DataHammer/scimrc huggingface dataset:
Question: How do you play blackjack?
Answer: Blackjack is a card game, among players and a dealer. Each player is initially given two cards and the dealer shows one card. Each player takes a turn deciding to request one new card, "double down" by taking one extra card and doubling their bet amount, or "splitting" if the player's two cards are identical by doubling their bet and subsequently splitting the two cards into two new games. Cards with kings, queens, or jacks count are considered to have.a value of 10. Cards with an ace can be considered to be either a 1 or 11, whichever benefits the player. The player has the choice to take one additional card at a time to increase their value, until their choose to stop and "stay", or lose the game if their cards total more than 21. Once the player chooses to "stay", the dealer goes through a similar process by taking new cards until they either go over 21 and lose, or reach 17, 18, 19, 20, or 21. The player wins if the sum of the value of their cards  is greater than the sum of the value of the dealer's card. The the players sum of the value equals the dealers sum their value, then the game is a "draw" and a tie. A "blackjack" is when a player's first two cards total 21, and the player typically receives 150% the value of their bet. If both the player and the dealer have a blackjack on their first two cards, then it is considered a tie.
Question: What are the differences between FHIR and RDF?
Evidence: One of the several formats into which FHIR can be serialized is RDF. However, because RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting, there is the potential for a slight mismatch between the models. This comes up in two ways: One, RDF makes statements of fact, whereas FHIR makes records of events. The example given in the FHIR documentation is the difference between "patient x has viral pneumonia" (statement of fact) and "Dr. Jones diagnosed patient x with viral pneumonia" (record of event). Two, RDF is intended to have the property of monotonicity, meaning that previous facts cannot be invalidated by new facts. The example given for this mismatch is "a modifier extension indicates that the surrounding element's meaning will likely be misunderstood if the modifier extension is not understood." The potential for serious error resulting from this mismatch is small, but it is worth bearing in mind when designing information systems.
One of the several formats into which FHIR can be serialized is RDF. However, because RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting, there is the potential for a slight mismatch between the models
Answer: The answers are shown as follows:
* One of the several formats into which FHIR can be serialized is RDF
* there is the potential for a slight mismatch between the models
Question: How many stories is the Empire State Building?
Answer: The Empire State Building is 102 stories
Question: Does the paper describe experiments with real humans?
Evidence: Thus, when size adjectives are noisier than color adjectives, the model produces overinformative referring expressions with color, but not with size – precisely the pattern observed in the literature BIBREF5 , BIBREF0 . Note also that no difference in adjective cost is necessary for obtaining the overinformativeness asymmetry, though assuming a greater cost for size than for color does further increase the observed asymmetry. We defer a discussion of costs to Section "Experiment 1: scene variation in modified referring expressions" , where we infer the best parameter values for both the costs and the semantic values of size and color, given data from a reference game experiment.
We recruited 58 pairs of participants (116 participants total) over Amazon's Mechanical Turk who were each paid $1.75 for their participation. Data from another 7 pairs who prematurely dropped out of the experiment and who could therefore not be compensated for their work, were also included. Here and in all other experiments reported in this paper, participants' IP address was limited to US addresses and only participants with a past work approval rate of at least 95% were accepted.
Experiment 1: scene variation in modified referring expressions
We recruited 58 pairs of participants (116 participants total) over Amazon's Mechanical Turk who were each paid $1.75 for their participation.
Answer: Yes.
Question: What is freedom?
Answer: Freedom is what you make out of it. The dictionary could provide you with one definition but at a personal level freedom for me means to be able to walk in to a coffee shop at a time I would like, express my thoughts and have the money to pay for that coffee.
Question: When is the best time to visit the Jersey shore in New Jersey?
Answer: New Jersey is known to have some of the best beach vacation spots in the United States. Sitting along the eastern coast of New Jersey, the Jersey shore includes many different towns and styles from the boardwalks in Asbury Park, the casinos of Atlantic City to the southern most tip of New Jersey, Cape May. The best time to travel to the Jersey shore is between the months of June and September. These months provide the warmest times to head to the beach for both hot sunny days in the sand and clear cool water in the Atlantic Ocean.
Question: When do schools close and reopen from summer holidays in India?
Evidence: Schools and universities typically have a summer break to take advantage of the warmer weather and longer days. In almost all countries, children are out of school during this time of year for summer break, although dates vary. Many families will take holidays for a week or two over summer, particularly in Southern Hemisphere Western countries with statutory Christmas and New Year holidays.
In the United States, public schools usually end in late May in Memorial Day weekend, while colleges finish in early May. Public school traditionally resumes near Labor Day, while higher institutions often resume in mid-August.
In England and Wales, school ends in mid-July and resumes again in early September. In Scotland, the summer holiday begins in late June and ends in mid-to late-August. Similarly, in Canada the summer holiday starts on the last or second-last Friday in June and ends in late August or on the first Tuesday of September, with the exception of when that date falls before Labour Day, in which case, ends on the second Tuesday of the month. In Russia the summer holiday begins at the end of May and ends on 31 August.
In the Southern Hemisphere, school summer holiday dates include the major holidays of Christmas and New Year's Day. School summer holidays in Australia, New Zealand and South Africa begin in early December and end in early February, with dates varying between states. In South Africa, the new school year usually starts during the second week of January, thus aligning the academic year with the Calendar year. In India, school ends in late April and resumes in early or mid-June. In Cameroon and Nigeria, schools usually finish for summer vacation in mid-July and resume in the later weeks of September or the first week of October.
Answer: In India, school ends in late April and resumes in early or mid-June.
Question: If you were falling from an airplane and your parachute failed, list some things that you could try to increase your odds of surviving the fall.
Answer: 1. Use the reserve parachute.
2. Try to untangle the primary parachute.
3. Spread your arms and legs to maximize wind resistance.
4. Aim for softer surfaces like bushes, snow, or tall grass. Avoid water or concrete.
5. Land with an impact posture that helps distribute force away from your head.
Question: Give me an overview of the inspiration of the development of Barbie based on this passage
Evidence: Ruth Handler watched her daughter Barbara play with paper dolls, and noticed that she often enjoyed giving them adult roles. At the time, most children's toy dolls were representations of infants. Realizing that there could be a gap in the market, Handler suggested the idea of an adult-bodied doll to her husband Elliot, a co-founder of the Mattel toy company. He was unenthusiastic about the idea, as were Mattel's directors.

During a trip to Europe in 1956 with her children Barbara and Kenneth, Ruth Handler came across a German toy doll called Bild Lilli. The adult-figured doll was exactly what Handler had in mind, so she purchased three of them. She gave one to her daughter and took the others back to Mattel. The Lilli doll was based on a popular character appearing in a comic strip drawn by Reinhard Beuthin for the newspaper Bild. Lilli was a blonde bombshell, a working girl who knew what she wanted and was not above using men to get it. The Lilli doll was first sold in Germany in 1955, and although it was initially sold to adults, it became popular with children who enjoyed dressing her up in outfits that were available separately.
Answer: The Barbie Doll was invented by Ruth Handler, who was inspired by a German Doll called Bild Lilli. Unlike most American dolls, which were infants, Lilli was an adult. Ruth was inspired to make a similar type of doll for her daughter because she noticed her daughter liked to give her dolls adult rolls.
Question: What is the best hotel in Munnar
Answer: 1, Amber Dale Resort 
2. Chandy Windy woods 

Amber Dale for best view
Chandy Windy for best Hospitality
Question: How do I get in shape?
Answer: In order to get in good physical shape, you need to take multiple aspects of physical health into account. Some keys components that influence one's physical health are diet, exercising, and recovery. All of these need to be considered. In order to improve your physical health, you should put your body under a manageable amount of physical stress. For this stress to lead to improved health, you must remember to also recover! Recovery happens through eating high quality foods in the appropriate portions and by getting an adequate amount of sleep. Without also prioritizing these components of recovery, you will not "build up" your body, but rather continue to "break it down". Remember this saying, "Stress + Rest = Growth".
Question: Give me the top 5 golf equipment company names.
Answer: Titleist, Taylormade, Callaway, Ping, Cobra
Question: Write the first paragraph of a blog post describing the appeal of Boston as a tourist destination
Answer: Boston is a vibrant city with a rich history, incredible architecture, and diverse culture. Its cobblestone streets, charming neighborhoods, and vibrant nightlife make it an ideal destination for visitors. From the Freedom Trail to the Boston Common, Boston offers visitors a unique experience unlike any other. From historic sites to vibrant nightlife, Boston is a great place to visit and explore. The suburbs are great too.
Question: Why do people like to drink tea?
Answer: People enjoy drinking tea for many reasons. Drinking tea may be a part of a person's morning routine, and steeping a cup of tea may be a very grounding ritual. Tea also contains caffeine, which many people rely on as a source of energy. However, a cup of tea typically has less caffeine than a cup of coffee, and there are herbal options for those who do not want to consume a caffeinated beverage. Green tea in particular may also provide beneficial antioxidants. A warm cup of tea can bring comfort and be a be a great way to de-stress during the day.
