DataHammer/scimrc huggingface dataset:
Question: What is the KeyVec introduced in this paper?
Evidence: In recent years, the use of word representations, such as word2vec BIBREF0 , BIBREF1 and GloVe BIBREF2 , has become a key “secret sauce” for the success of many natural language processing (NLP), information retrieval (IR) and machine learning (ML) tasks. The empirical success of word embeddings raises an interesting research question: Beyond words, can we learn fixed-length distributed representations for pieces of texts? The texts can be of variable-length, ranging from paragraphs to documents. Such document representations play a vital role in a large number of downstream NLP/IR/ML applications, such as text clustering, sentiment analysis, and document retrieval, which treat each piece of text as an instance. Learning a good representation that captures the semantics of each document is thus essential for the success of such applications.
In this paper, we introduce KeyVec, a neural network model that learns densely distributed representations for documents of variable-length. In order to capture semantics, the document representations are trained and optimized in a way to recover key information of the documents. In particular, given a document, the KeyVec model constructs a fixed-length vector to be able to predict both salient sentences and key words in the document. In this way, KeyVec conquers the problem of prior embedding models which treat every word and every sentence equally, failing to identify the key information that a document conveys. As a result, the vectorial representations generated by KeyVec can naturally capture the topics of the documents, and thus should yield good performance in downstream tasks.
We evaluate our KeyVec on two text understanding tasks: document retrieval and document clustering. As shown in the experimental section SECREF5 , KeyVec yields generic document representations that perform better than state-of-the-art embedding models.
In this paper, we introduce KeyVec, a neural network model that learns densely distributed representations for documents of variable-length.
Answer: It is a neural network model that learns densely distributed representations for documents of variable-length.
Question: What makes a good sales account executive?
Answer: A good Sales account executive will be able to multi-task, manage customer engagement activity for multiple teams, and expand relationships within a customer account by sharing the value proposition of the Company.
Question: From when are many VQA datasets collected?
Evidence: VQA research began in earnest in late 2014 when the DAQUAR dataset was released BIBREF0 . Including DAQUAR, six major VQA datasets have been released, and algorithms have rapidly improved. On the most popular dataset, `The VQA Dataset' BIBREF1 , the best algorithms are now approaching 70% accuracy BIBREF2 (human performance is 83%). While these results are promising, there are critical problems with existing datasets in terms of multiple kinds of biases. Moreover, because existing datasets do not group instances into meaningful categories, it is not easy to compare the abilities of individual algorithms. For example, one method may excel at color questions compared to answering questions requiring spatial reasoning. Because color questions are far more common in the dataset, an algorithm that performs well at spatial reasoning will not be appropriately rewarded for that feat due to the evaluation metrics that are used.
VQA research began in earnest in late 2014 when the DAQUAR dataset was released BIBREF0
Answer: late 2014
Question: Based on the reference text, what colors do the Tampa Bay Rowdies wear and what stadium do they play in?
Evidence: The Tampa Bay Rowdies are an American professional soccer team based in St. Petersburg, Florida. The club was founded in 2008 and first took the pitch in 2010. Since 2017, the Rowdies have been members of the USL Championship in the second tier of the American soccer pyramid. They formerly played in USSF Division 2 (in 2010) and the North American Soccer League (NASL) (from 2011 to 2016), which were also second-tier leagues. The Rowdies play their home games at Al Lang Stadium on St. Petersburg's downtown waterfront.

The current club is a phoenix club of the original Tampa Bay Rowdies, who were active from 1975 until 1993, most notably in the original North American Soccer League. It shares its name, logo, and some of its club culture with the original club. The owners of the current club announced their intention to use the old Rowdies' trademarks at its introductory press conference in 2008. However, licensing issues forced the club to use the name FC Tampa Bay until December 2011, when it gained full rights to the Rowdies name and other intellectual property. The current Rowdies have always used the same green and yellow color scheme and "hoops" as the original team, even when they could not yet use the Rowdies name.

