the request is localized in accordance with the regionality5 rule.

Characterizes the request for the degree of change from the addition of a fixed word (number of some year), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

Characterizes a request for the degree of change from the addition of a fixed word ('online' for Kirilitsa), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

Characterizes the request for the degree of change from removing a fixed word ('site' for Kirilitsa), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

For each word offline, the average Hasnotr meaning is calculated for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average ISLJ value is calculated for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average BCLMLITE value is calculated for 3 months. Further, in all words of the request, a minimum of this value is taken.

For each word offline, the average DBM40 value is calculated for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

For each word offline, the average ISDESKTOPREQUEST value is calculated for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

For each word offline, the average value of RLQAVGHASNOLLWORDSTRSYN is calculated at the request for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average DSSMAGGRETEDANNREG value is calculated at the request for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average meaning of MetanumurlSperhostfixed is calculated in demands in 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average value of MaxsdisnavmxqueryMax is calculated at the request for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

DSSM prediction of google specificity for query

The average value of Randomlogqueryavgaddtime of the closest KNN queries.

The average value of RandomlogqueryavgtXthirelsy nearest KNN queries.

The average value of Randomlogqueryavgtextlike nearest KNN queries.

The average value of Randomlogqueryavgisforum of the closest KNN queries.

The average value of Randomlogqueryavghaspayments closest to KNN queries.

The average value of Randomlogqueryavgdiferentinternallinks of the nearest KNN queries.

The average value of RandomlogqueryavgistargetbussinessCard of the nearest KNN queries.

The average value is RandomlogqueryavgquerytododoCallsumfcountTextBM11NORM16384 of the nearest KNN queries.

The average value is Randomlogqueryavgxfdtshowallsumwfsumwbodyminwindowsize closest KNN queries.

Sigmoid rationed the value of a textual classifier of porn according to Toloka Porn

Binarized value of a textual classifier text classifier according to Toloka estimates

The value of the text classifier of porn according to the classifier of the web and add. dictionaries

Binarized with the use of networks, the value of a textual classifier of porn according to the estimates of the web and additional classifier. dictionaries

The presence of obscene vocabulary in the request. 0 - absent, 0.5 - non -seated, 1 - hard

The presence of porn markers in the request (0 - is, 1/3 - no, 1 - request 'gray')

The presence of an official word in a lemmetized request

The presence of Wikipedia word in a lemmetized request

The presence in the lemmetized request of the word not and similar in meaning

The presence in a lemmetized request to buy words, price and similar in meaning

Median dwelltai request in history. Dwelltaym is cut to 6000. The request is normalized by doppelgangers

The number of shows of the request with more than one click in history. The request is normalized by doppelgangers

The share of shows with more than one click from all shows in history. The request is normalized by doppelgangers

The average value for the query factor according to Querytotext linguosting data is calculated in the LingBoostqueryFeatures Hotemaway Rules

The average value for the query factor according to Querytotextbyxfdtshowknn linguosting data is calculated in the LingBoostqueryFeatures Hotemail Rules

SUM / (SUM + 10) for the requested factor according to the XFDTSHOW linguosting data, is calculated in the LingBoostqueryFeatures Hotemaura

Quantile 0.1 for the request factor according to the XFDTSHOW linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

Quantile 0.1 for the request factor according to the XFDTSHOWKNN linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

Quantile 0.9 for the quantity factor according to the XFDTSHOWKNN linguosting data, is calculated in the LingBoostqueryFeatures Hotemail Rules

SUM / (SUM + 10) for the requested factor according to QFUF linguosting data, is calculated in the LingBoostqueryFeatures Hotemaura

The average value for the quoted factor according to QFUF lingvobusting data is calculated in the LingBoostqueryFeatures Hotemail Rules

Relev_locale == Us

25% time quantile from the previous request to the current one. The request is normalized by doppelgangers

The request was recognized as having intent to copyright objects protected by anti-Pirate memorandum.

Transferred Ethos predicted classifier for marking on the relevance of video.

Reduced ETHOS predictive classifier, trained in the synthetic sample 'Request characteristic of the pirate site' VS 'Request is characteristic of the site far from this'

DSSM regression for the separation of memorandum and no requests

Transferred Ethos predicted classifier trained to distinguish a memorandum queries from a random

DSSM regression of embeds to separate Pirato specific and no requests

Weekday query probability

The average ISBLOG value for the year. It is calculated in offline.

The value of the freshness detector calculated in the hippo. Always 0 with a detector value less than the threshold.

