DataHammer/scimrc huggingface dataset:
Question: Who is the best soccer player in the world?
Answer: There are many ways to determine who the best soccer player is at the current time.

Some would say that the Ballon d'Or award does that job for us, labeling the best player in the game at the current time. At this juncture, that is Karim Benzema of Real Madrid, who won the 2021/22 award after leading Los Blancos to the La Liga and Champions League double.

Others would say that the highest-paid player is the best. That would be Kylian Mbappe whose new PSG contract could see the French side shell out an eye-watering €630 million over the course of his new three-year contract he signed in the summer of 2022. Mbappe is thought to be the best young player in the game now, gracing the cover of the popular FIFA video game for the last two years.

Still more could argue that the player with the most goals scored would be the best player. That would make Cristiano Ronaldo the top player in the world, with his all-time record of more than 800 goals for club and country. Right behind him is his rival Lionel Messi, who also has collected far more assists than his Portuguese counterpart. Young striker Erling Haaland may have something to say about that, however, as his incredible start to the Premier League season has seen him on pace to shatter the league's single-season goalscoring record.

However, that seems to discount those who play other positions, like 2018 Ballon d'Or winner Luka Modric, whose midfield brilliance led Croatia to the last World Cup final, or 2022 Yashin trophy winner Thibaut Courtois, whose goalkeeping heroics earned him Man of the Match in the 2022 Champions League final.
Question: Identify which instrument is string or percussion: Mirwas, Esraj
Answer: Esraj is string, Mirwas is percussion.
Question: List five titles by Gertrude Chandler Warner
Answer: Surprise Island, Blue Bay Mystery, Mystery Ranch, Benny Uncovers a Mystery, The Boxcar Children
Question: Name some books of the Bible.
Answer: Some of the books of the Bible include: 1 Samuel, Lamentations, Song of Solomon, Genesis, Leviticus, Deuteronomy, Esther, Proverbs
Question: What neural machine translation models can learn in terms of transfer learning?
Evidence: Various multilingual extensions of NMT have already been proposed in the literature. The authors of BIBREF18 , BIBREF19 apply multitask learning to train models for multiple languages. Zoph and Knight BIBREF20 propose a multi-source model and BIBREF21 introduces a character-level encoder that is shared across several source languages. In our setup, we will follow the main idea proposed by Johnson et al. BIBREF22 . The authors of that paper suggest a simple addition by means of a language flag on the source language side (see Figure 2 ) to indicate the target language that needs to be produced by the decoder. This flag will be mapped on a dense vector representation and can be used to trigger the generation of the selected language. The authors of the paper argue that the model enables transfer learning and supports the translation between languages that are not explicitly available in training. This ability gives a hint of some kind of vector-based “interlingua”, which is precisely what we are looking for. However, the original paper only looks at a small number of languages and we will scale it up to a larger variation using significantly more languages to train on. More details will be given in the following section.
Various multilingual extensions of NMT have already been proposed in the literature. The authors of BIBREF18 , BIBREF19 apply multitask learning to train models for multiple languages. Zoph and Knight BIBREF20 propose a multi-source model and BIBREF21 introduces a character-level encoder that is shared across several source languages. In our setup, we will follow the main idea proposed by Johnson et al. BIBREF22 . The authors of that paper suggest a simple addition by means of a language flag on the source language side (see Figure 2 ) to indicate the target language that needs to be produced by the decoder. This flag will be mapped on a dense vector representation and can be used to trigger the generation of the selected language. The authors of the paper argue that the model enables transfer learning and supports the translation between languages that are not explicitly available in training.
Answer: Multilingual Neural Machine Translation Models
Question: What are the four statistical functionals computed for each descriptor?
Evidence: The features extracted from the transitions include 12 Mel-Frequency Cepstral Coefficients (MFCCs) with their first and second derivatives, and the log energy of the signal distributed into 22 Bark bands. The total number of descriptors corresponds to 58. Four statistical functionals (mean, standard deviation, skewness, and kurtosis) are computed for each descriptor, obtaining a 232-dimensional feature-vector per utterance. The classification of PD patients and HC speakers is performed with a radial basis SVM with margin parameter $C=10$ and a Gaussian kernel with parameter $\gamma =0.0001$. The SVM is tested following a 10-fold Cross-Validation strategy, speaker independent.
Four statistical functionals (mean, standard deviation, skewness, and kurtosis) are computed for each descriptor, obtaining a 232-dimensional feature-vector per utterance.
Answer: Mean, standard deviation, skewness, and kurtosis.
Question: How many times do the authors evaluate the method and baseline methods with different train/test splits?
Evidence: We conduct experiments on a simplified Chinese emotion cause corpus BIBREF31 , the only publicly available dataset on this task to the best of our knowledge. The corpus contains 2,105 documents from SINA city news. Each document has only one emotion word and one or more emotion causes. The documents are segmented into clauses manually. The main task is to identify which clause contains the emotion cause.

