DataHammer/scimrc huggingface dataset:
Question: Given a reference text about Our Common Future, what was the purpose of the report?
Evidence: Our Common Future, also known as the Brundtland Report, was published on October 1987 by the United Nations through the Oxford University Press. This publication was in recognition of Gro Harlem Brundtland's, former Norwegian Prime Minister, role as Chair of the World Commission on Environment and Development (WCED).

Its targets were multilateralism and interdependence of nations in the search for a sustainable development path. The report sought to recapture the spirit of the Stockholm Conference which had introduced environmental concerns to the formal political development sphere. Our Common Future placed environmental issues firmly on the political agenda; it aimed to discuss the environment and development as one single issue.

The document was the culmination of a "900-day" international exercise which catalogued, analysed, and synthesised written submissions and expert testimony from "senior government representatives, scientists and experts, research institutes, industrialists, representatives of non-governmental organizations, and the general public" held at public hearings throughout the world.

The report defined 'sustainable development' as "Development that meets the needs of the present without compromising the ability of future generations to meet their own needs".
Answer: The purpose of the Our Common Future report was to focus on environmental issues on the political agenda and to discuss sustainable development.
Question: Identify which animal is domesticated or wild: Pig, Arabian ostrich
Answer: Pig is domesticated, Arabian ostrich is wild
Question: Given a reference text about Britney Spears, tell me when they were born and why they are famous.
Evidence: Britney Jean Spears (born December 2, 1981) is an American singer, actress, and dancer. Often referred to as the "Princess of Pop", she is credited with influencing the revival of teen pop during the late 1990s and early 2000s. After appearing in stage productions and television series, Spears signed with Jive Records in 1997 at age fifteen. Her first two studio albums, ...Baby One More Time (1999) and Oops!... I Did It Again (2000), are among the best-selling albums of all time and made Spears the best-selling teenage artist of all time. With first-week sales of over 1.3 million copies, Oops!... I Did It Again held the record for the fastest-selling album by a female artist in the United States for fifteen years. Spears adopted a more mature and provocative style for her albums Britney (2001) and In the Zone (2003), and starred in the 2002 film Crossroads.
Answer: Britney Spears was born December 2, 1981 she is famous for being the "princess of pop" and being the best selling teenage artist of all time.
Question: what are the three methods presented in the paper?
Evidence: FLOAT SELECTED: Table 2: MAP scoring of new methods. The timings are in seconds for the whole dev-set, and the BERT Re-ranking figure includes the initial Iterated TF-IDF step.
FLOAT SELECTED: Table 2: MAP scoring of new methods. The timings are in seconds for the whole dev-set, and the BERT Re-ranking figure includes the initial Iterated TF-IDF step.
Answer: Optimized TF-IDF, iterated TF-IDF, BERT re-ranking.
Question: What proxies for data annotation were used in previous datasets?
Evidence: Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.
In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter.
Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8.
In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content.
Answer: The answers are shown as follows:
* widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet
* Natural Language Processing (NLP) models can be used to automatically label text content
Question: What does zan zendegi azadi mean?
Answer: Zan zendegi azadi translates from Farsi to woman, life, freedom.
Question: How many layers and sublayers does the encoder block comprise of?
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.
The encoder block comprises of 6 layers, with each containing two sublayers of multi-head self-attention mechanism followed by a fully connected feed forward neural network.
Answer: 6 layers, with each containing two sublayers.
Question: What is the best way to earn money on the side?
Answer: Sell your unused belongings.
Buy, refurbish and then sell other peoples' belongings for a profit.
Tutor primary school students as the curriculum should be fairly straightforward and the main value-add is being present to guide children on school subjects or on homework.
Turn your hobby into a business.
Start your own website or blog and earn money through selling advertising on your website or blog.
If you are a dog owner, start a dog walking business.
Question: Given the following paragraph about New York City, how many people live in the city?
Evidence: New York, often called New York City or NYC, is the most populous city in the United States. With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), New York City is the most densely populated major city in the United States and more than twice as populous as Los Angeles, the nation's second-largest city. New York City is located at the southern tip of New York State. It constitutes the geographical and demographic center of both the Northeast megalopolis and the New York metropolitan area, the largest metropolitan area in the U.S. by both population and urban area. With over 20.1 million people in its metropolitan statistical area and 23.5 million in its combined statistical area as of 2020, New York is one of the world's most populous megacities, and over 58 million people live within 250 mi (400 km) of the city. New York City is a global cultural, financial, entertainment, and media center with a significant influence on commerce, health care and life sciences, research, technology, education, politics, tourism, dining, art, fashion, and sports. Home to the headquarters of the United Nations, New York is an important center for international diplomacy, and is sometimes described as the capital of the world.