Count of utterance hypothesises from ASR

Max count of words in utterance hypothesises from ASR

Min count of words in utterance hypothesises from ASR

Average count of words in utterance hypothesises from ASR

Standard deviation of words count in utterance hypothesises from ASR

Count of words in winner utterance hypothesis from ASR

Max length of utterance hypothesises from ASR

Min length of utterance hypothesises from ASR

Average length of utterance hypothesises from ASR

Standard deviation of utterance hypothesises lengths from ASR

Length of winner utterance hypothesis from ASR

Number of speakers deduced from ASR hypothesises

Mean distance to winner ASR hypothesis from other hypothesises

Standard Deviation of distances to winner ASR hypothesis from other hypothesises

Maximum acoustic score

Minimum acoustic score

Average acoustic score

Standard deviation of the acoustic score

Median of the acoustic score

Winner's acoustic score

ASR acoustic model type (0 == undefined, 1 == seq2seq, 2 == grapheme)

Duration of the audio request

Word LSTM trained on quasar toloka data (alarms_timers prediction)

Word LSTM trained on quasar toloka data (commands prediction)

Word LSTM trained on quasar toloka data (fairy_tails prediction)

Word LSTM trained on quasar toloka data (games_skills prediction)

Word LSTM trained on quasar toloka data (geo prediction)

Word LSTM trained on quasar toloka data (info_request prediction)

Word LSTM trained on quasar toloka data (iot prediction)

Word LSTM trained on quasar toloka data (market prediction)

Word LSTM trained on quasar toloka data (music prediction)

Word LSTM trained on quasar toloka data (new_functionality prediction)

Word LSTM trained on quasar toloka data (news prediction)

Word LSTM trained on quasar toloka data (ontofacts prediction)

Word LSTM trained on quasar toloka data (other prediction)

Word LSTM trained on quasar toloka data (radio prediction)

Word LSTM trained on quasar toloka data (search prediction)

Word LSTM trained on quasar toloka data (taxi prediction)

Word LSTM trained on quasar toloka data (tell_something prediction)

Word LSTM trained on quasar toloka data (time_tables prediction)

Word LSTM trained on quasar toloka data (toloka_gc prediction)

Word LSTM trained on quasar toloka data (translate prediction)

Word LSTM trained on quasar toloka data (video prediction)

Word LSTM trained on quasar toloka data (weather prediction)

Word LSTM trained on quasar toloka data (alarms_timers prediction)

Word LSTM trained on quasar toloka data (commands prediction)

Word LSTM trained on quasar toloka data (fairy_tails prediction)

Word LSTM trained on quasar toloka data (games_skills prediction)

Word LSTM trained on quasar toloka data (geo prediction)

Word LSTM trained on quasar toloka data (info_request prediction)

Word LSTM trained on quasar toloka data (iot prediction)

Word LSTM trained on quasar toloka data (market prediction)

Word LSTM trained on quasar toloka data (music prediction)

Word LSTM trained on quasar toloka data (new_functionality prediction)

Word LSTM trained on quasar toloka data (news prediction)

Word LSTM trained on quasar toloka data (ontofacts prediction)

Word LSTM trained on quasar toloka data (other prediction)

Word LSTM trained on quasar toloka data (player_commands prediction)

Word LSTM trained on quasar toloka data (radio prediction)

Word LSTM trained on quasar toloka data (sound_commands prediction)

Word LSTM trained on quasar toloka data (taxi prediction)

Word LSTM trained on quasar toloka data (tell_joke prediction)

Word LSTM trained on quasar toloka data (tell_something prediction)

Word LSTM trained on quasar toloka data (toloka_gc prediction)

Word LSTM trained on quasar toloka data (translate prediction)

Word LSTM trained on quasar toloka data (tv_intents prediction)

Word LSTM trained on quasar toloka data (video prediction)

Word LSTM trained on quasar toloka data (vins_intents prediction)

Word LSTM trained on quasar toloka data (weather prediction)

Word LSTM traind on toloka data (music_play prediction)

Word LSTM traind on toloka data (video_play prediction)

Word LSTM traind on scenarios (VINS) data (music_play prediction)

Word LSTM traind on scenarios (VINS) data (video_play prediction)

Result of CustomEntities rule has video_action entity

Result of CustomEntities rule has tv_genere entity

Result of CustomEntities rule has market_vendor entity

Result of CustomEntities rule has channel_suggest entity

Result of CustomEntities rule has rewind_type entity

Result of CustomEntities rule has video_provider entity

Result of CustomEntities rule has language entity

Result of CustomEntities rule has news_topic entity

Result of CustomEntities rule has route_action_type entity

Result of FstNum rule is not empty

Result of FstAlbum rule is not empty

Result of CustomEntities rule has player_type entity

Result of CustomEntities rule has video_film_genre entity

Result of FstSoft rule is not empty

Result of CustomEntities rule has ambient_sound entity

Result of EntityFinder rule has fb:film.film_subject entity

Result of EntityFinder rule has fb:award.award_winning_work entity

Result of EntityFinder rule has film entity

Result of EntityFinder rule has fb:music.artist entity

Result of Granet rule has form for goto_video_screen intent

Result of Granet rule has form for payment_confirmed intent

Result of Granet rule has form for open_current_video intent

Word LSTM traind on toloka data (search prediction)

Word LSTM traind on scenarios (VINS) data (search prediction)

Word LSTM trained on toloka data (music_fairy_tale prediction)