[id=lq]Details of the corpus are shown in Table 1. The metrics we used in evaluation follows lee2010text. It is commonly accepted so that we can compare our results with others. If a proposed emotion cause clause covers the annotated answer, the word sequence is considered correct. The precision, recall, and F-measure are defined by INLINEFORM0 
In the experiments, we randomly select 90% of the dataset as training data and 10% as testing data. In order to obtain statistically credible results, we evaluate our method and baseline methods 25 times with different train/test splits.
In order to obtain statistically credible results, we evaluate our method and baseline methods 25 times with different train/test splits.
Answer: 25 times
Question: What did the author decide to use to achieve the best performance in the CAp 2018 data science challenge?
Evidence: Automatically predicting the level of English of non-native speakers from their written text is an interesting text mining task. Systems that perform well in the task can be useful components for online, second-language learning platforms as well as for organisations that tutor students for this purpose. In this paper I present the system balikasg that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems. In order to achieve the best performance in the challenge, I decided to use a variety of features that describe an essay's readability and syntactic complexity as well as its content. For the prediction step, I found Gradient Boosted Trees, whose efficiency is proven in several data science challenges, to be the most efficient across a variety of classifiers.
The rest of the paper is organized as follows: in Section 2 I frame the problem of language level as an ordinal classification problem and describe the available data. Section 3 presents the feature extaction and engineering techniques used. Section 4 describes the machine learning algorithms for prediction as well as the achieved results. Finally, Section 5 concludes with discussion and avenues for future research.
In order to achieve the best performance in the challenge, I decided to use a variety of features that describe an essay's readability and syntactic complexity as well as its content.
Answer: In order to achieve the best performance in the CAp 2018 data science challenge, the author decided to use a variety of features that describe an essay's readability and syntactic complexity as well as its content.
Question: What are the Regulations of Football?
Answer: The Fédération Internationale de Football Association rules football. Their headquarters were first founded in 1904 in Switzerland.

