DataHammer/scimrc huggingface dataset:
Question: What was the Byzantine Empire?
Evidence: The Byzantine Empire, also referred to as the Eastern Roman Empire or Byzantium, was the continuation of the Roman Empire primarily in its eastern provinces during Late Antiquity and the Middle Ages, when its capital city was Constantinople. It survived the fragmentation and fall of the Western Roman Empire in the 5th century AD and continued to exist for an additional thousand years until the fall of Constantinople to the Ottoman Empire in 1453. During most of its existence, the empire remained the most powerful economic, cultural, and military force in Europe. The terms "Byzantine Empire" and "Eastern Roman Empire" were coined after the end of the realm; its citizens continued to refer to their empire as the Roman Empire and to themselves as Romans[note 2]—a term which Greeks continued to use for themselves into Ottoman times. Although the Roman state continued and its traditions were maintained, modern historians prefer to differentiate the Byzantine Empire from Ancient Rome as it was centered on Constantinople instead of Rome, oriented towards Greek rather than Latin culture, and was characterized by Eastern Orthodox Christianity.
Answer: After the fall of the Roman Empire in the 5th century AD, it split into many nations. The eastern part of the Roman Empire became the Byzantine Empire with Constantinople as its capital city rather than Rome. The Byzantine citizens continued to refer to themselves as Romans, and believed themselves as the successor to the former empire, however their culture was more Greek than Latin. The Byzantine Empire continued to be the most powerful nation in Europe until the fall of Constantinople to the Ottoman Empire in 1453.
Question: Where was William José born?
Evidence: Born in Porto Calvo, Alagoas, Willian José started his career at local CRB's youth setup, before joining Grêmio Barueri in 2008, aged 17. On 1 August 2009 he made his first team – and Série A – debut, coming on as a late substitute in a 2–1 loss at Botafogo.

Willian José scored his first goal as a professional on 17 January of the following year, netting his side's only in a 1–1 draw at Sertãozinho for the Campeonato Paulista championship. He scored six further times during the campaign, which ended in relegation.
Answer: William José was born in Porto Calvo, in the State of Alagoas in Brazil. He started his career at the local Gremio Barueri soccer team.
Question: What is Doña Barbara?
Evidence: Doña Bárbara is a novel written by the Venezuelan Rómulo Gallegos and published by Editorial Araluce, on February 15 , 1929 . 1 It has been republished more than forty times and translated into other languages. It consists of three parts and is set in Apure plains in Venezuela , on the Arauca river property .
Answer: Doña Bárbara is a novel written by the Venezuelan Rómulo Gallegos and published by Editorial Araluce, on February 15 , 1929 . 1 It has been republished more than forty times and translated into other languages. It consists of three parts and is set in Apure plains in Venezuela , on the Arauca river property .

