DataHammer/scimrc huggingface dataset:
Question: Imagine that one day, a universally acknowledged property of time is bended. What is that property? Write about the new world under that setting.
Answer: Rather than being linear, time is a loop in this new world. People moving forward to the direction of "future" are only making ways to their past. This is a good news for those with a joyful past, but not so much for those with a rather dark history. People who seek to avoid their history are now ruminating over the causes of their misery, while those who seek to relive their past are doing the same thing, only to amplify those factors behind their happiness. Yet, the happiness of some is built on top of the exploitation and manipulation of others, and it is under this setting that a war over the past commences.
Question: What is the circumference of the earth?
Answer: 40,075km or  24,855 miles depending on which metric system you use.
Question: What was the cause of Michael Jackson's death?
Answer: On August 28, 2009, the Los Angeles County coroner classified Jackson's death as a homicide, determining that Jackson died from acute propofol intoxication, exacerbated by the anxiolytic lorazepam, and less significantly midazolam, diazepam, lidocaine, and ephedrine.
Question: What is a Downcountry Mountain Bike?
Answer: Downcountry mountain bikes first came onto the scene around 2020 as an intersection between hard tail XC bikes and longer travel downhill/enduro bikes. They are usually full suspension with front and rear shocks. However, shorter travel than an enduro bike but much more efficient at climbing. They appeal to the rider who likes to get out and explore on their bike whilst also enjoying trail riding. Expect to see suspension travel in the range 120-130mm.
Question: Who won the 2022 Masters golf tournament?
Answer: Scotty Scheffler
Question: Which datasets are used in this work?
Evidence: There have been several workshops on biomedical natural language processing. We focus on the BioNLP Shared Tasks in recent years that had competitions on event extraction. There have been three BioNLP Shared Task competitions so far: 2009, 2011, and 2013. The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells. There was a second BioNLP Shared Task competition organized in 2011 to measure the advances in approaches and associated results BIBREF197 . The third BioNLP ST was held in 2013. We discuss some notable systems from BioNLP ST 2011 and 2013.
The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells.
Answer: The answers are shown as follows:
* GENIA corpus
Question: Q: Products online claim to prevent or treat COVID-19. Where can I report websites selling products with fraudulent claims?
Answer: A: The FDA advises consumers to be beware of websites and stores selling products that claim to prevent, treat, or cure COVID-19. If you have a question about a product sold online that claims to prevent, treat, or cure COVID-19, talk to your health care provider or doctor.

Read this Consumer Update to learn how to protect yourself and your family from coronavirus fraud. 

Please report websites selling products with fraudulent claims about treatment or prevention of COVID-19. If you have experienced a bad reaction to a product sold with COVID-19 claims, report it to the FDA’s MedWatch Adverse Event Reporting program:

Complete and submit the report online; or
Download and complete the form, then submit it via fax at 1-800-FDA-0178.
Include as much information as you can about the product that caused the reaction, including the product name, the manufacturer, and the lot number (if available).
Question: What are some of the pros and cons of owning a pet?
Answer: There are many pros and cons of pet ownership. On the positive side interacting with your pet can provide a great way of relaxing and reducing stress in your life. If your pet is a dog it can be a great way to encourage you to get exercise. If you have a young child it can be a way of teaching them responsibility. On the down side, owning a per means you are obligated to feed, clean up after and otherwise take care of them - responsibilities you will have no matter what else going on in your life.
Question: Tell me if this a universal blood group:  A+, A-,  B+, B-,  O+, O-,  AB+, AB-
Answer: A+ = No
A- = No
B+ = No
B- = No
O+ = No
O-  = Yes
AB+= No
AB-= No
Question: What kind of model does Tweet2Vec use to learn and generate vector representation of tweets?
Evidence: In recent years, the micro-blogging site Twitter has become a major social media platform with hundreds of millions of users. The short (140 character limit), noisy and idiosyncratic nature of tweets make standard information retrieval and data mining methods ill-suited to Twitter. Consequently, there has been an ever growing body of IR and data mining literature focusing on Twitter. However, most of these works employ extensive feature engineering to create task-specific, hand-crafted features. This is time consuming and inefficient as new features need to be engineered for every task.
In this paper, we present Tweet2Vec, a method for generating general-purpose vector representation of tweets that can be used for any classification task. Tweet2Vec removes the need for expansive feature engineering and can be used to train any standard off-the-shelf classifier (e.g., logistic regression, svm, etc). Tweet2Vec uses a CNN-LSTM encoder-decoder model that operates at the character level to learn and generate vector representation of tweets. Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics BIBREF0 , BIBREF1 ).
There has been several works on generating embeddings for words, most famously Word2Vec by Mikolov et al. BIBREF2 ). There has also been a number of different works that use encoder-decoder models based on long short-term memory (LSTM) BIBREF3 , and gated recurrent neural networks (GRU) BIBREF4 . These methods have been used mostly in the context of machine translation. The encoder maps the sentence from the source language to a vector representation, while the decoder conditions on this encoded vector for translating it to the target language. Perhaps the work most related to ours is the work of Le and Mikolov le2014distributed, where they extended the Word2Vec model to generate representations for sentences (called ParagraphVec). However, these models all function at the word level, making them ill-suited to the extremely noisy and idiosyncratic nature of tweets. Our character-level model, on the other hand, can better deal with the noise and idiosyncrasies in tweets. We plan to make our model and the data used to train it publicly available to be used by other researchers that work with tweets.
Tweet2Vec uses a CNN-LSTM encoderdecoder model that operates at the character level to learn and generate vector representation of tweets.
Answer: A CNN-LSTM encoder-decoder model.
Question: How many subjects have been used to create the annotations?
Evidence: We used the WebAnno annotation tool BIBREF2 for our project. The stories from each scenario were distributed among four different annotators. In a calibration phase, annotators were presented with some sample texts for test annotations; the results were discussed with the authors. Throughout the whole annotation phase, annotators could discuss any emerging issues with the authors. All annotations were done by undergraduate students of computational linguistics. The annotation was rather time-consuming due to the complexity of the task, and thus we decided for single annotation mode. To assess annotation quality, a small sample of texts was annotated by all four annotators and their inter-annotator agreement was measured (see Section "Inter-Annotator Agreement" ). It was found to be sufficiently high.
The stories from each scenario were distributed among four different annotators. 
Answer: The answers are shown as follows:
*  four different annotators
Question: Who was Lem Tucker?
Evidence: Lemuel Tucker (May 26, 1938 – March 2, 1991) was an American journalist.

Born in Saginaw, Michigan, Tucker graduated from Central Michigan University in 1960.

Tucker was one of the first African Americans to work as a television network reporter. He began his career at NBC in 1965 as a general assignment news correspondent, and served as news director at WOR-TV in New York City from 1970 to 1971. He moved to ABC in 1972 and to CBS in 1977, where he worked until his death.

He was winner of two Emmy Awards, and made the master list of Nixon political opponents for his reporting.

Although Lem Tucker's life was brief, his career accomplishments were memorable.

Tucker was the first African American elected to the position of student body president at CMU. That election took place when less than one percent of the student body was black. Previous to his election, only one other African American had been elected to a similar position in the United States. While at Central, Tucker was considered one of the university's top forensic students. He served as a chapter president of Pi Kappa Delta, a national speech honorary fraternity.

He won his first Emmy in 1979 for his coverage of black America and his second in 1983 for a series about hunger in America. While working for the networks, he covered numerous major news events, including the Vietnam War, the Kent State University shootings and the Iran hostage crisis. He covered social unrest and riots in Newark, Miami and Harlem. He also reported from the Woodstock Festival. In 1981, he was covering President Ronald Reagan's speech at the Washington Hilton Hotel and witnessed the shooting of the president. He was broadcasting live reports on CBS Radio within minutes of the incident. He was applauded by his colleagues and others in the media for his continuous and thorough coverage, which he provided well into the next morning.