Answer: As of 2020, there are over 20.1 million people living in the metropolitan statistical area of New York City. When considering the combined statistical area of New York City as of 2020, it is 23.5 million people.
Question: what state of the accuracy did they obtain?
Evidence: FLOAT SELECTED: Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015).
FLOAT SELECTED: Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015).
Answer: 51.5
Question: In what month did Jens Stub die?
Evidence: Jens Stub (3 March 1764 – 24 October 1819) was a Norwegian priest and politician. He was vicar on the island of Veøya and served as a representative at the Norwegian Constitutional Assembly at Eidsvoll in 1814.
Answer: Jens Stub died in October.
Question: How to tag each missing hypothesis?
Evidence: In the process, we have gather a total of 449 expert judgments (one expert failed to answer a given problem), 146 missing hypotheses and 47 explanations for negative judgments.
Despite being marked as “yes”, the problems with a reported missing hypothesis should really be classified as “no”, if one does not assume external knowledge. (See below for further discussion on the reported missing hypotheses.) Thus crunching the numbers, we see that more that half of the responses express some doubt about entailment. Remember that all problems were marked as “yes” by the creators of the RTE3 testsuite — we find here that one average, one expert in two is likely to cast a doubt over this “yes”.
However, each problem was classified by three experts. The histogram below shows the distribution of number of experts casting doubt on entailment, over all problems.
[ybar interval, ymax=0.5,ymin=0, minor y tick num = 0] coordinates (0, 0.26666666666666666) (1, 0.17333333333333334) (2, 0.3466666666666667) (3, 0.21333333333333335) (4,0) ;
Unfortunately we can only draw preliminary conclusions, due to the limited number of respondents for each problem. However, we can make the following observations:
We find this level of agreement indicative of a good level of reliability. Additionally, with three experts per problem, we are very likely to discover most missing hypotheses and incorrect entailments.
In our compilation of answers, we have marked 42 problems as straight “No”, 64 as “Yes” with missing implicit hypotheses and “44” as plain “Yes”. This means that, we expect, in our opinion, 28% of problems to be incorrectly labeled in RTE3 even assuming reasonable world knowledge. An additional 42% of problems require additional (yet reasonable to assume) hypotheses for entailment to hold formally, as prescribed by RTE3. This leaves only 30% of problems to acceptable as such. The reason that the amount of doubt is larger than in the average numbers quoted above is that, for many problems, certain missing hypotheses and/or error were not detected by a majority experts, but, after careful inspection, we judge that the minority report is justified.
We have additionally tagged each missing hypothesis according to the following classification:
We have additionally tagged each missing hypothesis according to the following classification: 1. Linguistic subtleties (Labeled “Language”; Example: “ownership in the past is enough to justify the possessive in the present”; 9 occurrences in our sample) 2. Lexical meaning, sometimes specific to the context of the problem; (Labeled “Lexicon”; Example: “buying entails selling”; 15 occurrences in our sample) 3. World knowledge (Labeled “World”; Example: “Increased amounts of CO2 and other greenhouse gases cause Greenhouse effect.”; 13 occurrences in our sample) 4. Other missing hypothesis, see below for further details.
Answer: The authors have additionally tagged each missing hypothesis according to the following classification: 1. Linguistic subtleties; 2. Lexical meaning; 3. World knowledge; 4. Other missing hypothesis.
Question: Jill, a 60-year-old personal training client, wants to improve her hip flexibility. Identify two exercises commonly done in a Pilates class that would address core strength and flexibility, thus improving Jill's lower back health.
Answer: One exercise is the criss-cross, which helps target the participant’s core strength and flexibility. The participant alternates bringing one shoulder towards the opposing knee, while keeping their hands on the back of their head, lying face-up, and curling their head up. Another exercise would be plank leg lift, which starts as a regular high plank. The participant alternates lifting one leg off the floor, keeping their core engaged to stabilize the movement.
Question: What is "woke" and why is it so controversial?
Answer: Woke is a general political term used to group a set of similar ideologies in an attempt to characterize the political motivations typically ascribed to the far left. Although many definitions of woke exist, these definitions run along a few common threads. Woke is typically used to describe the ideals underpinning what is known as equality of outcome. This is to be contrasted with the opposing political forces who support equality of opportunity.

