DataHammer/scimrc huggingface dataset:
Question: From the given text, suggest some good activities for passing time during summer season.
Evidence: People generally take advantage of the high temperatures by spending more time outdoors during summer. Activities such as travelling to the beach and picnics occur during the summer months. Sports including cricket, association football (soccer), horse racing, basketball, American football, volleyball, skateboarding, baseball, softball, tennis and golf are played.

Water sports also occur. These include water skiing, wakeboarding, swimming, surfing, tubing and water polo. The modern Olympics have been held during the summer months every four years since 1896. The 2000 Summer Olympics, in Sydney, were held in spring and the 2016 Summer Olympics, in Rio de Janeiro, were held in winter.

In the United States, many television shows made for children are released during the summer, as children are off school.

Conversely, the music and film industries generally experience higher returns during the summer than other times of the year and market their summer hits accordingly. Summer is popular for animated movies to be released theatrically in movie theaters.
Answer: 1. During summer season, Outdoor activities such as travelling to beach/picnics are popular.
2. Outdoor games such as Cricket, Soccer, Horse racing, Basketball, American football, Volleyball, Skateboarding, Baseball, Softball, Tennis and Golf are often played in summer season.
3. Water sports such as Water skiing, Wakeboarding, Swimming, Surfing, Tubing and Water polo are also favoured by people in summer months.
4. In United States, children can enjoy watching television as many television shows are released during summer as children are off school.
5. Animated movies are often launched across theatres in summer.
Question: You state in section 3.1 that you do not consider Cysteine for experimental reasons? Why? Can you elaborate on the implied limitations of this choice?
Evidence: We report all timing estimations, unless stated otherwise, based on using an RTX 2080 Ti graphics card on the same single machine. We report all sample speed timings in seconds per example. Sample timing estimations were calculated with batches of 100 sequences, and calculated on the same single machine. The only example were this was not the case was for DARK0-Grad which was performed in batches of 10 due to memory constraints. Model training and sample refinement was performed with resources available at the time but these typically consisted of V100 graphics cards. As such, we report timing for training, refinement, and simulated annealing experiments as they would take on V100 graphics cards. The average time taken for refinement is 51.3 seconds
Answer: Thank you, we agree this should have been explained in more detail and we have added further explanation to the text in 3.1 that additionally links to further detail in Appendix A.6. In short, not including cysteine is common in de novo protein design studies and does not pose any significant limitations to our work or the conclusions drawn from it. It also provides a benefit. Its inclusion makes expression of any designed sequence in bacteria (the most common way to produce designs in the lab) more challenging.
Cysteine is special among amino acids, as it is mainly occurs in proteins to form disulfide bonds, particularly secreted proteins and outwards facing membrane proteins. This happens between pairs of cysteines, and forms a strong, usually stabilizing, bond between them. This bond cannot form in the cytosol of the cell, so disulfide containing proteins are expressed in an otherwise different way. This is the root cause of why it complicates validation of designed sequences. In de novo design literature the inclusion of cysteine is typically discussed as the inclusion of disulfide bonds. De novo designed proteins are known for being hyperstable without disulfides, and it is often a question of if the inclusion of disulfide bonds is justified. In other words, its common that the default stance is to not include disulfide bonds or to add them to preexisting candidates. For example, the Rosetta software package has separate protocols explicitly for adding disulfides to designs. Similarly to trDesign, we don't feel that including them here provides a clear benefit compared to the complications it brings.
Question: What is heavier, 1 centimetre or 5 miles?
Answer: Unfortunately, I can't answer that question. A centimetre and a mile are both units of measurement and therefore don't have an associated weight.
Question: How can I improve Kafka performance with Spark?
Answer: To improve performance, you can adjust the consumer and producer settings to match your use case. Additionally, you can use the latest versions of Kafka and Spark and increase the number of partitions in the topics. You can also optimize the code by using efficient transformations and caching.
Question: Which publicly available NLU dataset is used?
Evidence: We tested the system on two datasets, different in size and complexity of the addressed language.
Experimental Evaluation ::: Datasets ::: NLU-Benchmark dataset
The first (publicly available) dataset, NLU-Benchmark (NLU-BM), contains $25,716$ utterances annotated with targeted Scenario, Action, and involved Entities. For example, “schedule a call with Lisa on Monday morning” is labelled to contain a calendar scenario, where the set_event action is instantiated through the entities [event_name: a call with Lisa] and [date: Monday morning]. The Intent is then obtained by concatenating scenario and action labels (e.g., calendar_set_event). This dataset consists of multiple home assistant task domains (e.g., scheduling, playing music), chit-chat, and commands to a robot BIBREF7.
Experimental Evaluation ::: Datasets ::: ROMULUS dataset
The second dataset, ROMULUS, is composed of $1,431$ sentences, for each of which dialogue acts, semantic frames, and corresponding frame elements are provided. This dataset is being developed for modelling user utterances to open-domain conversational systems for robotic platforms that are expected to handle different interaction situations/patterns – e.g., chit-chat, command interpretation. The corpus is composed of different subsections, addressing heterogeneous linguistic phenomena, ranging from imperative instructions (e.g., “enter the bedroom slowly, turn left and turn the lights off ”) to complex requests for information (e.g., “good morning I want to buy a new mobile phone is there any shop nearby?”) or open-domain chit-chat (e.g., “nope thanks let's talk about cinema”). A considerable number of utterances in the dataset is collected through Human-Human Interaction studies in robotic domain ($\approx $$70\%$), though a small portion has been synthetically generated for balancing the frame distribution.
We tested the system on two datasets, different in size and complexity of the addressed language.

