DataHammer/scimrc huggingface dataset:
Question: What is table tennis and why is it popular
Evidence: Table tennis, also known as ping-pong and whiff-whaff, is a racket sport derived from tennis but distinguished by its playing surface being atop a stationary table, rather than the court on which players stand. Either individually or in teams of two, players take alternating turns returning a light, hollow ball over the table's net onto the opposing half of the court using small rackets until they fail to do so, which results in a point for the opponent. Play is fast, requiring quick reaction and constant attention, and is characterized by an emphasis on spin relative to other ball sports, which can heavily affect the ball's trajectory.
Owed to its small minimum playing area, its ability to be played indoors in all climates, and relative accessibility of equipment, table tennis is enjoyed worldwide not just as a competitive sport, but as a common recreational pastime among players of all levels and ages.
Answer: Table tennis is a sport that plays a ball on a table with rackets. Its rule has evolved over the years, like the material of the racket and balls, size of the balls. Like tennis, it can also play simple or double games. It is popular because it doesn't require a big area, and normally people can play it even at an older age.
Question: Who won the National Championship for Men's Basketball in 2023?
Answer: The University of Connecticut won the National Championship for Men's Basketball in 2023. UConn was not favored coming into the tournament and was a #4 seed to start the tournament. UConn won every one of its tournament games by a large margin and quickly became the favorite as it reached the Final Four. Uconn beat the San Diego State Aztecs to win the title. This is the 5th national championship for Uconn in 25 years. They have also won the championship under 3 different coaches during that time. This year's coach is Dan Hurley who is from New Jersey and played college basketball himself
Question: Write an example message asking your friend Tony to go to a baseball game with you
Answer: Hi Tony!  Baseball season just started and I am looking for people to go to a game with.  Would you be interested?  There are a bunch of games coming up in town that would be good options.  We could go on a weekend, but the tickets will be more expensive.  Alternatively, if we can make it to a game during the week, we might be able to either save some money, or get better seats for the same price as worse tickets on a weekend game.  Let me know if you are interested at all.  Thanks!
Question: Quel a été l'impact de la révolution française ?
Evidence: La Révolution française est une période de bouleversements sociaux et politiques de grande envergure en France, dans ses colonies et en Europe à la fin du xviiie siècle. La période habituellement comprise s'étend entre l'ouverture des États généraux, le 5 mai 1789, et au plus tard le coup d'État de Napoléon Bonaparte le 9 novembre 1799 (18 brumaire de l'an VIII). Cette période de l'histoire de France a mis fin à l'Ancien Régime en remplaçant la monarchie absolue par une suite de régimes plus ou moins définis, dont la Première République un peu plus de trois ans après la prise de la Bastille.

La Révolution française a légué de toutes nouvelles formes politiques, notamment au travers de la Déclaration des droits de l'homme et du citoyen de 1789 qui proclame l'égalité des citoyens devant la loi, les libertés fondamentales, et la souveraineté de la Nation, et se constituant autour d'un État. « Mythe national », ses valeurs et les institutions de la Révolution dominent encore aujourd'hui la vie politique française. La Révolution a entraîné la suppression de la société d'ordres (féodalité, privilèges…), une plus grande division de la propriété foncière, la limitation de l'exercice du pouvoir politique, le rééquilibrage des relations entre l'Église et l'État et la redéfinition des structures familiales.

Elle fut marquée par des périodes de grande violence, notamment pendant la Terreur, dans le cadre de la tentative de contre-révolution de la guerre de Vendée, au cours de laquelle plusieurs centaines de milliers de personnes trouvèrent la mort, lors des insurrections fédéralistes ou dans le cadre de luttes entre factions révolutionnaires rivales, qui ont abouti à la mort successive des principales figures révolutionnaires.
Answer: la révolution française a eu plusieurs impacts, sociaux et politiques. On doit à la révolution française la déclaration des droits de l'homme et du citoyen, ainsi que l'égalité des chances pour accéder au pouvoir politique, la séparation de l' église et l'état et une meilleure posture pour les femmes au sein de la société
Question: What is so good about Japan?
