DataHammer/scimrc huggingface dataset:
Question: Which social media platform is the best?
Answer: Instagram, YouTube, Facebook, Twitter, TikTok, Pinterest, Snapchat, and LinkedIn are all social media platforms.  It may be hard to pick which one is the best because they are all used for different purposes.  If your goal is entertainment you might want to consider youtube and tiktok.  If your goal is to find job then you should consider linkedin.  If your goal is to share family photos, person status updates, or marketing material for your business you will want to consider facebook, instagram, or pinterest.  If you goal is to get information on political issues or current events you will want to consider twitter.  If your goal is to stay up to date with your favorite celebrities you will want to consider instagram.  If you goal is to talk with family and friends you will want to consider snapchat.
Question: From the passage note down the various stars of the movie Rise of the Planet of the Apes. List the results in comma separated format.
Evidence: Rise of the Planet of the Apes is a 2011 American science fiction film directed by Rupert Wyatt and written by Rick Jaffa and Amanda Silver. The film is a reboot of the Planet of the Apes film series and its seventh installment overall. It stars Andy Serkis as Caesar, alongside James Franco, Freida Pinto, John Lithgow, Brian Cox, Tom Felton, and David Oyelowo. In the film, Caesar, a chimpanzee genetically enhanced and raised by William Rodman (Franco), goes from son to sheltered, and eventually leads an ape uprising against members of humanity.Rise of the Planet of the Apes first entered development in 2006 when Jaffa and Silver wrote a spec script and sold it to 20th Century Fox, the producers and distributors of the original film series. Production struggled until Franco, Serkis, and Wyatt were hired by late 2009, and principal photography started in July 2010 and finished that September, with filming locations including Vancouver, San Francisco, and Oahu. The apes were created using extensive visual effects and performance capture commissioned by Weta Digital.
Answer: Andy Serkis, James Franco, Freida Pinto, John Lithgow, Brian Cox, Tom Felton, David Oyelowo
Question: What kind of label do the users like? The long one or the short one?
Evidence: Currently, probabilistic topic models are important tools for improving automatic text processing including information retrieval, text categorization, summarization, etc. Besides, they can be useful in supporting expert analysis of document collections, news flows, or large volumes of messages in social networks BIBREF0 , BIBREF1 , BIBREF2 . To facilitate this analysis, such approaches as automatic topic labeling and various visualization techniques have been proposed BIBREF1 , BIBREF3 .
Boyd-Graber et al. BIBREF4 indicate that to be understandable by humans, topics should be specific, coherent, and informative. Relationships between the topic components can be inferred. In BIBREF1 four topic visualization approaches are compared. The authors of the experiment concluded that manual topic labels include a considerable number of phrases; users prefer shorter labels with more general words and tend to incorporate phrases and more generic terminology when using more complex network graph. Blei and Lafferty BIBREF3 visualize topics with ngrams consisting of words mentioned in these topics. These works show that phrases and knowledge about hyponyms/hypernyms are important for topic representation.
In this paper we describe an approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics. The structure of the paper is as follows. In Section 2 we consider related works. Section 3 describes the proposed approach. Section 4 enumerates automatic quality measures used in experiments. Section 5 presents the results obtained on several text collections according to automatic measures. Section 6 describes the results of manual evaluation of combined topic models for Islam Internet-site thematic analysis.
The authors of the experiment concluded that manual topic labels include a considerable number of phrases; users prefer shorter labels with more general words and tend to incorporate phrases and more generic terminology when using more complex network graph.
Answer: Users prefer shorter labels.
Question: What is Nematus?
Evidence: Neural Machine Translation (NMT) BIBREF0 , BIBREF1 has recently established itself as a new state-of-the art in machine translation. We present Nematus, a new toolkit for Neural Machine Translation.
Nematus has its roots in the dl4mt-tutorial. We found the codebase of the tutorial to be compact, simple and easy to extend, while also producing high translation quality. These characteristics make it a good starting point for research in NMT. Nematus has been extended to include new functionality based on recent research, and has been used to build top-performing systems to last year's shared translation tasks at WMT BIBREF2 and IWSLT BIBREF3 .
Nematus is implemented in Python, and based on the Theano framework BIBREF4 . It implements an attentional encoder–decoder architecture similar to DBLP:journals/corr/BahdanauCB14. Our neural network architecture differs in some aspect from theirs, and we will discuss differences in more detail. We will also describe additional functionality, aimed to enhance usability and performance, which has been implemented in Nematus.
We present Nematus, a new toolkit for Neural Machine Translation.
Answer: A new toolkit for Neural Machine Translation.
Question: What is in the model search space?
Evidence: Our search space consists of two stackable cells, one for the model encoder and one for the decoder (see Figure 1 ). Each cell contains NASNet-style blocks, which receive two hidden state inputs and produce new hidden states as outputs; the encoder contains six blocks and the decoder contains eight blocks, so that the Transformer can be represented exactly. The blocks perform separate transformations to each input and then combine the transformation outputs together to produce a single block output; we will refer to the transformations applied to each input as a branch. Our search space contains five branch-level search fields (input, normalization, layer, output dimension and activation), one block-level search field (combiner function) and one cell-level search field (number of cells).
Our search space consists of two stackable cells, one for the model encoder and one for the decoder
Each cell contains NASNet-style blocks, which receive two hidden state inputs and produce new hidden states as outputs
 Our search space contains five branch-level search fields (input, normalization, layer, output dimension and activation), one block-level search field (combiner function) and one cell-level search field (number of cells).
Answer: The answers are shown as follows:
* Our search space consists of two stackable cells, one for the model encoder and one for the decoder 
* Each cell contains NASNet-style blocks, which receive two hidden state inputs and produce new hidden states as outputs
* Our search space contains five branch-level search fields (input, normalization, layer, output dimension and activation), one block-level search field (combiner function) and one cell-level search field (number of cells).
Question: Give me a list of some US Army training schools
Answer: 1) Airborne School: aka "jump school" is a three week course in which soldiers are trained to conduct airborne operations by jumping out of planes. A graduate of this school is commonly known as a paratrooper. 