The Rowdies captured the NASL championship in Soccer Bowl 2012, and their team shield includes two stars: one for their 2012 win and one for the 1975 Soccer Bowl championship won by the original Rowdies. The club has had a long-standing rivalry with the Fort Lauderdale Strikers, with whom they have contested the Florida Derby since the original Rowdies and Strikers first met in 1977. The Rowdies were also named co-league champions in 2020 after winning the USL Regular Season title and Eastern Conference Championship, but the title game was canceled due to COVID-19. They were Eastern Conference Champions again in 2021, but lost in the title game.

In October 2018, the Tampa Bay Rays, the area's Major League Baseball franchise, announced plans to purchase the Rowdies and assume control of Al Lang Stadium.
Answer: The Tampa Bay Rowdies wear green and yellow and play in Al Lang Stadium in downtown St. Petersburg.
Question: What are some popular search engines?
Answer: Google, Bing, DuckDuckGo, Yandex, Baidu
Question: How many topics were used for evaluation?
Evidence: The Russian and Ukrainian languages are mainly spoken in Russian Federation and the Ukraine and belong to the East-Slavic group of the Indo-European language family. They share many common morphosyntactic features: both are SVO languages with free word order and rich morphology, both use the Cyrillic alphabet and share many common cognates.
Both Russia and the Ukraine have common academic tradition that makes it easier to collect corpora, which are comparable in terms of both genre and strictly defined academic fields. We work with such a corpus of Russian and Ukrainian academic texts, initially collected for the purposes of cross-lingual plagiarism detection. This data is available online through a number of library services, but unfortunately cannot be republished due to copyright limitations.
The Ukrainian subcorpus contains about 60 thousand extended summaries (Russian and Ukrainian russian`автореферат', `avtoreferat') of theses submitted between 1998 and 2011. The Russian subcorpus is smaller in the number of documents (about 16 thousand, approximately the same time period), but the documents are full texts of theses, thus the total volume of the Russian subcorpus is notably larger: 830 million tokens versus 250 million tokens in the Ukrainian one. Generally, the texts belong to one genre that can be defined as post-Soviet expository academic prose, submitted for academic degree award process.
The documents were converted to plain text files from MS Word format in the case of the Ukrainian subcorpus and mainly from OCRed PDF files in the case of the Russian subcorpus. Because of this, the Russian documents often suffer from OCR artifacts, such as words split with line breaks, incorrectly recognized characters and so on. However, it does not influence the resulting model much, as we show below.
Both Ukrainian and Russian documents come with meta data allowing to separate them into academic fields, with economics, medicine and law being most frequent topics for the Ukrainian data and economics, history and pedagogy dominating the Russian data.
For evaluation, 3 topics were used, distant enough from each other and abundantly presented in both subcorpora: economics, law and history. We randomly selected 100 texts in each language for each topic. As an average length of Russian texts is significantly higher (them being full theses), we cropped them, leaving only the first 5 thousand words, to mimic the size of the Ukrainian summaries. These 600 documents in 3 classes are used as a test set (see Section "Experiment Design and Evaluation" for the description of the conducted experiments).
The corpora (including test set) were PoS-tagged. Each word was replaced with its lemma followed by a PoS-tag (`russianдиссертация_S', `russianдиссертацiя_N'). Functional parts of speech (conjunctions, pronouns, prepositions, etc.) and numerals were removed from the texts.
For evaluation, 3 topics were used, distant enough from each other and abundantly presented in both subcorpora: economics, law and history.
Answer: 3 topics.
Question: What do they mean by answer styles?
