DataHammer/scimrc huggingface dataset:
Question: Given a reference text about a president, tell me when he died.
Evidence: William Henry Harrison (February 9, 1773 – April 4, 1841) was an American military officer and politician who served as the ninth president of the United States. Harrison died just 31 days after his inauguration in 1841, and had the shortest presidency in United States history. He was also the first United States president to die in office, and a brief constitutional crisis resulted as presidential succession was not then fully defined in the United States Constitution. Harrison was the last president born as a British subject in the Thirteen Colonies and was the paternal grandfather of Benjamin Harrison, the 23rd president of the United States.
Answer: April 4, 1841
Question: The type of bicycle you buy will depend on a few different variables to consider. Are you looking for a city, hybrid, folding or off roading bike? How much are you looking to spend on the bicycle is also important when making these decisions as there are several hundred options out there to choose from. There are also options to customize bicycles.
Answer: I would like to spend no more than $1000 for my hybrid bicycle purchase.
Question: Given this text about speakers of varieties of Louisiana French, has the number of French speakers in Texas increased or decreased since the middle of the twentieth century?
Evidence: Reliable counts of speakers of Louisiana French are difficult to obtain as distinct from other varieties of French. However, the vast majority of native residents of Louisiana and east and southeast Texas who speak French are likely speakers of Louisiana French.

In Louisiana, as of 2010, the population of French speakers was approximately 115,183. These populations were concentrated most heavily in the southern, coastal parishes.

In Texas, as of 2010, the French-speaking population was 55,773, though many of these were likely immigrants from France and other locations, living in the urban areas. Nevertheless, in the rural eastern/southeastern Texas counties of Orange, Jefferson, Chambers, Newton, Jasper, Tyler, Liberty, and Hardin alone—areas where it can be reasonably presumed that almost all French speakers are Louisiana French speakers—the total French-speaking population was composed of 3,400 individuals. It is likely a substantial portion of the 14,493 speakers in Houston's Harris county are also Louisiana French speakers. With this in mind, a marked decline in the number of French speakers in Texas has been noticed in the last half of the twentieth century. For example, at one point[when?] the French-speaking population of Jefferson County was 24,049 as compared to the mere 1,922 today. Likewise, in Harris County the French-speaking population has shifted from 26,796 to 14,493 individuals.

Louisiana French-speaking populations can also be found in southern Mississippi and Alabama, as well as pockets in other parts of the United States.
Answer: It has generally decreased.  For example, in the second half of the twentieth century, the French-speaking population of Jefferson County was 24,049 as compared to 1,922 today. Similarly, in Harris County the French-speaking population has shifted from 26,796 to 14,493.
Question: What is the popular TV show called where they send a number of people to a deserted island and they have to vote people off of the tribe?
Answer: The show is called Survivor, a reality-competition television franchise. Contestants are left somewhere in the world and they have to practice survival skills while also competing in challenges to make it to the final three to win a grand prize.
Question: Which are most valuable corporate brands in the world
Evidence: The following article lists the most valuable corporate brands in the world according to different estimates by Kantar Group, Interbrand, Brand Finance and Forbes. Factors that influence brand value are sales, market share, market capitalization, awareness of a brand, products, popularity, image, etc. Readers should note that lists like this, while informative, are somewhat subjective, as no single metric exists for determining valuation for brands.

Top 10 valuable companies according Kantar 2022 list are Apple Inc, Google Inc, Amazon.com Inc, Microsoft corp, Tencent, McDonald, Visa Inc, Facebook, Alibaba, Louis Vuitton
Answer: Kantar group, Interbrand, Brand Finance and Forbes every year release most valuable corporate brands in the world. 
Top 10 valuable companies according Kantar 2022 list are Apple Inc (US) , Google Inc (US), Amazon.com Inc (US), Microsoft corp (US), Tencent (China), McDonald (US), Visa Inc(US), Facebook (US), Alibaba group (China) and Louis Vuitton (France) 