Answer: Japan is a fascinating country with a long and rich history, filled with unique and interesting cultural customs. From its rich culinary culture to its breathtaking natural beauty, there is something for everyone to enjoy in Japan. With so much to see and do, it’s no wonder why Japan is so well known around the world.
Question: What kind of questions do the team need to be carefully considered when deciding to remove useless documents?
Evidence: After identifying the data source(s), the next step is compiling the data. This step is fundamental: if the sources cannot support a convincing result, no result will be convincing. In many cases, this involves defining a “core" set of documents and a “comparison" set. We often have a specific set of documents in mind: an author's work, a particular journal, a time period. But if we want to say that this “core" set has some distinctive property, we need a “comparison" set. Expanding the collection beyond the documents that we would immediately think of has the beneficial effect of increasing our sample size. Having more sources increases the chance that we will notice something consistent across many individually varying contexts.
Comparing sets of documents can sometimes support causal inference, presented as a contrast between a treatment group and a control. In BIBREF0 , the treatment consisted of the text written in the two forums that were eventually closed by Reddit. However, identifying a control group required a considerable amount of time and effort. Reddit is a diverse platform, with a wide variety of interactional and linguistic styles; it would be pointless to compare hate speech forums against forums dedicated to, say, pictures of wrecked bicycles. Chandrasekharan et al. used a matching design, populating the control group with forums that were as similar as possible to the treatment group, but were not banned from Reddit. The goal is to estimate the counterfactual scenario: in this case, what would have happened had the site not taken action against these specific forums? An ideal control would make it possible to distinguish the effect of the treatment — closing the forums — from other idiosyncratic properties of texts that were treated.
We also look for categories of documents that might not be useful. We might remove documents that are meta-discourse, like introductions and notes, or documents that are in a language that is not the primary language of the collection, or duplicates when we are working with archived web pages. However, we need to carefully consider the potential consequences of information we remove. Does its removal alter the data, or the interpretation of the data, we are analyzing? Are we losing anything that might be valuable at a later stage?
However, we need to carefully consider the potential consequences of information we remove. Does its removal alter the data, or the interpretation of the data, we are analyzing? Are we losing anything that might be valuable at a later stage?
Answer: Does its removal alter the data, or the interpretation of the data, they are analyzing? Are they losing anything that might be valuable at a later stage?
Question: Do they do quantitative quality analysis of learned embeddings?
Evidence: The high nearest neighbours accuracy indicates that syntax information was successfully captured by the embeddings. Table TABREF22 also shows that the syntactic information of multiple languages was captured by a single embedding model.
The high nearest neighbours accuracy indicates that syntax information was successfully captured by the embeddings.
Answer: Yes.
Question: What are their correlation results?
Evidence: FLOAT SELECTED: Table 1: Spearman’s ρ, Kendall’s τ and Pearson’s r correlations on DUC-05, DUC-06 and DUC-07 for Q1–Q5. BEST-ROUGE refers to the version that achieved best correlations and is different across years.
FLOAT SELECTED: Table 1: Spearman’s ρ, Kendall’s τ and Pearson’s r correlations on DUC-05, DUC-06 and DUC-07 for Q1–Q5. BEST-ROUGE refers to the version that achieved best correlations and is different across years.
Answer: High correlation results range from 0.472 to 0.936
Question: Is Chen Ruo Bing a Chinese or Japanese artist?
Evidence: Chen Ruo Bing (born 1970) is a Chinese artist whose abstract color painting merges Eastern tradition and Western contemporary art.
Answer: Chinese
Question: What is the difference between a flute and piccolo?
Answer: The main difference between a flute and piccolo is that the flute is a woodwind instrument and the piccolo is a brass instrument. The flute is typically made from wood and is played with the mouth, while the piccolo is typically made from brass and is played with the fingers. Additionally, the flute is usually pitched higher than the piccolo, and the flute is used for a variety of musical styles while the piccolo is typically used for jazz and classical music.
Question: What architecture is often chosen for the context encoding layer?