Those who engage in wokeness more often than not cite disparities as evidence of systemic biases or expressions of racism, sexism, classism, genderism, agism and other prejudices. A common example would be that females are disproportionately represented in elementary school teaching whereas males are disproportionately represented in railroad construction. Woke politics would lead us to believe that this is evidence of sexism: women and men are socialized differently so they are pushed into different positions by institutions. Woke opponents would lead us to believe this is evidence of inate preferences different between the sexes: males are generally stronger than females, so men are more suited to performing heavy labor.

Wokeness is controversial because its opponents do not believe disparity is evidence of bias. Disparities can exist due to natural or cultural differences, neither of which can be governed out of existence. The opponents of woke ideology also proclaim that it ignores the root cause of problems which plague society. If one's analysis stops at disparity and from disparity an answer is assumed, one can miss the nuances present in a situation. A common unavoidable disparity cited is that 100% of births are to females, but this is not sexism. Analysis past disparity is required to prove or disprove systemic or individual bias.
Question: Name some famous hindi film actresses
Answer: Here is a list of some famous hindi actresses  - Priyanka Chopra, Aishwarya Rai, Deepika Padukone, Alia Bhatt, Anushka Sharma and Katrina Kaif
Question: Please give me a short bulleted list of the key arguments the Shohei Ohtani in his career.
Evidence: Shohei Ohtani, nicknamed "Shotime", is a Japanese professional baseball pitcher, designated hitter and outfielder for the Los Angeles Angels of Major League Baseball (MLB). He previously played for the Hokkaido Nippon-Ham Fighters of Nippon Professional Baseball's (NPB) Pacific League.
Considered early on as an elite two-way player, Ohtani was the first pick of the Fighters in the 2012 draft. He played in NPB for the Fighters from 2013 through 2017 as a pitcher and an outfielder, and won the 2016 Japan Series with them. The Fighters posted Ohtani to MLB after the 2017 season, and he signed with the Angels, soon winning the 2018 American League (AL) Rookie of the Year Award.

Following an injury-plagued 2019 and 2020, Ohtani would go on to have a 2021 season widely considered to be historic, as he became the first in the history of MLB with 10+ home runs and 20+ stolen bases as a hitter and 100+ strikeouts and 10+ pitching appearances as a pitcher in the same season while also holding at least a share of the major league lead in home runs in fourteen starts. Because of his elite contributions both offensively and as a pitcher, a rarity for two-way players, Ohtani's peak is widely considered among the greatest in baseball history, with some comparing it favorably to the early career of Babe Ruth. For his efforts, he was awarded the 2021 American League Most Valuable Player Award. He followed this in 2022 by becoming the first player in the modern era to qualify for both the hitting and pitching leaderboards in one season, reaching the limits of 3.1 plate appearances and one inning pitched per game with 586 at bats against 166 innings pitched.