2) Air Assault School: an approximately two week course where soldiers are trained in Air Assault operations, which includes movement in helicopters, rappelling from helicopters, and rigging gear to helicopters. 

3) Ranger School: a 62 day leadership course that if commonly viewed as one of the  toughest schools in the military with only an approximate 50% graduation rate. This is a small unit leadership course that includes but is not limited to patrolling, small unit tactics, mountaineering skills, airborne operations, etc. Only about 1% of the entire military has graduated from this course and is considered "ranger qualified"
Question: What is an allele?
Answer: An allele is a variation of the same sequence of nucleotides at the same place on a long DNA molecule, as described in leading textbooks on genetics and evolution.

The word "Allele" is a short form of "allelomorph".

"The chromosomal or genomic location of a gene or any other genetic element is called a locus (plural: loci) and alternative DNA sequences at a locus are called alleles."

The simplest alleles are single nucleotide polymorphisms (SNP), but they can also be insertions and deletions of up to several thousand base pairs.

Popular definitions of 'allele' typically refer only to different alleles within genes. For example, the ABO blood grouping is controlled by the ABO gene, which has six common alleles (variants). In population genetics, nearly every living human's phenotype for the ABO gene is some combination of just these six alleles.

Most alleles observed result in little or no change in the function of the gene product it codes for. However, sometimes, different alleles can result in different observable phenotypic traits, such as different pigmentation. A notable example of this is Gregor Mendel's discovery that the white and purple flower colors in pea plants were the result of a single gene with two alleles.