Factors that influence brand value are sales, market share, market capitalization, awareness of a brand, products, popularity, image, etc. Readers should note that lists like this, while informative, are somewhat subjective, as no single metric exists for determining valuation for brands.
Question: What datasets was the method evaluated on?
Evidence: To support this hypothesis, we first demonstrate that the permutation and word-dropping noise used by BIBREF19 do not improve or significantly degrade NMT accuracy, corroborating that noise might act as an indicator that the source is back-translated, without much loss in mutual information between the source and target. We then train models on WMT English-German (EnDe) without BT noise, and instead explicitly tag the synthetic data with a reserved token. We call this technique “Tagged Back-Translation" (TaggedBT). These models achieve equal to slightly higher performance than the noised variants. We repeat these experiments with WMT English-Romanian (EnRo), where NoisedBT underperforms standard BT and TaggedBT improves over both techniques. We demonstrate that TaggedBT also allows for effective iterative back-translation with EnRo, a technique which saw quality losses when applied with standard back-translation.
We perform our experiments on WMT18 EnDe bitext, WMT16 EnRo bitext, and WMT15 EnFr bitext respectively. We use WMT Newscrawl for monolingual data (2007-2017 for De, 2016 for Ro, 2007-2013 for En, and 2007-2014 for Fr). For bitext, we filter out empty sentences and sentences longer than 250 subwords. We remove pairs whose whitespace-tokenized length ratio is greater than 2. This results in about 5.0M pairs for EnDe, and 0.6M pairs for EnRo. We do not filter the EnFr bitext, resulting in 41M sentence pairs.
We then train models on WMT English-German (EnDe) without BT noise, and instead explicitly tag the synthetic data with a reserved token.
We repeat these experiments with WMT English-Romanian (EnRo), where NoisedBT underperforms standard BT and TaggedBT improves over both techniques.
We perform our experiments on WMT18 EnDe bitext, WMT16 EnRo bitext, and WMT15 EnFr bitext respectively. We use WMT Newscrawl for monolingual data (2007-2017 for De, 2016 for Ro, 2007-2013 for En, and 2007-2014 for Fr). For bitext, we filter out empty sentences and sentences longer than 250 subwords. We remove pairs whose whitespace-tokenized length ratio is greater than 2. This results in about 5.0M pairs for EnDe, and 0.6M pairs for EnRo. We do not filter the EnFr bitext, resulting in 41M sentence pairs.
Answer: The answers are shown as follows:
* WMT18 EnDe bitext
* WMT16 EnRo bitext
* WMT15 EnFr bitext
* We perform our experiments on WMT18 EnDe bitext, WMT16 EnRo bitext, and WMT15 EnFr bitext respectively. We use WMT Newscrawl for monolingual data (2007-2017 for De, 2016 for Ro, 2007-2013 for En, and 2007-2014 for Fr). For bitext, we filter out empty sentences and sentences longer than 250 subwords. We remove pairs whose whitespace-tokenized length ratio is greater than 2. This results in about 5.0M pairs for EnDe, and 0.6M pairs for EnRo. We do not filter the EnFr bitext, resulting in 41M sentence pairs.
Question: What does the simplified Chinese emotion cause corpus contain?
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.
The corpus contains 2,105 documents from SINA city news.
Answer: The corpus contains 2,105 documents from SINA city news.
Question: Which of the following are cities from South India: Bangalore, Hyderabad, Mumbai, Delhi, Chennai, Srinagar, Jaipur, Kolkata, Pune, Kochi?
Answer: Bangalore, Hyderabad, Chennai, Kochi are cities in South India.
Question: What is a quantum computer?
Answer: A quantum computer is a computer that exploits quantum mechanical phenomena. At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is still largely experimental and impractical.

The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states, which loosely means that it is in both states simultaneously. When measuring a qubit, the result is a probabilistic output of a classical bit. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently.

Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. National governments have invested heavily in experimental research that aims to develop scalable qubits with longer coherence times and lower error rates. Two of the most promising technologies are superconductors (which isolate an electrical current by eliminating electrical resistance) and ion traps (which confine a single atomic particle using electromagnetic fields).

Any computational problem that can be solved by a classical computer can also be solved by a quantum computer. Conversely, any problem that can be solved by a quantum computer can also be solved by a classical computer, at least in principle given enough time. In other words, quantum computers obey the Church–Turing thesis. This means that while quantum computers provide no additional advantages over classical computers in terms of computability, quantum algorithms for certain problems have significantly lower time complexities than corresponding known classical algorithms. Notably, quantum computers are believed to be able to solve certain problems quickly that no classical computer could solve in any feasible amount of time—a feat known as "quantum supremacy." The study of the computational complexity of problems with respect to quantum computers is known as quantum complexity theory.
Question: How to understand or classify Indian film industry?
Answer: India has a big and vibrant movie industry  producing between 1500 to 2000 movies per year. But not all of them are Bollywood movies - a term given to movies made in Hindi out of Bombay (mumbai). Only 300-500 of the moves are bollywood movies, the rest are made in over 20 different languages in different states of India. Apart from Bollywood ( movies made in Bombay), there is Tollywood (Telegu and Tamil films made in Telangana and TamilNadu), Kollywood ( Movies made in Kolkota in Bengali), Pollywood (movies made in Punjabi in the state of Punjab) , Mollywood ( movies made in Malayalam in the state of Kerala) etc.  