Doña Bárbara is the most popular Venezuelan novel: since its appearance in 1929, it has been read avidly perhaps because between the lines Gallegos expressed his rebellion against the dictatorial regime of Juan Vicente Gómez and the backwardness that the country was experiencing. The novel examines the sociological topic, rooted in positivism, civilization versus barbarism in rural Venezuelan life. Among other merits, the writer's mastery in terms of character creation stands out, as well as the description of the llanero landscape.
Question: what datasets were used?
Evidence: The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; we further have 761 testing examples. However, there is 98% correlation between fake news and click-baits, i.e., a model trained on fake news would do well on click-baits and vice versa. Thus, below we focus on fake news detection only.
The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; we further have 761 testing examples.
Answer: The answers are shown as follows:
*  training dataset contains 2,815 examples
* 761 testing examples
Question: What is the largest estuary in the United States?
Evidence: The Chesapeake Bay (/ˈtʃɛsəpiːk/ CHESS-ə-peek) is the largest estuary in the United States. The Bay is located in the Mid-Atlantic region and is primarily separated from the Atlantic Ocean by the Delmarva Peninsula, including parts of the Eastern Shore of Maryland, the Eastern Shore of Virginia, and the state of Delaware. The mouth of the Bay at its southern point is located between Cape Henry and Cape Charles. With its northern portion in Maryland and the southern part in Virginia, the Chesapeake Bay is a very important feature for the ecology and economy of those two states, as well as others surrounding within its watershed. More than 150 major rivers and streams flow into the Bay's 64,299-square-mile (166,534 km2) drainage basin, which covers parts of six states, New York, Pennsylvania, Delaware, Maryland, Virginia, and West Virginia, and all of Washington, D.C.
Answer: The Chesapeake Bay is the largest estuary in the United States.
Question: What does the encoder consist of?
Evidence: Our datasets can be used for various hate speech tasks. In this paper, we focus on generative hate speech intervention.
The goal of this task is to generate a response to hate speech that can mitigate its use during a conversation. The objective can be formulated as the following equation:
where $c$ is the conversation, $r$ is the corresponding intervention response, and $D$ is the dataset. This task is closely related to the response generation and dialog generation, though several differences exist including dialog length, language cadence, and word imbalances. As a baseline, we chose the most common methods of these two tasks, such as Seq2Seq and VAE, to determine the initial feasibility of automatically generate intervention responses. More recent Reinforcement Learning method for dialog generation BIBREF21 can also be applied to this task with slight modification. Future work will explore more complex, and unique models.
Similar to BIBREF21, a generative model is considered as an agent. However, different from dialog generation, generative intervention does not have multiple turns of utterance, so the action of the agent is to select a token in the response. The state of the agent is given by the input posts and the previously generated tokens. Another result due to this difference is that the rewards with regard to ease of answering or information flow do not apply to this case, but the reward for semantic coherence does. Therefore, the reward of the agent is:
where $rw(c,r)$ is the reward with regard to the conversation $c$ and its reference response $r$ in the dataset. $p(r|c)$ denotes the probability of generating response $r$ given the conversation $c$, and $p_{back}(c|r)$ denotes the backward probability of generating the conversation based on the response, which is parameterized by another generation network. The reward is a weighted combination of these two parts, which are observed after the agent finishing generating the response. We refer the readers to BIBREF21 for details.
The encoder consists of 2 bidirectional GRU layers.
Answer: The encoder consists of 2 bidirectional GRU layers.
Question: Why do some scholars think that campaigns do not matter much for presidential election outcomes? Why do other scholars and practitioners think that campaigns do matter?
Answer: Scholars argue that there are three main reasons that campaigns do not matter in elections.  One, that other factors have a greater influence than campaigns, such as if a candidate is an incumbent, party identification of the candidate, state of the economy, and finally, macro-trends like wars and presidential approval.  Second, the public does not care about politics so they don't pay attention to campaign functions like speeches, candidate gaffes, or advertising.  Third, in presidential elections, each side has about the same amount of experts and money which cancels out any advantage those resources might bring.
Other scholars argue that campaigns do matter, and they also have three main reasons for this.  One, that after controlling for incumbency, campaign spending does impact a candidates performance. Secondly, Gerber and Green argue that campaigns personalized outreach, such as, door-to-door or digital contact, will influence the person contacted to go and vote for the candidate. Finally, for non-presidential elections, the campaign resources vary enormously between candidates and the campaign with the most resources is favored to win the election.
Question: Why is supporting fact supervision necessary for DMN?
Evidence: We speculate that there are two main reasons for this performance disparity, all exacerbated by the removal of supporting facts. First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.
First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.
Answer: The answers are shown as follows:
* First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.
Question: Give me a list of things to enable a support team on their technical strengths
Answer: 1) Setup a daily stand up that becomes a platform for people to discuss their challenges. This platform will be a significant source of technical brainstorming. It enables people with technical competencies to guide the person asking for help. At the same time it allows for every one else to get familiar with an issue and its solution even if they haven't experienced it yet. Culturally this should be a judgement free zone. It should eliminate inhibitions to raise challenges.

2) Setup a cadence (weekly, bi weekly, monthly) for members of team to discuss their most interesting customer issues. They should cover the problem statement, symptom, assessment/diagnosis of the issue, approaches to solve the problem. The last piece around approaches to solve the problem is key as it equips others to incorporate effective options in their troubleshooting toolbox

3) Collaborate with product managers to setup a broader feature level enablement cadence.

4) Create mentorship programs where junior members can shadow seniors on their customer calls and learn technical cops as well as communication cops by just being a fly on the wall.  At the same time seniors should shadow juniors on their customer calls and give them direct feedback on what needs to be improved
Question: Can I travel to Canada from the United States with my dog?
Answer: You can travel into Canada with a dog as long as you have proof of an unexpired rabies vaccination.
Question: Given a reference text about Scrub Daddy, tell me the details on the deal the founder made on Shark Tank.
Evidence: Scrub Daddy is a cleaning product company best known for an eponymous sponge it manufactures in the shape of a smiley face. The product is made of a polymer which changes texture – firm in cold water and soft in warm water. As of 2019, Scrub Daddy had the highest revenue of any product successfully pitched on the ABC reality show Shark Tank.

History
After damaging the exterior of a vehicle when cleaning the outside, detailer Aaron Krause invented a line of buffing and polishing pads. The company was acquired by 3M in August 2008. 3M did not purchase a line of sponges Krause had invented, leaving them to sit in his factory. Five years later, Krause used the remaining sponges to clean his dishes and lawn furniture. According to Krause, this is when he "realized his multi-million dollar idea."

According to the company's website, Scrub Daddy, Inc. was founded in 2012 with grassroots marketing. On the Shark Tank episode originally airing in October 2012, Lori Greiner made a 20% equity stake deal with Krause for $200,000. The following day, Greiner and Krause sold out of 42,000 sponges in under seven minutes on QVC. Greiner then helped Scrub Daddy to be sold in retail stores such as Bed, Bath & Beyond. In January 2017, Scrub Daddy's total revenues surpassed $100 million – the highest of any Shark Tank product. As of October 2019, the company's lifetime sales were $209 million. In Season 14 Episode 13, it was reported that Scrub Daddy in 10 years has 273 employees, 160 products, sold in 257,000 retail locations, top 5 grossing company in shark tank history, doing over $670m in retail sales since launch.
Answer: Aaron Krause, the founder of Scrub Daddy, made a deal with Lori Greiner on Shark Tank, giving her 20% equity in the company for $200,000.
Question: What methods have other researchers proposed to explore the combinatorial functions of different kinds on tree structures?
Evidence: One thread of related work is the exploration of different kinds of compositional function over tree structures. BIBREF11 [ BIBREF11 ] proposed the recursive neural network with standard compositional function. After that, some extensions are introduced to enhance the expressive power of compositional function, such as MV-RecNN BIBREF23 , SU-RNN BIBREF13 , RNTN BIBREF23 , while these models suffer from the problem of hard-coded compositional operations and overfitting.
Another thread of work is the idea of using one network to direct the learning of another network BIBREF16 . BIBREF33 [ BIBREF33 ] introduce a meta neural network to provide another network with a step size and a direction vector, which is helpful for parameter optimization. BIBREF16 [ BIBREF16 ] propose the dynamic filter network to implicitly learn a variety of filtering operations. BIBREF17 [ BIBREF17 ] introduce a learnet for one-shot learning, which can predict the parameters of a second network given a single exemplar. BIBREF18 [ BIBREF18 ] propose the model hypernetwork, which uses a small network to generate the weights for a larger network.
Different from these models, we employ the idea of parameter generation to address the limitation of weight-sharing or partially sharing paradigm of tree-based compositional models.
One thread of related work is the exploration of different kinds of compositional function over tree structures. Socher et al. proposed the recursive neural network with standard compositional function. After that, some extensions are introduced to enhance the expressive power of compositional function, such as MV-RecNN, SURNN, RNTN, while these models suffer from the problem of hard-coded compositional operations and overfitting.
Answer: Socher et al. proposed the recursive neural network with standard compositional function.
Question: Tell me about the discovery of coffee bean?
Answer: Coffee was first discovered by the 9th century Ethiopian goat-herder Kaldi. According to a story written down in 1671. Kaldi noticed that his goats behaving erratically when eating the coffee bean in a region called Kaffa in Ethiopia. It made its way north, across the red sea into Yemen in the 15th Century. It then started to be grown across the world.
Question: Why didn’t people like the end of Game of Thrones?
Answer: Note:  this answer contains spoilers for the end of the HBO television series, “Game of Thrones.”  The end of the Game of Thrones television series was a bit divisive among viewers for a number of reasons.  First, some believed that one of the series’ principal and most popular characters, Daenerys Targaryen, had been – if flawed – a protagonist throughout most of the series until the last few episodes of the final season, and her switch to a villainous persona felt rushed.  Similarly, some viewers believed that arcs for other main characters, including Jaime, Cersei, and Tyrion Lannister, Brienne of Tarth, Jon Snow, and others, were adversely impacted by the quick pacing of the final season and did away with much of those characters’ development over the course of many years.  Others were confused and disappointed that Bran Stark became king of the Seven Kingdoms (and ultimate winner of the titular “game of thrones”), as there seemed to be little build-up or motivation for that narrative decision.  Many suspect that George R.R. Martin, author of the “Song of Ice and Fire” series of novels on which the show was based, may have anticipated providing more narrative context and development for these choices in his then-unpublished final novels in the series, and that the television series simply needed to end well before Martin could complete his novels.
Question: How is quality of the citation measured?
Evidence: In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.
At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.
In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.
At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.
Answer: The answers are shown as follows:
* it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.
Question: What is Nevada City known for?
Evidence: European-Americans first settled Nevada City in 1849, during the California Gold Rush, as Nevada (Spanish for "snow-covered", a reference to the snow-topped mountains in the area). The Gold Tunnel on the north side of Deer Creek was the city's first mine, built in 1850. The first sawmill in Nevada City was built on Deer Creek, just above town, in August 1850, by Lewis & Son, with a water wheel. In 1850–51, Nevada City was the state's most important mining town, and Nevada County the state's leading gold-mining county. In 1851, The Nevada Journal became the first newspaper published in the town and county. The first cemetery in town, the Pioneer Cemetery, was founded around 1851 behind the Nevada City United Methodist Church, Nevada County's first denominational church.