Question: What datasets are used to evaluate the model?
Evidence: FLOAT SELECTED: Table 1: The knowledge base completion (link prediction) results on WN18 and FB15k.
FLOAT SELECTED: Table 1: The knowledge base completion (link prediction) results on WN18 and FB15k.
Answer: WN18 and FB15k
Question: What is Sunshine Recession?
Answer: It is known as the deepest period in which sunspots are not virtually visible. Deepest period is related to sun cycle's process called solar minimum
Question: How to evaluate word discovery?
Evidence: In order to provide an extrinsic metric to evaluate the quality of the acoustic units discovered by our different methods, we performed an unsupervised word segmentation task on the acoustic units sequences, and evaluated the accuracy of the discovered word boundaries. We also wanted to experiment using lattices as an input for the word segmentation task, instead of using single sequences of units, so as to better mitigate the uncertainty of the AUD task and provide a companion metric that would be more robust to noise. A model capable of performing word segmentation both on lattices and text sequences was introduced by BIBREF6 . Building on the work of BIBREF17 , BIBREF18 they combine a nested hierarchical Pitman-Yor language model with a Weighted Finite State Transducer approach. Both for lattices and acoustic units sequences, we use the implementation of the authors with a bigram language model and a unigram character model. Word discovery is evaluated using the Boundary metric from the Zero Resource Challenge 2017 BIBREF20 and BIBREF21 . This metric measures the quality of a word segmentation and the discovered boundaries with respect to a gold corpus (Precision, Recall and F-score are computed).
Word discovery is evaluated using the Boundary metric from the Zero Resource Challenge 2017 [21] and [22].
Answer: Word discovery is evaluated using the Boundary metric from the Zero Resource Challenge 2017.
Question: What kind of neural network architecture do they use?
Evidence: We adopt the approach in BIBREF4 , modifying it to synchronise audio with UTI data. Our model, UltraSync, consists of two streams: the first takes as input a short segment of ultrasound and the second takes as input the corresponding audio. Both inputs are high-dimensional and are of different sizes. The objective is to learn a mapping from the inputs to a pair of low-dimensional vectors of the same length, such that the Euclidean distance between the two vectors is small when they correlate and large otherwise BIBREF21 , BIBREF22 . This model can be viewed as an extension of a siamese neural network BIBREF23 but with two asymmetrical streams and no shared parameters. Figure FIGREF1 illustrates the main architecture. The visual data INLINEFORM0 (ultrasound) and audio data INLINEFORM1 (MFCC), which have different shapes, are mapped to low dimensional embeddings INLINEFORM2 (visual) and INLINEFORM3 (audio) of the same size: DISPLAYFORM0
FLOAT SELECTED: Figure 1: UltraSync maps high dimensional inputs to low dimensional vectors using a contrastive loss function, such that the Euclidean distance is small between vectors from positive pairs and large otherwise. Inputs span '200ms: 5 consecutive raw ultrasound frames on one stream and 20 frames of the corresponding MFCC features on the other.
Figure FIGREF1 illustrates the main architecture. 
FLOAT SELECTED: Figure 1: UltraSync maps high dimensional inputs to low dimensional vectors using a contrastive loss function, such that the Euclidean distance is small between vectors from positive pairs and large otherwise. Inputs span '200ms: 5 consecutive raw ultrasound frames on one stream and 20 frames of the corresponding MFCC features on the other.
Answer: CNN
Question: Write me an email to a professor respectfully declining their request for my attendance at a conference
Answer: Hi Professor,

It is so great to hear from you. 

I am so glad you thought of me to attend this conference. Unfortunately, I have a tentative vacation planned that week but please do keep me in mind for future opportunities. 

Thank you,
Question: What three publicly available coropora are used?
Evidence: Intuitively, a well-organized curriculum should provide the model learning with easy dialogues first, and then gradually increase the curriculum difficulty. However, currently, there is no unified approach for dialogue complexity evaluation, where the complexity involves multiple aspects of attributes. In this paper, we prepare the syllabus for dialogue learning with respect to five dialogue attributes. To ensure the universality and general applicability of the curriculum, we perform an in-depth investigation on three publicly available conversation corpora, PersonaChat BIBREF12, DailyDialog BIBREF13 and OpenSubtitles BIBREF7, consisting of 140 248, 66 594 and 358 668 real-life conversation samples, respectively.
To ensure the universality and general applicability of the curriculum, we perform an in-depth investigation on three publicly available conversation corpora, PersonaChat BIBREF12, DailyDialog BIBREF13 and OpenSubtitles BIBREF7, consisting of 140 248, 66 594 and 358 668 real-life conversation samples, respectively.
