DataHammer/scimrc huggingface dataset:
Question: Write a monologue describing a young woman’s journey to self-actualization in America.
Answer: A young woman in America may feel a strong connection to her ancestors before her who fought tirelessly for the 19th Amendment to be added to the U.S. Constitution, protecting her right to vote, among many of the rights and liberties she enjoys today. She realizes that it’s because of them she can dream of a world of opportunities for herself beyond being a mother. 

A young, attractive woman in America will devote herself to proving she is more than a pretty face. Driven by ambition and overwhelmed with a duty to honor those who came before her, she will work hard to land the career of her dreams. When she does, one shouldn’t be surprised to see she has accidentally fallen asleep at her desk for the third time in one week and well past working hours. During her sleep, she will surely be dreaming of the day she finally makes it through all the hoops she will have had to jump through to finally achieve the American dream. 

It won’t be until her mid-20s or 30s that she will realize somewhere along the way her drive evolved into another entity entirely, one that made her single-minded and cold-hearted. She will realize that the emotional traits and feelings she denied herself for so long because she didn’t associate them with societal norms of success, were the traits that would eventually yield her the most power. 

When she does realize this is when she will have made a pivotal step toward her self-actualization. Depending on who she is, she will let her hair loose and swap her loafers for stilettos, unlocking a new level of confidence she didn’t realize was once weighed down by societal prejudices and expectations. She will occasionally use her good looks to her advantage because she will have the confidence to decide for herself which trials to endure and the grace to let herself have flaws. 

She will still worship the likes of Elizabeth Cady Stanton, but alongside them, she will find a soft spot for Cardi B and the confidence and pride her songs ignite in women and in their perception of their bodies. She will find a new respect for her friends who chose to work as stay-at-home moms, though that was not her path. 

A young, attractive woman in America might take inspiration from her ancestors on the kind of life she should lead, but she should be just as steadfast in her resolve to stay true to herself and the women of her own generation in pursuit of her goals.
Question: How is η in VMBPO chosen?
Evidence: For VMBPO we used η = 1.
Answer: We use η= 1. We have added this detail to Appendix C.1.
Question: What are the rivers in Rajkot area?
Evidence: Rajkot is located at 22.3°N 70.78°E. It has an average elevation of 128 metres (420 ft). The city is located on the bank of Aji River and Nyari River which remains dry except the monsoon months of July to September. The city is spread in the area of 170.00 km2.
Answer: There are two rivers - Aaji and Nyari - in the vicinity of Rajkot.
Question: Write a creative writing prompt to give to students for an exam about creative non-fiction about nature.
Answer: The prompt for this exam is to write a minimum of four pages addressing the following prompt: write a passage about the discovery of a fairy grove in the forest while hiking near a small creek, and the flora and fauna you see there.
Question: Does the LD2NL generate texts which can be easily understood by humans?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
The results suggest that LD2NL generates texts which can be easily understood by humans.
Answer: Yes, it does.
Question: What are some plastic use reduction ideas
Answer: Plastic is a pollutant that won't easily degrade. However; its versatility can't be dismissed and its not easy to get away from its usage in our day to day lives. We can however optimize and reduce the usage. 
1) Invest in a solid reusable grocery bag made out of non plastic material
2) If you don't have your own reusable grocery bag please insist your grocery store to bag your groceries in paper bags
3) This may be a surprise to you but the inner lining of canned beverages is plastic. So getting canned beverages may not be a 100% plastic free experience it's a much effective one than having the same beverage in a plastic bottle
4)  Offices can install filter based water machines rather than storing plastic water bottles.
5) Invest in a reusable water jug or water bottle to avoid use and throw plastic water bottles
Question: What are the main characteristics of a Haiku poem?
Evidence: Haiku (俳句, listen (help·info)) is a type of short form poetry originally from Japan. Traditional Japanese haiku consist of three phrases composed of 17 phonetic units (called on in Japanese, which are similar to syllables) in a 5, 7, 5 pattern; that include a kireji, or "cutting word"; and a kigo, or seasonal reference. Similar poems that do not adhere to these rules are generally classified as senryū.

Haiku originated as an opening part of a larger Japanese poem called renga. These haiku written as an opening stanza were known as hokku and over time they began to be written as stand-alone poems. Haiku was given its current name by the Japanese writer Masaoka Shiki at the end of the 19th century.

Originally from Japan, haiku today are written by authors worldwide. Haiku in English and haiku in other languages have different styles and traditions while still incorporating aspects of the traditional haiku form. Non-Japanese haiku vary widely on how closely they follow traditional elements. Additionally, a minority movement within modern Japanese haiku (現代俳句, gendai-haiku), supported by Ogiwara Seisensui and his disciples, has varied from the tradition of 17 on as well as taking nature as their subject.