Experimental Evaluation ::: Datasets ::: NLU-Benchmark dataset
The first (publicly available) dataset, NLU-Benchmark (NLU-BM), contains $25,716$ utterances annotated with targeted Scenario, Action, and involved Entities.
Experimental Evaluation ::: Datasets ::: ROMULUS dataset
The second dataset, ROMULUS, is composed of $1,431$ sentences, for each of which dialogue acts, semantic frames, and corresponding frame elements are provided. This dataset is being developed for modelling user utterances to open-domain conversational systems for robotic platforms that are expected to handle different interaction situations/patterns – e.g., chit-chat, command interpretation.
Answer: The answers are shown as follows:
* ROMULUS dataset
* NLU-Benchmark dataset
Question: At what text unit/level were documents processed?
Evidence: We extend BERT Base-Chinese (12-layer, 768-hidden, 12-heads, 110M parameters) for sequence labeling. All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end. All inputs are then padded to a length of 256 tokens. After feeding through BERT, we obtain the hidden state of the final layer, denoted as ($h_{1}$, $h_{2}$, ... $h_{N}$) where $N$ is the max length setting. We add a fully-connected layer and softmax on top, and the final prediction is formulated as:
All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end.
Answer: The answers are shown as follows:
* documents are segmented into paragraphs and processed at the paragraph level
Question: Who are the known members of the student group named Dumbledor's Army, from the Harry Potter book series.
Answer: The known list of students in Dumbledor's Army is as follows:

Harry Potter (leader and teacher)
Ron Weasley (co-founder)
Hermione Granger (co-founder)
Neville Longbottom (Leader)
Ginny Weasley (Leader)
Luna Lovegood (Leader)
Angelina Johnson
Alice Tolipan
Alicia Spinnet
Anthony Goldstein
Cho Chang
Colin Creevey
Cormac McLaggen
Dean Thomas
Dennis Creevey
Ernie Macmillan
Fred Weasley
George Weasley
Hannah Abbott
Justin Finch-Fletchley
Katie Bell
Lavender Brown
Leanne
Lee Jordan
Luca Caruso
Maisy Reynolds
Marietta Edgecombe
Michael Corner
Nigel Wolpert
Padma Patil
Parvati Patil
Romilda Vane
Seamus Finnigan
Susan Bones
Terry Boot
Zacharias Smith
Question: What is the name of the season that begins in the summer and ends in the winter?
Answer: Fall
Question: how to express love without saying it out loud?
Answer: The book Five Love Languages talks about ways to express and experience love between partners are compliments, quality time, gifting, acts of service, physical touch. Although you do need to speak out your compliments, the other four can be done without a word. Some people also claim that preparing food and enjoying food together is another good way of bonding. Other suggestions include having a date routine like watching favorite movies or going to a dance hall and dancing to some Barry White on a fixed day every month.
Question: Tell me more about congratulatory message.
Answer: A message of congratulations or best wishes sent to someone after they have accomplished a noteworthy feat or milestone is known as a congratulatory message. Congratulatory messages can be conveyed via a variety of media, including social media posts, emails, cards, emails, and text messages.

It's customary to send congratulations to recognise and congratulate someone's accomplishment of a goal or significant milestone. They can be delivered for a range of events, such as graduations, promotions, new employment, marriages, births, or personal triumphs.

Depending on the relationship between the sender and the recipient as well as the type of accomplishment being honoured, the tone and style of a congrats message may change. Depending on the congratulations, some remarks could be formal and professional.
Question: why is Mahatma Gandhi loved by everyone?
Answer: Mahatma Gandhi preached non-cooperation using non-violent methods as a way to win against the oppressive rulers without resorting to guns.  This is difficult to do and requires public's cooperation.
Question: What is an API?
Answer: An application programming interface (API) is a way for two or more computer programs to communicate with each other. It is a type of software interface, offering a service to other pieces of software.
Question: 2022 UEFA European Under 19 Championship qualification
Answer: The 2022 UEFA European Under-19 Championship qualifying competition was a men's under-19 football competition that determined the seven teams joining the automatically qualified hosts Slovakia in the 2022 UEFA European Under-19 Championship final tournament. Players born on or after 1 January 2003 were eligible to participate.

Originally, the qualifying competition would use a new format with teams split into three different leagues with promotion and relegation, with the draw of the first round under the new format already held in December 2019. However, on 17 June 2020, UEFA announced that the introduction of the new format had been postponed to the next edition due to the COVID-19 pandemic, and qualification for this edition would use the previous format involving two rounds only.