They currently have over 200 members around the world. FIFA governs all the rules and regulations of football as played in the English Premier League, United Kingdom.
Question: How do the authors measure performance?
Evidence: FLOAT SELECTED: Table 2: Accuracies of different methods for various benchmarks on two classifier architectures. CBERT, which represents conditional BERT, performs best on two classifier structures over six datasets. “w/” represents “with”, lines marked with “*” are experiments results from Kobayashi(Kobayashi, 2018).
FLOAT SELECTED: Table 2: Accuracies of different methods for various benchmarks on two classifier architectures. CBERT, which represents conditional BERT, performs best on two classifier structures over six datasets. “w/” represents “with”, lines marked with “*” are experiments results from Kobayashi(Kobayashi, 2018).
Answer: Accuracy across six datasets
Question: Given this paragraph about Kevum, explain when it is typically consumed?
Evidence: Kevum or Kavum is a deep-fried Sri Lankan sweet made from rice flour and kithul (sugar-palm) treacle, with a number of variants adding additional ingredients. It is also known as oil cake. Kevum is traditionally given and consumed during celebrations of Sinhala and Tamil New Year.
Answer: It's consumed usually for Sinahala and Tamil celebrations. Particularly, the New Year.
Question: What are some disadvantages of the way the tax code treats incentive stock options?
Answer: There are three primary problems with the way the tax code treats incentive stock options. First, the tax code gives an employee a strong incentive to exercise the options early on, to achieve the tax benefit relating to the special option treatment. This means the tax code encourages young employees to write a check to their employer at a time when their entire financial life is likely to be dependent on that employer. This is the exact opposite of diversification, a key goal of personal financial management. The second issue is that the alternative minimum tax (AMT) is imposed at the time of exercise. This requires a potentially large cash outlay, at a time when the employee is unlikely to have sufficient liquidity to meet the burden. So not only does the employee have to pay his or her employer to cover the exercise premium, he or she has to send funds to the Internal Revenue Service as well. Finally, the requirement that an employee terminating employment must exercise within three months of termination date to preserve the tax advantage of the options encourages exercise at a time when the employee may not have sufficient funds, and may be looking at financial uncertainty if he or she does not yet have a new job lined up. The advantages of incentive stock options are significant, but they encourage the wrong behavior.
Question: Given this paragraph about Museo Municipal Taurino Francisco Montes, where is it located?
Evidence: Museo Municipal Taurino Francisco Montes is located in Chiclana de la Frontera, in the province of Cádiz, Andalusia, southwestern Spain. Situated on San Agustín Street, the bullfighting museum is dedicated to the matador Francisco Montes Reina (1805–1851), nicknamed "Paquiro", who was born in this town. He is considered the most important 19th century bullfighter, because of his skill at the sport, on foot or on horseback. The bullfighting displays include pieces of great historical and artistic value. It was inaugurated in 2003 and founded by Pedro Leal Aragon who for seven years collected objects, souvenirs and bullfighting implements. One of its first events was a group show of the artists Antonio Vela, Carlos Quevedo, and Paloma Garcia. After a renovation, it re-opened in June 2005 with miniature models of the bullring in Ronda, a head bust of Paquiro, and an original painting by Antonio Cavanna.The expansion also housed Spain's most important bullfighting collection, Collection Sagnier, which was acquired by the city to improve the historical and artistic quality of the municipal bullfighting museum. There are four rooms: bullfighting origins, Paquiro and his time, contemporary bullfighting, and the bull and the arts. The museum's garden is used for book presentations, press conferences, weddings and small concerts. It is closed on Sundays.
Answer: Museo Municipal Taurino Francisco Montes is located in Chiclana de la Frontera, in the province of Cádiz, Andalusia, southwestern Spain.
Question: Is a tomato a fruit or a vegetable?
Answer: A tomato is technically a fruit. We know this because the seeds of a tomato are on the inside. However, tomatoes are typically thought of and treated as a vegetable. We roast them in the oven, add them to salads, and include them on vegetable trays with other vegetables. If you want to win a technicality content, you can call a tomato a fruit. But don't put them in a fruit salad.
Question: Given this reference test, what is the daily recommended amount of magnesium for women in the U.S.?
Evidence: In the UK, the recommended daily values for magnesium are 300 mg for men and 270 mg for women. In the U.S. the Recommended Dietary Allowances (RDAs) are 400 mg for men ages 19–30 and 420 mg for older; for women 310 mg for ages 19–30 and 320 mg for older.
Answer: The daily recommended amount of magnesium for women in the U.S. is 310 mg for ages 19-30 and 320 mg for older.
Question: Let’s solve the following math problem. Each rabbit has 4 feets and each duck has 2 feets. If there are in total 25 heads and 88 feets, how many rabbits and how many ducks are there?
Answer: We can use equations to solve this problem. Let the number of ducks be X and the number of rabbits be Y. We have 
X+Y = 25 (1) 
2X + 4Y = 88 (2). 
From the first equation, we know that X = 25-Y and we can substitute this into the second equation (2), and we get
2(25-Y) + 4Y = 88
Which can be simplified to 50 + 2Y = 88 and we have Y = 19.
We substitute the value into (1), we will then get X = 6. 
There are 6 ducks and 19 rabbits!
Question: Given a reference text that describes the air travel disruption after the 2010 Eyjafjallajökull eruption, how long was the air travel over Europe disrupted for?
Evidence: In response to concerns that volcanic ash ejected during the 2010 eruptions of Eyjafjallajökull in Iceland would damage aircraft engines, the controlled airspace of many European countries was closed to instrument flight rules traffic, resulting in what at the time was the largest air-traffic shut-down since World War II. The closures caused millions of passengers to be stranded not only in Europe, but across the world. With large parts of European airspace closed to air traffic, many more countries were affected as flights to, from, and over Europe were cancelled.

After an initial uninterrupted shutdown over much of northern Europe from 15 to 23 April, airspace was closed intermittently in different parts of Europe in the following weeks, as the path of the ash cloud was tracked. The ash cloud caused further disruptions to air travel operations in Scotland and Ireland on 4 and 5 May and in Spain, Portugal, northern Italy, Austria, and southern Germany on 9 May. Irish and UK airspace closed again on 16 May and reopened on 17 May.

The eruption occurred beneath glacial ice. The cold water from the melting ice chilled the lava quickly, causing it to fragment into very small particles of glass (silica) and ash, which were carried into the eruption plume. The extremely fine ash particles and the large volume of steam from the glacial meltwater sent an ash plume hazardous to aircraft rapidly high into the upper atmosphere. The presence and location of the plume depended upon the state of the eruption and the winds. The large amount of glacial meltwater flowing into the eruption vent made this eruption so explosive that it ejected its ash plume directly into the jet stream, which was unusually stable and south-easterly. The ash was then carried over Europe into some of the busiest airspace in the world.

The International Air Transport Association (IATA) estimated that the airline industry worldwide would lose €148 million (US$200 million, £130 million) a day during the disruption. IATA stated that the total loss for the airline industry was around US$1.7 billion (£1.1 billion, €1.3 billion). The Airport Operators Association (AOA) estimated that airports lost £80 million over the six-and-a-half days. Over 95,000 flights had been cancelled all across Europe during the six-day airspace ban, with later figures suggesting 107,000 flights cancelled during an 8-day period, accounting for 48% of total air traffic and roughly 10 million passengers.
Answer: Initially there was an uninterrupted shutdown of the air travel from April 15 until April 23. There were intermittent disruptions in different parts of Europe in the following weeks until May 17. Overall, the air travel over Europe disrupted for 33 days between April 15 and May 17 2010.
Question: Give me a title of one of the "Harry Potter" novels.
Answer: Harry Potter and the Deathly Hallows
Question: Which three components does the components consist of?
Evidence: Since we focus on single-relation question answering in this work, our model decodes every question into a KB query that consists of exactly two elements–the topic entity, and the predicate. More formally, our model is a function $f(q, \lbrace e\rbrace , \lbrace p\rbrace )$ that takes as input a question $q$ , a set of candidate entities $\lbrace e\rbrace =e_1, ...,e_n$ , a set of candidate predicates $\lbrace p\rbrace =p_1,..., p_m$ , and produces a likelihood score $p(e_i, p_j|q)$ of generating entity $e_i$ and predicate $p_j$ given question $q$ for all $i\in {1...n}, j\in {1...m}$ .
As illustrated in Figure 1, our model consists of three components:
The details of each component are described in the following sections.
As illustrated in Figure 1, our model consists of three components: 1. A character-level LSTM-based encoder for the question which produces a sequence of embedding vectors, one for each character (Figure 1a). 2. A character-level convolutional neural network (CNN)-based encoder for the predicates/entities in a knowledge base which produces a single embedding vector for each predicate or entity (Figure 1c). 3. An LSTM-based decoder with an attention mechanism and a relevance function for generating the topic entity and predicate to form the KB query (Figure 1b).
Answer: 1. A character-level LSTM-based encoder for the question which produces a sequence of embedding vectors, one for each character (Figure 1a). 2. A character-level convolutional neural network (CNN)-based encoder for the predicates/entities in a knowledge base which produces a single embedding vector for each predicate or entity (Figure 1c). 3. An LSTM-based decoder with an attention mechanism and a relevance function for generating the topic entity and predicate to form the KB query (Figure 1b).
Question: What is the size of the dataset?
Evidence: To develop the dataset, we extracted 200 sentences from Sorani Kurdish books of grades one to three of the primary school in the Kurdistan Region of Iraq. We randomly created 2000 sentences from the extracted sentences.
To develop the dataset, we extracted 200 sentences from Sorani Kurdish books of grades one to three of the primary school in the Kurdistan Region of Iraq. We randomly created 2000 sentences from the extracted sentences.
Answer: The answers are shown as follows:
* 2000 sentences
Question: What are the sources of the data?
Evidence: We compile three Chinese text corpora from online data for three domains, namely, “hotel", “mobile phone (mobile)", and “travel". All texts are about user reviews. Each text sample collected is first partitioned into clauses according to Chinese tokens. Three clause sets are subsequently obtained from the three text corpora.
We compile three Chinese text corpora from online data for three domains, namely, “hotel", “mobile phone (mobile)", and “travel". All texts are about user reviews. 
Answer: User reviews written in Chinese collected online for hotel, mobile phone, and travel domains
Question: What is American Red Cross
Evidence: The American Red Cross (ARC), also known as the American National Red Cross, is a non-profit humanitarian organization that provides emergency assistance, disaster relief, and disaster preparedness education in the United States. It is the designated US affiliate of the International Federation of Red Cross and Red Crescent Societies and the United States movement to the International Red Cross and Red Crescent Movement.
Answer: The American Red Cross is a non profit humanitarian organization that provides emergency assistance, disaster relief, and disaster preparedness education in the United States
Question: What is an adjustable rate mortgage?
Answer: A mortgage whose interest rate and monthly payments vary throughout the life of the loan
Question: How is the score of the acoustic model computed?
Evidence: We use the beam-search decoder presented in BIBREF11 to generate word sequences given the output from our acoustic model. The decoder finds the word transcription INLINEFORM0 to maximize: INLINEFORM1 
where INLINEFORM0 is the value for the INLINEFORM1 th frame in the path leading to INLINEFORM2 and INLINEFORM3 is the (unnormalized) acoustic model score of the transcription INLINEFORM4 . The hyperparameters INLINEFORM5 respectively control the weight of the language model, the word insertion reward, and the silence insertion penalty. The other parameters are the beam size and the beam score, a threshold under which candidates are discarded even if the beam is not full. These are chosen according to a trade-off between (near-)optimality of the search and computational cost.
The score of the acoustic model is computed based on the score of paths of letters (including silences) that are compatible with the output sequence.
Answer: The score of the acoustic model is computed based on the score of paths of letters (including silences) that are compatible with the output sequence.
Question: What is the difference between a donut and a scone?
Answer: A donut is a fried piece of dough, generally made from flour, eggs and liquor. It tends to be airy and soft. A scone is a dense pastry baked in the oven, as opposed to fried. Scones are more akin to bread and can be either savory or sweet.