During the course of his career, Tucker returned numerous times to his alma mater to speak to students about politics, share reflections on college life and offer insight regarding work as a national network correspondent.

Tucker had a keen sense of humor. In an address given to the CMU community in the early 1980s, he told his audience he was amused to have been asked back to speak at the university, given that during his days as student body president, he "quibbled a lot" with the administration. He said, "How rare for me to be given a microphone and be invited to speak at CMU. Now I am on stage in front of administration and applauded."

Tucker has one daughter, Linn Carol Tucker.

His death in 1991 was due to liver failure.
Answer: Lemuel Tucker was an American journalist. He was born in Saginaw, Michigan, Tucker graduated from Central Michigan University in 1960. Tucker was one of the first African Americans to work as a television network reporter. He won his first Emmy in 1979 for his coverage of black America and his second in 1983 for a series about hunger in America. His death in 1991 was due to liver failure.
Question: Order these cars as either 'fast', 'slow' or 'dangerously fast'. A Doge Viper, Toyota 4Runner, Lamborghini Aventador, Chrysler Pacifica, Go-Kart, Lime Scooter, Porsche 911, Corvette
Answer: fast: Lamborghini Aventador, Porsche 911, Corvette
slow: Toyota 4Runner, Chrysler Pacifica, Go-Kart, Lime Scooter
dangerously fast: Dodge Viper
Question: Locate what the player should defend against in The Last of Us using the sample as a reference, extract them, and place them in a numbered list.
Evidence: The Last of Us is an action-adventure game played from a third-person perspective. The player traverses post-apocalyptic environments such as towns, buildings, forests, and sewers to advance the story. The player can use firearms, improvised weapons, hand-to-hand combat, and stealth to defend against hostile humans and cannibalistic creatures infected by a mutated strain of the Cordyceps fungus. For most of the game, the player takes control of Joel, a man tasked with escorting a young girl, Ellie, across the United States. The player also controls Ellie throughout the game's winter segment and briefly controls Joel's daughter, Sarah, in the opening sequence.
Answer: 1. Hostile humans;
2. Cannibalistic creatures infected by a mutated strain of the Cordyceps fungus.
Question: What is the best day for cold calling?
Answer: Data suggests that there is no difference in which day of the week you cold call a prospect.
Question: What are the things I should consider when purchasing an electric car?
Answer: Here are the things you should consider when purchasing an electric car:

* the range - make sure it’s enough to cover your expected use. Range anxiety is real. 
* the efficiency - the more efficient it is, the less batteries, and therefore weight, you have to carry around
* the charging network - will you have a reliable place to charge when you’re not able to charge at home?
* the price - does it fit your budget?
* the fun factor - will you enjoy the driving experience?
* the buying experience - is the company known for an easy or painful buying experience?
Question: Why are fast text embeddings used in the experiment?
Evidence: We have used Word2Vec BIBREF16, GloVe BIBREF17 and FastText BIBREF18 word vectors of 300 dimensions. These vectors were trained on the corpus obtained from Nepali National Corpus. This pre-lemmatized corpus consists of 14 million words from books, web-texts and news papers. This corpus was mixed with the texts from the dataset before training CBOW and skip-gram version of word2vec using gensim libraryBIBREF24. This trained model consists of vectors for 72782 unique words.
Light pre-processing was performed on the corpus before training it. For example, invalid characters or characters other than Devanagari were removed but punctuation and numbers were not removed. We set the window context at 10 and the rare words whose count is below 5 are dropped. These word embeddings were not frozen during the training session because fine-tuning word embedding help achieve better performance compared to frozen oneBIBREF20.
We have used fasttext embeddings in particular because of its sub-word representation ability, which is very useful in highly inflectional language as shown in Table TABREF25. We have trained the word embedding in such a way that the sub-word size remains between 1 and 4. We particularly chose this size because in Nepali language a single letter can also be a word, for example e, t, C, r, l, n, u and a single character (grapheme) or sub-word can be formed after mixture of dependent vowel signs with consonant letters for example, C + O + = CO, here three different consonant letters form a single sub-word.
The two-dimensional visualization of an example word npAl is shown in FIGREF14. Principal Component Analysis (PCA) technique was used to generate this visualization which helps use to analyze the nearest neighbor words of a given sample word. 84 and 104 nearest neighbors were observed using word2vec and fasttext embedding respectively on the same corpus.
We have used fasttext embeddings in particular because of its sub-word representation ability, which is very useful in highly inflectional language as shown in Table 3.
Answer: Because its sub-word representation ability, which is very useful in highly inflectional language.
Question: Who was Alexander the Great?
Evidence: Alexander III of Macedon (Ancient Greek: Ἀλέξανδρος, romanized: Alexandros; 20/21 July 356 BC – 10/11 June 323 BC), commonly known as Alexander the Great, was a king of the ancient Greek kingdom of Macedon
Answer: Alexander III of Macedon, commonly known as Alexander the Great, was a king of the ancient Greek kingdom of Macedon. He succeeded his father Philip II to the throne in 336 BC at the age of 20, and spent most of his ruling years conducting a lengthy military campaign throughout Western Asia and Egypt. By the age of 30, he had created one of the largest empires in history, stretching from Greece to northwestern India. He was undefeated in battle and is widely considered to be one of history's greatest and most successful military commanders.
Question: What are the three biggest cities by population in Spain?
Answer: The three biggest cities by population in Spain are Madrid, Barcelona, and Valencia.
Question: What is caramelization?
Answer: Caramelization is a chemical process that happens to carbohydrates and sugars when they are heated to certain temperatures. At these temperatures new compounds are formed and browning happens. For example during caramelization, complex sugars degrade to simple sugars. Caramelization is an important process in cooking and shows ups in many common recipes
Question: What did José María Arizmendiarrieta do?
Evidence: In 1941, a young Catholic priest, José María Arizmendiarrieta settled in Mondragón, a town with a population of 7,000 that had not yet recovered from the poverty, hunger, exile, and tension of the Spanish Civil War. In 1943, Arizmendiarrieta established a technical college which became a training ground for managers, engineers and skilled labour for local companies, and primarily for the co-operatives.
Answer: José María Arizmendiarrieta was a Catholic Preist who settled in Mondragón, a town with a population of 7,000 that had not yet recovered from the poverty, hunger, exile, and tension of the Spanish Civil War.
Question: What should be an men do in weekend
Answer: Saturday : Late Wakeup , have a branch and sleep. wake up and Booze. , sleep late night. 
Sunday - Wake up late , Have a handful lunch and sleep. and Sunday night - think about tomorrow is Monday
Question: List the seven episode titles from the seventh season of "Game of Thrones" in broadcast order.
Answer: Dragonstone, Stormborn, The Queen's Justice, The Spoils of War, Eastwatch, Beyond the Wall, The Dragon and the Wolf
Question: Who will win the women’s final four matchup between Iowa and South Carolina?
Answer: While South Carolina is undefeated on the season, Iowa enters the game with newly crowned player of the year Caitlin Clark. South Carolina is favored, but if Clark and the Hawkeyes can stay hot from behind the arc they could pull off the upset.
Question: What is the minimum weight of a coswain?
Evidence: It is an advantage for the coxswain to be light as this requires less effort for the crew to propel the boat. In many competitive events there is a minimum weight, 55 kilograms (121 lb) under World Rowing rules, set for the coxswain to prevent unfair advantage. If a coxswain is under the minimum weight allowance (underweight), they may have to carry weights in the boat such as sandbags.
Answer: The minimum weight of a coxswain if 55 kilograms (121 pounds) under World Rowing rules.
Question: What is shared in the joint model?
Evidence: Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots)
Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords)
Using sequence-to-sequence networks, the approach here is jointly training annotated utterance-level intents and slots/intent keywords by adding / tokens to the beginning/end of each utterance, with utterance-level intent-type as labels of such tokens. Our approach is an extension of BIBREF2 , in which only an term is added with intent-type tags associated to this sentence final token, both for LSTM and Bi-LSTM cases. However, we experimented with adding both and terms as Bi-LSTMs will be used for seq2seq learning, and we observed that slightly better results can be achieved by doing so. The idea behind is that, since this is a seq2seq learning problem, at the last time step (i.e., prediction at ) the reverse pass in Bi-LSTM would be incomplete (refer to Fig. FIGREF24 (a) to observe the last Bi-LSTM cell). Therefore, adding token and leveraging the backward LSTM output at first time step (i.e., prediction at ) would potentially help for joint seq2seq learning. An overall network architecture can be found in Fig. FIGREF30 for our joint models. We will report the experimental results on two variations (with and without intent keywords) as follows:

Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots)

Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords)
Answer: The answers are shown as follows:
* jointly trained with slots
Question: Given this paragraph about Lake Michigan, tell me where Lake Michigan ranks in terms of size by volume.
Evidence: Lake Michigan is one of the five Great Lakes of North America. It is the second-largest of the Great Lakes by volume (1,180 cu mi (4,900 km3)) and the third-largest by surface area (22,404 sq mi (58,030 km2)), after Lake Superior and Lake Huron. To the east, its basin is conjoined with that of Lake Huron through the 3+1⁄2 miles (5.6 kilometres) wide, 295 feet (90 metres; 49 fathoms) deep, Straits of Mackinac, giving it the same surface elevation as its easterly counterpart; the two are technically a single lake.
Answer: Lake Michigan is ranked second by volume
Question: Which university has Purdue Pete as their mascot?
Evidence: Purdue Pete is a mascot of Purdue University. Despite his on-field presence at Purdue sporting events, Pete is only the athletic mascot of the university, and the official mascot of Purdue University is the Boilermaker Special.
Answer: The university that has Purdue Pete as their mascot is Purdue University.
Question: What was the hit single on Nek's 1996 Album Lei, gli amici e tutto il resto?
Evidence: Lei, gli amici e tutto il resto (She, friends and everything else), is the fourth studio album by Italian singer-songwriter Nek. It was released in 1996, as his first studio album with Warner Music Group. It was re-released on 1 March 1997, after the success of the single "Laura non c'è", which had not been included in the album.

Since 1999 there have been reported sales of over 2 million copies all over the world.
Answer: Laura non c'è" was the hit single, but wasn't added until the album was released in 1997.
Question: What was Mark Twain's actual name?
Answer: Samuel Langhorne Clemens was known the pen name Mark Twain, a nom de plume he coined to reflect his time as a river boat pilot.  On the Mississippi in the 1800's it was common to measure river depth in terms of 6 foot increments demarcated on a rope by knots.  Thus, when the river was of depth 12 feet, or two fathoms, the boatmen would call out, "Mark Twain" indicating the second of two knots was submerged.
Question: Where did Kamlaksha Rama Naik receive his Business Management degree?
Evidence: Kamlaksha Rama Naik, better known as K R Naik (Hindi: क़ रा नायक़) is an Indian industrial engineer. He founded D-Link Ltd. (India) in 1993. He has been in the IT Industry for 50 years and has played a key role in creating the IT networking market and the surrounding channel ecosystem in India. He pioneered several new businesses and distribution models as early as 1990, when IT was a nascent industry and the concept of an IT distribution channel was just conceived. 

Naik was born in Karwar, Karnataka on 19 November 1947. He completed his schooling in Karwar and later shifted to Mumbai. Naik is a mechanical engineer with a P.G. Diploma in Industrial Engineering and Licenciate in Plastic Engineering. He earned a Business Management degree from the Jamnalal Bajaj Institute in Mumbai
Answer: The Jamnalal Bajaj Institute in Mumbai