India is a diverse country with each state having its own distinct culture, language and food and now that is reflected in its movies as well.More and more Indians are enjoying different films from across their contries now thanks to not only cinema but newer OTT platforms like  Netflix India, Amazon Prime India, HotStar etc. 

RRR is a prime example of a Tollywood regional film winning a few oscars this year - and  it is important to note that it is NOT a bollywood or a hindi film. 

So this is usual way to classify the film industry in India - apart from other classifications like Art films or mainstream films.
Question: First, it's important to know what your goal is with a bicycle. If you go out with the family, you can choose a regular bicycle, but if you go to use for competition, it is better to choose a more sophisticated carbon fiber bicycle, for gain more performance in races.
Answer: It is correct, but I would also recommend checking the purpose of buying a bicycle, and evaluate the cost-benefit.
Question: What is ASEAN?
Answer: ASEAN stands for the Association of South East Asian Nations. It was formed as an economic and political union on the 8th of August 1967 through the Bangkok Declaration. It has a secondary objective of promoting peace. It currently consists of ten member countries in South East Asia, namely Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam
Question: Classify the below car manufacturing companies based on their countries of origin (Germany or India).
Audi, BMW, Tata Motors, Mahindra and Mahindra
Answer: Germany - Audi, BMW
India - Tata Motors, Mahindra and Mahindra
Question: What is result of their attention distribution analysis?
Evidence: In this section, we analyze the visual and text based attention mechanisms. We find that the visual attention is very sparse, in that just one source encoding is attended to (the maximum visual attention over source encodings, across the test set, has mean 0.99 and standard deviation 0.015), thereby limiting the use of modulation. Thus, in practice, we find that a small weight ($\gamma =0.1$) is necessary to prevent degradation due to this sparse visual attention component. Figure FIGREF18 & FIGREF19 shows the comparison of visual and text based attention for two sentences, one long source sentence of length 21 and one short source sentence of length 7. In both cases, we find that the visual component of the attention hasn't learnt any variation over the source encodings, again suggesting that the visual embeddings do not lend themselves to enhancing token-level discriminativess during prediction. We find this to be consistent across sentences of different lengths.
We find that the visual attention is very sparse, in that just one source encoding is attended to (the maximum visual attention over source encodings, across the test set, has mean 0.99 and standard deviation 0.015), thereby limiting the use of modulation.
In both cases, we find that the visual component of the attention hasn't learnt any variation over the source encodings, again suggesting that the visual embeddings do not lend themselves to enhancing token-level discriminativess during prediction. We find this to be consistent across sentences of different lengths.
Answer: The answers are shown as follows:
* visual attention is very sparse
*  visual component of the attention hasn't learnt any variation over the source encodings
Question: Who is the largest coatings company in the world by revenue?
Evidence: PPG Industries, Inc. is an American Fortune 500 company and global supplier of paints, coatings, and specialty materials. With headquarters in Pittsburgh, Pennsylvania, PPG operates in more than 70 countries around the globe. By revenue it is the largest coatings company in the world followed by AkzoNobel. It is headquartered in PPG Place, an office and retail complex in downtown Pittsburgh, and is known for its glass facade designed by Postmodern architect Philip Johnson.
Answer: PPG Industries, Inc.
Question: What happened to Ned Stark's ancestral family greatsword, Ice?
Answer: After he was beheaded, Ned Stark's family sword was taken by Tywin Lannnister and reforged to make two longswords: Widow's Wail, which was gifted to his eldest grandson, Joffrey, and Oathkeeper, which was gifted to his son, Jaime.
Question: In Asia Cricket Cup 2022 ,Player of the match was
Answer: Bhanuka Rajapaksa
Question: Does XLNet outperform RoBERTa?
Evidence: Speculation is a naturally occurring phenomena in textual data, forming an integral component of many systems, especially in the biomedical information retrieval domain. Previous work addressing cue detection and scope resolution (the two subtasks of speculation detection) have ranged from rule-based systems to deep learning-based approaches. In this paper, we apply three popular transformer-based architectures, BERT, XLNet and RoBERTa to this task, on two publicly available datasets, BioScope Corpus and SFU Review Corpus, reporting substantial improvements over previously reported results (by at least 0.29 F1 points on cue detection and 4.27 F1 points on scope resolution). We also experiment with joint training of the model on multiple datasets, which outperforms the single dataset training approach by a good margin. We observe that XLNet consistently outperforms BERT and RoBERTa, contrary to results on other benchmark datasets. To confirm this observation, we apply XLNet and RoBERTa to negation detection and scope resolution, reporting state-of-the-art results on negation scope resolution for the BioScope Corpus (increase of 3.16 F1 points on the BioScope Full Papers, 0.06 F1 points on the BioScope Abstracts) and the SFU Review Corpus (increase of 0.3 F1 points).