Internationally, Ohtani also represented Japan in the 2023 World Baseball Classic, winning the MVP Award for the tournament following Team Japan's victory over Team USA.
Answer: Shohei Otani is a baseball player in the history of MLB. He did:
・Won the 2018 American League (AL) Rookie of the Year Award.
・10+ home runs and 20+ stolen bases as a hitter
・100+ strikeouts and 10+ pitching appearances as a pitcher in the same season.
・Awarded the 2021 American League Most Valuable Player Award. 
・Awarded the 2023 World Baseball Classic Most Valuable Player Award
Question: Given this paragraph about different types of eyes, how are eye types categorized and why?
Evidence: There are ten different eye layouts—indeed every technological method of capturing an optical image commonly used by human beings, with the exceptions of zoom and Fresnel lenses, occur in nature. Eye types can be categorised into "simple eyes", with one concave photoreceptive surface, and "compound eyes", which comprise a number of individual lenses laid out on a convex surface. Note that "simple" does not imply a reduced level of complexity or acuity. Indeed, any eye type can be adapted for almost any behaviour or environment. The only limitations specific to eye types are that of resolution—the physics of compound eyes prevents them from achieving a resolution better than 1°. Also, superposition eyes can achieve greater sensitivity than apposition eyes, so are better suited to dark-dwelling creatures. Eyes also fall into two groups on the basis of their photoreceptor's cellular construction, with the photoreceptor cells either being cilliated (as in the vertebrates) or rhabdomeric. These two groups are not monophyletic; the cnidaria also possess cilliated cells,  and some gastropods, as well as some annelids possess both.

Some organisms have photosensitive cells that do nothing but detect whether the surroundings are light or dark, which is sufficient for the entrainment of circadian rhythms. These are not considered eyes because they lack enough structure to be considered an organ, and do not produce an image.
Answer: Eyes are categorized into two types, "simple eyes" and "compound eyes". In this case, "simple eyes" do not mean that they have reduced complexity or acuity, but rather that they have one concave photoreceptive surface. This is in contrast to "compound eyes" which are comprised of a number of individual lenses on a convex surface. In other words, eyes fall into two groups based on their photoreceptor's cellular construction either being cilliated (as in the vertebrates) or rhabdomeric.
Question: Given a reference text about S26, tell me where it begins and ends.
Evidence: S26 is a line on the Berlin S-Bahn. It runs from Teltow in the district (Kreis) of Potsdam-Mittelmark via Südkreuz, through the Berlin Nord-Süd Tunnel with a stop at Friedrichstraße, and ends in Waidmannslust in the borough of Reinickendorf. On weekends the S26 runs from Teltow to Potsdamer Platz.

Trains on this line run every 20 minutes. This interval is in sections shortened to a 10-minute interval by the S25. There is no night service on this line.
Answer: S26 begins in Tetlow and ends in Waidmannslust.
Question: How does proposed word embeddings compare to Sindhi fastText word representations?
Evidence: Generally, closer words are considered more important to a word’s meaning. The word embeddings models have the ability to capture the lexical relations between words. Identifying such relationship that connects words is important in NLP applications. We measure that semantic relationship by calculating the dot product of two vectors using Eq. DISPLAY_FORM48. The high cosine similarity score denotes the closer words in the embedding matrix, while less cosine similarity score means the higher distance between word pairs. We present the cosine similarity score of different semantically or syntactically related word pairs taken from the vocabulary in Table TABREF77 along with English translation, which shows the average similarity of 0.632, 0.650, 0.591 yields by CBoW, SG and GloVe respectively. The SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText. This shows that along with performance, the vocabulary in SdfastText is also limited as compared to our proposed word embeddings.
Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391. The first query word China-Beijing is not available the vocabulary of SdfastText. However, the similarity score between Afghanistan-Kabul is lower in our proposed CBoW, SG, GloVe models because the word Kabul is the name of the capital of Afghanistan as well as it frequently appears as an adjective in Sindhi text which means able.
The SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText.
Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391.
Answer: Proposed SG model vs SINDHI FASTTEXT:
Average cosine similarity score: 0.650 vs 0.388
Average semantic relatedness similarity score between countries and their capitals: 0.663 vs 0.391
Question: What is rationing?
Answer: Rationing is the restriction of goods. 
It is mostly done when there are insufficient goods/products to meet the demand or needs of the people. 
It is used to save resources.