Apart from Slovakia, 53 of the remaining 54 UEFA member national teams entered the qualifying competition, where the original format consisted of a qualifying round that took place in autumn 2021, and an elite round that took place in spring 2022.
Question: Categorize these bands as either 'rock', 'pop', 'metal' or 'indie'. Metallica, Queen, One direction, Korn, Coldplay, Arcade fire, backstreet boys
Answer: metal: Metallica, Korn
rock: Queen, Coldplay
pop: One direction, backstreet boys
indie: Arcade fire
Question: How many and which states were US Colonies?
Evidence: The Thirteen Colonies, also known as the Thirteen British Colonies, the Thirteen American Colonies, were a group of British colonies on the Atlantic coast of North America. Founded in the 17th and 18th centuries, the American Enlightenment led these colonies to revolution, and declaring full independence in July 1776. With victory over Britain in the American Revolutionary War the former colonies were confirmed as the new United States of America. Just prior to declaring independence, the Thirteen Colonies in their traditional groupings were: New England (New Hampshire; Massachusetts; Rhode Island; Connecticut); Middle (New York; New Jersey; Pennsylvania; Delaware); Southern (Maryland; Virginia; North Carolina; South Carolina; and Georgia). The Thirteen Colonies came to have very similar political, constitutional, and legal systems, dominated by Protestant English-speakers. The first of these colonies was Virginia Colony in 1607, a Southern colony. While all these colonies needed to become economically viable, the founding of the New England colonies, as well as the colonies of Maryland and Pennsylvania, were substantially motivated by their founders' concerns related to the practice of religion. The other colonies were founded for business and economic expansion. The Middle Colonies were established on an earlier Dutch colony, New Netherland. All the Thirteen Colonies were part of Britain's possessions in the New World, which also included territory in Canada, Florida, and the Caribbean.
Answer: There were 13 British Colonies that became States in the United States of America in 1776:  Connecticut, Delaware, Georgia, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, South Carolina, and Virginia.
Question: What are the two lines of work that are related to this paper?
Evidence: Two lines of work are related to our paper: research on context-aware translation (described in Section SECREF8 ) and research on focused evaluation of pronoun translation (described in Section SECREF11 ).
Two lines of work are related to our paper: research on context-aware translation (described in Section 2.1) and research on focused evaluation of pronoun translation (described in Section 2.2).
Answer: They are research on context-aware translation and research on focused evaluation of pronoun translation.
Question: Describe the benefits of riding the bus to work instead of driving a car.
Answer: Riding the bus to work instead of driving a car to work reduces greenhouse gas emissions. While you are riding the bus, you can also do other tasks that you could not do while driving, such as catching up on work, messaging your friends, or taking a nap. In areas where bus lanes exist, they can make the bus as fast or faster than a car for your commute because the bus won't get stuck in traffic. Finally, if you take the bus, you don't have to worry about finding parking for your car at your destination.
Question: What are the results of the author's experiment?
Evidence: In this paper, we have shown that using the designed RNN based network we could increase the performance of classification dramatically. We showed that keeping the sequential nature of the data can be hugely beneficial when working with textual data especially faced with the hard task of detecting more complex phenomena like emotions. We accomplished that by using a recurrent network in the process of generating our hidden representation. We have also used a max-pooling layer to capture the most relevant features and an average pooling layer to capture the text as a whole proving that we can achieve better performance by focusing on creating a more informative hidden representation. In future we can focus on improving these representations for example by using attention networks BIBREF37 , BIBREF38 to capture a more contextual representation or using language model based methods like BERT BIBREF39 that has been shown very successful in various NLP tasks.
We showed that keeping the sequential nature of the data can be hugely beneficial when working with textual data especially faced with the hard task of detecting more complex phenomena like emotions.
Answer: Keeping the sequential nature of the data can be hugely beneficial when working with textual data especially faced with the hard task of detecting more complex phenomena like emotions.
Question: What are four movies with Sandra Bullock.
Answer: Four movies with Sandra Bullock are The Blind Side, Bird Box, Gravity, and Miss Congeniality.
Question: What is the Accuracy ± k?
Evidence: Negotiations, either between individuals or entities, are ubiquitous in everyday human interactions ranging from sales to legal proceedings. Being a good negotiator is a complex skill, requiring the ability to understand the partner's motives, ability to reason and to communicate effectively, making it a challenging task for an automated system. While research in building automatically negotiating agents has primarily focused on agent-agent negotiations BIBREF0, BIBREF1, there is a recent interest in agent-human negotiations BIBREF2 as well. Such agents may act as mediators or can be helpful for pedagogical purposes BIBREF3.