We observe that XLNet consistently outperforms BERT and RoBERTa, contrary to results on other benchmark datasets.
Answer: Yes.
Question: Identify which animal species is alive or extinct: Pelycosaurus, Hippopotamus
Answer: Hippopotamus is alive, Pelycosaurus is extinct.
Question: What are the two ways of ensembling BERT and E-BERT?
Evidence: We ensemble BERT and E-BERT by (a) mean-pooling their outputs (AVG) or (b) concatenating the entity and its name with a slash symbol (CONCAT), e.g.: Jean_Marais / Jean Mara ##is.
We ensemble BERT and E-BERT by (a) mean-pooling their outputs (AVG) or (b) concatenating the entity and its name with a slash symbol (CONCAT), e.g.: Jean_Marais / Jean Mara ##is.
Answer: The answers are shown as follows:
* mean-pooling their outputs (AVG)
* concatenating the entity and its name with a slash symbol (CONCAT)
Question: What is the PEDALS method
Answer: PEDALS is a mnemonic to help in system design interviews, which stands for Process, Estimate, Design, Articulate Data Model, List Architecture and Scale
Question: What is the best way to get a good night of rest.
Answer: Do not look at any screens with blue light 1 hour before bed. Drink tea. Meditate. Ensure there is no light in the room and it is a quiet space. Optimal temperature is 65 degrees.
Question: How do you get good at a hobby?
Answer: You can get good at a hobby with a lot of practice.  Some hobbies that require a lot of practice to get good are gymnastics, swimming, sports, and running.  The practice required for to become skilled at these hobbies is done through drills and repetition.  This creates the muscle memory needed to perform at the highest levels.  The practice also changes the body and increases your cardiovascular's system to handle high stress loads on the body with ease in the presence of less oxygen and high physical intensity.
Question: Who are the Seven Dwarfs?
Answer: The seven dwarfs are commonly referred as seven fictional characters in the fairy tale Snow White. According to the fairy tale, the seven dwarfs live in a cottage in the wood. Snow White's step mother, the Queen of the kingdom, wants Snow White dead and sends a huntsman to kill her. Snow White escapes form the Queen and hides in the dwarfs cottage. 
When the Queen knows Snow White is still alive, she goes to the dwarfs' cottage in disguise and offer Snow White a magic poisoned apple that causes her to faint. 
When the dwarfs return back that day from work in the mines, they find Snow White unconscious and believe she is dead. The dwarfs place Snow White in a glass coffin.
After some time, a prince finds Snow White in the coffin and kisses her. Snow White wakes up, marries the prince and live happily ever after.
Question: How is the accuracy of the system measured?
Evidence: The graphs in figure 1 show the distances between duplicate and non-duplicate questions using different embedding systems. The X axis shows the euclidean distance between vectors and the Y axis frequency. A perfect result would be a blue peak to the left and an entirely disconnected orange spike to the right, showing that all non-duplicate questions have a greater euclidean distance than the least similar duplicate pair of questions. As can be clearly seen in the figure above, Elmo BIBREF23 and Infersent BIBREF13 show almost no separation and therefore cannot be considered good models for this problem. A much greater disparity is shown by the Google USE models BIBREF14 , and even more for the Google USE Large model. In fact the Google USE Large achieved a F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates.
In order to test whether these results generalised to our domain, we devised a test that would make use of what little data we had to evaluate. We had no original data on whether sentences were semantically similar, but we did have a corpus of articles clustered into stories. Working on the assumption that similar claims would be more likely to be in the same story, we developed an equation to judge how well our corpus of sentences was clustered, rewarding clustering which matches the article clustering and the total number of claims clustered. The precise formula is given below, where INLINEFORM0 is the proportion of claims in clusters from one story cluster, INLINEFORM1 is the proportion of claims in the correct claim cluster, where they are from the most common story cluster, and INLINEFORM2 is the number of claims placed in clusters. A,B and C are parameters to tune. INLINEFORM3
The graphs in figure 1 show the distances between duplicate and non-duplicate questions using different embedding systems.
Large achieved a F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates.
In order to test whether these results generalised to our domain, we devised a test that would make use of what little data we had to evaluate.
Answer: The answers are shown as follows:
* F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates
* distances between duplicate and non-duplicate questions using different embedding systems