Answer: The answers are shown as follows:
* PersonaChat BIBREF12
* DailyDialog BIBREF13
* OpenSubtitles BIBREF7
Question: What was the Great Depression?
Answer: The Great Depression (1929–1939) was an economic shock that impacted most countries across the world. It was a period of economic depression that became evident after a major fall in stock prices in the United States. The economic contagion began around September and led to the Wall Street stock market crash of October 24 (Black Thursday). It was the longest, deepest, and most widespread depression of the 20th century.

Between 1929 and 1932, worldwide gross domestic product (GDP) fell by an estimated 15%. By comparison, worldwide GDP fell by less than 1% from 2008 to 2009 during the Great Recession. Some economies started to recover by the mid-1930s. However, in many countries, the negative effects of the Great Depression lasted until the beginning of World War II. Devastating effects were seen in both rich and poor countries with falling personal income, prices, tax revenues, and profits. International trade fell by more than 50%, unemployment in the U.S. rose to 23% and in some countries rose as high as 33%.
Question: What model do the authors present in the paper?
Evidence: In this paper, we present a deep neural network model for the task of fine-grained named entity classification using ELMo embeddings and Wikidata. The proposed model learns representations for entity mentions based on its context and incorporates the rich structure of Wikidata to augment these labels into finer-grained subtypes. We can see comparisons of our model made on Wiki(gold) in Table TABREF20 . We note that the model performs similarly to existing systems without being trained or tuned on that particular dataset. Future work may include refining the clustering method described in Section 2.2 to extend to types other than person, location, organization, and also to include disambiguation of entity types.
In this paper, we present a deep neural network model for the task of fine-grained named entity classification using ELMo embeddings and Wikidata.
Answer: A deep neural network model for the task of fine-grained named entity classification using ELMo embeddings and Wikidata.
Question: Who is Ramona Singer?
Evidence: Ramona Singer (née Mazur; born November 18, 1956) is an American television personality, businesswoman, and author. She is best known as a main cast member on the reality television series The Real Housewives of New York City, in which she has starred in the first 13 seasons from 2008 to 2021.
Answer: Ramona Singer is best known as a Real Housewife of New York City. She was the founder of RMS Fashion and helped her husband Mario Singer start True Faith Jewelry, the couple has since divorced.
Question: How do you explain cloud computing to a 5 year old?
Answer: Imagine you are building some thing using lego pieces. All of your legos are in basket.  As you start to build your desired structure you start to take 1-3 or couple of handful of legos from the basket. As you start to give some shape to the structure you are designing you start to put back some of the legos into the basket you don't need. Overtime you keep changing the shape slightly here and there; sometimes making the structure smaller and sometimes a little bigger. And you are doing all of these from the lego collection in the basket of yours. Cloud computing in a similar way is compute power that you can rent out from a cloud provider who has huge data centers spread across the world (This is the basket). As your application needs compute resources to meet the demand; you borrow compute resources from the cloud providers data center and give it back when you don't need it.
Question: How large is the dataset?
Evidence: Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. 
Answer: The answers are shown as follows:
*  $150,000$ tweets
Question: What models have provided and achieved incredible accuracy so far?
Evidence: Emotion detection has long been a topic of interest to scholars in natural language processing (NLP) domain. Researchers aim to recognize the emotion behind the text and distribute similar ones into the same group. Establishing an emotion classifier can not only understand each user's feeling but also be extended to various application, for example, the motivation behind a user's interests BIBREF0. Based on releasing of large text corpus on social media and the emotion categories proposed by BIBREF1, BIBREF2, numerous models have provided and achieved fabulous precision so far. For example, DeepMoji BIBREF3 which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER BIBREF4 learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.
As methods become mature, text-based emotion detecting applications can be extended from a single utterance to a dialogue contributed by a series of utterances. Table TABREF2 illustrates the difference between single utterance and dialogue emotion recognition. The same utterances in Table TABREF2, even the same person said the same sentence, the emotion it convey may be various, which may depend on different background of the conversation, tone of speaking or personality. Therefore, for emotion detection, the information from preceding utterances in a conversation is relatively critical.
In SocialNLP 2019 EmotionX, the challenge is to recognize emotions for all utterances in EmotionLines dataset, a dataset consists of dialogues. According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT) BIBREF5, FriendsBERT and ChatBERT. In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning.
Based on releasing of large text corpus on social media and the emotion categories proposed by [Ekman et al., 1987; Plutchik, 2001], numerous models have provided and achieved fabulous precision so far. For example, DeepMoji which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.
Answer: DeepMoji which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.