DataHammer/scimrc huggingface dataset:
Question: What would be the summary of the story of Oedipus, which serves as the basis for the psychoanalytic theory of the Oedipus complex?
Evidence: The story of Oedipus is the subject of Sophocles' tragedy Oedipus Rex, which is followed in the narrative sequence by Oedipus at Colonus and then Antigone. Together, these plays make up Sophocles' three Theban plays. Oedipus represents two enduring themes of Greek myth and drama: the flawed nature of humanity and an individual's role in the course of destiny in a harsh universe.

In the best-known version of the myth, Oedipus was born to King Laius and Queen Jocasta of Thebes. Laius wished to thwart the prophecy, so he sent a shepherd-servant to leave Oedipus to die on a mountainside. However, the shepherd took pity on the baby and passed him to another shepherd who gave Oedipus to King Polybus and Queen Merope to raise as their own. Oedipus learned from the oracle at Delphi of the prophecy that he would end up killing his father and marrying his mother but, unaware of his true parentage, believed he was fated to murder Polybus and marry Merope, so left for Thebes. On his way, he met an older man and killed him in a quarrel. Continuing on to Thebes, he found that the king of the city (Laius) had recently been killed and that the city was at the mercy of the Sphinx. Oedipus answered the monster's riddle correctly, defeating it and winning the throne of the dead king – and the hand in marriage of the king's widow, who was also (unbeknownst to him) his mother Jocasta.


Detail of ancient fresco in which Oedipus solves the riddle of the Sphinx. Egyptian Museum, 2nd c. CE
Years later, to end a plague on Thebes, Oedipus searched to find who had killed Laius and discovered that he himself was responsible. Jocasta, upon realizing that she had married her own son, hanged herself. Oedipus then seized two pins from her dress and blinded himself with them.

The legend of Oedipus has been retold in many versions and was used by Sigmund Freud to name and give mythic precedent to the Oedipus complex.
Answer: Oedipus killed his father and got married to his mother, as announced by the prophecy of the Oracle of Delphi.
Question: Where do we go when we die?
Answer: In some religions, it is believed we go to heaven after die.  There are passages that talk about the pearly gates and being greeted by St. Peter who will check you into heaven and allow to pass through the gates once vetted.  The ancient egyptians believed in three afterlife ideologies; the underworld, eternal life and the rebirth of the soul.  However, for the average person today who does not partake in mummification and ancient egyptians, it is widely believed that once the body dies, the soul leaves and goes to a place of peace.  In a perfect world, we would be reunited with our loved ones, both human and pet, where we would no longer be in pain and can run free with those we care about most.  Alternatively, the more literal answer to this question would be that the physical body either goes into a coffin or some type of container or it is cremated into ashes after death.
Question: Why is the Great Salt Lake salty?
Answer: The Great Salt Lake is salty because water only flows into the lake, not out. This is called a “terminal lake.” Over time, the water in the lake evaporates, leaving behind the minerals that flowed in with the water. Because the amount of water in the lake remains relatively constant but the amount of minerals increases over time, the lake becomes more salty over time.
Question: How in YouTube content translated into a vector format?
Evidence: We obtain word vectors of size 300 from the learned word embeddings. To represent a Twitter profile, we retrieve word vectors for all the words that appear in a particular profile including the words appear in tweets, profile description, words extracted from emoji, cover and profile images converted to textual formats, and words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline. Those word vectors are combined to compute the final feature vector for the Twitter profile. To combine the word vectors, we consider five different methods. Letting the size of a word vector be INLINEFORM0 , for a Twitter profile INLINEFORM1 with INLINEFORM2 unique words and the vector of the INLINEFORM3 word in INLINEFORM4 denoted by INLINEFORM5 , we compute the feature vector for the Twitter profile INLINEFORM6 by:
We obtain word vectors of size 300 from the learned word embeddings. To represent a Twitter profile, we retrieve word vectors for all the words that appear in a particular profile including the words appear in tweets, profile description, words extracted from emoji, cover and profile images converted to textual formats, and words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline. Those word vectors are combined to compute the final feature vector for the Twitter profile.
Answer: The answers are shown as follows:
* words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline
Question: What scoring function does the model use to score triples?
Evidence: Let $\mathcal {E}$ denote the set of entities and $\mathcal {R}$ the set of relation types. For each triple $(h, r, t)$ , where $h, t \in \mathcal {E}$ and $r \in \mathcal {R}$ , the STransE model defines a score function $f_r(h, t)$ of its implausibility. Our goal is to choose $f$ such that the score $f_r(h,t)$ of a plausible triple $(h,r,t)$ is smaller than the score $f_{r^{\prime }}(h^{\prime },t^{\prime })$ of an implausible triple $\mathcal {R}$0 . We define the STransE score function $\mathcal {R}$1 as follows:
$ f_r(h, t) & = & \Vert \textbf {W}_{r,1}\textbf {h} + \textbf {r} - \textbf {W}_{r,2}\textbf {t}\Vert _{\ell _{1/2}} $
using either the $\ell _1$ or the $\ell _2$ -norm (the choice is made using validation data; in our experiments we found that the $\ell _1$ norm gave slightly better results). To learn the vectors and matrices we minimize the following margin-based objective function: $ \mathcal {L} & = & \sum _{\begin{array}{c}(h,r,t) \in \mathcal {G} \\ (h^{\prime },r,t^{\prime }) \in \mathcal {G}^{\prime }_{(h, r, t)}\end{array}} [\gamma + f_r(h, t) - f_r(h^{\prime }, t^{\prime })]_+ $
We define the STransE score function $\mathcal {R}$1 as follows:

$ f_r(h, t) & = & \Vert \textbf {W}_{r,1}\textbf {h} + \textbf {r} - \textbf {W}_{r,2}\textbf {t}\Vert _{\ell _{1/2}} $

using either the $\ell _1$ or the $\ell _2$ -norm (the choice is made using validation data; in our experiments we found that the $\ell _1$ norm gave slightly better results).
Answer: The answers are shown as follows:
* $ f_r(h, t) & = & \Vert \textbf {W}_{r,1}\textbf {h} + \textbf {r} - \textbf {W}_{r,2}\textbf {t}\Vert _{\ell _{1/2}} $
Question: Classify each of the following as a "fiat money" or "cryptocurrency": Bitcoin, US Dollar, British Pound, Ethereum, Dogecoin, Euro
Answer: Bitcoin: Cryptocurrency
US Dollar: Fiat Money
British Pound: Fiat Money
Ethereum: Cryptocurrency
Dogecoin: Cryptocurrency
Euro: Fiat Money
Question: What facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL.
Answer: An open-source holistic NLG framework for the SW, named LD2NL.
Question: Why is Andrew Jackson criticized for racial policies?
Evidence: Jackson used the power of the federal government to enforce the separation of the Native American tribes and whites. In May 1830, Jackson passed the Indian Removal Act through Congress. It gave the president the right to negotiate treaties to buy tribal lands in the eastern part of the United States in exchange for lands set aside for Native Americans west of the Mississippi, as well as broad discretion on how to use the federal funds allocated to the negotiations. The law was supposed to be a voluntary relocation program, but it was not implemented as one. Jackson's administration often achieved agreement to relocate through bribes, fraud and intimidation, and the leaders who signed the treaties often did not represent the entire tribe. The relocations could be a source of misery too: the Choctaw relocation was rife with corruption, theft, and mismanagement that brought great suffering to that people.
Answer: During Andrew Jackson's presidency the Indian Removal Act was passed by Congress. Even though this legislation was designed to give the President flexibility in negotiating with native American tribes, the President chose a harsher course of actions as a matter of course. Instead of offering voluntary relocation, there was a great deal of forced tribal relocation, as evidenced by the experience of the Cherokees and the "Trail of Tears." Other examples of unfair treatment by President Jackson including reaching agreements with leaders from tribes who often did not fully represent the tribe. A number of Native American leaders were corrupt in their dealings, benefiting individually at tremendous cost to their people. Ultimately, Native Americans in the eastern United States experienced remarkable hardships as a result of Andrew Jackson's policies and actions.
Question: Give me a list of environment conscious clothing brands.
Answer: 1. Patagonia
2. Harvest & Mill
3. Armedangels
4. Honest Basics
5. LangerChen
6. Vaude
7. Organic Basics
8. Eileen Fisher
9. Everlane
10. Nudie Jeans
Question: Why Disney should be acquiring smaller movie studios like Pixar, Marvel, and Lucasfilm?
Answer: Combined business capabilities have valuable synergies 

Before the acquisition of Pixar, Disney lagged in computer-generated films. This is a growing market, and Pixar has unique capabilities like its engineers and a culture where the engineers can innovate freely and creatively. The capabilities of Pixar are difficult to imitate as it is challenging to determine what resources are used and how they can be recreated. At the same time, Pixar lacks marketing and distribution muscle. This is an area where Disney can complement Pixar. Disney acquiring studios like Pixar, Marvel, and Lucasfilm will enable Disney to leverage the characters created by these studios. 

Increased market power 

With Marvel’s 5000+ characters, Disney can do what it does best. In total, Marvel movies have generated over 22 billion dollars for Disney. 

