DataHammer/scimrc huggingface dataset:
Question: What is the Natural Language Generation (NLG)?
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.
Natural Language Generation (NLG) is the process of automatically generating coherent Natural Language (NL) text from non-linguistic data (Reiter and Dale, 2000a).
Answer: Natural Language Generation (NLG) is the process of automatically generating coherent Natural Language (NL) text from non-linguistic data.
Question: Where can I find Yukigassen tournaments?
Evidence: Yukigassen (雪合戦) is a snowball fighting-competition from Japan. Today there are annual tournaments in Sōbetsu, Hokkaidō in Japan, Kemijärvi in Finland, Vardø in Norway, Murmansk in Russia, Mount Buller, Victoria in Australia, Luleå in Sweden, Anchorage in Alaska, Aparan in Armenia, Jasper, Alberta and Saskatoon, Saskatchewan in Canada.
Answer: Sōbetsu, Hokkaidō, Japan
Kemijärvi, Finland
Vardø, Norway
Murmansk, Russia
Mount Buller, Victoria, Australia
Luleå, Sweden
Anchorage, Alaska
Aparan, Armenia
Jasper, Alberta, Canada
Saskatoon, Saskatchewan, Canada
Question: Give me a list of the best romantic comedies
Answer: -My Big Fat Greek Wedding
-How to Lose a Guy in 10 Days
-Clueless
-13 Going On 30
-She's The Man
-Just Go With It
-The Sweetest Thing
-Set It Up
Question: Based on this passage, please extract a bulleted list of the factors of GPU's constructions that enter into the performance of the card for real-time rendering.
Evidence: Several factors of the GPU's construction enter into the performance of the card for real-time rendering. Common factors can include the size of the connector pathways in the semiconductor device fabrication, the clock signal frequency, and the number and size of various on-chip memory caches. Additionally, the number of Streaming Multiprocessors (SM) for NVidia GPUs, or Compute Units (CU) for AMD GPUs, which describe the number of core on-silicon processor units within the GPU chip that perform the core calculations, typically working in parallel with other SM/CUs on the GPU. Performance of GPUs are typically measured in floating point operations per second or FLOPS, with GPUs in the 2010s and 2020s typically delivering performance measured in teraflops (TFLOPS). This is an estimated performance measure as other factors can impact the actual display rate.
Answer: -The size of the connector pathways in the semiconductor device fabrication
-The clock signal frequency
-The number/size of various on-chip memory caches
-Number of SM or CU GPUs
Question: What are the main football teams in the city of Madrid?
Answer: There are two world-class football/soccer teams in the city of Madrid, Spain: Real Madrid and Atletico de Madrid.
Question: How much better does the model behave than baselines?
Evidence: Suppose a user wants to write a sentence “I will be 10 minutes late.” Ideally, she would type just a few keywords such as “10 minutes late” and an autocomplete system would be able to infer the intended sentence (Figure FIGREF1). Existing left-to-right autocomplete systems BIBREF0, BIBREF1 can often be inefficient, as the prefix of a sentence (e.g. “I will be”) fails to capture the core meaning of the sentence. Besides the practical goal of building a better autocomplete system, we are interested in exploring the tradeoffs inherent to such communication schemes between the efficiency of typing keywords, accuracy of reconstruction, and interpretability of keywords.
One approach to learn such schemes is to collect a supervised dataset of keywords-sentence pairs as a training set, but (i) it would be expensive to collect such data from users, and (ii) a static dataset would not capture a real user's natural predilection to adapt to the system BIBREF2. Another approach is to avoid supervision and jointly learn a user-system communication scheme to directly optimize the combination of efficiency and accuracy. However, learning in this way can lead to communication schemes that are uninterpretable to humans BIBREF3, BIBREF4 (see Appendix for additional related work).
In this work, we propose a simple, unsupervised approach to an autocomplete system that is efficient, accurate, and interpretable. For interpretability, we restrict keywords to be subsequences of their source sentences based on the intuition that humans can infer most of the original meaning from a few keywords. We then apply multi-objective optimization approaches to directly control and achieve desirable tradeoffs between efficiency and accuracy.
We observe that naively optimizing a linear combination of efficiency and accuracy terms is unstable and leads to suboptimal schemes. Thus, we propose a new objective which optimizes for communication efficiency under an accuracy constraint. We show this new objective is more stable and efficient than the linear objective at all accuracy levels.
As a proof-of-concept, we build an autocomplete system within this framework which allows a user to write sentences by specifying keywords. We empirically show that our framework produces communication schemes that are 52.16% more accurate than rule-based baselines when specifying 77.37% of sentences, and 11.73% more accurate than a naive, weighted optimization approach when specifying 53.38% of sentences. Finally, we demonstrate that humans can easily adapt to the keyword-based autocomplete system and save nearly 50% of time compared to typing a full sentence in our user study.
We empirically show that our framework produces communication schemes that are 52.16% more accurate than rule-based baselines when specifying 77.37% of sentences, and 11.73% more accurate than a naive, weighted optimization approach when specifying 53.38% of sentences.
Answer: It is 52.16% more accurate than rule-based baselines when specifying 77.37% of sentences.
Question: What datasets are used for evaluation?
Evidence: The Swissmetro dataset consists of survey data collected on the trains between St. Gallen and Geneva, Switzerland, during March 1998. According to its description BIBREF0, the respondents provided information in order to analyze the impact of the modal innovation in transportation, represented by the Swissmetro, a revolutionary mag-lev underground system, against the usual transport modes represented by car and train. After discarding respondents for which some variables were not available (e.g. age, purpose), a total of 10469 responses from 1188 individuals were used for the experiments.
The Swissmetro dataset consists of survey data collected on the trains between St. Gallen and Geneva, Switzerland, during March 1998. 
Answer: The answers are shown as follows:
* Swissmetro dataset
Question: What's the easiest way to make friends when traveling alone?
Answer: Traveling alone to anywhere in the world can be very intimidating and cal also feel lonely at times, but the great news is that it's extremely easy to make friends abroad! A couple of ways to make friends are: 
1. Visit your neighborhood bar and sit at the bar. More than likely there will be other folks going alone and you'll be able to chat with them. You can strike a conversation by easily asking them about their order or if they're from the area. 
2. Join travel groups on Facebook, Instagram and TikTok. With social media, it's much easier to find people who are traveling in the same location as you are and willing to meet up. A great group is The Travel Squad. You can join their Facebook community with thousands of solo travelers 
3. Do walking tours! There are a ton of free walking tours that you can do so make sure to sign up. 
4. Excursions is another great way to meet others. Don't be afraid to ask people where they are from or what got them interested in doing that excursion. 
5. Hostels. If you don't mind the noise levels, hostels are one of the top ways to meet fellow friends while solo traveling. They always host great music, events and have a common dining room area where people tend to hang out. 

Traveling alone can feel scary but remember that all it takes is getting out of your comfort zone and striking up a conversation to make long lasting friendships!
Question: From the passage provided, extract the list of software products sold by Adobe in the order they appear in the passage.
Evidence: Adobe Inc., originally called Adobe Systems Incorporated, is an American multinational computer software company incorporated in Delaware and headquartered in San Jose, California. It has historically specialized in software for the creation and publication of a wide range of content, including graphics, photography, illustration, animation, multimedia/video, motion pictures, and print. Its flagship products include Adobe Photoshop image editing software; Adobe Illustrator vector-based illustration software; Adobe Acrobat Reader and the Portable Document Format (PDF); and a host of tools primarily for audio-visual content creation, editing and publishing. Adobe offered a bundled solution of its products named Adobe Creative Suite, which evolved into a subscription software as a service (SaaS) offering named Adobe Creative Cloud. The company also expanded into digital marketing software and in 2021 was considered one of the top global leaders in Customer Experience Management (CXM).

Adobe was founded in December 1982 by John Warnock and Charles Geschke, who established the company after leaving Xerox PARC to develop and sell the PostScript page description language. In 1985, Apple Computer licensed PostScript for use in its LaserWriter printers, which helped spark the desktop publishing revolution. Adobe later developed animation and multimedia through its acquisition of Macromedia, from which it acquired Adobe Flash; video editing and compositing software with Adobe Premiere, later known as Adobe Premiere Pro; low-code web development with Adobe Muse; and a suite of software for digital marketing management.

As of 2022, Adobe has more than 26,000 employees worldwide. Adobe also has major development operations in the United States in Newton, New York City, Arden Hills, Lehi, Seattle, Austin and San Francisco. It also has major development operations in Noida and Bangalore in India.
Answer: - Adobe Photoshop
- Adobe Illustrator
- Adobe Acrobat Reader
- Adobe Creative Suite
- Adobe Flash
- Adobe Premiere Pro
- Adobe Muse
Question: Based on the following paragraph on squat lobster fisheries, where do such fisheries exist today?
Evidence: Flesh from these animals is often commercially sold in restaurants as "langostino" or sometimes dishonestly called "lobster" when incorporated in seafood dishes. As well as being used for human consumption, there is demand for squat lobster meat to be used as feed in fish farms and shrimp or prawn farms. This is in part because they contain astaxanthin, a pigment that helps to colour the meat of farmed salmon and trout.
Despite their worldwide distribution and great abundance, there are few functioning fisheries for squat lobsters. Experimental fisheries have occurred in several countries, including Argentina, Mexico, and New Zealand, but commercial exploitation is currently restricted to Latin America, and chiefly to Chile. The main target species are Pleuroncodes monodon, P. planipes, and Cervimunida johni.
In Central America, the primary species of squat lobster targeted by fisheries is a species of Pleuroncodes. There is a great deal of confusion over both scientific names and common names, and the exact species is often unknown. In El Salvador, for instance, the commercial catch is generally referred to as "P. planipes", but is in fact P. monodon. Commercial fishing for squat lobsters in El Salvador began in the early 1980s; production increased markedly in the 2001 season, and has continued to grow, now making up 98% of the demersal resources landed in El Salvador, with annual catches peaking at 13,708 t in 2005. In Costa Rica, aggregations of squat lobsters are avoided, as the fishermen fear the squat lobsters will clog their nets. In Nicaragua, squat lobsters are heavily exploited, especially following a large increase in fishing effort in the 2007 season. In Panama, production reached 492 t in 2008. Chilean squat lobster fisheries initially targeted Cervimunida johni, beginning in 1953. By the mid-1960s, effort had largely switched to P. monodon. In an effort to conserve stocks, the Chilean government instituted quotas for squat lobsters, and the fishery is closely monitored. In New Zealand, Munida gregaria has been considered as a potential fisheries resource, particularly to feed farmed Chinook salmon (Oncorhynchus tshawytscha).
Answer: Commercial fisheries for squat lobsters currently exist only in Latin America, mainly in Chile due to the restriction on commercial exploitation of these animals. There are, however, a number of experimental fisheries in countries such as Argentina, Mexico, and New Zealand.
Question: In his dataset coding process, what techniques does he use?
Evidence: Automated coding of political event data, or the record of who-did-what-to-whom within the context of political actions, has existed for roughly two decades. The approach has remained largely the same during this time, with the underlying coding procedures not updating to reflect changes in natural language processing (NLP) technology. These NLP technologies have now advanced to such a level, and with accompanying open-source software implementations, that their inclusion in the event-data coding process comes as an obvious advancement. When combined with changes in how news content is obtained, the ability to store and process large amounts of text, and enhancements based on two decades worth of event-data experience, it becomes clear that political event data is ready for a next generation dataset.
In this chapter, I provide the technical details for creating such a next-generation dataset. The technical details lead to a pipeline for the production of the Phoenix event dataset. The Phoenix dataset is a daily updated, near-real-time political event dataset. The coding process makes use of open-source NLP software, an abundance of online news content, and other technical advances made possible by open-source software. This enables a dataset that is transparent and replicable, while providing a more accurate coding process than previously possible. Additionally, the dataset's near-real-time nature also enables many applications that were previously impossible with batch-updated datasets, such as monitoring of ongoing events. Thus, this dataset provides a significant improvement over previous event data generation efforts.
In the following sections I briefly outline the history of computer-generated political event data to this point in history. I then outline what the “next generation” of event data should look like. Following this, I discuss the many facets of creating a real-time political event dataset, mainly from a technological and infrastructure standpoint. Finally, the paper concludes with a brief empirical view of the Phoenix event dataset, which is the output of the previously-discussed technological pipeline.
Phoenix dataset is a daily updated, nearreal-time political event dataset. The coding process makes use of open-source NLP software, an abundance of online news content, and other technical advances made possible by open-source software.
Answer: An abundance of online news content, and other technical advances made possible by open-source software.
Question: What is MapReduce?
Answer: MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. A MapReduce program is composed of a map procedure, which performs filtering and sorting, and a reduce method, which performs a summary operation.
Question: Imagine you are a grandparent who wants a grandchild. Write a persuasive argument to your children.
Answer: As a grandparent, one of my greatest joys has been raising my children. I want you to experience the same in your life. While there is hard work involved, seeing your children grow up is worth it all. I want you to be able to grow old with those memories and be surrounded by family who can spend time with you and help take care of you. I hope you consider having children.
Question: What are some of the ways society can be improved?
Answer: Each country have their own customs, political systems, economic systems and beliefs. But some of the common ways we can improve society are to give population of a country clean water, clean air, nutritious food, basic education, health care and security. Additionally, creating economic system where individuals can work and contribute to their society's cultural, economic, and social growth may also improve society overall.
Question: What is Delta Lake?
Answer: Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
Question: Categorize  the below names into fishes and insects.
 Hilsa, Catla, Maggot, Mite
Answer: Insects - Maggot, Mite
Fish - Hilsa, Catla
Question: What language is HP GL
Answer: HP-GL, short for Hewlett-Packard Graphics Language and often written as HPGL, is a printer control language created by Hewlett-Packard (HP). HP-GL was the primary printer control language used by HP plotters. It was introduced with the plotter HP-8972 in 1977 and became a standard for almost all plotters. Hewlett-Packard's printers also usually support HP-GL/2 in addition to PCL.
Question: What Peloton instructor should I take a cycling class with for the following moods and why? Happy, Sad, Angry, Unmotivated
Answer: There are 48 Peloton instructors who teach a variety of classes including: strength, tread, cycling, yoga, boxing, rowing, running, stretching, meditation, cardio, pilates, barre, and bootcamp. They have both male and female instructors representing a variety of body types. The classes are high energy in nature and feature music from todays hits, genres, decades, and more. There are classes on the platform in the following languages: German, Spanish, and English. There is a workout set to a musical playlist for every type of athlete on the Peloton platform. They have four types of intensity levels for classes: beginner, intermediate,  advanced, and all levels. As a rule of thumb, all instructors teach classes across all levels. The classes vary in length from 5 minutes to 60+ minutes long. The most popular class lengths are 10, 20, and 30 minute long classes. Many instructors on the Peloton platform have a large following on social media. Devout fans often delineate their fandom by displaying the instructor's fan hashtag within the Peloton app. An athlete looking to take a class when feeling unmotivated should take a class with the instructor Robin Arzon, she's a no none-sense instructor who will motivate you from your core, you'll often find latino beats and hip hop music as the the themes in her playlists, which translate to a high energy environment pulling even the most unmotivated person into a workout. An athlete looking to take a class when sad should take a class with Christine D'Ercole. Her reflection ride classes might bring out a cry if you're feeling sad, but she leaves space for you to feel all your feelings and work through them with soothing tunes and motion. An athlete looking to take a class when they are angry should take a class with Kendal Toole. Kendal's class will help you channel that anger into an intense workout where you will be able to get all of the anger into the movements. Plus, she has an emotions themed class on anger. For an athlete looking to take a class when happy they should take a class with Callie Gullickson, her happy go lucky personality and goofy jokes will keep the good mood going. She's known for pop playlists with upbeat tunes. She loves Trader Joes and often makes references to it in her workout classes.
Question: How do they quantify moral relevance?
Evidence: To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words.
To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words.
Answer: By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence
Question: Extract the teams that Bob Sanders played throughout his career, and put them in a comma-separated list.
Evidence: Demond "Bob" Sanders (born February 24, 1981) is an American former professional football player who was a safety in the National Football League (NFL) for eight seasons. He played college football for the Iowa Hawkeyes and was drafted by the Indianapolis Colts in the second round of the 2004 NFL Draft. He was named NFL Defensive Player of the Year in 2007 and also played for the San Diego Chargers in his final season in 2011.

Sanders was nicknamed "The Sandman" because of his hard hits and tackles, and was also called "The Eraser" by former Colts' coach Tony Dungy because of his tendency to erase the mistakes of his teammates. He spent the majority of his career battling injuries, having played more than six games in a single season only twice, in 2005 and 2007.
Answer: Iowa Hawkeyes, Indianapolis Colts, San Diego Chargers