Word LSTM trained on scenarios (VINS) data (music_fairy_tale prediction)

Result of Granet rule has form for music_fairy_tale intent

Word LSTM trained on toloka data (music_ambient_sound prediction)

Word LSTM trained on scenarios (VINS) data (music_ambient_sound prediction)

Result of Granet rule has form for music_ambient_sound intent

Word LSTM trained on toloka data (radio_play prediction)

Word LSTM trained on scenarios (VINS) data (radio_play prediction)

Result of Granet rule has form for radio_play intent

Result of Granet rule has form for alarm_general intent

Result of Granet rule has form for alarm_set_sound intent

Result of Granet rule has form for alarm_set_with_sound intent

Result of Granet rule has form for translation intent

Result of Granet rule has form for how_much intent

Result of Granet rule has form for tv_stream intent

Result of Granet rule has form for player_rewind intent

Result of Granet rule has form for player_shuffle intent

Result of Granet rule has form for player_replay intent

Result of Granet rule has form for get_news intent

Result of Granet rule has form for get_free_news intent

Result of Granet rule has form for create_reminder intent

Result of Granet rule has form for get_weather intent

Result of Granet rule has form for select_video_by_number

Result of Granet rule has form for music_podcast intent

Result of Granet rule has form for music key words

Result of Granet rule has form for music_sing_song

Word LSTM trained on scenarios (VINS) data (music_sing_song prediction)

Word LSTM trained on toloka data (music_sing_song prediction)

Word LSTM trained on scenarios (VINS) data (find_poi prediction)

Word LSTM trained on toloka data (find_poi prediction)

Word LSTM trained on scenarios (VINS) data (open_site_or_app prediction)

Word LSTM trained on toloka data (open_site_or_app prediction)

Word LSTM trained on scenarios (VINS) data (how_much prediction)

Word LSTM trained on toloka data (how_much prediction)

Word LSTM trained on scenarios (VINS) data (translate prediction)

Word LSTM trained on toloka data (translate prediction)

Word LSTM trained on scenarios (VINS) data (tv_broadcast prediction)

Word LSTM trained on toloka data (tv_broadcast prediction)

Word LSTM trained on scenarios (VINS) data (convert prediction)

Word LSTM trained on toloka data (convert prediction)

Word LSTM trained on scenarios (VINS) data (player_dislike prediction)

Word LSTM trained on scenarios (VINS) data (create_reminder prediction)

Word LSTM trained on toloka data (create_reminder prediction)

Word LSTM trained on scenarios (VINS) data (tv_stream prediction)

Word LSTM trained on toloka data (tv_stream prediction)

Word LSTM trained on scenarios (VINS) data (recite_a_poem prediction)

Word LSTM trained on toloka data (recite_a_poem prediction)

Result has frame containing slots with trusted values only and no other unexpected slots

Gc DSSM classifier distilled from BERT

Result of Granet rule has form for player_dislike

Word LSTM trained on factoid markup from toloka

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

Filled frames found useful for classification

query-reply dssm score from gc

is seq2seq reply from gc

Max tokens frequency in music scenarios

Max tokens frequency in video scenarios

Average tokens frequency in music scenarios

Average tokens frequency in video scenarios

Max tokens frequency in music scenarios (without high frequency tokens)

Max tokens frequency in video scenarios (without high frequency tokens)

Average tokens frequency in music scenarios (without high frequency tokens)

Average tokens frequency in video scenarios (without high frequency tokens)

Max tokens frequency in video select from gallery scenario

Avg tokens frequency in video select from gallery scenario

Max tokens frequency in general conversation scenarios

Avg tokens frequency in general conversation scenarios

Max tokens frequency in search scenarios

Avg tokens frequency in search scenarios

Max tokens frequency in other scenarios

Avg tokens frequency in other scenarios

Max tokens bigrams frequency in music scenarios

Max tokens bigrams frequency in video scenarios

Average tokens bigrams frequency in music scenarios

Average tokens bigrams frequency in video scenarios

Max tokens bigrams frequency in music scenarios (without high frequency tokens)

Max tokens bigrams frequency in video scenarios (without high frequency tokens)

Average tokens bigrams frequency in music scenarios (without high frequency tokens)

Average tokens bigrams frequency in video scenarios (without high frequency tokens)

Max tokens bigrams frequency in video select from gallery scenario

Avg tokens bigrams frequency in video select from gallery scenario

Max tokens bigrams frequency in general conversation scenarios

Avg tokens bigrams frequency in general conversation scenarios

Max tokens bigrams frequency in search scenarios

Avg tokens bigrams frequency in search scenarios

Max tokens bigrams frequency in other scenarios

Avg tokens bigrams frequency in other scenarios

The value of the freshness detector calculated in the hippo. Always 0 with a detector value less than the threshold.

The value of the news detector calculated in the Hippo. Always 0 with a detector value less than the threshold.

The value of the pirate detector calculated in the hippo.