In Japanese, haiku are traditionally printed as a single line, while haiku in English often appear as three lines, although variations exist. There are several other forms of Japanese poetry related to haiku, such as tanka, as well as other art forms that incorporate haiku, such as haibun and haiga.
Answer: Haiku is a form of poetry which is composed of 17 syllables arranged in a 5 - 7 - 5 pattern. A Haiku includes a seasonal reference.
Question: Tell me which of these things are used as rock climbing gear: lamps, guardrails, ropes, power cords, nuts, anchors, puppies, cams, belay devices.
Answer: Ropes, nuts, anchors, cams, belay devices.
Question: What is the most common air or gas mixture used in recreational scuba diving.
Evidence: For some diving, gas mixtures other than normal atmospheric air (21% oxygen, 78% nitrogen, 1% trace gases) can be used, so long as the diver is competent in their use. The most commonly used mixture is nitrox, also referred to as Enriched Air Nitrox (EAN), which is air with extra oxygen, often with 32% or 36% oxygen, and thus less nitrogen, reducing the risk of decompression sickness or allowing longer exposure to the same pressure for equal risk. The reduced nitrogen may also allow for no stops or shorter decompression stop times or a shorter surface interval between dives. A common misconception is that nitrox can reduce narcosis, but research has shown that oxygen is also narcotic.: 304
Answer: For most recreational diving atmospheric air with 21% oxygen and 78% nitrogen is used. The second most common mixture is Enriched Air Nitrox which contains extra oxygen with 32% or 36% and less Nitrogen than is found in atmospheric air, which permits divers to have shorter safety stops or shorter surface intervals.
Question: What is the training-test ratio？
Evidence:  This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified.
While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:
Scientific articles were selected from four different research areas as defined in the ACM classification, and were equally distributed into training (144 articles) and test (100 articles) sets.
Answer: 144:100.
Question: Tell me which of the following objects are shaped like spheres: beach ball, banana, pencil, marble, credit card.
Answer: Objects shaped like spheres include: beach ball and marble.
Question: How to cook Filipino chicken adobo?
Answer: Sauté two lbs. of chicken for about five minutes in a little olive oil stirring constantly. Add in 6 cloves of roughly chopped garlic and stir. Add in 3/4 c low-sodium soy sauce, a few whole peppercorns, a teaspoon of ground black pepper, a couple of dried bay leaves, and 1/4 cup of water. Bring to a boil, stir, and let simmer for an additional ten minutes. Add in 1/2 tablespoon of brown sugar and 2 tablespoon of vinegar - do not stir. You can add in half a teaspoon of crushed red pepper flakes. Let it simmer for another 5 minutes and stir. If it's getting a bit dry, add a tablespoon more of soysauce and a tablespoon of water. Simmer until the chicken is cooked through. Enjoy over warm rice.
Question: What is a sememe?
Evidence: In this work, we introduce a new dataset for the Chinese definition modeling task that we call Chinese Definition Modeling Corpus cdm(CDM). CDM consists of 104,517 entries, where each entry contains a word, the sememes of a specific word sense, and the definition in Chinese of the same word sense. Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed of several sememes, as is illustrated in Figure 1 . For a given word sense, CDM annotates the sememes according to HowNet BIBREF5 , and the definition according to Chinese Concept Dictionary (CCD) BIBREF6 . Since sememes have been widely used in improving word representation learning BIBREF7 and word similarity computation BIBREF8 , we argue that sememes can benefit the task of definition modeling.
Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed of several sememes, as is illustrated in Figure 1 .
Answer: The answers are shown as follows:
* Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed of several sememes
Question: What are the best brunch places in Burlingame, CA?
Answer: Crepevine, Stacks, Coopenhagen, Mints and Honey, Alanas, and Caffe Central.
Question: From the passage provided, extract the age at which Taylor Swift moved to Nashville
Evidence: Taylor Alison Swift (born December 13, 1989) is an American singer-songwriter. Her genre-spanning discography, songwriting, and artistic reinventions have received critical praise and wide media coverage. Born in West Reading, Pennsylvania, Swift moved to Nashville at age 14 to become a country artist. She signed a songwriting deal with Sony/ATV Music Publishing in 2004 and a recording contract with Big Machine Records in 2005. Her 2006 self-titled debut album made her the first female country artist to write a U.S. platinum-certified album.
Answer: Swift moved to Nashville at age 14
Question: Make me a list of cocktails I can order at the bar
Answer: Old Fashioned, Mojito, Cosmo, Sidecar, Martini, Whiskey Sour
Question: What is the recipe for a successful hackathon?
Answer: LabCorp had its second annual “Innovation Days” (or hackathon) event in February, 2020. The event was a success, and this post describes the secret ingredient that helped make it a success: Lipton® Onion Soup Mix. The soup mix captures the essence of a successful hackathon… some functional, and some personal to the LabCorp leadership.

The first reason that the soup mix embodies the spirit of the hackathon is simple: the product is rarely used as it was intended: to make soup. In fact, the soup mix launched into culinary notoriety in the early 1950s for its role in a non-soup recipe: potato chip dip. Referred to as “California Dip” originally, this recipe takes bland sour cream and gives it a flavorful zing and distinctive texture. For over half a century, this soup mix has been a party favorite thanks to people hacking a recipe and using materials in an unexpected manner. The benefit of the soup mix as a dip enhancer is that it helps speed up the process of making a consistently flavorful dip. For people participating in a hackathon, this is something to look for: can hackathon ideas or aspects of the hackathon projects be applied to existing processes to help streamline them?

The journey of “inventing” the soup mix also captures the essence of the hackathon. The engineer who helped create this soup mix had failed years earlier. He was not trying to make soup during the Second World War, he was trying to save lives. So he invented a - sadly ineffective - method for freeze-drying blood that could be reconstituted on the battlefield to save lives. His vision for “dried blood” being used on the battlefield was not viable. Let’s hope it did not require too many “QA” folks to realize this product was DOA. Years later, however, it was the method of freeze-drying that was in his mind when he helped design Lipton® Onion Soup Mix.

LabCorp CIO, Lance Berberian told the origin story of Lipton® Onion Soup Mix as the keynote speaker during the Innovation Days event. Mr. Berberian recounted the story from his uncle, and, like many stories told by uncles, we hope it to be true. The take-away message, as it relates to hackathons: remember your failed attempts. Aspects of your failed attempts may be core to your future successes.

Mr. Berberian was very engaged in the hackathon. In fact, his leadership team was front-and-center on launch day as well as on presentation day. The story about his uncle was powerful because it set the tone for the event: experiment, learn, network, and expect the unexpected. And the unexpected did happen during the event. Near the end of presentation day, a participant in the audience suddenly collapsed and began seizing. Many folks rushed to help and call 911. As a compassionate leader, Mr. Berberian was on the front-lines of the incident, and he stayed with the sick colleague while we waited for an ambulance to arrive. This, I believe, captures the true secret ingredient of a successful hackathon: engaged leadership.
Question: Provide a bulleted list of 5 cool things to do in Europe
Answer: The following are 5 cool things to do in Europe:
1. Go running with the bulls in Pamplona, Spain.
2. Go to a Real Madrid vs Barcelona soccer game.
3. Party in the night clubs of Ibiza.
4. Go to yacht week in Croatia. 
5. Go to Germany for Oktoberfest.
Question: How have the differences in communication styles between Twitter and Facebook increase the complexity of the problem?
Evidence: Most of the above-discussed systems either shows high performance on (a) Twitter dataset or (b) Facebook dataset (given in the TRAC-2018), but not on both English code-mixed datasets. This may be due to the text style or level of complexities of both datasets. So, we concentrated to develop a robust system for English code-mixed texts, and uni-lingual texts, which can also handle different writing styles. Our approach is based on three main ideas:
Most of the above-discussed systems either shows high performance on (a) Twitter dataset or (b) Facebook dataset (given in the TRAC-2018), but not on both English code-mixed datasets. This may be due to the text style or level of complexities of both datasets.
Answer: Systems do not perform well both in Facebook and Twitter texts
Question: How were topics of interest about DDEO identified?
Evidence: To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning “diabetes", “cancer", and “influenza" into a topic that has an overall “disease" theme BIBREF44 , BIBREF45 . Topic modeling has a wide range of applications in health and medical domains such as predicting protein-protein relationships based on the literature knowledge BIBREF46 , discovering relevant clinical concepts and structures in patients' health records BIBREF47 , and identifying patterns of clinical events in a cohort of brain cancer patients BIBREF48 .
Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . LDA assumes that a corpus contains topics such that each word in each document can be assigned to the topics with different degrees of membership BIBREF53 , BIBREF54 , BIBREF55 .
We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets. Before identifying the opinions, two pre-processing steps were implemented: (1) using a standard list for removing stop words, that do not have semantic value for analysis (such as “the"); and, (2) finding the optimum number of topics. To determine a proper number of topics, log-likelihood estimation with 80% of tweets for training and 20% of tweets for testing was used to find the highest log-likelihood, as it is the optimum number of topics BIBREF57 . The highest log-likelihood was determined 425 topics.
To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning “diabetes", “cancer", and “influenza" into a topic that has an overall “disease" theme BIBREF44 , BIBREF45 .
Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 .
We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
Answer: using topic modeling model Latent Dirichlet Allocation (LDA)
Question: What are a few ways that I can keep my house cool when it is hot in the summer?
Answer: Here are a few thoughts on how you could keep your house cool when it is hot in the summer. First, you could draw all of your blinds and shades and keep the house as dark as possible. Second, you could open the windows to let in a cool breeze if one is available. You could also turn on a fan to circulate the air. If these or a combination of these ideas are not working, then you could turn on the air conditioning.
Question: When does the world  celebrates  Hindi diwas every year
Answer: On January 10
Question: What architecture was their model based on?
Evidence: Based on the models described in Section we experiment with eight variants: (a) baseline transformer model (base); (b) base with AIC (base+sum); (c) base with AIF using spacial (base+att) or object based (base+obj) image features; (d) standard deliberation model (del); (e) deliberation models enriched with image information: del+sum, del+att and del+obj.
We base our model on the transformer architecture (Vaswani et al., 2017) for neural machine translation.
Answer: The transformer architecture (Vaswani et al., 2017) for neural machine translation.