DataHammer/scimrc huggingface dataset:
Question: What is the history of the Masters Golf Tournament
Answer: The Masters was started by amateur champion Bobby Jones and investment banker Clifford Roberts. After his grand slam in 1930, Jones acquired the former plant nursery and co-designed Augusta National with course architect Alister MacKenzie. First played 89 years ago in 1934, the tournament is an official money event on the PGA Tour, the European Tour, and the Japan Golf Tour. The field of players is smaller than those of the other major championships because it is an invitational event, held by the Augusta National Golf Club.

The tournament has a number of traditions. Since 1949, a green jacket has been awarded to the champion, who must return it to the clubhouse one year after his victory, although it remains his personal property and is stored with other champions' jackets in a specially designated cloakroom. In most instances, only a first-time and currently reigning champion may remove his jacket from the club grounds. A golfer who wins the event multiple times uses the same green jacket awarded upon his initial win (unless they need to be re-fitted with a new jacket). The Champions Dinner, inaugurated by Ben Hogan in 1952, is held on the Tuesday before each tournament, and is open only to past champions and certain board members of the Augusta National Golf Club. Beginning in 1963, legendary golfers, usually past champions, have hit an honorary tee shot on the morning of the first round to commence play. These have included Fred McLeod, Jock Hutchinson, Gene Sarazen, Sam Snead, Byron Nelson, Arnold Palmer, Jack Nicklaus, Gary Player, Lee Elder, and Tom Watson. Since 1960, a semi-social contest at the par-3 course has been played on Wednesday, the day before the first round.

Nicklaus has the most Masters wins, with six between 1963 and 1986. Tiger Woods won five between 1997 and 2019. Palmer won four between 1958 and 1964. Five have won three titles at Augusta: Jimmy Demaret, Sam Snead, Gary Player, Nick Faldo, and Phil Mickelson. Player, from South Africa, was the first non-American player to win the tournament, in 1961; the second was Seve Ballesteros of Spain, the champion in 1980 and 1983.

The Augusta National course first opened 90 years ago in 1933 and has been modified many times by different architects. Among the changes: greens have been reshaped and, on occasion, entirely re-designed, bunkers have been added, water hazards have been extended, new tee boxes have been built, hundreds of trees have been planted, and several mounds have been installed.
Question: What is Microfinance?
Answer: Microfinance refers to the financial services provided to low-income individuals or groups who are typically excluded from traditional banking. Most microfinance institutions focus on offering credit in the form of small working capital loans, sometimes called microloans or microcredit.
Question: What is FIFA?
Evidence: The recognised international governing body of football (and associated games, such as futsal and beach soccer) is FIFA. The FIFA headquarters are located in Zürich, Switzerland. Six regional confederations are associated with FIFA; these are:

Asia: Asian Football Confederation (AFC)
Africa: Confederation of African Football (CAF)
Europe: Union of European Football Associations (UEFA)
North/Central America & Caribbean: Confederation of North, Central American and Caribbean Association Football (CONCACAF)
Oceania: Oceania Football Confederation (OFC)
South America: Confederación Sudamericana de Fútbol (South American Football Confederation; CONMEBOL)
National associations (or national federations) oversee football within individual countries. These are generally synonymous with sovereign states (for example, the Cameroonian Football Federation in Cameroon), but also include a smaller number of associations responsible for sub-national entities or autonomous regions (for example, the Scottish Football Association in Scotland). 211 national associations are affiliated both with FIFA and with their respective continental confederations.

While FIFA is responsible for arranging competitions and most rules related to international competition, the actual Laws of the Game are set by the IFAB, where each of the UK Associations has one vote, while FIFA collectively has four votes.
Answer: FIFA stands for Fédération internationale de football association. It is the main international governing body for the sport of soccer (called football internationally outside the US)
Question: Extract all of the dates mentioned in this paragraph and list them using bullets in the format {Date} - {Description}
Evidence: William Brown Foley (November 15, 1855 – November 12, 1916) was a Major League Baseball third baseman. He played all or part of seven seasons in the majors, playing for five different teams in three different leagues. His career began in the National Association in 1875 with the Chicago White Stockings, and ended in the Union Association in 1884 with the Chicago Browns/Pittsburgh Stogies. From 1876 until 1879, he was the starting third baseman for the Cincinnati Reds and Milwaukee Grays.
Answer: • November 15, 1855 - William Brown Foley was born on this date.
• November 12, 1916 - William Brown Foley passed away on this date.
• 1875 - This is when Foley's career began in the National Association with the Chicago White Stockings.
• 1884 - This is when Foley's career ended.
• 1876 until 1879 - This is when he was a starting third baseman for the Cincinnati Reds and Milwaukee Grays.
Question: Which datasets are used in the paper?
Evidence: To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text. We use the MFD for moral polarity classification by dividing seed words into positive and negative sets, and for fine-grained categorization by splitting them into the 10 MFT categories.
To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words.
We divide historical time into decade-long bins, and use two sets of embeddings provided by BIBREF30, each trained on a different historical corpus of English:
Google N-grams BIBREF31: a corpus of $8.5 \times 10^{11}$ tokens collected from the English literature (Google Books, all-genres) spanning the period 1800–1999.
COHA BIBREF32: a smaller corpus of $4.1 \times 10^8$ tokens from works selected so as to be genre-balanced and representative of American English in the period 1810–2009.
To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text.
To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words.
We divide historical time into decade-long bins, and use two sets of embeddings provided by BIBREF30, each trained on a different historical corpus of English:

Google N-grams BIBREF31: a corpus of $8.5 \times 10^{11}$ tokens collected from the English literature (Google Books, all-genres) spanning the period 1800–1999.

COHA BIBREF32: a smaller corpus of $4.1 \times 10^8$ tokens from works selected so as to be genre-balanced and representative of American English in the period 1810–2009.
Answer: Google N-grams
COHA
Moral Foundations Dictionary (MFD)
Question: Which of the following countries are in Europe, and which are in the rest of the world: Nicaragua, Australia, Austria, New Zealand, Marocco, Spain, Greece, China, Chile?
Answer: Austria, Spain and Greece are in Europe. Nicaragua, Australia, New Zealand, Marocco, China and Chile are in the rest of the world.
Question: List all former departments that were formerly Cabinet-level in the United States.
Evidence: Former executive and Cabinet-level departments
Department of War (1789–1947), headed by the secretary of war: renamed Department of the Army by the National Security Act of 1947.
Department of the Navy (1798–1949), headed by the secretary of the Navy: became a military department within the Department of Defense.
Post Office Department (1829–1971), headed by the postmaster general: reorganized as the United States Postal Service, an independent agency.
National Military Establishment (1947–1949), headed by the secretary of Defense: created by the National Security Act of 1947 and recreated as the Department of Defense in 1949.
Department of the Army (1947–1949), headed by the secretary of the Army: became a military department within the Department of Defense.
Department of the Air Force (1947–1949), headed by the secretary of the Air Force: became a military department within the Department of Defense.
Answer: The Department of War, the Department of the Navy, the Post Office Department, the National Military Establishment, the Department of the Army, and the Department of the Air Force.
Question: From the text provided, find all the names of game consoles. Separate them with a comma.
Evidence: Redout is a science fiction racing video game developed and published by Italian studio 34BigThings, and co-published by Nicalis and 505 Games. It is inspired by racing games such as F-Zero, Wipeout, Rollcage, and POD as stated on the game page on Steam.

The game was released for Microsoft Windows in September 2016, while the PlayStation 4 and Xbox One released in August 2017. The Nintendo Switch port was originally slated for a Q2 2017 release, but got delayed and was released in May 2019. The game was released for Amazon Luna on October 20, 2020.
Answer: PlayStation 4, Xbox One, Nintendo Switch
Question: What is Family Mart Japan?
Evidence: The FamilyMart Company, Ltd. (株式会社ファミリーマート, Kabushikigaisha Famirīmāto) is a Japanese convenience store franchise chain. It is Japan's second largest convenience store chain, behind 7-Eleven. There are now 24,574 stores worldwide in Japan, Taiwan, China, Philippines, Thailand, Vietnam, South Korea, Indonesia, and Malaysia. Its headquarters is on the 17th floor of the Sunshine 60 building in Ikebukuro, Toshima, Tokyo. There are some stores in Japan with the name Circle K Sunkus under the operation of FamilyMart.
Answer: Family Mart is Japan's second largest convenience store chain, behind 7-Eleven.
Question: What is Dogecoin?
Answer: Dogecoin is a cryptocurrency created by software engineers Billy Markus and Jackson Palmer, who decided to create a payment system as a "joke", making fun of the wild speculation in cryptocurrencies at the time. It is considered both the first "meme coin", and more specifically the first "dog coin". Despite its satirical nature, some consider it a legitimate investment prospect. Dogecoin features the face of the Shiba Inu dog from the "doge" meme as its logo and namesake.
Question: Why do people chose to homeschool their children?
Answer: Choosing how and where to educate your child is a very important family decision.  Parents have different values and make different decisions accordingly.  Homeschooling is on the rise due to the pandemic, safety, quality of the curriculum and ideological factors.  This trend is documented in the United States and many other countries such as Canada, Australia, Mexico, France, South Korea, and the United Kingdom. According to the National Home Education Research Institute, homeschooled children usually score 15 to 30 percentile points above public school students on standardized tests.  Some research has found that overall, 78% of the compared and reviewed studies confirm that homeschooled children performed significantly better than their public schooled peers in terms of academic performance.  Social and Emotional Skills are an area where homeschools have perceived weakness, but that is not true.  Infact,  87% of peer-reviewed studies on the subject indicate that they perform statistically significantly better than those in conventional schools.
Question: What do FHIR and RDF stand for?
Evidence: Background ::: Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR)
FHIR BIBREF5 is a new open standard for healthcare data developed by the same company that developed HL7v2. However, whereas HL7v2 uses an idiosyncratic data exchange format, FHIR uses data exchange formats based on those already in wide use on the World-Wide Web such as Extensible Markup Language (XML) and JavaScript Object Notation (JSON) BIBREF6, as well as the web's familiar transfer control protocols such as HyperText Transfer Protocol Secure (HTTPS) and Representational State Transfer (REST) BIBREF6 and system of contextual hyperlinks implemented with Uniform Resource Locators / Identifiers (URL/URI) BIBREF7. This design choice simplifies interoperability and discoverability and enables applications to be built rapidly on top of FHIR by the large number of engineers already familiar with web application design without a steep learning curve.
Background ::: Resource Description Framework (RDF)
RDF is the backbone of the semantic webBIBREF8. It is described as a framework, rather than a protocol or a standard, because it is an abstact model of information whose stated goal is "to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines (a priori) the semantics of any application domain." BIBREF12 Its concrete realization is typically a serialization into one of several formats including XML, JSON, TTL, etc.
Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR)
FHIR BIBREF5 is a new open standard for healthcare data developed by the same company that developed HL7v2.
Resource Description Framework (RDF)
RDF is the backbone of the semantic webBIBREF8.
Answer: The answers are shown as follows:
* Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR)
* Resource Description Framework (RDF)
Question: Identify which instrument is string or percussion: Bass drum, Goje
Answer: Goje is string, Bass drum is percussion.
Question: What approaches without reinforcement learning have been tried?
Evidence: We conducted cross-validation experiments using various values of $t$ and $m$. Table TABREF26 shows the results for the best values of $t$ and $m$ obtained. The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. To enable a fair comparison we used the same input features in all systems. These input features combine information from the question and the input sentence and are shown in Fig. FIGREF16. The features are based on BIBREF12, and are the same as in BIBREF1, plus the addition of the position of the input snippet. The best SVC and SVR parameters were determined by grid search.
The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: “NNC top 5” uses classification labels as described in Section SECREF3, and “NNC SU4 F1” uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence. Of interest is the fact that “NNC SU4 F1” outperforms the neural regressor. We have not explored this further and we presume that the relatively good results are due to the fact that ROUGE values range between 0 and 1, which matches the full range of probability values that can be returned by the sigmoid activation of the classifier final layer.
The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively.
The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC). 
Answer: classification, regression, neural methods
Question: What items can be easily composted at home?
Answer: For a home compost bin, it is important to only compost things that can be easily turned to soil an won't attract animals or pest. Thus, you can compost fruits and vegetables, egg shells, coffee grounds, paper products like newspaper or cardboard (if ripped into smaller pieces), and yard waste like grass clippings and leaves. 

In a commercial compost, oily food scraps, dairy, meat, and bones can also be composted. It is better to leave these items out of a home compost bin.
Question: Given a reference text about Sean Dunphy, tell me where he is buried.
Evidence: Sean Dunphy (30 November 1937 – 17 May 2011) was an Irish singer who represented Ireland at the 1967 Eurovision Song Contest, achieving second place with "If I Could Choose". He was also the first Irish singer to record in Nashville.

Career
Born in Whitehall, Dublin, Dunphy first became famous in his home country as lead singer with The Hoedowners, a showband led by trumpeter Earl Gill. Between 1966 and 1973, fourteen singles by Sean Dunphy and The Hoedowners entered the Irish Charts including, in 1969, two number ones: "Lonely Woods of Upton" and "When The Fields Were White With Daisies". In the late 1970s, Dunphy went on to have two further hits as a solo artist.

Despite undergoing a quadruple heart bypass operation in 2007, Sean Dunphy continued to give live performances. In March 2009, he sang many of his greatest hits in a one-off concert at Dublin's National Concert Hall. His last public engagement was at a charity event twenty-four hours before his death.

Dunphy died at his home in Baldoyle, County Dublin and is buried at Greenogue cemetery in Ashbourne, County Meath.

Sean Dunphy and his wife Lily had four children. His son Brian is a member of the Irish band, The High Kings.
Answer: Sean Dunphy is buried at Greenogue cemetery in Ashbourne, County Meath.
Question: What state-of-the-art models are compared against?
Evidence: As a comparison to the state of the art methods, Table 1 presents accuracy results of the best Collective Matrix Factorization model, with a latent space dimension of 350, which has been determined by cross-validation on a development set, where the value of each slot is instantiated as the most probable w.r.t the inference procedure presented in Section "Spectral decomposition model for state tracking in slot-filling dialogs" . In our experiments, the variance is estimated using standard dataset reshuffling. The same results are obtained for several state of the art methods of generative and discriminative state tracking on this dataset using the publicly available results as reported in BIBREF22 . More precisely, as provided by the state-of-the-art approaches, the accuracy scores computes $p(s^*_{t+1}|s_t,z_t)$ commonly name the joint goal. Our proposition is compared to the 4 baseline trackers provided by the DSTC organisers. They are the baseline tracker (Baseline), the focus tracker (Focus), the HWU tracker (HWU) and the HWU tracker with “original” flag set to (HWU+) respectively. Then a comparison to a maximum entropy (MaxEnt) proposed in BIBREF23 type of discriminative model and finally a deep neural network (DNN) architecture proposed in BIBREF24 as reported also in BIBREF22 is presented.
Then a comparison to a maximum entropy (MaxEnt) proposed in BIBREF23 type of discriminative model and finally a deep neural network (DNN) architecture proposed in BIBREF24 as reported also in BIBREF22 is presented.


Answer: The answers are shown as follows:
* a deep neural network (DNN) architecture proposed in BIBREF24 
* maximum entropy (MaxEnt) proposed in BIBREF23 type of discriminative model
Question: Which German word means lightning war used in WW2
Answer: Blitzkrieg
Question: What baselines did they consider?
Evidence: In this work we investigate state-of-the-art methods for language modelling and sentence classification. Our contributions are centred around developing transformer-based fine-tuning approaches tailored to SR tasks. We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. We demonstrate that models based on the BERT architecture solve problems related to ambiguous sentence labels by learning to predict multiple labels reliably. Further, we show that the improved feature representation and contextualization of embeddings lead to improved performance in biomedical data extraction tasks. These fine-tuned models show promising results while providing a level of flexibility to suit reviewing tasks, such as the screening of studies for inclusion in reviews. By predicting on multilingual and full text contexts we showed that the model's capabilities for transfer learning can be useful when dealing with diverse, real-world data.
Figure FIGREF23 shows the same set of sentences, represented by concatenations of SCIBERT outputs. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences. When clustered, its embeddings yielded an adjusted rand score of 0.57 for a concatenation of the two layers, compared with 0.25 for BERT-base.
We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13.
SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences. 
Answer: The answers are shown as follows:
* LSTM
* SCIBERT
Question: Which knowledge graph completion tasks do they experiment with?
Evidence: We evaluate the effectiveness of our LAN model on two typical knowledge graph completion tasks, i.e., link prediction and triplet classification. We compare our LAN with two baseline aggregators, MEAN and LSTM, as described in the Encoder section. MEAN is used on behalf of pooling functions since it leads to the best performance in BIBREF9 ijcai2017-250. LSTM is used due to its large expressive capability BIBREF26 .
We evaluate the effectiveness of our LAN model on two typical knowledge graph completion tasks, i.e., link prediction and triplet classification.
Answer: The answers are shown as follows:
* link prediction 
* triplet classification
Question: Brainstorm a list of snacks to pack for a 3 year old and a 1 year old on a road trip
Answer: Juice boxes, fruit pouches, water bottles, gold fish, cheese it’s, cheese and crackers, strawberries, blueberries, rice crispy treats, milk, fruit snacks
Question: Why do you think humans need pets?
Answer: Humans need pets because they can provide companionship. Owning a pet means the owner must have a sense of responsibility. They need to take care of their pets every day. This has a benefit for mental health,  as the owner must take some time off from other focuses, such as work.  Pets also provide emotional support.  They can help owner calm down when they are feeling down.
Question: What is the difference between a pomelo and a grapefruit?
Answer: Both a pomelo and a grapefruit have similar citrus tastes, but a pomelo is a larger fruit that is usually greenish yellow and its outer peel bruises much more easily than the pink peel of a grapefruit.
Question: Who (not Peter Sellers) played Inspector Clouseau in 1968
Answer: Alan Arkin