Efforts in agent-human negotiations involving free-form natural language as a means of communication are rather sparse. Researchers BIBREF4 recently studied natural language negotiations in buyer-seller bargaining setup, which is comparatively less restricted than previously studied game environments BIBREF5, BIBREF6. Lack of a well-defined structure in such negotiations allows humans or agents to express themselves more freely, which better emulates a realistic scenario. Interestingly, this also provides an exciting research opportunity: how can an agent leverage the behavioral cues in natural language to direct its negotiation strategies? Understanding the impact of natural language on negotiation outcomes through a data-driven neural framework is the primary objective of this work.
We focus on buyer-seller negotiations BIBREF4 where two individuals negotiate the price of a given product. Leveraging the recent advancements BIBREF7, BIBREF8 in pre-trained language encoders, we attempt to predict negotiation outcomes early on in the conversation, in a completely data-driven manner (Figure FIGREF3). Early prediction of outcomes is essential for effective planning of an automatically negotiating agent. Although there have been attempts to gain insights into negotiations BIBREF9, BIBREF10, to the best of our knowledge, we are the first to study early natural language cues through a data-driven neural system (Section SECREF3). Our evaluations show that natural language allows the models to make better predictions by looking at only a fraction of the negotiation. Rather than just realizing the strategy in natural language, our empirical results suggest that language can be crucial in the planning as well. We provide a sample negotiation from the test set BIBREF4 along with our model predictions in Table TABREF1.
We define a metric called Accuracy ± k, measuring the %age of cases where the model performs ‘well’, as controlled by k. This allows us to compare our methods in a useful manner (Section 4).
Answer: It is metric that measures the %age of cases where the model performs ‘well’, as controlled by k.
Question: Please label which of the following appear in the game Clue as suspects, weapons, or rooms: Atrium, Billiard Room, Colonel Mustard, Conservatory, General Ketchup, Lead Pipe, Mrs. Peacock, Rope
Answer: In the classic edition of board game Clue, Colonel Mustard and Mrs. Peacock are suspects, the Lead Pipe and Rope are weapons, and Billiard Room and Conservatory are rooms. While the Atrium and General Ketchup seem like a plausible room and suspect respectively, neither is in game.
Question: What is the plot of Harry Potter?
Answer: The books concern a wizard called Harry Potter and his journey through Hogwarts School of Witchcraft and Wizardry. The stories tell of him overcoming dangerous obstacles to defeat the Dark Wizard Lord Voldemort, who killed his parents when Harry was 15 months old.
Question: What team has won the most Stanley Cups?
Answer: The Montreal Canadiens have won the most Stanley Cups with 24.
Question: What size of snowboard should I use?
Answer: The size of snowboard you should use is based on many different factors such as the skill of the rider, the weight and height of the rider, as well as the type of terrain you plan on riding. It is best to consult the manufacturer of the snowboard maker in order to make the best decision on the size of snowboard to use.
Question: Should I use microservices for my project? How many microservices should I have?
Answer: Every microservice you add increases the testing and deployment complexity for your project. However there are a few reasons you would want to split off separate microservices:
1. the scaling requirements are different (e.g., maybe part of your code needs to be deployed to 100 servers and other parts just to 5 servers).
2. the deployment model is different (e.g., maybe part of your code needs to be sharded by customer id, and other parts not sharded)
3. they are logically different parts of the code or of the company and they can benefit from moving independently for each other.

So split off as many microservices as required by the rules above, but don't split off too many.
Question: Name some fruits that have green skin.
Answer: Grapes, Green Apples and Avocados all have green skin.
Question: A farmer is on his way to market. He's going to sell a wolf, a goat, and a cabbage. On the way, he comes to a river crossing. The river is too wide to ford, so he has to take a tiny boat across. The boat is so small that he can take at most one of the three items with him in the boat at a time. Left alone, the wolf would devour the goat, and the goat would devour the cabbage. How can the farmer safely transport all three items across the river?
Answer: The wolf and the cabbage can be left together with no risk to either. So the farmer first takes the goat across. He returns to the near side, retrieves the cabbage, then takes it across to the far side. Then he returns to the near side with the goat. At the near side, he leaves the goat and takes the wolf to the far side, leaving the wolf with the cabbage. Finally, he returns to the near side to take the goat across for a second time. Problem solved!
Question: What is the difference between bourbon and whiskey?
Answer: While all bourbons are whiskeys, they have to be made with at least 51% corn to be classified as a bourbon.
Question: Which of the current Girl Scout cookies is based on one of the original Girl Scout cookies?
Answer: Trefoils are based on the original shortbread recipe