Evidence: Various neural networks have been proposed for sentence pair modeling, all of which fall into two types of approaches. The sentence encoding approach encodes each sentence into a fixed-length vector and then computes sentence similarity directly. The model of this type has advantages in the simplicity of the network design and generalization to other NLP tasks. The sentence pair interaction approach takes word alignment and interactions between the sentence pair into account and often show better performance when trained on in-domain data. Here we outline the two types of neural networks under the same general framework:
•The Context Encoding Layer incorporates word context and sequence order into modeling for better vector representation. This layer often uses CNN (He et al., 2015), LSTM (Chen et al., 2017), recursive neural network (Socher et al., 2011), or highway network (Gong et al., 2017). The sentence encoding type of model will stop at this step, and directly use the encoded vectors to compute the semantic similarity through vector distances and/or the output classification layer.
Answer: CNN (He et al., 2015), LSTM (Chen et al., 2017), recursive neural network (Socher et al., 2011), or highway network (Gong et al., 2017) are often used for context encoding layer.
Question: Tell me about plant life cycle
Answer: Like any living being, a plant is programmed for its specie to last over generations.
With winter coming, the plant will generate seeds, what is called « fructification ». 
These seeds are basically a strong capsule containing the plant’s genetic material and enough energy for the germ to grow the first leaf which will start photosynthesis.
The energy contained in the seed is stabilised under the form of starch. It is basically a long chain of sugar polymerised.
During winter, the seed hibernate, but when spring comes, specific weather conditions (humidity, temperature) degrade the starch and release elementary sugar molecules for the new growing plant to create its first leaf.
Chlorophyll contained in leaves will enable the plant to produce its own energy; glucose sugar, from carbon dioxyde, water and light, and continue to grow, until the next cycle.
There is a bit of magic with plants: creating oxygen and energy from air and sun!
Question: What makes up the data set the author collected?
Evidence: We collected the dataset with 1900 dialogs and 8533 turns. Topics discussed in dialogs are questions randomly chosen from training examples of Simple questions BIBREF7 dataset. From this dataset we also took the correct answers in form of Freebase entities.
Our dataset consists of standard data split into training, development and test files. The basic properties of those files are as follows:
Each file contains complete dialogs enriched by outputs of NLU (see Section "Natural Language Understanding (NLU)" ) that were used during the data collection. On top of that, each dialog is labeled by the correct answer for the question and expert annotation of the user answer hint which tells whether the hint points to the correct answer, incorrect answer, or no answer at all.
351 of all collected dialogs contain correct answer provided by users and 702 dialogs have incorrect answer. In the remaining 847 dialogs users did not want to answer the question. The collected dialogs also contain 1828 paraphrases and 1539 explanations for 1870 questions.
An answer for a question was labeled as correct by annotators only when it was evident to them that the answer points to the same Freebase entity that was present in Simple questions dataset for that particular question. However, a large amount of questions from that dataset is quite general - with many possible answers. Therefore lot of answers from users were labeled as incorrect even though those answers perfectly fit the question. Our annotators identified that 285 of the incorrect answers were answers for such general questions. Example of this situation can be demonstrated by question 'Name an actor' which was correctly answered by 'Brad Pitt is an actor', however, to be consistent with Simple questions annotation, which is 'Kelly Atwood', annotators were forced to mark it as an incorrect answer.
Our dataset consists of standard data split into training, development and test files.
Answer: Standard data split into training, development and test files.
Question: Where is their baseline from?
Evidence: Laughter is a special, higher-order function that only humans possess. In the analysis of laughter, as Wikipedia says, “Laughter is thought to be a shift of composition (schema)", and laughter frequently occurs when there is a change from a composition of receiver. However, the viewpoint of laughter differs greatly depending on the position of the receiver. Therefore, the quantitative measurement of laughter is very difficult. Image Ogiri on web services such as "Bokete" BIBREF0 have recently appeared, where users post funny captions for thematic images and the captions are evaluated in an SNS-like environment. Users compete to obtain the greatest number of “stars”. Although quantification of laughter is considered to be a very difficult task, the correspondence between evaluations and images on Bokete allows us to treat laughter quantitatively. Image captioning is an active topic in computer vision, and we believe that humorous image captioning can be realized. The main contributions of the present paper are as follows:
BoketeDB
In the experimental section, we compare the proposed method based on Funny Score and BoketeDB pre-trained parameters with a baseline provided by MS COCO Pre-trained CNN+LSTM. We also compare the results of the NJM with funny captions provided by humans. In an evaluation by humans, the results provided by the proposed method were ranked lower than those provided by humans (22.59% vs. 67.99%) but were ranked higher than the baseline (9.41%). Finally, we show the generated funny captions for several images.
The main contributions of the present paper are as follows: BoketeDB In the experimental section, we compare the proposed method based on Funny Score and BoketeDB pre-trained parameters with a baseline provided by MS COCO Pre-trained CNN+LSTM.
Answer: MS COCO Pre-trained CNN+LSTM.
Question: What is cuban cuisine?
Answer: Cuban cuisine refers to the food eaten in the country of Cuba. Popular dishes usually include a protein/meat, rice and some type of veggie. Cuban food tends to have a combination of the following spices; salt, pepper, cumin, onion, garlic and oregano. Some of the most popular traditional dishes include picadillo, ropa vieja, media noche and empanadas. Many dishes also tend to include black beans.
Question: Define Part of Speech
Answer: In grammar, a part of speech or part-of-speech (abbreviated as POS or PoS, also known as word class or grammatical category) is a category of words (or, more generally, of lexical items) that have similar grammatical properties. Words that are assigned to the same part of speech generally display similar syntactic behavior (they play similar roles within the grammatical structure of sentences), sometimes similar morphological behavior in that they undergo inflection for similar properties and even similar semantic behavior. Commonly listed English parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, interjection, numeral, article, and determiner.

Almost all languages have the word classes noun and verb, but beyond these two there are significant variations among different languages. For example:

Japanese has as many as three classes of adjectives, where English has one.
Chinese, Korean, Japanese and Vietnamese have a class of nominal classifiers.
Many languages do not distinguish between adjectives and adverbs, or between adjectives and verbs.
Because of such variation in the number of categories and their identifying properties, analysis of parts of speech must be done for each individual language. Nevertheless, the labels for each category are assigned on the basis of universal criteria
Question: Why is CH the Country Code for Switzerland?
Answer: It is because the Latin name of the country is Helvetia, and it can still be seen on various stamps. The full form of CH is Confoederatio Helvetica which means Swiss Confederation.
Question: What is the main cause of lightning?
Answer: Lightning happens when the negative charges (electrons) in the bottom of the cloud are attracted to the positive charges (protons) in the ground.
Question: What is Lawson in Japan?
Evidence: Lawson, Inc. (株式会社ローソン, Kabushiki gaisha Rōson) is a convenience store franchise chain in Japan. The store originated in the United States in Cuyahoga Falls, Ohio, but exists today as a Japanese company based in Shinagawa, Tokyo.
Answer: Lawson is a convenience store franchise chain in Japan
Question: Tell me whether these are alcoholic or non alcoholic drinks: sprite, mojito, beer, orange juice, water, manhattan, cranberry juice, milk
Answer: Sprite is a non alcoholic soft drink.
Mojitos are alcoholic cocktails that typically contain rum, lime juice, simple syrup, and fresh mint.
Beer is an alcoholic beverage,
Orange juice is a non alcoholic drink.
Water is a non alcoholic beverage.
A manhattan is a an alcoholic cocktail that typically contains whiskey, vermouth, bitters and a brandied cherry for garnish.
Cranberry juice is a non alcoholic drink.
Milk is a non alcoholic drink.
Question: Extract the names of individuals in the passage who have eponymous laws.
Evidence: This list of eponymous laws provides links to articles on laws, principles, adages, and other succinct observations or predictions named after a person. In some cases the person named has coined the law – such as Parkinson's law. In others, the work or publications of the individual have led to the law being so named – as is the case with Moore's law. There are also laws ascribed to individuals by others, such as Murphy's law; or given eponymous names despite the absence of the named person.
Answer: Parkinson, Moore, and Murphy are listed as examples of individuals who have an eponymous law.
Question: What are some sound personal financial practices to employ in the modern economy?
Answer: Although this is a vast topic there are some widely accepted best practices guidance for personal financial practices in the modern economy.  Generally speaking, individuals should first keep track of what income and expenses they have.  This often requires noting down monthly income and expenses in a spreadsheet.  Once we have an understanding of our monthly income and expenses we can then create a budget.  A budget is a financial plan meant to help us understand how much we can spend between paychecks and how much we can save.  If we have any high interest debt or any debt, the loan or bill with the highest accruing interest, that becomes the first priority for us to spend our budget on after we account for our expenses.  These are some sound personal financial practices to employ and we should try to keep our expenses and debt as low as we can while maximizing our income and savings as a general practice.
Question: how to become fit
Answer: Getting fit is a journey than just a milestone. It starts with maintaining a positive attitude, healthy lifestyle comprising of good eating habits, good sleep cycle and an active lifestyle. If you are trying to get fit, start with making small life style changes like including walking, running, a sport, an exercise routine or any activity that you think is fun in your every day life. Apart from that eat healthy and get good sleep for your body & mind to rest and recover.  Most importantly remember that there are no short cuts or a quick solution to getting fit. So start you journey today and enjoy yourself.
Question: What was the baseline?
Evidence: Table IV shows the result of our model for sentiment classification against other models. We compare our model performance with the approaches of BIBREF0 BIBREF5 on STS Corpus. BIBREF0 reported the results of Maximum Entropy (MaxEnt), NB, SVM on STS Corpus having good performance in previous time. The model of BIBREF5 is a state-of-the-art so far by using a CharSCNN. As can be seen, 86.63 is the best prediction accuracy of our model so far for the STS Corpus.
For Sanders and HCR datasets, we compare results with the model of BIBREF14 that used a ensemble of multiple base classifiers (ENS) such as NB, Random Forest (RF), SVM and Logistic Regression (LR). The ENS model is combined with bag-of-words (BoW), feature hashing (FH) and lexicons. The model of BIBREF14 is a state-of-the-art on Sanders and HCR datasets. Our models outperform the model of BIBREF14 for the Sanders dataset and HCR dataset.
Table IV shows the result of our model for sentiment classification against other models. We compare our model performance with the approaches of BIBREF0 BIBREF5 on STS Corpus. BIBREF0 reported the results of Maximum Entropy (MaxEnt), NB, SVM on STS Corpus having good performance in previous time. The model of BIBREF5 is a state-of-the-art so far by using a CharSCNN. As can be seen, 86.63 is the best prediction accuracy of our model so far for the STS Corpus.

For Sanders and HCR datasets, we compare results with the model of BIBREF14 that used a ensemble of multiple base classifiers (ENS) such as NB, Random Forest (RF), SVM and Logistic Regression (LR). The ENS model is combined with bag-of-words (BoW), feature hashing (FH) and lexicons. The model of BIBREF14 is a state-of-the-art on Sanders and HCR datasets. Our models outperform the model of BIBREF14 for the Sanders dataset and HCR dataset.


Answer: The answers are shown as follows:
* We compare our model performance with the approaches of BIBREF0 BIBREF5 on STS Corpus. BIBREF0 reported the results of Maximum Entropy (MaxEnt), NB, SVM on STS Corpus having good performance in previous time. The model of BIBREF5 is a state-of-the-art so far by using a CharSCNN.
* we compare results with the model of BIBREF14 that used a ensemble of multiple base classifiers (ENS) such as NB, Random Forest (RF), SVM and Logistic Regression (LR). The ENS model is combined with bag-of-words (BoW), feature hashing (FH) and lexicons. The model of BIBREF14 is a state-of-the-art on Sanders and HCR datasets.
Question: Tell me which of these stores sells furnitures: Safeway, dollar tree, HEB, IKEA, home depot, walmart
Answer: IKEA and Walmart sells all kinds of furnitures, and home depot only sells outdoor furnitures