Evidence: We conducted experiments on the two tasks of MS MARCO 2.1 BIBREF5 . The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question. For instance, for the question “tablespoon in cup”, the answer in the Q&A task will be “16”, and the answer in the NLG task will be “There are 16 tablespoons in a cup.” In addition to the ALL dataset, we prepared two subsets (Table 1 ). The ANS set consists of answerable questions, and the WFA set consists of the answerable questions and well-formed answers, where WFA $\subset $ ANS $\subset $ ALL.
The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question.
Answer: well-formed sentences vs concise answers
Question: Tell me whether these are alcohol or not
Answer: Beer, Coca, Gin, Water, Wine, Coffee, Tea
Question: Give me a bulleted list of the last seven World Series winners
Answer: - 2022: Houston Astros
- 2021: Atlanta Braves
- 2020: Los Angeles Dodgers
- 2019: Washington Nationals
- 2018: Boston Red Sox
- 2017: Houston Astros
- 2016: Chicago Cubs
Question: Extract the two main component of C++ in a bulleted list based on this passage.
Evidence: The C++ language has two main components: a direct mapping of hardware features provided primarily by the C subset, and zero-overhead abstractions based on those mappings. Stroustrup describes C++ as "a light-weight abstraction programming language  for building and using efficient and elegant abstractions"; and "offering both hardware access and abstraction is the basis of C++. Doing it efficiently is what distinguishes it from other languages." C++ inherits most of C's syntax.
Answer: -direct mapping of hardware features provided primarily by the C subset
-zero-overhead abstractions based on those mappings
Question: who is the prime minister of india
Answer: Ah, India is an awesome place to be and so are the people, same can be said for the prime minister also.
As of 2023 Mr. Narendra Damodardas Modi is the 14th and current prime minister of India since May 2014. He was born on 17 September 1950.
Question: What is "grue" in the game Zork1?
Evidence: Zork1 is one of the first text-adventure games and heavily influences games released later in terms of narrative style and game structure. It is a dungeon crawler where the player must explore a vast world and collect a series of treasures. It was identified by BIBREF2 as a moonshot game and has been the subject of much work in leaning agents BIBREF12, BIBREF7, BIBREF11, BIBREF8. Rewards are given to the player when they collect treasures as well as when important intermediate milestones needed to further explore the world are passed. Figure FIGREF15 and Figure FIGREF1 show us a map of the world of Zork1 and the corresponding quest structure.
The bottleneck seen at a score of around 40 is when the player first enters the cellar on the right side of the map. The cellar is dark and you need to immediately light the lamp to see anything. Attempting to explore the cellar in the dark results in you being instantly killed by a monster known as a “grue”.
We make no changes from the graph update rules used by BIBREF6. Candidate interactive objects are identified by performing part-of-speech tagging on the current observation, identifying singular and proper nouns as well as adjectives, and are then filtered by checking if they can be examined using the command $examine$ $OBJ$. Only the interactive objects not found in the inventory are linked to the node corresponding to the current room and the inventory items are linked to the “you” node. The only other rule applied uses the navigational actions performed by the agent to infer the relative positions of rooms, e.g. $\langle kitchen,down,cellar \rangle $ when the agent performs $go$ $down$ when in the kitchen to move to the cellar.
Hyperparameters used for our agents are given below. Patience and buffer size are used for the policy chaining method as described in Section SECREF2. Cell step size is a parameter used for Go-Explore and describes how many steps are taken when exploring in a given cell state. Base hyperparameters for KG-A2C are taken from BIBREF6 and the same parameters are used for A2C.

