princeton-nlp/datasets-for-simcse · Datasets at Hugging Face

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Lappanella guineensis
Lapanella guineensis is a species of marine ray-finned fish from the family Labridae, the wrasses.
It is found in the eastern Atlantic Ocean, at depths of no less than in rocky areas, off the coasts of Sierra Leone and Guinea.
Jean-Baptiste Barrez
Jean-Baptiste Barrez (28 November 1792 – 27 November 1868) was a French dancer and ballet master.
He was the son of surgeon Jean-Baptiste Barrez and Julie Jolivet.
Barrez studied under Jean-Francois Coulon and was the principal dancer at the Grand Théâtre de Bordeaux from 1817 to 1821.
He married Jeanne-Marie Blache, daughter of choreographer Jean-Baptiste Blache, on 28 July 1819.
The young couple had a son, Jean-Baptiste Hippolyte Barrez, (born 22 April 1820) who also become a dancer and dance teacher to Spanish dancer Lola Montez.
Jean-Baptiste Barrez began performing at the Paris Opera in 1821 and remained there until 1843.
He originated roles in several ballets of Jean Coralli and Joseph Mazilier, including "Le Diable boiteux" (1836), "La Tarentule" (1839), "Le Diable amoureux" (1840) and "La Péri" (1843).
He began teaching ballet at the opera in 1832; Danish ballerina Lucile Grahn was one of his students.
In the spring of 1844, he was called to Madrid, where he worked as a ballet master at the Teatro del Circo and shared the stage with Marie Guy-Stéphan, Clara Galby, Clotilde Laborderie, Ernest Gontié and Marius Petipa.
In 1847, he was hired as a ballet master at the Théâtre de la Monnaie in Brussels, a position he only occupied for one season.
He then settled in London the following year.
Acaena antarctica
Acaena antarctica is a small herbaceous plant in the Rosaceae family native to Argentina, Chile and the Falkland Islands.
"Acaena antarctica" was first formally described in 1846 by Joseph Dalton Hooker.
Kew holds specimens collected by Hooker from Hermite Island, Cape Horn on the Ross expedition.
The genus name "Acaena" is derived from the Ancient Greek word "akaina" meaning "thorn" or "spine", referring to the spiny calyx of many species of "Acaena".
The specific epithet, "antarctica", derives from the Greek ("anti", "opposite" and "arktos", "bear") and designates the place opposite the constellations of the Great and the Little Bear, thus describing the species as coming from south of the South Pole circle.
William Percillier
William Percillier (Born 30 January 1999) is a Canadian rugby union player.
His usual position is as a scrum-half, and he currently plays for Stade Français in the Top 14.
1992 Perth and Kinross District Council election
The 1992 Perth and Kinross District Council election took place on the 7 May 1992 to elect members of Perth and Kinross District Council, as part of that years Scottish local elections.
Dependency network (graphical model)
Dependency networks (DNs) are graphical models, similar to Markov networks, wherein each vertex (node) corresponds to a random variable and each edge captures dependencies among variables.
Unlike Bayesian networks, DNs may contain cycles.
Each node is associated to a conditional probability table, which determines the realization of the random variable given its parents.
In a Bayesian network, the Markov blanket of a node is the set of parents and children of that node, together with the children's parents.
The values of the parents and children of a node evidently give information about that node.
However, its children's parents also have to be included in the Markov blanket, because they can be used to explain away the node in question.
In a Markov random field, the Markov blanket for a node is simply its adjacent (or neighboring) nodes.
In a dependency network, the Markov blanket for a node is simply the set of its parents.
Dependency networks have advantages and disadvantages with respect to Bayesian networks.
In particular, they are easier to parameterize from data, as there are efficient algorithms for learning both the structure and probabilities of a dependency network from data.
Such algorithms are not available for Bayesian networks, for which the problem of determining the optimal structure is NP-hard.
Nonetheless, a dependency network may be more difficult to construct using a knowledge-based approach driven by expert-knowledge.
Consistent dependency networks and Markov networks have the same representational power.
Nonetheless, it is possible to construct non-consistent dependency networks, i.e., dependency networks for which there is no compatible valid joint probability distribution.
Markov networks, in contrast, are always consistent.
A consistent dependency network for a set of random variables formula_1 with joint distribution formula_2 is a pair formula_3 where formula_4 is a cyclic directed graph, where each of its nodes corresponds to a variable in formula_5, and formula_6 is a set of conditional probability distributions.
The parents of node formula_7, denoted formula_8, correspond to those variables formula_9 that satisfy the following independence relationships
The dependency network is consistent in the sense that each local distribution can be obtained from the joint distribution formula_2.
Dependency networks learned using large data sets with large sample sizes will almost always be consistent.
A non-consistent network is a network for which there is no joint probability distribution compatible with the pair formula_3.
In that case, there is no joint probability distribution that satisfies the independence relationships subsumed by that pair.
Two important tasks in a dependency network are to learn its structure and probabilities from data.
Essentially, the learning algorithm consists of independently performing a probabilistic regression or classification for each variable in the domain.
It comes from observation that the local distribution for variable formula_7 in a dependency network is the conditional distribution formula_14, which can be estimated by any number of classification or regression techniques, such as methods using a probabilistic decision tree, a neural network or a probabilistic support-vector machine.
Hence, for each variable formula_7 in domain formula_16, we independently estimate its local distribution from data using a classification algorithm, even though it is a distinct method for each variable.
Here, we will briefly show how probabilistic decision trees are used to estimate the local distributions.
For each variable formula_7 in formula_5, a probabilistic decision tree is learned where formula_7 is the target variable and formula_20 are the input variables.
To learn a decision tree structure for formula_7, the search algorithm begins with a singleton root node without children.
Then, each leaf node in the tree is replaced with a binary split on some variable formula_22 in formula_20, until no more replacements increase the score of the tree.
A probabilistic inference is the task in which we wish to answer probabilistic queries of the form formula_24, given a graphical model for formula_5, where formula_26 (the 'target' variables) formula_27 (the 'input' variables) are disjoint subsets of formula_5.
One of the alternatives for perform probabilistic inferences is using Gibbs sampling.
A naive approach for this uses an ordered Gibbs sampler, whose an important difficult is that if either formula_24 or formula_30 is small, then many iterations are required for an accurate probability estimate.
Another approach for estimating formula_24 when formula_30 is to use modified ordered Gibbs sampler, where it fix formula_33 during Gibbs sampling.
It may also happen that formula_34 is rare, e.g.
formula_26 contains many variables.
So, the law of total probability along with the independencies encoded in a dependency network can be used to decompose the inference task into a set of inference tasks on single variables.
This approach comes with the advantage that some terms may be obtained by directly lookup, thereby avoiding some Gibbs sampling.
You can see below an algorithm that can be used for obtain formula_36 for a particular instance of formula_37 and formula_38, where formula_26 and formula_27 are disjoint subsets.
In addition to the applications to probabilistic inference, the following applications are in the category of Collaborative Filtering (CF), which is the task of predicting preferences.
Dependency networks are a natural model class on which to base CF predictions, once an algorithm for this task only needs estimation of formula_58 to produce recommendations.
In particular, these estimates may be obtained by a direct lookup in a dependency network.
Another class of useful applications for dependency networks is related to data visualization, that is, visualization of predictive relationships.
Olga Kazakova
Olga Kazakova (; born May 30, 1968 in the city of Usolye-Sibirskoye, in the Soviet Union) is a Russian politician, was the Minister of Culture of the Stavropol Krai, since 2012, deputy of the State Duma, First Deputy Chairman of the Committee on Culture.
Born in the family of an officer in the Soviet army, she lived with her family in the city of Lugansk, Ukrainian SSR.
In 1990 she graduated from University of Luhansk with a degree in Russian language and literature.
She is a Komsomol member.
From 1984 to 1991 at the Komsomol work, head of the children's dance club, kindergarten teacher.
From 1992 to 1996, a primary school teacher in Vorkuta and Nevinnomyssk.
From 2000 to 2003, an assistant to a deputy of the city parliament of the city of Stavropol, executive director of the Slavyansk Sports Center.
From 2003 to 2009, he was the head of the youth affairs department of the Administration of the city of Stavropol.
From 2009 to 2011, chairman of the Committee on Youth Affairs of the Government of the Stavropol Krai.
From 2011 to 2012, the Minister of Culture of the Government of the Stavropol Krai.
May 22, 2012, deputy of the State Duma of the sixth convocation of the United Russia fraction on the All-Russia People's Front quota from the Stavropol Territory, member of the State Duma Committee on Family, Women and Children.
In 2016, according to the primaries of United Russia, it took 1st place (78% of the vote) in the single-member constituency.
Re-elected deputy of the State Duma of the seventh convocation, elected first deputy chairman of the Committee on Culture.
Lab 110
Lab 110 is one of North Korea's government hacking organizations, and it is an operation of the Reconnaissance General Bureau.
List of lakes in the United States Virgin Islands
The United States Virgin Islands have no natural lake-like bodies of water.
The islands have very few freshwater resources.
The U.S. virgin Islands is made up of 4 large islands and about 50 smaller islands.
The large islands are: St. Croix, St. Thomas, St. John and Water Island.
The Virgin Islands rely on ocean water desalination to supply fresh water to residents and toursts.
In addition all hotels collect rain water on their rooftops.
*There are no large rivers or reservoirs in the Virgin Islands.
Let's Dance (German season 13)
The thirteenth season of "Let's Dance" will start on February 21, 2020.
Daniel Hartwich and Victoria Swarovski returned as hosts.
Joachim Llambi, Motsi Mabuse, Jorge Gonzalez also returned as the judges.
On January 15, 2020, RTL announced the 14 "Let's Dance-celebrities" to be participating in the series.
This table only counts for dances scored on a traditional 30-points scale.
The best and worst performances in each dance according to the judges' marks are as follows:

Lappanella guineensis

Lapanella guineensis is a species of marine ray-finned fish from the family Labridae, the wrasses.

It is found in the eastern Atlantic Ocean, at depths of no less than in rocky areas, off the coasts of Sierra Leone and Guinea.

Jean-Baptiste Barrez

Jean-Baptiste Barrez (28 November 1792 – 27 November 1868) was a French dancer and ballet master.

He was the son of surgeon Jean-Baptiste Barrez and Julie Jolivet.

Barrez studied under Jean-Francois Coulon and was the principal dancer at the Grand Théâtre de Bordeaux from 1817 to 1821.

He married Jeanne-Marie Blache, daughter of choreographer Jean-Baptiste Blache, on 28 July 1819.

The young couple had a son, Jean-Baptiste Hippolyte Barrez, (born 22 April 1820) who also become a dancer and dance teacher to Spanish dancer Lola Montez.

Jean-Baptiste Barrez began performing at the Paris Opera in 1821 and remained there until 1843.

He originated roles in several ballets of Jean Coralli and Joseph Mazilier, including "Le Diable boiteux" (1836), "La Tarentule" (1839), "Le Diable amoureux" (1840) and "La Péri" (1843).

He began teaching ballet at the opera in 1832; Danish ballerina Lucile Grahn was one of his students.

In the spring of 1844, he was called to Madrid, where he worked as a ballet master at the Teatro del Circo and shared the stage with Marie Guy-Stéphan, Clara Galby, Clotilde Laborderie, Ernest Gontié and Marius Petipa.

In 1847, he was hired as a ballet master at the Théâtre de la Monnaie in Brussels, a position he only occupied for one season.

He then settled in London the following year.

Acaena antarctica

Acaena antarctica is a small herbaceous plant in the Rosaceae family native to Argentina, Chile and the Falkland Islands.

"Acaena antarctica" was first formally described in 1846 by Joseph Dalton Hooker.