The value of the commerce detector calculated in the Hippo.

The value of the chopped commerce detector calculated in the hippo.

The value of the health detector calculated in the Hippo.

The value of Purchasetotalpredict, calculated in the Hippo.

The value of serpsummarysurpluspredict, calculated in the hippo.

Requestwith120d3ClickpartPredict value, calculated in the Hippo.

The value of Halfecompredict, calculated in the Hippo.

The value of the conversion of the request calculated in the Hippo.

The value of the requester detector of the parasites calculated in the hippo.

Classifier of one product in the request

Classifier of several goods in the request

The value of the Proxima DCG-5, calculated in the hippo.

The request is fresh

The theme of the request determined ((http://wiki.yandex-team.ru/jandekspoisk/zarubezhnyjjinternet/dmozqueryClassifier1 The rule of the sorcerer Dmoztheme)), only a few of the most popular topics are taken into account.

The most likely theme of the request determined ((http://wiki.yandex-team.ru/jandekspoisk/zarubezhnyjjinternet/dmozqueryClassifier1 The rule of the sorcerer DmozTheme)), only the most popular topics are taken into account (but there are more than in the DMOZQUREMES factor). The factor contains the likelihood of a correspondence of the request of the theme, but for each topic, its own interval is taken on the segment [0..1]

The popularity of the request

The degree of centralization of the points from which the request is set

Geographical distribution of the request

The severity of the querial tasks at different times of the day

The average length of the logical session in which there was a request

The average number of found on request

The request is set mainly at night

The request is set mainly in the morning

The request is given mainly in the afternoon

The request is set mainly in the evening

The popularity of the request within the country

The degree of centralization of the points from which the request is set (inside the country)

Geographical distribution of the request within the country

The hour at which this request is given the most

The degree of severity of the querial tasks at different times of the day (inside the country)

The musicality of the request. The results of the sorcerer Anton Konygin.

The commercial request for the dictionary of phrases from Direct: 0 - maximum commercial, 1 - minimal.

The measure of 'commercial' request. It is a comprehensively calculated Matrixnet factor formula for the procurement vocabulary in direct + for user queries + add. Intensive dictionaries. Requests with intensity to buy a factor seeks to -> 1 commodity requests -> 0.6 with intensity cannot buy, reviews, etc. -> 0 ((http://wiki.yandex-team.ru/Faktorydljanovogokatorazaprosov Factors of the Classifier))) (HTTP : //wiki.yandex-team.ru/jandekspoisk/antispam/antiseo/klassifikATORCHESSKIXZAPROSOV STUNITURE OF HIM))

Rank 'navigation'

Binar non -profit request: Querynoncommerciality> 0.965.

Are there any words from Yweb/Pornofilter/Porno.query.

Classifier of Porn Causions, another dictionary than Pornoquery

Whether the request of blog vocabulary contains

Is the request for navigation, on the clicking of the answers

Request is not in Russian

Request about the video

Requestful factors - the result of work ((http://wiki.yandex-team.ru/jandekspoisk/kachestvopoiska/obshayaformula/tekushhiekomponenty/georegionality classifier of geolocalization of the request)))))))))))))

R- Georelevan - regional results in the issuance could be useful, but nothing more

V- geovital - regional issuance is of fundamental importance

Requestful factors - the result of the work ((http://wiki.yandex-team.ru/poiskovajaplatforma/lingvistika/zaprosnyjefefactory/localizovannyjezaprosya classifier of the request of the request)) - a new version of factors [328] - [328] - [328]: u - u - u - u - u - u - uceleless sites the request is meaningless;

Запросные факторы - результат работы ((http://wiki.yandex-team.ru/PoiskovajaPlatforma/Lingvistika/ZaprosnyjeFactory/LocalizovannyjeZaprosy классификатора геолокализованности запроса)) - новая версия факторов [328]-[330]: R - георелевантные - региональные результаты в issuing could be useful, but nothing more;

Requestful factors - the result of the work ((http://wiki.yandex-team.ru/poiskovajaplatforma/lingvistika/zaprosnyjefefactory/localizovannyjezaprosy classifier of the request of the request)) - a new version of factors [328]: Vegetable fundamental importance.

Regional and navigation request - in the user region there are one or more navigation results on it

The result of the work of the lexical classifier of requests predicting the likelihood of click on the page 3973 page

The result of the work of the lexical classifier of requests predicting the likelihood of click on the theme of 3561

The prediction of the share of “good” (at least two different cities and frequency> = 10) references to the request with geography in Turkey

When responding to a request, the results are important in close proximity ([pharmacies], [children's clinic])

When answering a request, the results within the city are important (the bulk of localized queries)

When responding to a request, the results from the region, the region of the user ([airport], [dairy]) are important

It launches on the basic search under the name ISGEO the maximum weight of the meters of the gelator in the request. A geo-object is understood as an object of the category GEO, Geo1, Geoaddr, Geoaddr1, Landmark, Landmark1 (see ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects kaovsky allocation))))))))))))))))))))))))))))))). wiki.yandex-team.ru/arsengadzhikurbanov/wares Read more))