Nearly all multicellular organisms have two sets of chromosomes at some point in their biological life cycle; that is, they are diploid. In this case, the chromosomes can be paired. Each chromosome in the pair contains the same genes in the same order, and place, along the length of the chromosome. For a given gene, if the two chromosomes contain the same allele, they, and the organism, are homozygous with respect to that gene. If the alleles are different, they, and the organism, are heterozygous with respect to that gene.
Question: Would you typically find each of the following items in a bathroom, bedroom, kitchen or living room? Couch, pillow, dish soap, bedside table, oven, shower, toilet, coffee table, coffee machine
Answer: Couch: living room, pillow: bedroom, dish soap: kitchen, bedside table: bedroom, oven: kitchen, shower: bathroom, toilet: bathroom, coffee table: living room, coffee machine: kitchen
Question: What is a database schema?
Answer: A database schema is a definition for how to organize data within a relational database.  a relational database can also have multiple schemas.  Schemas may also describe the source system the data in that schema comes from.  Schema includes table names, fields, data types, and the relationships between all these items.  Schemas are visually represented using Entity Relationship Diagrams (ERDs), and the process of creating schema definitions is called data modeling.
Question: Did Paul Ernest Adolph ever served the military?
Evidence: Paul Ernest Adolph (August 4, 1901 – 17 June 1972) was an American medical missionary born in Philadelphia, Pennsylvania. Paul served the China Inland Mission, now OMF International, from 1929 to 1941 then served in the US Army Medical Corps as a Major from 1941 to 1945. Paul returned to China to serve one last time from 1946 to 1949. After returning to the United States in 1949, he worked preparing missionaries for overseas work and restoring their health after contracting diseases. Once retired, Paul served at his sons' hospitals in central Ethiopia and Bangladesh. Paul opened two hospitals in Luan, Shansi and in Kaifeng, Henan and played a key role in the negotiations with Japanese military that kept hospitals open to Chinese populations during the Sino-Japanese war.
Answer: Yes, Paul Ernest Adolph served in the US Army Medical Corps as a Major from 1941 to 1945.
Question: Does the basic memory network model capture the sequential information in the context which is important in emotion cause extraction?
Evidence: The formal definition of emotion cause extraction is given in BIBREF31 . In this task, a given document, which [id=lq]is a passage about an emotion event, contains an emotion word INLINEFORM0 and the cause of the event. The document is manually segmented in the clause level. For each clause INLINEFORM1 consisting of INLINEFORM2 words, the goal [id=lq]is to identify which clause contains the emotion cause. [id=lq]For data representation, we can map each word into a low dimensional embedding space, a.k.a word vector BIBREF32 . All the word vectors are stacked in a word embedding matrix INLINEFORM3 , where INLINEFORM4 is the dimension of word vector and INLINEFORM5 is the vocabulary size.
For example, the sentence, “I lost my phone yesterday, I feel so sad now.” shown in Figure 1, consists of two clauses. The first clause contains the emotion cause while the second clause [id=lq]expresses the emotion of sadness. [id=lq]Current methods to emotion cause extraction cannot handle complex sentence structures where the expression of an emotion and its cause are not adjacent. We envision that the memory network can [id=lq]better model the relation between [id=lq]a emotion word and [id=lq]its emotion causes in such complex sentence structures. In our approach, we only select the clause with the highest probability to be [id=lq] thean emotion cause in each document.
However, the basic memory network model does not capture the sequential information in context which is important in emotion cause extraction.
Answer: No. it doesn't.
Question: Classify each as being used on water or land: jet ski, boat, car, bike, kayak, skateboard
Answer: Jet Ski: Water
Boat: Water
Car: Land
Bike: Land
Kayak: Water
Skateboard: Land
Question: Was Thanos right ?
Answer: In the 2018 sci-fi movie Avengers : Infinity war , the main protagonist “Thanos”, was obsessed with the idea of resource management . In a flash back to his story, he revealed that his planet suffered from overpopulation and hence lacked the necessary resources to maintain that society. The society and planet had subsequently ceased to exist. Thanos then believed that only by population control can you better manage the resources.  He set about to exterminate ½ the population of the galaxy. 

Thanos did succeed and as the 2019 blockbuster Avengers : Endgame and subsequent TV shows suggested , there were some positive environmental benefits to a reduced population. The issue is , and without stating the obvious that genocide is wrong , is that even with a reduced population, that rate that which humans would repopulate the world in the next one hundred years,  as a consequence of such staggering loss, would be exponentially faster than the last hundred years. Thereby defeating Thanos, plan for resource management. Instead Thanos should have explored technology for maximizing the longevity of those resources or looked at more sustainable solutions.
Question: What is Venture Capital?
Evidence: Venture capital (commonly abbreviated as VC) is a form of private equity financing that is provided by venture capital firms or funds to startups, early-stage, and emerging companies that have been deemed to have high growth potential or which have demonstrated high growth (in terms of number of employees, annual revenue, scale of operations, etc). Venture capital firms or funds invest in these early-stage companies in exchange for equity, or an ownership stake. Venture capitalists take on the risk of financing risky start-ups in the hopes that some of the companies they support will become successful. Because startups face high uncertainty, VC investments have high rates of failure. The start-ups are usually based on an innovative technology or business model and they are usually from high technology industries, such as information technology (IT), clean technology or biotechnology.
Answer: Venture capital firms invest in early-stage companies in exchange for equity.
Question: Are there any improvements to be done in the future?
Evidence: We use the SimpleQuestions BIBREF2 and WebQSP BIBREF25 datasets. Each question in these datasets is labeled with the gold semantic parse. Hence we can directly evaluate relation detection performance independently as well as evaluate on the KBQA end task.
SimpleQuestions (SQ): It is a single-relation KBQA task. The KB we use consists of a Freebase subset with 2M entities (FB2M) BIBREF2 , in order to compare with previous research. yin2016simple also evaluated their relation extractor on this data set and released their proposed question-relation pairs, so we run our relation detection model on their data set. For the KBQA evaluation, we also start with their entity linking results. Therefore, our results can be compared with their reported results on both tasks.
WebQSP (WQ): A multi-relation KBQA task. We use the entire Freebase KB for evaluation purposes. Following yih-EtAl:2016:P16-2, we use S-MART BIBREF26 entity-linking outputs. In order to evaluate the relation detection models, we create a new relation detection task from the WebQSP data set. For each question and its labeled semantic parse: (1) we first select the topic entity from the parse; and then (2) select all the relations and relation chains (length $\le $ 2) connected to the topic entity, and set the core-chain labeled in the parse as the positive label and all the others as the negative examples.
We tune the following hyper-parameters on development sets: (1) the size of hidden states for LSTMs ({50, 100, 200, 400}); (2) learning rate ({0.1, 0.5, 1.0, 2.0}); (3) whether the shortcut connections are between hidden states or between max-pooling results (see Section "Hierarchical Matching between Relation and Question" ); and (4) the number of training epochs.
For both the relation detection experiments and the second-step relation detection in KBQA, we have entity replacement first (see Section "Relation Detection" and Figure 1 ). All word vectors are initialized with 300- $d$ pretrained word embeddings BIBREF27 . The embeddings of relation names are randomly initialized, since existing pre-trained relation embeddings (e.g. TransE) usually support limited sets of relation names. We leave the usage of pre-trained relation embeddings to future work.
We leave the usage of pre-trained relation embeddings to future work.
Answer: Yes.
Question: What is special about the number 1729?
Evidence: The number 1729 is known as the Hardy–Ramanujan number after a famous visit by Hardy to see Ramanujan at a hospital. In Hardy's words:

I remember once going to see him when he was ill at Putney. I had ridden in taxi cab number 1729 and remarked that the number seemed to me rather a dull one, and that I hoped it was not an unfavorable omen. "No", he replied, "it is a very interesting number; it is the smallest number expressible as the sum of two cubes in two different ways."

Immediately before this anecdote, Hardy quoted Littlewood as saying, "Every positive integer was one of [Ramanujan's] personal friends."

The two different ways are:

1729
=
1
3
+
12
3
=
9
3
+
10
3
.
{\displaystyle 1729=1^{3}+12^{3}=9^{3}+10^{3}.}
Generalisations of this idea have created the notion of "taxicab numbers".
Answer: 1729, known as the Hardy–Ramanujan number, is the smallest integer that can be expressed as the sum of the cubes of two unique pairs of integers.
Question: What is the best swimming form?
Answer: It is hard to say what is the best swimming form as everyone's body may be more acclimated to a certain form. One of the most standard forms of swimming that works for most people is the breast stroke.
Question: When rock climbing, what is the difference between lead climbing and top rope climbing?
Answer: The difference between lead climbing and top rope climbing has to do with the location of the rope when the climb is started. In lead climbing the rope begins at the bottom attached to the climber as well as the belayer. The climber must then bring the rope from the bottom of the route to the top by clipping into a series of quick draws that are generally placed 10-15 feet apart. If a person were to fall while lead climbing, they would be caught by the last quick draw they clipped into. When top rope climbing, the rope is already at the top of the route. This means the climber must tie into one side of the rope while their belayer must clip into the other side of the rope. As the climber ascends higher up the route the belayer must take out the slack of the rope as opposed to lead climbing where the belayer would be giving slack. When a person falls in top rope climbing they will only fall a short distance as the rope is already clipped in above them at the top of the route, so how far they fall will be determined by how much slack the belayer has not taken in.
Question: Given a reference text about the Pitt Street Mall, tell me how big it is and how many specialty stores it has.
Evidence: Pitt Street Mall is the pedestrianised section of Pitt Street in the Sydney central business district, in the Australian state of New South Wales. Running for approximately 200 metres between Market Street and King Street, it is one block long and one of Australia's busiest and most cosmopolitan shopping precincts. Floorspace rents are the highest in Australia, in part due to other cities' shopping precincts being longer. In 2015, its rents were the fifth-highest in the world in terms of city streets.

Despite its small size, Pitt Street Mall hosts many flagship chain stores and more than 400 specialty stores. It is one of the flagship venues of Sydney Fashion Week held in the month of May.
Answer: The Pitt Street Mall is 200 meters long and has more than 400 specialty stores.
Question: Why are kids inquisitive?
Answer: As kids grow and have a sense of the world around them, they tend to be eager to understand the meaning of everything and events. In most cases, their primary source of information is the adults around them, usually their mom and data, hence they ask 'why' on a lot of things and events. This not only develops their intellectual skills, but also their social skills. It is worth mentioning that there could be several developmental and/or cognitive factors that affect this trait in some kids.
Question: Who is the little person in Game of Thrones?
Answer: Tyrion Lannister is a main character in George R.R. Martin's "Song of Ice and Fire" series. Tyrion is distinguished by his congenital dwarfism. In the "Game of Thrones" television series, which is adapted from Martin's books, Tyrion is portrayed by Peter Dinklage.
Question: How many planets are there in our solar system?
Answer: Our solar system consists of our star, the Sun and eight official planets. These planets are, in order of distance from the Sun, Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus and Neptune. There are also five dwarf planets, which are named Ceres, Pluto, Haumea, Makemake and Eres. Pluto was considered the ninth official planet but in 2006 the IAU demoted pluto from a planet to a dwarf planet, in a move that was considered controversial at the time.