Kew holds specimens collected by Hooker from Hermite Island, Cape Horn on the Ross expedition.

The genus name "Acaena" is derived from the Ancient Greek word "akaina" meaning "thorn" or "spine", referring to the spiny calyx of many species of "Acaena".

The specific epithet, "antarctica", derives from the Greek ("anti", "opposite" and "arktos", "bear") and designates the place opposite the constellations of the Great and the Little Bear, thus describing the species as coming from south of the South Pole circle.

William Percillier

William Percillier (Born 30 January 1999) is a Canadian rugby union player.

His usual position is as a scrum-half, and he currently plays for Stade Français in the Top 14.

1992 Perth and Kinross District Council election

The 1992 Perth and Kinross District Council election took place on the 7 May 1992 to elect members of Perth and Kinross District Council, as part of that years Scottish local elections.

Dependency network (graphical model)

Dependency networks (DNs) are graphical models, similar to Markov networks, wherein each vertex (node) corresponds to a random variable and each edge captures dependencies among variables.

Unlike Bayesian networks, DNs may contain cycles.

Each node is associated to a conditional probability table, which determines the realization of the random variable given its parents.

In a Bayesian network, the Markov blanket of a node is the set of parents and children of that node, together with the children's parents.

The values of the parents and children of a node evidently give information about that node.

However, its children's parents also have to be included in the Markov blanket, because they can be used to explain away the node in question.

In a Markov random field, the Markov blanket for a node is simply its adjacent (or neighboring) nodes.

In a dependency network, the Markov blanket for a node is simply the set of its parents.

Dependency networks have advantages and disadvantages with respect to Bayesian networks.

In particular, they are easier to parameterize from data, as there are efficient algorithms for learning both the structure and probabilities of a dependency network from data.

Such algorithms are not available for Bayesian networks, for which the problem of determining the optimal structure is NP-hard.

Nonetheless, a dependency network may be more difficult to construct using a knowledge-based approach driven by expert-knowledge.

Consistent dependency networks and Markov networks have the same representational power.

Nonetheless, it is possible to construct non-consistent dependency networks, i.e., dependency networks for which there is no compatible valid joint probability distribution.

Markov networks, in contrast, are always consistent.

A consistent dependency network for a set of random variables formula_1 with joint distribution formula_2 is a pair formula_3 where formula_4 is a cyclic directed graph, where each of its nodes corresponds to a variable in formula_5, and formula_6 is a set of conditional probability distributions.

The parents of node formula_7, denoted formula_8, correspond to those variables formula_9 that satisfy the following independence relationships

The dependency network is consistent in the sense that each local distribution can be obtained from the joint distribution formula_2.

Dependency networks learned using large data sets with large sample sizes will almost always be consistent.

A non-consistent network is a network for which there is no joint probability distribution compatible with the pair formula_3.

In that case, there is no joint probability distribution that satisfies the independence relationships subsumed by that pair.

Two important tasks in a dependency network are to learn its structure and probabilities from data.

Essentially, the learning algorithm consists of independently performing a probabilistic regression or classification for each variable in the domain.

It comes from observation that the local distribution for variable formula_7 in a dependency network is the conditional distribution formula_14, which can be estimated by any number of classification or regression techniques, such as methods using a probabilistic decision tree, a neural network or a probabilistic support-vector machine.

Hence, for each variable formula_7 in domain formula_16, we independently estimate its local distribution from data using a classification algorithm, even though it is a distinct method for each variable.

Here, we will briefly show how probabilistic decision trees are used to estimate the local distributions.

For each variable formula_7 in formula_5, a probabilistic decision tree is learned where formula_7 is the target variable and formula_20 are the input variables.

To learn a decision tree structure for formula_7, the search algorithm begins with a singleton root node without children.

Then, each leaf node in the tree is replaced with a binary split on some variable formula_22 in formula_20, until no more replacements increase the score of the tree.

A probabilistic inference is the task in which we wish to answer probabilistic queries of the form formula_24, given a graphical model for formula_5, where formula_26 (the 'target' variables) formula_27 (the 'input' variables) are disjoint subsets of formula_5.