It launches on the basic searches under the name ISMUSIC the maximum weight of the Music or Music1 category met in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/warees more))))))))))))))))))))))

The request is the name of the organization (example: Gazprom, Gazprom) ((http://wiki.yandex-team.ru/arsengadzhikurbanov/warees Description))

It launches on the basic search under the name ISHUM the maximum weight of the enclosed object of the Hum or Hum1 category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#ishum more)))))

It launches on the basic search under the name ISTEXT the maximum weight of the TEXT or Text1 category of the category of the category met in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#istext more)))

It launches on the basic search under the name Ispicture the maximum weight of the Picture or Picture1 category of the category of the category of the category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#ispicture))))))))))))))))))

Returns the maximum degree of household objects in the request under the name Wminone. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#minone more)))))

Returns the maximum degree of household objects in the request under the name Wmaxone. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#maxone more)))))))

Country classifier of localization - how much the request implies the context of the country

= 1 - v. Download formula. Class requests: download/watch online/play/photo/listen

The result of the classifier of the request - in the request there are words from the corresponding dictionary. brand

Medication Dictionary

question

Specific request for Moscow

organization

porn

trips

0 or 1, depending on the presence in the request of the clearly expressed intent Need_photo from the variety

0 or 1, depending on the presence in the request of the clearly expressed intent Need_map from the variety

0 or 1 - whether the request is matured by the tickt

0 or 1 - whether the request is matured by the tickt

0 or 1 - whether the request is matured by the tickt

There is a typo in the request

Double logarithm of the probability of a request for a language model of the Erratum typo service

Summary Skorov words of a request for a language model 3GRAMS-YANDEX-DIRECT.

Summary of the Skorov words of the request by the Web-Mt language model.

The angle in the Depth Nodes space, counted only by words (min for all)

Prediction of a share of requests with geography on a bag of words built for request

The value of the classifier of localization for request

The request is a Urle with an accuracy of the points and testing characters - the ISURL sorcerer's rule is used

Relev_locale == ru

Relev_locale == ua

relev_locale == by

relev_locale == kz

relev_locale == tr

relev_locale == id

relev_locale == world

The result of the work of Adult Rules for the Sorcerer.

Porn query classification result from Wizard (iad_vw flag, based on Vowpal Wabbit)

The probability that the VK.com host is popular for this request in accordance with the corresponding DSSM model.

The probability that the Onliner.by host is popular for this request according to the corresponding DSSM model.

The probability that the Rambler.ru host is popular for this request in accordance with the corresponding DSSM model.

The likelihood that the ExpertCen.ru host is popular for this request in accordance with the corresponding DSSM model.

The probability that the Sunhome.ru host is popular for this request according to the corresponding DSSM model.

The average value of News for the year. It is calculated in offline.

ADDTIME average value for the year. It is calculated in offline.

The average value of TXTHIRELSY for the year. It is calculated in offline.

The average TEXTLIKE value is for the year. It is calculated in offline.

The average Hasnoallwordstersy value for the year. It is calculated in offline.

The average value of ISFORUM for the year. It is calculated in offline.

The average value of Haspayments for the year. It is calculated in offline.

The average value is Yabarhostavgtime2 for the year. It is calculated in offline.

The average value of Yabarurlvisitors for the year. It is calculated in offline.

The average value of QueryDowneronlyClickRate for the year. It is calculated in offline.

The average value of Dateraage for the year. It is calculated in offline.

The average value of the LonGestText for the year. It is calculated in offline.

The average value is DifferentinTernallinks for the year. It is calculated in offline.

The average value of QueryDowneronlyClickRate_Rreg is for a year. It is calculated in offline.

The average ISHUB value is for the year. It is calculated in offline.

The average ISTARGETBUSSINESSCARD for the year. It is calculated in offline.

The average value is BM25_0 on request per year. It is calculated in offline.

The average value of BOCM for the year. It is calculated in offline.

The average ISindexpage is for the year. It is calculated in offline.

The average value of queriesavgcm2 for the year. It is calculated in offline.

The average value of BrowserhostDownloadProbabolyity for the year. It is calculated in offline.

The average value of Regbrowseruserhub for the year. It is calculated in offline.

Auxtitlebm25 average value for the year. It is calculated in offline.

The average value of QuryurlCorrectedctrxFactor for the year. It is calculated in offline.

The average value is QueryTodocallsumfcountTextbM11Norm16384 for the year. It is calculated in offline.

The average value of the XFDTSHOWALLSUMWFSUMWBODYMINWIDESIZE for the year. It is calculated in offline.

Maintened by clicks ISMainPage value for the year. It is calculated in offline.

Main -heated clicks of the Yabarurlavgtime value for the year. It is calculated in offline.

Maintened by clicks DifferentinTernallinks for the year. It is calculated in offline.

Malpanized Dwelltime-AMI Value of Urldomainfraction for the year. It is calculated in offline.

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

Predicted by demand and country, using a DSSM model, the length of the click from this country.

The average for the year for the year predicted using the neural network.

ADDTIME ADDTIME is predicted using a neural network for a year.

The average Txthirelesy value predicted using a neural network for the year.

The average Textlike is predicted using a neural network for the year.

The average HasnoallwordStrsy is predicted using a neural network for a year.

The average ISFORUM is predicted using a neural network for the year.

The average Haspayments is predicted using a neural network for the year.

The average value of Yabarhostavgtime2 for the year for the year.

The average yabarurlvisitors is predicted using a neural network for the year.

The average value of QueryDowneronlyClickRate for the year for the year.

The average Dateraage value for the year for a year predicted using a neural network.

The average LonGestText is predicted using a neural network for the year.

The average DifferentinTernallinks for the year for the year.

The average value of QueryDowneronlyClickRate_Rreg is predicted using a neural network for a year.

The average ISTARGETBUSSINESSCARD is predicted using a neural network for the year.

The average BOCM value predicted using a neural network for the year.

The average ISindEXPAGE is predicted using a neural network for the year.

The average value of QueriesavGCM2 for the year for the year predicted using a neural network.

The average BrowserhostdowLoadProbabolyti is predicted using a neural network for the year.

The average value of Regbrowseruserhub for the year for a year predicted using a neural network.

The average AuxtitlebM25 average value for the year for the year.

The average value of QueryurlCorrectrxFactor for the year for the year.

The average value of QueryTodoCallsumfcountTextbM11Norm16384 for the year for the year.

The average value of the XFDTSHOWALSUMWFSUMWBODYMINWINDOWSIZE for the year for the year.

The value of the ISMAINPAGE with clicks predicted using the neural network with clicks on request for the year.

A mid Yabarurlavgtime value predicted using a neural network with clicks for a year.

DiffferentinTernallinks, which is predicted using a neural network, is a weighted net with clicks for a year.

The Malue Network DwellTime-AMI predicted using the neural network is the value of Urldomainfraction for the year.

The prediction of the probability that the request is localized in accordance with the regionality5 rule.

The factor is an analogue of LongQuery (the sum of the IDF words of the request), but with the 'correct' accounting of synonyms. Specifically, a minimum of IDF (i.e. the most frequent) of synonyms and words is selected.

Characterizes the request for the degree of change from removing a fixed word ('site' for Kirilitsa), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

Characterizes the request for the degree of change from the addition of a fixed word (number of some year), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

Characterizes a request for the degree of change from the addition of a fixed word ('online' for Kirilitsa), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

For each word offline, the average Hasnotr meaning is calculated for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average ISLJ value is calculated for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average BCLMLITE value is calculated for 3 months. Further, in all words of the request, a minimum of this value is taken.

For each word offline, the average DBM40 value is calculated for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

For each word offline, the average ISDESKTOPREQUEST value is calculated for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

For each word offline, the average value of RLQAVGHASNOLLWORDSTRSYN is calculated at the request for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average DSSMAGGRETEDANNREG value is calculated at the request for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average meaning of MetanumurlSperhostfixed is calculated in demands in 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average value of MaxsdisnavmxqueryMax is calculated at the request for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

DSSM prediction of google specificity for query

The average value of Randomlogqueryavgaddtime of the closest KNN queries.

The average value of RandomlogqueryavgtXthirelsy nearest KNN queries.

The average value of Randomlogqueryavgtextlike nearest KNN queries.

The average value of Randomlogqueryavgisforum of the closest KNN queries.

The average value of Randomlogqueryavghaspayments closest to KNN queries.

The average value of Randomlogqueryavgdiferentinternallinks of the nearest KNN queries.

The average value of RandomlogqueryavgistargetbussinessCard of the nearest KNN queries.

The average value is RandomlogqueryavgquerytododoCallsumfcountTextbM11NORM16384 of the nearest KNN queries.

The average value is Randomlogqueryavgxfdtshowallsumwfsumwbodyminwindowsize closest KNN queries.

request came from yandsearch (rearr.is_desktop == 1) (https://wiki.yandex-team.ru/users/lpshka/platformfeatures)

request came from touchsearch (rearr.is_mobile == 1) (https://wiki.yandex-team.ru/users/lpshka/platformfeatures)

request came from padsearch (rearr.is_tablet == 1) (https://wiki.yandex-team.ru/users/lpshka/platformfeatures)

Median dwelltai request in history. Dwelltaym is cut to 6000. The request is normalized by doppelgangers

The number of shows of the request with more than one click in history. The request is normalized by doppelgangers

The share of shows with more than one click from all shows in history. The request is normalized by doppelgangers

The average value for the query factor according to Querytotext linguosting data is calculated in the LingBoostqueryFeatures Hotemaway Rules

The average value for the query factor according to Querytotextbyxfdtshowknn linguosting data is calculated in the LingBoostqueryFeatures Hotemail Rules

SUM / (SUM + 10) for the requested factor according to the XFDTSHOW linguosting data, is calculated in the LingBoostqueryFeatures Hotemaura

Quantile 0.1 For the request factor according to the XFDTSHOW linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

Quantile 0.1 for the request factor according to the XFDTSHOWKNN linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

Quantile 0.9 for the quantity factor according to the XFDTSHOWKNN linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

SUM / (SUM + 10) for the requested factor according to QFUF linguosting data, is calculated in the LingBoostqueryFeatures Hotemaura

The average value for the quoted factor according to QFUF lingvobusting data is calculated in the LingBoostqueryFeatures Hotemail Rules

Relev_locale == Us

25% time quantile from the previous request to the current one. The request is normalized by doppelgangers

The request was recognized as having intent to copyright objects protected by anti-Pirate memorandum.

Transferred Ethos predicted classifier for marking on the relevance of video.

Reduced ETHOS predictive classifier, trained in a synthetic sample 'Request characteristic of the pirate site' VS ', the request is characteristic of the site far from this'

DSSM regression for the separation of memorandum and no requests

Transferred Ethos predicted classifier trained to distinguish a memorandum queries from a random

DSSM regression of embeds to separate Pirato specific and no requests

Manual formula of the classifier of the pa*of the request of the request

Manual formula of the classifier M*Morandum of request

Time has passed after a sporting event in the request

Time remaining before the sporting event from the request

Time has passed after a sports event between several teams

Time remaining before a sports event between several teams

The difference between the current date, and the date in the request (1 - request about today's date, 0 - about the date 10 days ago and younger)

The difference between the current date, and the date in the request (1 - a request for today's date, 0 - about the date after 10 days and older)

There is one date in the request

Weekday query probability

The average ISBLOG value for the year. It is calculated in offline.

The request has the name of the series and a serial marker (series, series, all seasons).

Is serial query with season and episode specified

Function from number of episodes between requested and last released episodes

The query getting into the TOP 11 clusters on Kernel metric based on DSSM proximity.

The request mentions the product category. Not used (depreded)

The request mentions a vendor. Not used (depreded)

The presence of an album in a lemmetized request

DSSM distance from request to word match

DSSM distance from request to word hockey

DSSM distance from request to word broadcast

DSSM distance from request to word championship

DSSM distance from request to word series

DSSM distance from request to word today

DSSM distance from request to word last

DSSM Distance from request to the word accident

DSSM distance from request to word news

DSSM distance from request to word death

DSSM distance from request to word football

DSSM distance from request to word day

DSSM distance from request to word postcard

DSSM distance from request to word khl

DSSM distance from request to word UFC

DSSM distance from request to word results

DSSM distance from request to word schedule

DSSM distance from request to word fresh

DSSM distance from request to word biography

DSSM distance from request to word yesterday

DSSM distance from request to word forecast

Requests got into the 34th cluster based on CTR-DSSM.

DSSM distance from request to word coronavirus

DSSM distance from request to word buy

DSSM distance from request to word price

DSSM distance from request to word product

DSSM distance from the request to the word description

DSSM distance from request to word delivery

DSSM distance from request to word is paid

DSSM distance from request to word analogue

DSSM distance from request to word sale

DSSM distance from the request to the word installment

DSSM distance from request to word rent

DSSM distance from request to word catalog

DSSM distance from request to word store

Dssm distance from request to word watch

DSSM distance from request to word movie

DSSM distance from request to word season

DSSM distance from request to word free

The likelihood that the selected hosts from the Construction category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosts from the Wear category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosth hosts category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Food category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Furniture category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosts from the Travel category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hostplace hostplace category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosts from the Book category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Electron category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Board category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Service category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosto hosto are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Cinema category are popular for this request in accordance with the corresponding DSSM model

Matter coating of the request of the dictionary with the names of the series

Matter coating of a request from a dictionary with films names

DSSM distance from request to word treatment

DSSM distance from request to word pharmacy

DSSM distance from request to the word cold

DSSM distance from request to word diet

DSSM distance from request to word ointment

DSSM distance from request to word means

DSSM distance from request to word recipe

DSSM distance from request to word belly

DSSM Prediction of money spent on a request

Prediction of the classifier that the request is a medical

Prediction of the classifier that the request is a legal

Prediction of the classifier that the request is financial

The prediction of the classifier is that the request of SOS topics

The prediction of the classifier that the request is navigation

The prediction of the classifier is that the request is white (that is, it is not worth showing tin on it)

DSSM prediction of the complexity of the request

Are there any quotes in the request

Is there a minus

Is there a plus word in the request

Is the request completely with a quote

Indicator - is the request of the SEO -request

Indicator - is the request of the SEO -request from the list No. 1

Indicator - is the request of the SEO request from the list No. 2

Request contains full name from FIO Rules

Request contains a word song or track

Musical request from Alice

The prediction of the classifier is that upon request you need to show URL from a mobile market

Prediction of the classifier to especially commercial requests

Prediction of the CP Classifier Requests

Requested classifier of programmatic queries V2

Classifier of a request for equipment repair services: office, household, computers, phones

Phonenumberrule in the Hippo Parisilo phone number in the request.

Request classifier to Pro-Hoogle Expertise.

The request is Latinic.

Whether the request is porn.

Constant 1

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Prediction of the CP Classifier Requests

Prediction of the requesting classifier of violence

Prediction of the requesting classifier of weapons or drugs

The prediction of the classifier is that a culinary request request

The prediction of the classifier is that the request is strong international and culinary

The presence of an official word in a lemmetized request

The presence of Wikipedia word in a lemmetized request

The presence in the lemmetized request of the word not and similar in meaning

The presence in a lemmetized request to buy words, price and similar in meaning

Musical request for the script of Arab Alice

1/(1 + number of words of the request), attributes are not taken into account

Is an attribute request (looks at rearr = attrs)

Classifier of a request for pornographic for Indonesian

Classifier of requests for the requirement only English -speaking pages

Classifier of requests for the requirement of mixed English -speaking and Russian -speaking pages

The classifier of requests for the requirement of either only English -speaking pages, or English -speaking and Russian -speaking pages together

The maximum ratio of the amount of the request for the request and its bigrams per day, to the same amount in 30 days

The maximum ratio of the amount of the request for the request and its bigrams per day, to the same amount in 30 days (mobile version)

The ratio of shows in the stream per day and for 30 days, provided that it was more than 5 shows in 30 days

The ratio of the shows in the stream per day and for 30 days, provided that it was more than 5 shows in 30 days (mobile version)

The maximum ratio of the amount of the request for the request and its bigram per day, to the same amount in 30 days. With the value of the detector freshness below the threshold, the factor value is canonized

The maximum ratio of the amount of the request for the request and its bigrams per day, to the same amount in 30 days (mobile version). With the value of the detector freshness below the threshold, the factor value is canonized

The ratio of shows in the stream per day and for 30 days, provided that it was more than 5 shows in 30 days. With the value of the detector freshness below the threshold, the factor value is canonized

The ratio of shows in the stream per day and for 30 days, provided that it was more than 5 shows in 30 days (mobile version). With the value of the detector freshness below the threshold, the factor value is canonized

Wizdeteration Detector on top of DSSM Multiclick

WizDeteration service request detector on top of DSSM Multiclick

Wizdeteration Detector on top of DSSM ReformulationslonGestClicklogdt

Wizdeteration Detector on top of DSSM Multiclick on the cuts of the Docdoc.ru service shows

Telemedical Request detector from WizDeteration on top of DSSM Multiclick

Wizdeteration Detector on top of DSSM Multiclick

WizDeteration service request detector on top of DSSM Multiclick

Wizdeteration Detector on top of DSSM ReformulationslonGestClicklogdt

Wizdeteration Detector on top of DSSM Multiclick on the cuts of the Docdoc.ru service shows

Telemedical Request detector from WizDeteration on top of DSSM Multiclick

P+K+user's DC in the Request section. Suspended

P+K+user DC in the network in the request. Suspended

The presence of a house number in a request

The presence of the street in the request

The presence of a district in a request

The presence of a settlement in a request

The presence of a quarter in a request

The presence of a region in a request

The presence of a country in a request

The presence of parks in the request

The presence of water bodies in the request

Whether the request is disassembled as a rubric in at least one of the verticals

Result of rubric query classifier that is based on geosearch dssm

Result of query classifier that is based on geosearch dssm

Result of query classifier that is based on geosearch dssm

Result of query classifier that is based on geosearch dssm

Result of query classifier that is based on geosearch dssm

The search window is completely set

The search window was obtained from a given point

The search window was received from a given region

Request with Byak

Request from the plug

Request from mobile cards

Request from the navigator

A request from a sickle

There is some geography in the request

The request is exclusively geography

The number of words in the original request

The number of words when deleting geography

Full IDF in the original request

Full IDF when deleting geography

The number of words in the original request (excluding stop words)

The number of words when deleting geography (excluding stop words)

Full IDF in the original request (excluding stop words)

Full IDF when deleting geography (excluding stop-words)

The original request is completely undergoing rubric analysis

The request is completely undergoing rubric analysis after removing geography from the request

The original request is partially coated by the name Chain

The request is partially coated by the name of Chain after removing geography from the request

The original request consists entirely of the name of Chain

The request consists entirely of the name of Chain after removing geography from the request

The number of numbers in the original request

Number of numbers when deleting geography

User request window diameter (in meters)

Diameter of the highlighted geography window (in meters)

Result of rubric query classifier that is based on geosearch web dssm

The distance from the user to its nearest regular coordinate, normalized by 500 km. Equal 2. if regular coordinates are unknown

P+K+user's DC in the Request section. Suspended

P+K+user DC in the network in the request. Suspended