Decrease the cost of new product development 

The characters that Disney acquired, along with its acquisitions of LucasFilms, Marvel, and Pixar, will result in a cost decrease of new product development and increased speed to market. Also, Disney’s unique portfolio of brands provides significant long-term opportunities for growth and value creation. 

For these reasons, Disney must continue acquiring smaller studios like Pixar and Marvel.
Question: What material is suggested for the evaluation of a Vietnamese word corpus?
Evidence: Vietnamese, like many languages in continental East Asia, is an isolating language and one branch of Mon-Khmer language group. The most basic linguistic unit in Vietnamese is morpheme, similar with syllable or token in English and “hình vị” (phoneme) or “tiếng” (syllable) in Vietnamese. According to the structured rule of its, Vietnamese can have about 20,000 different syllables (tokens). However, there are about 8,000 syllables used the Vietnamese dictionaries. There are three methods to identify morphemes in Vietnamese text BIBREF10 .
Morpheme is the smallest meaningful unit of Vietnamese.
Morpheme is the basic unit of Vietnamese.
Morpheme is the smallest meaningful unit and is not used independently in the syntax factor.
In computational linguistics, morpheme is the basic unit of languages as Leonard Bloomfield mentioned for English BIBREF11 . In our research for Vietnamese, we consider the morpheme as syllable, called “tiếng” in Vietnamese (as Nguyen’s definition BIBREF12 ).
The next concept in linguistics is word which has fully grammar and meaning function in sentences. For Vietnamese, word is a single morpheme or a group of morphemes, which are fixed and have full meaning BIBREF12 . According to Nguyen, Vietnamese words are able classified into two types, (1) 1- syllable words with fully meaning and (2) n-syllables words whereas these group of tokens are fixed. Vietnamese syllable is not fully meaningful. However, it is also explained in the meaning and structure characteristics. For example, the token “kỳ” in “quốc kỳ” whereas “quốc” means national, “kỳ” means flag. Therefore, “quốc kỳ” means national flag.
Consider dictionary used for evaluating the corpus, extracting features for models, and evaluating the systems, there are many Vietnamese dictionaries, however we recommend the Vietnamese dictionary of Hoang Phe, so called Hoang Phe Dictionary. This dictionary has been built by a group of linguistical scientists at the Linguistic Institute, Vietnam. It was firstly published in 1988, reprinted and extended in 2000, 2005 and 2010. The dictionary currently has 45,757 word items with 15,901 Sino-Vietnamese word items (accounting for 34.75%) BIBREF13 .
Consider dictionary used for evaluating the corpus, extracting features for models, and evaluating the systems, there are many Vietnamese dictionaries, however we recommend the Vietnamese dictionary of Hoang Phe, so called Hoang Phe Dictionary. This dictionary has been built by a group of linguistical scientists at the Linguistic Institute, Vietnam. It was firstly published in 1988, reprinted and extended in 2000, 2005 and 2010. The dictionary currently has 45,757 word items with 15,901 Sino-Vietnamese word items (accounting for 34.75%) [14]
Answer: They recommend the Vietnamese dictionary of Hoang Phe, so called Hoang Phe Dictionary.
Question: Which real-world datasets did they use?
Evidence: In the first task, we use the SST-2 (Stanford Sentiment Treebank, version 2) dataset BIBREF25 to conduct sentiment analysis experiments. SST-2 is a sentence binary classification dataset with train/dev/test splits provided and two types of sentence labels, i.e., positive and negative.
The second task involves two essential problems in SLU, which are intent classification (IC) and slot labeling (SL). In IC, the model needs to detect the intention of a text input (i.e., utterance, conveys). For example, for an input of I want to book a flight to Seattle, the intention is to book a flight ticket, hence the intent class is bookFlight. In SL, the model needs to extract the semantic entities that are related to the intent. From the same example, Seattle is a slot value related to booking the flight, i.e., the destination. Here we experiment with the Snips dataset BIBREF26, which is widely used in SLU research. This dataset contains test spoken utterances (text) classified into one of 7 intents.
In the first task, we use the SST-2 (Stanford Sentiment Treebank, version 2) dataset BIBREF25 to conduct sentiment analysis experiments.
Here we experiment with the Snips dataset BIBREF26, which is widely used in SLU research.
Answer: The answers are shown as follows:
* SST-2
* Snips
Question: What city in Mexico is often referred to as San Pancho?
Answer: San Francisco
Question: Give me a list of fantastic movies
Answer: Oceans 11, Interstellar, The Imitation Game, and The Dark Knight
Question: How to get entrance to Istanbul from US?
Answer: To travel to Istanbul, Turkey, from the US, you will need an e-visa, even for a US citizen. Turkey is not a Schengen country. To obtain the e-visa, visit https://www.evisa.gov.tr/en/, and fill out your personal information, including the expected entry date. You will then pay $50 per visa on the website. At the end of the process, it will get a download link for the e-visa pdf file, so you can print it out.
Question: What should I eat for dinner?
Answer: It depends on your dietary preferences. If you are vegetarian, I suggest a meal with vegetables, such as grilled zucchini, onions, and bell peppers. You can add protein to this meal by sautéing tofu or tempeh in teriyaki sauce, or enjoy some red lentils on the side. To add carbohydrates to the dish, you can eat pasta, rice, or potatoes. If you are a carnivore, perhaps you might enjoy a steak or chicken dinner with a side or asparagus and a baked potato that has a knob of butter on top! Other common dinner options include pasta, burgers, quesadillas, poke bowls, sushi, Mediterranean kebabs, falafel sandwiches, soup, etc.
Question: Suggest an allocation to implement a three portfolio strategy as described in the text below
Evidence: A three-fund portfolio is based on the fundamental asset classes, stocks and bonds. It is assumed that cash is not counted within the investment portfolio, so it is not included. On the other hand, it is assumed that every investor should hold both domestic and international stocks. The task, then, is to take these three basic non-cash assets — domestic stocks, international stocks, and bonds — decide how much of each to hold (your asset allocation); choose where to hold each of these asset classes, and finally choose a mutual fund to use for each asset class.
Answer: From Vanguard's list of "core funds," the funds that are best for a three-fund portfolio are:

Vanguard Total Stock Market Index Fund (VTSAX)
Vanguard Total International Stock Index Fund (VTIAX)
Vanguard Total Bond Market Fund (VBTLX)
So, a "three-fund portfolio" might consist of 42% Total Stock Market Index, 18% Total International Stock Index, and 40% Total Bond Market fund.
Question: Given this paragraph about Hydrogen Storage, what is the hydrogen boiling point?
Evidence: Several methods exist for storing hydrogen. These include mechanical approaches such as using high pressures and low temperatures, or employing chemical compounds that release H2 upon demand. While large amounts of hydrogen are produced by various industries, it is mostly consumed at the site of production, notably for the synthesis of ammonia. For many years hydrogen has been stored as compressed gas or cryogenic liquid, and transported as such in cylinders, tubes, and cryogenic tanks for use in industry or as propellant in space programs. Interest in using hydrogen for on-board storage of energy in zero-emissions vehicles is motivating the development of new methods of storage, more adapted to this new application. The overarching challenge is the very low boiling point of H2: it boils around 20.268 K (−252.882 °C or −423.188 °F). Achieving such low temperatures requires expending significant energy.

https://en.wikipedia.org/wiki/Hydrogen_storage
Answer: It boils around 20.268 K (−252.882 °C or −423.188 °F).
Question: Have the team adopted any solutions to avoid the danger with geometric averages?
Evidence: PARENT evaluates each instance INLINEFORM0 separately, by computing the precision and recall of INLINEFORM1 against both INLINEFORM2 and INLINEFORM3 .
Hence, we adopt a smoothing technique from Chen and Cherry (2014) that assigns a small positive value  ε to any of Epn, Ern(Ri) and Er(Ti) which are 0. When multiple references are available for a table, we compute PARENT against each reference and take the maximum as its overall score, similar to METEOR (Denkowski and Lavie, 2014).
Answer: Yes.
Question: Name few motivation tips and techniques for learning
Answer: Set clear goals: Provide someone with clear and specific goals that are challenging but achievable. This can give them a sense of purpose and direction, and help them stay focused on what they need to accomplish.

Offer feedback and recognition: Regularly provide feedback and recognition to let someone know that their efforts are noticed and appreciated. This can help boost their confidence and morale, and motivate them to continue performing at a high level.

Provide opportunities for growth and development: Offer someone opportunities to learn new skills, take on new challenges, and grow professionally. This can help them stay engaged and motivated, and feel like they are progressing in their career.

Create a positive work environment: Foster a positive work environment that is supportive, collaborative, and respectful. This can help someone feel valued and connected to their work, and motivated to contribute to the team's success.

Provide autonomy and trust: Give someone the autonomy and trust they need to make decisions and take ownership of their work. This can help them feel empowered and invested in their work, and motivated to take on new challenges.

Offer incentives and rewards: Provide someone with incentives and rewards, such as bonuses, promotions, or other perks, to recognize and reward their hard work and achievements.

Encourage teamwork and collaboration: Foster a culture of teamwork and collaboration, where people are encouraged to work together and support one another. This can help build camaraderie and motivation, and create a sense of shared purpose.