DataHammer/scimrc huggingface dataset:
Question: Which club has won the most number of UEFA Champions League?
Evidence: The UEFA Champions League is a seasonal football competition established in 1955. Prior to the 1992–93 season, the tournament was named the European Cup. The UEFA Champions League is open to the league champions of all UEFA (Union of European Football Associations) member associations (except Liechtenstein, which has no league competition), as well as to the clubs finishing from second to fourth position in the strongest leagues. Originally, only the champions of their respective national league and the defending champions of the competition were allowed to participate. However, this was changed in 1997 to allow the runners-up of the stronger leagues to compete as well, and again in 1999 when third and fourth-placed teams of the said leagues also became eligible. In the Champions League era, the defending champions of the competition did not automatically qualify until the rules were changed in 2005 to allow title holders Liverpool to enter the competition.

Teams that have won the UEFA Champions League three consecutive times, or five times overall, receive a multiple-winner badge. Six teams have earned this privilege: Real Madrid, Ajax, Bayern Munich, Milan, Liverpool, and Barcelona. Until 2009, clubs that had earned that badge were allowed to keep the European Champion Clubs' Cup and a new one was commissioned; since 2009, the winning team each year has received a full-size replica of the trophy, while the original is retained by UEFA.

A total of 22 clubs have won the Champions League/European Cup. Real Madrid hold the record for the most victories, having won the competition fourteen times, including the inaugural edition. They have also won the competition the most consecutive times, with five straight titles from 1956 to 1960. Juventus have been runners-up the most times, losing seven finals. Atlético Madrid is the only team to reach three finals without having won the trophy while Reims and Valencia have finished as runners-up twice without winning. Spain has provided the most champions, with nineteen wins from two clubs. England have produced fourteen winners from five clubs and Italy have produced twelve winners from three clubs. English teams were banned from the competition for five years following the Heysel disaster in 1985. The current champions are Real Madrid, who beat Liverpool 1–0 in the 2022 final for a record-extending fourteenth title.
Answer: Real Madrid has won 14 of UEFA Champions League titles, make it the club that has won the most number of the tile, among all European clubs.
Question: What are the first 21 cryptocurrencies by trading year?
Answer: 2009 - Bitcoin (BTC) the original cryptocurrency using 'proof-of-work' consensus,
2011 - Namecoin (NMC) forked from bitcoin which allows users to protect DNS,
2011 - Litecoin (LTC) forked from bitcoin to increase transactions speed and minimize fees,
2012 - Ripple (XRP) developed to improve payments across the world,
2012 - Peercoin (PPC) pioneer of 'proof-of-stake',
2013 - Primecoin (XPM) proof-of-work system that searches for chains of prime numbers,
2013 - Emercoin (EMC) developed first dSDKs (Decentralized Software Development Kit),
2013 - Gridcoin (GRC) which rewards volunteer computing for science,
2013 - Dogecoin (DOGE) fork of Litecoin and first 'meme coin',
2014 - Vertcoin (VTC) open source GPU Bitcoin variant that mirrors BTC development,
2014 - Auroracoin (AUR) Icelandic alternative to bitcoin and the Icelandic króna,
2014 - DigitalNote (XDN) first hybrid blockchain with instant transactions,
2014 - Monero (XMR) first 'privacy coin' allowing users to achieve greater anonymity,
2014 - Tether (USDT) the first 'stablecoin' pegged 1:1 to the US dollar running on Ethereum,
2015 - IOTA for Internet of Things using directed acyclic graph to store transactions,
2015 - SixEleven (SIL) Bitcoin based decentralized key/value registration & transfer system,
2015 - Ethereum (ETH) the first 'smart-contract' functionality,
2016 - BitConnect (BCC), the first Ponzi Scheme cryptocurrency,
2016 - Waves (WAVES) 'proof-of-stake' based platform for decentralized applications,
2016 - Zcash (ZEC) 'privacy coin' forked from Bitcoin,
2017 - Cardano (ADA) first full 'proof-of-stake' based blockchain for 'smart-contracts'
Question: Kingsway in India was made in the honour of
Answer: George V the Emperor of India
Question: I've started doing individual training to get fit and now I'd like to learn a team sport, which of these are team sports? hockey, yoga, baseball, basketball, netball, running, handball, weightlifting, and ballet.
Answer: Hockey, baseball, basketball, netball, handball are all team sports
Question: what are some things you can do on a snowy day
Answer: - skip school
- make a snowman
- make a snow angel
- make a snow castle
- have a snow ball fight
- shovel snow
- use a snow blower
- go sledding
- go cross country skiing 
- slip and fall
Question: What is R language?
Answer: R is a language and environment for statistical computing and graphics.
Question: What are some ways that I can conserve water at home?
Answer: Some ways you can conserve water at home include limiting or eliminating taking baths, installing high pressure shower heads in your shower, and reducing toilet flushing. Some more creative ways include reusing water you use to rinse your vegetables to water houseplants and replacing your lawn with drought-tolerant plants.
Question: What is the standard procedure for speech recognition and synthesis datasets?
Evidence: The resource is comprised of 142 hours of spoken Mapudungun that was recorded during the AVENUE project BIBREF6 in 2001 to 2005. The data was recorded under a partnership between the AVENUE project, funded by the US National Science Foundation at Carnegie Mellon University, the Chilean Ministry of Education (Mineduc), and the Instituto de Estudios Indígenas at Universidad de La Frontera, originally spanning 170 hours of audio. We have recently cleaned the data and are releasing it publicly for the first time (although it has been shared with individual researchers in the past) along with NLP baselines.
The recordings were transcribed and translated into Spanish at the Instituto de Estudios Indígenas at Universidad de La Frontera. The corpus covers three dialects of Mapudungun: about 110 hours of Nguluche, 20 hours of Lafkenche and 10 hours of Pewenche. The three dialects are quite similar, with some minor semantic and phonetic differences. The fourth traditionally distinguished dialect, Huilliche, has several grammatical differences from the other three and is classified by Ethnologue as a separate language, iso 639-3: huh, and as nearly extinct.
The recordings are restricted to a single domain: primary, preventive, and treatment health care, including both Western and Mapuche traditional medicine. The recording sessions were conducted as interactive conversations so as to be natural in Mapuche culture, and they were open-ended, following an ethnographic approach. The interviewer was trained in these methods along with the use of the digital recording systems that were available at the time. We also followed human subject protocol. Each person signed a consent form to release the recordings for research purposes and the data have been accordingly anonymized. Because Machi (traditional Mapuche healers) were interviewed, we asked the transcribers to delete any culturally proprietary knowledge that a Machi may have revealed during the conversation. Similarly, we deleted any names or any information that may identify the participants.
The corpus is culturally relevant because it was created by Mapuche people, using traditional ways of relating to each other in conversations. They discussed personal experiences with primary health care in the traditional Mapuche system and the Chilean health care system, talking about illnesses and the way they were cured. The participants ranged from 16 years old to 100 years old, almost in equal numbers of men and women, and they were all native speakers of Mapudungun.
We also moved the boundaries of the turns to standardize the amount of leading and trailing silence in each turn. This is a standard procedure for speech recognition and synthesis datasets.
Answer: The authors applied standardized power normalization. They also moved the boundaries of the turns to standardize the amount of leading and trailing silence in each turn.
Question: Under what circumstances would they modify topic probabilities for related words and phrases locally in specific texts?
Evidence: In our approach we develop the idea of BIBREF16 that proposed to construct similarity sets between ngram phrases between each other and single words. Phrases and words are included in the same similarity set if they have the same component word, for example, weapon – nuclear weapon – weapon of mass destruction; discrimination – racial discrimination. It was supposed that if expressions from the same similarity set co-occur in the same document then their contribution into the document's topics is really more than it is presented with their frequencies, therefore their frequencies should be increased. In such an approach, the algorithm can "see" similarities between different multiword expressions with the same component word.
In our approach, at first, we include related single words and phrases from a thesaurus such as WordNet or EuroVoc in these similarity sets. Then, we add preliminarily extracted ngrams into these sets and, this way, we use two different sources of external knowledge. We use the same LDA-SIM algorithm as described in BIBREF16 but study what types of semantic relations can be introduced into such similarity sets and be useful for improving topic models. The pseudocode of LDA-SIM algorithm is presented in Algorithm SECREF3 , where INLINEFORM0 is a similarity set, expressions in similarity sets can comprise single words, thesaurus phrases or generated noun compounds.
We can compare this approach with the approaches applying the generalized Polya urn model BIBREF8 , BIBREF9 , BIBREF10 . To add prior knowledge, those approaches change topic distributions for related words globally in the collection. We modify topic probabilities for related words and phrases locally, in specific texts, only when related words (phrases) co-occur in these texts.
[ht!] collection INLINEFORM0 , vocabulary INLINEFORM1 , number of topics INLINEFORM2 , initial INLINEFORM3 and INLINEFORM4 , sets of similar expressions INLINEFORM5 , hyperparameters INLINEFORM6 and INLINEFORM7 , INLINEFORM8 is the frequency of INLINEFORM9 in the document INLINEFORM10 distributions INLINEFORM11 and INLINEFORM12 not meet the stop criterion INLINEFORM13 INLINEFORM14 
 INLINEFORM0 INLINEFORM1 
 INLINEFORM0 
 INLINEFORM0 
 LDA-SIM algorithm
We modify topic probabilities for related words and phrases locally, in specific texts, only when related words (phrases) co-occur in these texts.
Answer: Only when related words (phrases) co-occur in these texts.
Question: How are maximum recall numbers of 3 preprocessing ways are obtained?
Evidence: The SemEval-2010 benchmark dataset BIBREF0 is composed of 244 scientific articles collected from the ACM Digital Library (conference and workshop papers). The input papers ranged from 6 to 8 pages and were converted from PDF format to plain text using an off-the-shelf tool. The only preprocessing applied is a systematic dehyphenation at line breaks and removal of author-assigned keyphrases. Scientific articles were selected from four different research areas as defined in the ACM classification, and were equally distributed into training (144 articles) and test (100 articles) sets. Gold standard keyphrases are composed of both author-assigned keyphrases collected from the original PDF files and reader-assigned keyphrases provided by student annotators.
Long documents such as those in the SemEval-2010 benchmark dataset are notoriously difficult to handle due to the large number of keyphrase candidates (i.e. phrases that are eligible to be keyphrases) that the systems have to cope with BIBREF6 . Furthermore, noisy textual content, whether due to format conversion errors or to unusable elements (e.g. equations), yield many spurious keyphrase candidates that negatively affect keyphrase extraction performance. This is particularly true for systems that make use of core NLP tools to select candidates, that in turn exhibit poor performance on degraded text. Filtering out irrelevant text is therefore needed for addressing these issues.
In this study, we concentrate our effort on re-assessing keyphrase extraction performance on three increasingly sophisticated levels of document preprocessing described below.
Table shows the average number of sentences and words along with the maximum possible recall for each level of preprocessing. The maximum recall is obtained by computing the fraction of the reference keyphrases that occur in the documents. We observe that the level 2 preprocessing succeeds in eliminating irrelevant text by significantly reducing the number of words (-19%) while maintaining a high maximum recall (-2%). Level 3 preprocessing drastically reduce the number of words to less than a quarter of the original amount while interestingly still preserving high recall.
Table 1 shows the average number of sentences and words along with the maximum possible recall for each level of preprocessing. The maximum recall is obtained by computing the fraction of the ref- erence keyphrases that occur in the documents.
Answer: The maximum recall is obtained by computing the fraction of the ref- erence keyphrases that occur in the documents.
Question: What is the meaning of life?
Answer: The meaning of life is a subject that has been studied, pondered, written about, fought over, proven, disproven, dismissed and even expressed in the metaverse.  Some people pursue the meaning of life throughout their entire life while others spend no time whatsoever thinking about the concept.  Many people find meaning within things they experience in life such as religion, sport, academia, helping others, or just being kind.  One thing is certain about the meaning of life and that is it is deeply personal and unique to each of us.  I personally believe that the meaning of life is to pursue continual improvement and challenging oneself to bring joy to our relationships with others.  I also feel there is meaning in nature's energy that must be protected and we are losing our connection to it.
Question: Tell me whether the following are foods or beverages: coffee, water, corn, tea, rice, beef.
Answer: Beef, corn, and rice are foods.  Coffee water, and tea are beverages.
Question: Given this paragraph about rivers in Europe, tell me what are the two longest rivers in Europe.
Evidence: The commercial and geopolitical importance of rivers is not ranked here. As a transportation artery, a river may unite a region commercially and economically, but major rivers, as barriers to travel, may also form political boundaries between states. The Danube, the second longest river in Europe, is notable for flowing through or past ten countries; the Rhine through or past six. The Volga, the longest river in Europe, unites a huge region of European Russia; eleven of the twenty largest cities in Russia lie on its banks. The Loire and the Po unite important regions within France and Italy respectively. The most important rivers in Europe include Rhone, Elbe, Oder, Tagus, Thames, Don and Dnieper, among others.
Answer: The two longest rivers in Europe are the Volga and the Danube.
Question: Write me a cover letter for an application for a role I'm applying for. The role is with a technology company that sells software that helps consumers find the perfect hat based on the size and shape of their head. My name is Roger, I am passionate about hats and have a background in niche technology sales. The hiring manager's name is Jamiroquai. The companies name is Hats4U
Answer: Dear Jamiroquai, 

Even though I have not be actively looking for a new role, when I saw that you are hiring for Enterprise Sales Executives I had to throw my hat in the ring. I have been a huge fan of Hats4U for many years because it's not an exaggeration to say your company changed my life. 

I have been always believed I was a "Hat Person" but due to the odd shape of my head I have always struggled to find a hat that really worked for me. That's when I discovered your innovative solution and it changed everything. Hats4U helped me find the perfect hat that gave me the confidence I felt that I had always been missing. With this new found confidence I felt empower to tell my best friend that I had actually been in love with her since primary school and we are now married with twins on the way. 

Outside of my passion for your company I also have an extensive background in niche technology sales and believe I am well positioned to help scale your company to reach the millions of potential customers that I know are out there. 

Please find attached my resume, I look forward to hearing from you. 

Kind regards,
Roger
Question: Give me 3 ways you can cook eggs
Answer: 1. One can scramble an egg. Add some salt and pepper to taste. 2. One can make an omelette. You can add onions, dices tomatoes, cheese, salt and pepper to taste. 3. You can make an egg sunny side up. Just sprinkle some salt and pepper as usual.
Question: How were breast cancer related posts compiled from the Twitter streaming API?
Evidence: We collected tweets from two distinct Spritzer endpoints from September 15th, 2016 through December 9th, 2017. The primary feed for the analysis collected INLINEFORM0 million tweets containing the keywords `breast' AND `cancer'. See Figure FIGREF2 for detailed Twitter frequency statistics along with the user activity distribution. Our secondary feed searched just for the keyword `cancer' which served as a comparison ( INLINEFORM1 million tweets, see Appendix 1), and helped us collect additional tweets relevant to cancer from patients. The numeric account ID provided in tweets helps to distinguish high frequency tweeting entities.
The primary feed for the analysis collected INLINEFORM0 million tweets containing the keywords `breast' AND `cancer'. 
Answer: By using  keywords `breast' AND `cancer' in tweet collecting process.
Question: Do they conduct a user study where they show an NL interface with and without their explanation?
Evidence: We have examined the effect to which our query explanations can help users improve the correctness of a baseline NL interface. Our user study compares the correctness of three scenarios:
Parser correctness - our baseline is the percentage of examples where the top query returned by the semantic parser was correct.
User correctness - the percentage of examples where the user selected a correct query from the top-7 generated by the parser.
Hybrid correctness - correctness of queries returned by a combination of the previous two scenarios. The system returns the query marked by the user as correct; if the user marks all queries as incorrect it will return the parser's top candidate.
Our user study compares the correctness of three scenarios:

Parser correctness - our baseline is the percentage of examples where the top query returned by the semantic parser was correct.

User correctness - the percentage of examples where the user selected a correct query from the top-7 generated by the parser.

Hybrid correctness - correctness of queries returned by a combination of the previous two scenarios. The system returns the query marked by the user as correct; if the user marks all queries as incorrect it will return the parser's top candidate.
Answer: No.
Question: What is a bond in finance?
Answer: In finance, a bond is a type of security under which the issuer (debtor) owes the holder (creditor) a debt, and is obliged – depending on the terms – to provide cash flow to the creditor (e.g. repay the principal (i.e. amount borrowed) of the bond at the maturity date as well as interest (called the coupon) over a specified amount of time).The timing and the amount of cash flow provided varies, depending on the economic value that is emphasized upon, thus giving rise to different types of bonds. The interest is usually payable at fixed intervals: semiannual, annual, and less often at other periods. Thus, a bond is a form of loan or IOU. Bonds provide the borrower with external funds to finance long-term investments or, in the case of government bonds, to finance current expenditure.

Bonds and stocks are both securities, but the major difference between the two is that (capital) stockholders have an equity stake in a company (i.e. they are owners), whereas bondholders have a creditor stake in a company (i.e. they are lenders). As creditors, bondholders have priority over stockholders. This means they will be repaid in advance of stockholders, but will rank behind secured creditors, in the event of bankruptcy. Another difference is that bonds usually have a defined term, or maturity, after which the bond is redeemed, whereas stocks typically remain outstanding indefinitely. An exception is an irredeemable bond, which is a perpetuity, that is, a bond with no maturity. Certificates of deposit (CDs) or short-term commercial paper are classified as money market instruments and not bonds: the main difference is the length of the term of the instrument.

The most common forms include municipal, corporate, and government bonds. Very often the bond is negotiable, that is, the ownership of the instrument can be transferred in the secondary market. This means that once the transfer agents at the bank medallion-stamp the bond, it is highly liquid on the secondary market. The price of a bond in the secondary market may differ substantially from the principal due to various factors in bond valuation.
Question: Give me a bulleted list of the last ten U.S. Open Golf Champions
Answer: Here is a bulleted list of the past ten U.S. Open Golf Champions

Winners:
- Matt Fitzpatrick
- Jon Rahm	
- Bryson DeChambeau
- Gary Woodland	
- Brooks Koepka	
- Brooks Koepka 
- Dustin Johnson	
- Jordan Spieth 
- Martin Kaymer
- Justin Rose
- Webb Simpson
- Rory McIlroy
Question: What is Violet Jessop well know for?
Evidence: Violet Constance Jessop (2 October 1887 – 5 May 1971), often referred to as the "Queen of sinking ships" or "Miss Unsinkable," was an Argentine woman of Irish heritage who worked as an ocean liner stewardess, memoirist, and nurse in the early 20th century. Jessop is most well known for having survived the sinking of both the RMS Titanic in 1912 and her sister ship the HMHS Britannic in 1916, as well as having been onboard the eldest of the three sister ships, the RMS Olympic, when it collided with the British warship HMS Hawke in 1911.
Answer: Violet Jessop is well known for been onboard the RSM Olympic when it collided in 1911, and letter surviving the RMS Titanic in 1912, and the HMHS Britannic in 1916.