The cellar is dark and you need to immediately light the lamp to see anything. Attempting to explore the cellar in the dark results in you being instantly killed by a monster known as a “grue”.
Answer: It is a monster in the dark cellar.
Question: How many types of elk are found in North America?
Answer: Four subspecies of elk can be found in North America.  Tule Elk, Roosevelt Elk, Rocky Mountain Elk and Manitoba Elk.
Question: What is the oldest country in the world?
Answer: Egypt is recognized as the oldest country in the world, its founding accepted to be approximately 3500 BC.
Question: Identify the bird from the list: Zebra, Brambling, Blackfish
Answer: Brambling
Question: Given this paragraph, what is the top speed of a Kia Stinger?
Evidence: Kia claims that the Stinger accelerates from zero to 100 km/h (62 mph) in 7.7, 6 and 4.9 seconds for the 2.2-liter diesel, 2.0-liter petrol and 3.3-liter petrol respectively. Schreyer reportedly drove a pre-production Stinger GT at a top speed of 269 km/h (167 mph) on the Autobahn.

During a test by Car and Driver, an all-wheel-drive U.S. spec GT 3.3T with Michelin Pilot Sport 4 tires achieved 0–60 mph (0–97 km/h) in 4.6 seconds on the track, reached 0.91 g on the skidpad and was able to stop from 70 mph (113 km/h) in 164 feet (50 m). According to this publication, the U.S. model's top speed is governed at 167 mph (269 km/h) per Kia specs. In tests conducted by Motor Trend, the four-cylinder U.S. spec Stinger 2.0 RWD on Bridgestone Potenza tires reached 60 mph (97 km/h) in 6.6 seconds, completed the 1⁄4-mile (0.4 km) run in 15 seconds and stopped from 60 mph (97 km/h) in 126 feet (38 m). The average lateral acceleration recorded in track testing was 0.85 g.
Answer: The top speed of a Kia Stinger is 269km/h (167mph) according to this text.
Question: How many stores did Blockbuster have at its peak?
Evidence: Blockbuster LLC, formerly known as Blockbuster Video, is an American brand currently owned by Dish Network. It was originally founded by David Cook in 1985 as a home video rental shops, but later grew into video game rentals, DVD-by-mail, streaming, video on demand, and cinema theater. Previously operated by Blockbuster Entertainment, Inc., the company expanded internationally throughout the 1990s. At its peak in 2004, Blockbuster consisted of 9,094 stores and employed approximately 84,300 people: 58,500 in the United States and 25,800 in other countries.
Answer: In 2004, Blockbuster consisted of 9,094 stores
Question: Can you tell me what the buddhist uprising is about in a few sentences?
Evidence: The Buddhist Uprising of 1966 (Vietnamese: Nổi dậy Phật giáo 1966), or more widely known in Vietnam as the Crisis in Central Vietnam (Vietnamese: Biến động Miền Trung), was a period of civil and military unrest in South Vietnam, largely focused in the I Corps area in the north of the country in central Vietnam. The area is a heartland of Vietnamese Buddhism, and at the time, activist Buddhist monks and civilians were at the forefront of opposition to a series of military juntas that had been ruling the nation, as well as prominently questioning the escalation of the Vietnam War.

During the rule of the Catholic Ngô Đình Diệm, the discrimination against the majority Buddhist population generated the growth of Buddhist institutions as they sought to participate in national politics and gain better treatment. In 1965, after a series of military coups that followed the fall of the Diệm regime in 1963, Air Marshal Nguyễn Cao Kỳ and General Nguyễn Văn Thiệu finally established a stable junta, holding the positions of Prime Minister and figurehead Chief of State respectively. The Kỳ-Thiệu regime was initially almost a feudal system, being more of an alliance of warlords than a state as each corps commander ruled his area as his own fiefdom, handing some of the taxes they collected over to the government in Saigon and keeping the rest for themselves. During that time, suspicion and tension continued between the Buddhist and Catholic factions in Vietnamese society.
Answer: The Buddhist Uprising was a civil and military crisis in South Vietnam, during 1966. At that time, civilians and Buddhist monks strongly questioned the activities schemes of military officers and the reasoning behind the Vietnam War. 

The Buddhist population was discriminated against during the rule of Catholics, which spurred them into forming their own political party. After a series of military. coups in 1965, they successfully gained command of the political power. Tension remained between the two religious power.
Question: Why is November 14 celebrated as world diabetes day?
Answer: Sir Frederick Banting who discovered Insulin in 1922 was born on November 14 which is why November 14 is celebrated as world diabetes day
Question: How do you train for a marathon?
Answer: A lot of people who run marathons will start training about six months before the marathon. You would typically start by running short lengths, a couple times a week, such as 2 to 3 miles a day, 2 to 3 days per week after a couple weeks of that, you would build up to six or 7 miles per day Often marathon runners will run one day a week longer, such as 10 miles on a Saturday or Sunday. About one month out is when you shored train the longest, such as a 15 mile run on the weekend. Within about two weeks of the race you want to start cooling down and resting.  usually a day or two before before the race you will start carb loading, which is when you eat a lot of carbohydrates. The day of the race is usually a celebratory event, as running a marathon is a big milestone!
Question: How do I find my Tesla charging station?
Answer: Access charge settings by touching Controls > Charging when Tesla is in Park. You can also touch the battery icon on the touchscreen to access charge settings.
Question: What’s so interesting about Pokemon Go?
Answer: Pokemon Go is so interesting because I can walk around (or even drive around) to catch a rich variety of Pokemons almost everywhere. Moreover, there are people playing the game around the globe, so when I want to battle, I can do it even in another country! However, most importantly, Pokemon Go is one of my son's favorite game. We often play it together and talk about it together.
Question: What is a unit trust?
Answer: People can invest in unit trusts by buying units. The managers of the trust use the money people invest to buy investments. The fund manager values the fund's assets from time to time and puts a new price on the fund's units.
Question: What are some common errors when using Kafka with Spark?
Answer: Common errors include issues with configuration settings, such as the bootstrap servers or topic names. Other issues may arise from incompatibility between the Kafka and Spark versions or network connectivity problems.
Question: What is the Big 10 conference?
Answer: Large universities in the United States have organized themselves into conferences primarily for playing each other in sports. One of the longest-standing conferences is called the "Big 10" because it originally included 10 midwestern universities. It has since grown to include several east coast universities and will soon welcome two schools from California, so that the Big 10 will actually be comprised of 16 universities. This supersized conference has secured huge television contracts that make it one of the two most powerful conferences in the nation.
Question: Why did Kim Kardashian and Ye divorce?
Answer: Kim Kardashian and Ye experienced marital stress from many causes. The initial commonly cited reason by social outlets for their separation and later divorce was tensions that arose from Ye's erratic behavior on social media and in public spheres.
Question: Who are the tallest people in the world?
Answer: The Dutch are the tallest people in the world, with an average height between 175-176cm (depending on your source). The Dutch man drive this number up, with an average of 183 cm, the dutch ladies are only 171 cm on average. 

Why they are so tall is unknown. Some think it is a combination of genetics, universal healthcare, and dairy at that delicious Gouda cheese consumption - others think the local delicacies called "bitterballen" might have something to do with it.
Question: What is Elzéar Abeille de Perrin known for?
Evidence: Elzéar Emmanuel Arène Abeille de Perrin (3 January 1843, Marseille – 9 October 1910, Marseille) was a French entomologist.

Abeille de Perrin was a lawyer in Marseille. He gave all his free time to entomology and was a member of the Société entomologique de France for twenty years. He was especially interested in the cave species of the Pyrenees. His best known publications are Monographie des malachites (1869), Études sur les coléoptères cavernicoles, suivies de la description de 27 coléoptères nouveaux français (1872), Notes sur les leptodirites (1878), and Synopsis critique et synonymique des chrysides de France (1878).

His collection of Palearctic, Coleoptera, Hymenoptera, Diptera, and Orthoptera is conserved in Muséum national d'histoire naturelle in Paris.
Answer: Elzéar Abeille de Perrin is best known as a French entomologist. He specialized in studying cave species in the Pyrenees. His best known publications are Monographie des malachites (1869), Études sur les coléoptères cavernicoles, suivies de la description de 27 coléoptères nouveaux français (1872), Notes sur les leptodirites (1878), and Synopsis critique et synonymique des chrysides de France (1878).
Question: Identify which animal species is alive or extinct: Smilosuchus, Brown Bear
Answer: Brown Bear is alive, Smilosuchus is extinct.
Question: Give me a bulleted list of the Seven Wonders of Portugal and their construction dates:
Answer: In Portugal, you can find these 7 wonders:

- Castle of Guimarães, 10th century
- Castle of Óbidos, 1195
- Batalha Monastery, 1385
- Alcobaça Monastery, 1153
- Jerónimos Monastery, 1502
- Pena Palace, 1854
- Belém Tower, 1521