One of the alternatives for perform probabilistic inferences is using Gibbs sampling.

A naive approach for this uses an ordered Gibbs sampler, whose an important difficult is that if either formula_24 or formula_30 is small, then many iterations are required for an accurate probability estimate.

Another approach for estimating formula_24 when formula_30 is to use modified ordered Gibbs sampler, where it fix formula_33 during Gibbs sampling.

It may also happen that formula_34 is rare, e.g.

formula_26 contains many variables.

So, the law of total probability along with the independencies encoded in a dependency network can be used to decompose the inference task into a set of inference tasks on single variables.

This approach comes with the advantage that some terms may be obtained by directly lookup, thereby avoiding some Gibbs sampling.

You can see below an algorithm that can be used for obtain formula_36 for a particular instance of formula_37 and formula_38, where formula_26 and formula_27 are disjoint subsets.

In addition to the applications to probabilistic inference, the following applications are in the category of Collaborative Filtering (CF), which is the task of predicting preferences.

Dependency networks are a natural model class on which to base CF predictions, once an algorithm for this task only needs estimation of formula_58 to produce recommendations.

In particular, these estimates may be obtained by a direct lookup in a dependency network.

Another class of useful applications for dependency networks is related to data visualization, that is, visualization of predictive relationships.

Olga Kazakova

Olga Kazakova (; born May 30, 1968 in the city of Usolye-Sibirskoye, in the Soviet Union) is a Russian politician, was the Minister of Culture of the Stavropol Krai, since 2012, deputy of the State Duma, First Deputy Chairman of the Committee on Culture.

Born in the family of an officer in the Soviet army, she lived with her family in the city of Lugansk, Ukrainian SSR.

In 1990 she graduated from University of Luhansk with a degree in Russian language and literature.

She is a Komsomol member.

From 1984 to 1991 at the Komsomol work, head of the children's dance club, kindergarten teacher.

From 1992 to 1996, a primary school teacher in Vorkuta and Nevinnomyssk.

From 2000 to 2003, an assistant to a deputy of the city parliament of the city of Stavropol, executive director of the Slavyansk Sports Center.

From 2003 to 2009, he was the head of the youth affairs department of the Administration of the city of Stavropol.

From 2009 to 2011, chairman of the Committee on Youth Affairs of the Government of the Stavropol Krai.

From 2011 to 2012, the Minister of Culture of the Government of the Stavropol Krai.

May 22, 2012, deputy of the State Duma of the sixth convocation of the United Russia fraction on the All-Russia People's Front quota from the Stavropol Territory, member of the State Duma Committee on Family, Women and Children.

In 2016, according to the primaries of United Russia, it took 1st place (78% of the vote) in the single-member constituency.

Re-elected deputy of the State Duma of the seventh convocation, elected first deputy chairman of the Committee on Culture.

Lab 110

Lab 110 is one of North Korea's government hacking organizations, and it is an operation of the Reconnaissance General Bureau.

List of lakes in the United States Virgin Islands

The United States Virgin Islands have no natural lake-like bodies of water.

The islands have very few freshwater resources.

The U.S. virgin Islands is made up of 4 large islands and about 50 smaller islands.

The large islands are: St. Croix, St. Thomas, St. John and Water Island.

The Virgin Islands rely on ocean water desalination to supply fresh water to residents and toursts.

In addition all hotels collect rain water on their rooftops.

*There are no large rivers or reservoirs in the Virgin Islands.

Let's Dance (German season 13)

The thirteenth season of "Let's Dance" will start on February 21, 2020.

Daniel Hartwich and Victoria Swarovski returned as hosts.

Joachim Llambi, Motsi Mabuse, Jorge Gonzalez also returned as the judges.

On January 15, 2020, RTL announced the 14 "Let's Dance-celebrities" to be participating in the series.

This table only counts for dances scored on a traditional 30-points scale.

The best and worst performances in each dance according to the judges' marks are as follows: