DataHammer/scimrc huggingface dataset:
Question: What are the key components to landscaping?
Evidence: Landscaping requires both study and observation, and the process varies in different parts of the world. Landscaping varies according to different regions. Therefore, normally local natural experts are recommended if it is done for the first time. Understanding of the site is one of the chief essentials for successful landscaping. Different natural features and phenomena, like the position of the Sun, terrain, topography, soil qualities, prevailing winds, depth of the frost line, and the system of native flora and fauna must be taken into account
Answer: Landscaping is a blend of art and science. In order to be a proficient landscaper, one must scientifically understand which types of plants react to which types of elements and conditions. In order to create a thriving landscape, the science must make sense amongst these elements for the ecosystem to thrive. Landscaping is also artistic in the sense of creating something that fits into the surroundings and is pleasing to the eye. The best landscapers understand the science and also have an artistic ability to express beauty.
Question: What does this text say about the first season of Billions?
Evidence: On Rotten Tomatoes, the series holds an overall approval rating of 88%, with a rating of 77% for season 1, 89% for season 2, 93% for season 3, 97% for season 4, and 86% for season 5. The site's critical consensus for the first season reads: "Despite a lack of likable characters, Billions' soapy melodrama and larger-than-life canvas offer plenty of repeat viewing potential." For season 2 it reads: "An influx of new characters and a fresh narrative twist give Billions an added boost during its thoroughly entertaining second season." The site's critical consensus for the third season states: "Anchored by the performances of Paul Giamatti and Damian Lewis, the third season of Billions continues a convincing fable of greed, power and competition." On Rotten Tomatoes, the fourth season reads, "With tables turned and alliances formed, Billions's fourth season goes full throttle with sharp dialogue, better rivalries, and bigger stakes." On Metacritic, the show has an overall score of 72. The first season has a weighted average score of 69 out of 100, based on reviews from 37 critics, indicating "generally favorable reviews", while the second season has a score of 67 out of 100, based on reviews from 6 critics, also indicating "generally favorable reviews". Praise for the series has steadily grown over time—also on Metacritic, its third season scored a 77 out of 100, while its fourth holds a score of 87 out 100 based on reviews from 4 critics, indicating "universal acclaim". The show's coverage of the hedge fund industry has been widely recognized as realistic.
Answer: The show Billions received a Rotten Tomatoes approval rating of 77% for season 1. The site's critical consensus for the first season reads: "Despite a lack of likable characters, Billions' soapy melodrama and larger-than-life canvas offer plenty of repeat viewing potential". On Metacritic, the first season has a weighted average score of 69 out of 100, based on reviews from 37 critics, indicating "generally favorable reviews".
Question: What is Rhetorical Structure Theory?
Evidence: Rhetorical Structure Theory (RST) BIBREF0 is a technique of Natural Language Processing (NLP), in which a document can be structured hierarchically according to its discourse. The generated hierarchy, a tree, provides information associated with the boundaries of the discourse segments and related to their importance and dependencies. The figure FIGREF1 shows an example of such a rethorical tree. In the rethorical parsing process, the text has been divided into five units. In the figure FIGREF1, the arrow that leaves the unit (2) towards the unit (1) symbolizes that the unit (2) is the satellite of the unit (1), which is the core in a “Concession” relationship. In turn, the units (1) and (2) comprise the nucleus of three “Demonstration” relationships.
The discursive analysis of a document normally includes three consecutive steps: 1) discursive segmentation; 2) detection of the discursive relations; 3) construction of the hierarchical rhetorical tree. Regarding the discursive segmentation, there are segmenters in several languages. However, each piece depends on sofisticated linguistic resources, which complicates the reproduction of the experiments in other languages. Consequently, the development of multilingual systems using discursive analysis are yet to be developed. Diverse applications based on the latest technologies require at least one of the three steps mentioned above BIBREF1, BIBREF2, BIBREF3. In this context, the idea of exploring the architecture of a generic system that is able not only of segmenting a text correctly but also of adapting it to any language, was a great motivation of this research work.
In this article we show the preliminary results of a generic segmenter composed of several systems (different segmentation strategies). In addition, we describe an automatic evaluation protocol of discursive segmentation. The article is composed by the following sections: state of the art (SECREF2), which presents a brief bibliographic review; Description of the Annodis (SECREF3) corpus used in our tests and of the general architecture of the proposed systems (SECREF4); Segmentation strategies (SECREF5), which characterizes the different methods implemented to segment the text; results of our numerical experiments (sec:experiments); and we conclude with our conclusions and perspectives (SECREF7).
Rhetorical Structure Theory (RST) [8] is a technique of Natural Language Processing (NLP), in which a document can be structured hierarchically according to its discourse. The generated hierarchy, a tree, provides information associated with the boundaries of the discourse segments and related to their importance and dependencies.
Answer: It is a technique of NLP, in which a document can be structured hierarchically by its discourse.
Question: Extract from the passage, the first engine in the Jeep Compass
Evidence: The Jeep Compass is a compact crossover SUV introduced for the 2007 model year, and is currently in its second generation. The first generation Compass and Patriot, its rebadged variant, were among Jeep's first crossover SUVs. The second-generation Compass debuted in September 2016 in Brazil and at the Los Angeles International Auto Show in November 2016, sharing a modified platform with the Renegade. It is positioned between the smaller Renegade and the larger Cherokee globally or the Commander in South America.

Concept
Four years prior to the introduction of the production Jeep Compass, a concept vehicle with the same name had its world premiere in Detroit at the 2002 North American International Auto Show. It featured a two-door body, all-wheel drive and a 3.7-liter V6 engine.

The 3.7 L Power-Tech V6 engine from the Jeep Liberty was not available in the new Compass for 2007, though the production model Compass kept the concept car's styling cues and some of its features. The production model Compass also had four doors instead of the two doors on the concept car.
Answer: The initial engine in the concept was a 3.7-liter V6 engine.
Question: Is Realpolitik diplomacy strategy based on pragmatism or idealism?
Evidence: Realpolitik (German: [ʁeˈaːlpoliˌtiːk]; from German real 'realistic, practical, actual', and Politik 'politics') is the approach of conducting diplomatic or political policies based primarily on considerations of given circumstances and factors, rather than strictly following explicit ideological notions or moral and ethical premises. In this respect, it shares aspects of its philosophical approach with those of realism and pragmatism. It is often simply referred to as pragmatism in politics, e.g. "pursuing pragmatic policies" or "realistic policies".
Answer: Pragmatism
Question: What languages are seen in the news and TED datasets?
Evidence: As one of our aims is to compare coreference chain properties in automatic translation with those of the source texts and human reference, we derive data from ParCorFull, an English-German corpus annotated with full coreference chains BIBREF46. The corpus contains ca. 160.7 thousand tokens manually annotated with about 14.9 thousand mentions and 4.7 thousand coreference chains. For our analysis, we select a portion of English news texts and TED talks from ParCorFull and translate them with the three NMT systems described in SECREF4 above. As texts considerably differ in their length, we select 17 news texts (494 sentences) and four TED talks (518 sentences). The size (in tokens) of the total data set under analysis – source (src) and human translations (ref) from ParCorFull and the automatic translations produced within this study (S1, S2 and S3) are presented in Table TABREF20.
As one of our aims is to compare coreference chain properties in automatic translation with those of the source texts and human reference, we derive data from ParCorFull, an English-German corpus annotated with full coreference chains BIBREF46.
Answer: The answers are shown as follows:
* English
* German
Question: Who is Elizabeth Warren?
Evidence: Elizabeth Ann Warren (née Herring; born June 22, 1949) is an American politician and former law professor who is the senior United States senator from Massachusetts, serving since 2013. A member of the Democratic Party and regarded as a progressive, Warren has focused on consumer protection, equitable economic opportunity, and the social safety net while in the Senate. Warren was a candidate in the 2020 Democratic Party presidential primaries, ultimately finishing third.
Answer: Elizabeth Ann Warren (née Herring; born June 22, 1949) is an American politician and former law professor who is the senior United States senator from Massachusetts, serving since 2013. A member of the Democratic Party and regarded as a progressive, Warren has focused on consumer protection, equitable economic opportunity, and the social safety net while in the Senate. Warren was a candidate in the 2020 Democratic Party presidential primaries, ultimately finishing third.

Born and raised in Oklahoma, Warren is a graduate of the University of Houston and Rutgers Law School and has taught law at several universities, including the University of Houston, the University of Texas at Austin, the University of Pennsylvania, and Harvard University. She was one of the most influential professors in commercial and bankruptcy law before beginning her political career. Warren has written 12 books and more than 100 articles.

Warren's first foray into public policy began in 1995, when she worked to oppose what eventually became a 2005 act restricting bankruptcy access for individuals. During the late 2000s, her national profile grew after her forceful public stances in favor of more stringent banking regulations after the financial crisis of 2007–08. She served as chair of the Congressional Oversight Panel of the Troubled Asset Relief Program, and proposed and established the Consumer Financial Protection Bureau, for which she served as the first special advisor under President Barack Obama.

In 2012, Warren defeated incumbent Republican Scott Brown and became the first female U.S. senator from Massachusetts. She won re-election by a wide margin in 2018, defeating Republican nominee Geoff Diehl. On February 9, 2019, Warren announced her candidacy in the 2020 United States presidential election. She was briefly considered the front-runner for the Democratic nomination in late 2019, but support for her campaign dwindled. She withdrew from the race on March 5, 2020, after Super Tuesday.
Question: Where is Lake Manitou?
Evidence: Ivry-sur-le-Lac is a village and municipality in the Laurentides region of Quebec, Canada, part of the Les Laurentides Regional County Municipality. The municipality completely encompasses Lake Manitou which is a popular cottage vacation location.

The village of Ivry-sur-le-Lac is located at the head of the north-east bay (Lacasse Bay), and the hamlet of Lac-Manitou-Sud is at the head of the south-east bay of Lake Manitou.
Answer: Lake Manitou is in Quebec Canada, in the municipality of Ivry-sur-le-Lac.
Question: What are the New Rules for 2022?
Answer: IFAB introduced twelve new football rules for the 2019/20 season. They include changes to the handball rule and sterner punishments for an 'illegal' goal celebration.

The official Laws of the Game 2022 PDF contains 228 pages. Check out a summarisation of 12 new FIFA rule changes brought into play from the 1st of June.
Question: Which of the following are used in cricket? Classify as 'cricket' and 'not cricket' - bat, wickets, ball, pitch, boundary, six, four, goal, touchdown, strike, out, duck, powerplay, set, defender, goalkeeper.
Answer: In order - 'cricket', 'cricket', 'cricket', 'cricket', 'cricket', 'cricket', 'cricket', 'not cricket', 'not cricket', 'not cricket', 'cricket', 'cricket', 'cricket', 'not cricket', 'not cricket', 'not cricket'.
Question: Classify each of the following as either a chip or a cracker: Goldfish, Wheat Thins, Pringles, Doritos, Cheez-Its, Lays, Ruffles
Answer: Goldfish: Cracker
Wheat Thins: Cracker
Pringles: Chip
Doritos: Chip
Cheez-Its: Cracker
Lays: Chip
Ruffles: Chip
Question: Name a few Bhakti poets from the passage.
Evidence: The Bhakti movement of late medieval Hinduism started in the 7th century, but rapidly expanded after the 12th century. It was supported by the Puranic literature such as the Bhagavata Purana, poetic works, as well as many scholarly bhasyas and samhitas.This period saw the growth of Vashnavism Sampradayas (denominations or communities) under the influence of scholars such as Ramanujacharya, Vedanta Desika, Madhvacharya and Vallabhacharya. Bhakti poets or teachers such as Manavala Mamunigal, Namdev, Ramananda, Sankardev, Surdas, Tulsidas, Eknath, Tyagaraja, Chaitanya Mahaprabhu and many others influenced the expansion of Vaishnavism.Even Meera (princess of Mehwar and Rajasthan) took part in this specific movement. These Vaishnavism sampradaya founders rejected Shankara's doctrines of Advaita Vedanta, particularly Ramanuja in the 12th century, Vedanta Desika and Madhva in the 13th, building their theology on the devotional tradition of the Alvars (Sri Vaishnavas).In North and Eastern India, Vaishnavism gave rise to various late Medieval movements Ramananda in the 14th century, Sankaradeva in the 15th and Vallabha and Chaitanya in the 16th century. Historically, it was Chaitanya Mahaprabhu who founded congregational chanting of holy names of Krishna in the early 16th century after becoming a sannyasi.
Answer: Manavala Mamunigal, Namdev, Ramananda, Sankardev, Surdas, Tulsidas, Eknath, Tyagaraja, Chaitanya Mahaprabhu
Question: By how much did they outperform the other methods?
Evidence: FLOAT SELECTED: Table 6: Comparison of ACC of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
FLOAT SELECTED: Table 7: Comparison of NMI of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
FLOAT SELECTED: Table 6: Comparison of ACC of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
FLOAT SELECTED: Table 7: Comparison of NMI of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
Answer: on SearchSnippets dataset by 6.72% in ACC, by 6.94% in NMI; on Biomedical dataset by 5.77% in ACC, 3.91% in NMI
Question: Who is the youngest driver to win a Formula 1 race?
Evidence: Max Verstappen is the youngest winner of a Grand Prix; he was 18 years and 228 days old when he won the 2016 Spanish Grand Prix
Answer: Max Verstappen is the youngest driver to win a F1 race with 18 years and 228 days old at the 2016 Spanish Grand Prix, during his first race at Red Bull Racing Team
Question: Write an intro to a meetup about music, medicine, and machines
Answer: Music, Medicine, and Machines

Join us for an imaginative, authentic, and offbeat networking experience that will advance your knowledge of the technologies that impact the health and well-being of our human condition.  To make things interesting (but keep things real), our meetup incorporates an element of music to remind us, first and foremost, we want to advance technology for the greater good, but we don’t want to become robots ourselves.  Instead, we believe that “music is medicine for the soul” so our events will use a dose of music to unite us and to keep things fun and grounded in our humanity.  We’ll explore hot and emerging technologies such as:

Machine Learning and all things AI
Computer and Machine Vision
Telemedicine
Genomics
AR/VR/MR 
Robotics
Cloud 
DevOps, CI/CD, and Robotic Process Automation (RPA)
Infrastructure as Code (IaC) 
Chatbots
Wearable Tech
3D Printing
Blockchain
And many more

We’ll talk about how these disruptive technologies improve Health & Life Sciences and discuss the tenuous balance of innovation + opportunities vs privacy, security, open data, regulations, etc.  We’ll network and get to know each other to explore how each of us can get involved to ensure “the machines” benefit the communities we serve. We encourage attendees such as developers, clinicians, researchers, industry experts, students, educators, industry analysts, regulators, investors, startups, musicians, and all those willing to contribute meaningfully to our mission.
Question: Suggest a road trip in Washington state
Answer: Visiting Olympic National Park, Mt Rainier, Mt Baker, Lake Chelan, Snoqualmie Falls, Moses Lake, and Bainbridge Island are all road trips that are very scenic and beautiful
Question: Which experiments are perfomed?
Evidence: In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias is pervasive in these texts. More than $56\%$ of Americans believe that news sources do not report the news objectively , thus implying the prevalence of the bias. Therefore, when presenting factual information, it becomes necessary to differentiate subjective language from objective language.
In this work, we investigate the application of BERT-based models for the task of subjective language detection. We explore various BERT-based models, including BERT, RoBERTa, ALBERT, with their base and large specifications along with their native classifiers. We propose an ensemble model exploiting predictions from these models using multiple ensembling techniques. We show that our model outperforms the baselines by a margin of $5.6$ of F1 score and $5.95\%$ of Accuracy.
Experiments ::: Dataset and Experimental Settings
We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019. We randomly shuffled these sentences and split this dataset into two parts in a $90:10$ Train-Test split and perform the evaluation on the held-out test dataset.
In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints.
In this work, we investigate the application of BERT-based models for the task of subjective language detection.
Experiments ::: Dataset and Experimental Settings
We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019
Answer: They used BERT-based models to detect subjective language in the WNC corpus
Question: Why is gluten bad for weight loss
Answer: For centuries, gluten-free foods have provided people with protein, soluble fiber, and nutrients. 
Gluten, especially gluten found in whole grains, is not bad for healthy people whose bodies can tolerate it.
It's common in bread, pasta, pizza, and cereal. Gluten provides no essential nutrients, and people with celiac disease have an immune reaction triggered by eating gluten.
But if you eat a gluten-free diet, you may make healthier food choices because you're more aware of how to read food labels.
Question: What color do most people perceive the sky to be on a sunny day?
Answer: Most people perceive the sky to be blue on a sunny day.
Question: Which of the following ski resorts are in Colorado and which are in Utah: Alta Ski Area, Brighton Ski Resort, Snowbird, Solitude Mountain Resort, Deer Valley Resort, Park City Mountain,  Woodward Park City, Nordic Valley Resort, Powder Mountain, Snowbasin, Sundance Resort, Beaver Mountain Resort, Cherry Peak Resort, Brian Head Resort, Eagle Point Resort, Keystone, Copper Mountain, Vail Resort, Beaver Creek, Eldora
Answer: The following ski resorts are in Utah: Alta Ski Area, Brighton Ski Resort, Snowbird, Solitude Mountain Resort, Deer Valley Resort, Park City Mountain,  Woodward Park City, Nordic Valley Resort, Powder Mountain, Snowbasin, Sundance Resort, Beaver Mountain Resort, Cherry Peak Resort, Brian Head Resort, Eagle Point Resort. The following ski resorts are in Colorado: Keystone, Copper Mountain, Vail Resort, Beaver Creek, Eldora
Question: How does the well-resourced language impact the quality of the output?
Evidence: In this work we train bilingual UWS models using the endangered language Mboshi as target and different well-resourced languages as aligned information. Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved. This might be due to the different language-dependent structures that are captured by using more than one language. Lastly, we extend the bilingual Mboshi-French parallel corpus, creating a multilingual corpus for the endangered language Mboshi that we make available to the community.
In this work we train bilingual UWS models using the endangered language Mboshi as target and different well-resourced languages as aligned information. Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved. This might be due to the different language-dependent structures that are captured by using more than one language. 
Answer: The answers are shown as follows:
* Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved.