Tag: TG_SRC_FACTOID

ski uniramic text classifier trained for factskeeping queries from Toloka

The similarity of Snippet to a request, calculated on the basis of an unelematic Word2Vec Runet

The value of logistics regression predicting the question of the request for the average vector of hosts to issue

The value of logistics regression predicting that the answer is correct, according to the concatenation of the Khost vector and the medium vector of hosts to issue

The value of logistics regression predicting that the answer is correct, according to the concatenation of the Khost vector and the medium vector of hosts to issue

The value of logistics regression predicting that the answer is correct, according to the concatenation of the Khost vector and the medium vector of hosts to issue

checks the operation of the impyscar rule of the encyclopedicity of the request

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

The meaning of the regression trained in bigrams in which the first word is taken from the request, the second is from the answer

The meaning of the regression trained in bigrams in which the first word is taken from the request, the second is from the answer

The meaning of the regression trained at the frequencies of words in a snippet predicting that Snippet contains the answer

a sign that the request came from the assistant

Cosinus between emblems of the request and availability of wiz-images on a Serpa

Cosinus between emblems of the request and availability of Wiz-Video on a Serpa

Cosinus between embezzles of request and availability of Union-Facts on a Serpa

The cosine between the embedding of the request and the availability of Wiz-MusicPlayer on the Serpa

Cosinus between embezzles of request and availability of Wiz-Maps on a sickle

Cosinus between the embezzle of the request and the presence of a positive Query MX

MAXM DSSM-SCOROV by models bert_factsnip_answer

Average DSSM-School according to the Bert_FACTSNIP_ANSWER model

The minimum word frequency from the request, by the frequencies of words in factary requests

The maximum frequency of a word from the request, by the frequencies of words in factual requests

Squeezed inverted median frequencies from a request, according to the frequencies of words in factary requests

The minimum word frequency from the request, by the frequencies of words in factual requests, relative to the frequency of the word in general texts

The average frequency of words from the request, by the frequencies of words in factary requests, relative to the frequencies of words in general texts

Squeezed inverted median frequencies from a request, according to the frequencies of words in factary requests, relative to the frequencies of words in general texts

The maximum frequency of the bigma from the request, according to the frequencies of birams in factary queries

Request length after Normalizetext in symbols

Request length after Normalizetext in words

The number of interrogative words in the request

Hesh of the first pronoun dialect in the request

Hesh of the first preposition in the request

The number of words, the length of which is more than 3, in a normalized request

(length of_nabrication_biez_normalization - length_vseh_normalized_glines) / length_normalized_Prosa (in lengths of words, arithmic progression coefficients)

the number of answers in which there are numbers from this

ski uniramic text classifier trained for factskeeping queries from Toloka

The average similarity of the response to others, counted on the basis of the non -metered Word2VEC Runet

The similarity of Snippet to a request, calculated on the basis of an unelematic Word2Vec Runet

The value of logistics regression predicting the question of the request for the average vector of hosts to issue

The value of logistics regression predicting that the answer is correct, according to the concatenation of the Khost vector and the medium vector of hosts to issue

checks the operation of the impyscar rule of the encyclopedicity of the request

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

the proximity of the total host vector to one of the clusters built according to the answers from Toloka

The meaning of the regression trained in bigrams in which the first word is taken from the request, the second is from the answer

The meaning of the regression trained at the frequencies of words in a snippet predicting that Snippet contains the answer

The ratio of the number of times when the host was shown on a Serpa with a fact to a total number of times when the host was present on the Serpa.

The number of words in the factual fracture of Snippet (CI, the punctuation and words are also not taken into account of 3 characters not containing numbers)

Average similarity to SNiPPTs to switch words

The best coincidence according to the words in Actsnipp (CI, the punctuation and words are also not taken into account of 3 characters not containing numbers)

The second coincidence in the words in Actsnippe (CI, the punctuation and words are also not taken into account of 3 characters not containing numbers)

The third coincidence according to the words in Actsnipp (CI, the punctuation and words are also not taken into account of 3 characters not containing numbers)

The fourth coincidence according to Actsnippe (CI, the punctuation and words are also not taken into account of 3 characters not containing numbers)

the number of coincidences according to the acts differing from the best not more than 90%

the number of coincidences according to the acts differing from the best by 80-90%

the number of coincidences according to the acts in the act of the best by 70-80%

the number of coincidences according to the acts in the act of the best by 60-70%

the number of coincidences according to the acts in the act of the best by 50-60%

The prediction of the NeoCortEx model trained in TextTotext on factory logs

The prediction of the NeoCortex model trained in TextTotext on answers

The prediction of the large NEOCORTEX model trained in TextTotext on factory logs

a sign that the request came from the assistant

The cosinus between the embezzle of the request and Snippte built by the Bert_estimate_answer.dssm DSSM-Nevi-network, trained on the answers of the search bart, who is learned from the Toloca

Cosinus between emblems of the request and availability of wiz-images on a Serpa

Cosinus between emblems of the request and availability of Wiz-Video on a Serpa

Cosinus between embezzles of request and availability of Union-Facts on a Serpa

The cosine between the embedding of the request and the availability of Wiz-MusicPlayer on the Serpa

Cosinus between embezzles of request and availability of Wiz-Maps on a sickle

Cosinus between the embezzle of the request and the presence of a positive Query MX

Number of proposals in Snippet

The number of poorly formed sentences in Snippet (does not begin on the capital letter or does not end at the point or contains less than one sentence)

The number of words starting with the title letter / number of all words in snippet

Bert value on an assessment of an act, zero head of multitargete

The value of BERT on an assessment of the act, the first head of the multitargete

Info Bert value on an assessment of an act