The town of Nevada was incorporated on April 19, 1856. In 1864, the word “City” was added to its name to relieve confusion with the nearby state of Nevada, and the town has legally been known as Nevada City ever since. The former town of Coyoteville later became Nevada City's northwestern section.
Answer: Nevada City is a town in Northern California that was famous during the California Gold Rush that was first settled in 1849.
Question: How is face and audio data analysis evaluated?
Evidence: Table TABREF16 shows the confusion matrices for facial and audio emotion recognition on our complete AMMER data set and Table TABREF17 shows the results per class for each method, including facial and audio data and micro and macro averages. The classification from facial expressions yields a macro-averaged $\text{F}_1$ score of 33 % across the three emotions joy, insecurity, and annoyance (P=0.31, R=0.35). While the classification results for joy are promising (R=43 %, P=57 %), the distinction of insecurity and annoyance from the other classes appears to be more challenging.
Table TABREF16 shows the confusion matrices for facial and audio emotion recognition on our complete AMMER data set and Table TABREF17 shows the results per class for each method, including facial and audio data and micro and macro averages. The classification from facial expressions yields a macro-averaged $\text{F}_1$ score of 33 % across the three emotions joy, insecurity, and annoyance (P=0.31, R=0.35).
Answer: The answers are shown as follows:
* confusion matrices
* $\text{F}_1$ score
Question: Is it fun to play guitar? How do you start playing guitar?
Answer: Playing guitar is a worthwhile and super fun exercise for anyone, but especially those that are musically inclined.  Even for a beginner, its not difficult to be able to start playing simple songs from great artists like the Rolling Stones, The Beatles, Jimi Hendrix, or even Led Zeppelin.  The best way to get started is by understanding open chords, both major and minor, and simple string picking patterns.
Question: What is their baseline?
Evidence: For logistic regression model implementation, we use l2 loss. We adopt the balanced class weight as described in Scikit learn. Logistic regression model with character-level n-gram features is presented as a strong baseline for comparison since it was shown very effective. BIBREF0 , BIBREF9
 Logistic regression model with character-level n-gram features is presented as a strong baseline for comparison since it was shown very effective.
Answer: The answers are shown as follows:
* Logistic regression model with character-level n-gram features
Question: Name some of the bravolebrities from Vanderpump Rules
Answer: James Kennedy, Katie Maloney, Raquel Leviss, Tom Sandoval, Ariana Madix, Tom Schwartz and Lala Kent