DataHammer/scimrc huggingface dataset:
Question: Why zurich is so beautiful
Answer: Because of it's lake, The most beautiful place in Zurich
Question: Based on this paragraph on Tardigrades when did we first send them to space?
Evidence: Tardigrades are the first known animal to survive after exposure to outer space. In September 2007, dehydrated tardigrades were taken into low Earth orbit on the FOTON-M3 mission carrying the BIOPAN astrobiology payload. For 10 days, groups of tardigrades, some of them previously dehydrated, some of them not, were exposed to the hard vacuum of outer space, or vacuum and solar UV radiation. Back on Earth, more than 68% of the subjects protected from solar UV radiation were reanimated within 30 minutes following rehydration, although subsequent mortality was high; many of these produced viable embryos. In contrast, hydrated samples exposed to the combined effect of vacuum and full solar UV radiation had significantly reduced survival, with only three subjects of Milnesium tardigradum surviving. Also, it was found that the space vacuum did not have a significant effect on egg-laying in either R. coronifer or M. tardigradum. However, M. tardigradum exposed to UV radiation had a lower egg laying rate. In May 2011, Italian scientists sent tardigrades on board the International Space Station along with extremophiles on STS-134, the final flight of Space Shuttle Endeavour. Their conclusion was that microgravity and cosmic radiation "did not significantly affect survival of tardigrades in flight, and stated that tardigrades represent a useful animal for space research." In November 2011, they were among the organisms to be sent by the U.S.-based Planetary Society on the Russian Fobos-Grunt mission's Living Interplanetary Flight Experiment to Phobos; however, the launch failed. In August 2019, scientists reported that a capsule containing tardigrades in a cryptobiotic state may have survived for a while on the Moon after the April 2019 crash landing of Beresheet, a failed Israeli lunar lander, but in May 2021 it was reported that they were unlikely to have survived the impact.
Answer: The FOTON-M3 mission in September of 2007 is where Tardigrades were exposed to the hard vacuum of outer space for the first time.
Question: Are Grizzly bears and Brown bears the same?
Answer: Grizzly bears are actually a subspecies of brown bears (Ursus arctos horribilis). The difference is regional:  Bears found inland are referred to as "grizzlies", while bears found on the coast are known as brown bears.
Question: Tell me about the Qingdao Qianwan Container Terminal
Evidence: Qingdao Qianwan Container Terminal is a port of China, located at Qianwan (literally The Front Bay), Qingdao, Shandong province. Qingdao Port ranked 10th in 2008 world's busiest container ports with 10,320 thousands TEU, current projects were expected to raise the capacity to 15,000 thousands or more.
Answer: The Qingdao Qianwan Container Terminal is a port of China, located at Qianwan (literally The Front Bay), Qingdao, Shandong province. Qingdao Port ranked 10th in 2008 world's busiest container ports with 10,320 thousands TEU, current projects were expected to raise the capacity to 15,000 thousands or more.
Question: What are the top 3 largest countries by territory?
Answer: Russia, Canada and China
Question: You are a young citizen of a developing country. Write a letter in less than 150 words to a newspaper, highlighting your concern over incorrect disposal of biomedical waste and to spread awareness of the same.
Answer: The Covid19 pandemic has changed the lives of all human beings, and has driven massive consumption of personal protective equipment worldwide. People have adapted to wearing face masks as a 'new normal'. At the same time, the disposal of biomedical waste is a major concern, especially in developing countries with limited resources. Biomedical waste includes used Rapid Antigen Tests, PCR Tests, used injections, and used masks. We have observed that people who use the test kits and masks at home, often fail to dispose them off properly. This is counter productive, as it poses the risk of aiding the virus' transmission, and can potentially be life threatening to others. Hospitals, too, produce a huge amount of waste and it is imperative they dispose it off properly following all norms. 

If not scientifically handled and disposed, it could cause serious risk not just to human health, but also pose a serious threat to terrestrial and marine ecosystems.

Thank you for highlighting my concerns to your readership, and I request you to keep highlighting these concerns to your audience regularly via your articles.
Question: What kind of Toys should be prepare for a new puppy
Answer: • Puppy’s teeth can be strong, and they can easily destroy soft
toys. They can also accidentally swallow part of the toys. Do
monitor the puppy when they are playing with toys to
prevent accidental choking.

• As the puppy is teething, they will need bite toys to chew
on.

So prepare the toy for the puppy base on the above consideration.
Question: Given this paragraph about triathlon, who founded ironman
Evidence: On Wednesday, September 25, 1974, the race started. It began with a run of a 4.8 km (3 mi) loop, followed by biking twice around Fiesta Island for a total of 8.0 km (5 mi). Entrants would then get off the bikes, take their shoes off and run into the water to swim to the mainland. That was followed by running in bare feet, then swimming again along the bay, then one last swim up to the entrance of Fiesta Island, and a final crawl up a steep dirt bank to finish. Most participants were not skilled swimmers, so Johnstone recruited his 13-year-old son to float on his surfboard and act as lifeguard. Some participants took longer than expected, and it began to get dark as they finished their swims. Shanahan recalls they pulled up a few cars and turned on the headlights so the athletes could see. Johnstone and Shanahan were surprised by the large number of entrants (46), mainly coming from local running clubs. Two notable entrants, Judy and John Collins, would four years later found the event which brought international attention to the new sport: the Hawaii Ironman
Answer: Ironman was founded by Judy and John Collins four years after the first known modern day triathlon on Wednesday, September 25th, 1974.
Question: What's the role of the prediction layer?
Evidence: In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps. Then, the two-staged CNN architecture extracts features through word-level and sentence-level. At the same time, attention mechanism helps CNN focus more on the important part of the passage based on the query information. Finally, the prediction layer find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.
Finally, the prediction layer find out the most possible answer choice.
Answer: To find out the most possible answer choice.
Question: Which of these are dog and which are birds: Phoenix, Siberian Husky, Alaskan Malamute, Hawk, Border Collie, Labrador,  Araucana
Answer: Birds: Phoenix, Siberian Husky, Alaskan Malamute, Border Collie, Labrador
Dogs:  Siberian Husky, Alaskan Malamute, Border Collie, Labrador
Question: We are expecting a baby. Can you describe your experience and provide some pointers as a first time parent?
Answer: * Baby sleeps all the time. You might end up hearing this a lot from others but in reality it might not be true. As everything in life, it depends. Some babies “sleep like a baby” but others might not. Our baby only sleeps if we hold him in our hands and he also had some reflex issues. Due to this reflex issue, as soon as we put him in the bed, he wakes up immediately and started to cry as the milk comes up from his stomach to his mouth. We are at-least a bit lucky here as he sleeps on our hands. Some babies may have colic and cries with no reason. That would have been much worse situation to be in.
* You might be wondering how to hold the baby properly and how to change diapers and so on and forth. Trust me, you will learn all these things in no time.
* Babies mainly cries for the following three reasons. 1. When they are hungry 2. When they are sleepy 3. When they need diaper change. Hence you should rule out all these three before you escalate the baby crying issue.
* Team work is very important. Work with your partner. Plan, schedule your time and execute it. Taking care of baby is really a two or more persons job. Your partner needs all the help that is available. Utilise your parental leave.
* Don’t get stressed out if the baby do not reach the milestones that you read on the internet. Every baby is unique and they reach milestones in their own timeline
* It is very important to enjoy the journey as you will never get those moments back and trust me, these will be very precious moment in your life.
Question: What is a Rhapsa scotosialis
Evidence: Rhapsa scotosialis, the slender owlet moth, is a moth of the family Noctuidae. This species is endemic to New Zealand and is found throughout the country. It is regarded as one of the most common forest moths found in New Zealand. The larval host species for R. scotosialis is Piper excelsum.
Answer: A Rhapsa scotosialis, the slender owlet moth, is a moth of the family Noctuidae. This species is endemic to New Zealand and is found throughout the country. It is regarded as one of the most common forest moths found in New Zealand. The larval host species for R. scotosialis is Piper excelsum.
Question: From the passage provided, extract the length and width of tennis court in metres.
Evidence: Tennis is played on a rectangular, flat surface. The court is 78 feet (23.77 m) long, and 27 feet (8.2 m) wide for singles matches and 36 ft (11 m) for doubles matches. Additional clear space around the court is required in order for players to reach overrun balls. A net is stretched across the full width of the court, parallel with the baselines, dividing it into two equal ends. It is held up by either a cord or metal cable of diameter no greater than 0.8 cm (1⁄3 in). The net is 3 feet 6 inches (1.07 m) high at the posts and 3 feet (0.91 m) high in the centre. The net posts are 3 feet (0.91 m) outside the doubles court on each side or, for a singles net, 3 feet (0.91 m) outside the singles court on each side.
Answer: Length : 23.77 m
Width: 8.2 m
Question: Classify each of the following as cold or hot: snow, fire, refrigerator, lava, stove top, freezer
Answer: Snow: Cold
Fire: Hot
Refrigerator: Cold
Lava: Hot
Stove Top: Hot
Freezer: Cold
Question: What's the name of the authors' bilingual language models?
Evidence: We build our bilingual LMs, named RAMEN, starting from BERT$_{\textsc {base}}$, BERT$_{\textsc {large}}$, RoBERTa$_{\textsc {base}}$, and RoBERTa$_{\textsc {large}}$ pre-trained models. Using BERT$_{\textsc {base}}$ allows us to compare the results with mBERT model. Using BERT$_{\textsc {large}}$ and RoBERTa allows us to investigate whether the performance of the target LM correlates with the performance of the source LM. We evaluate our models on two cross-lingual zero-shot tasks: (1) Cross-lingual Natural Language Inference (XNLI) and (2) dependency parsing.
We choose BERT and RoBERTa (Liu et al., 2019) as the source models for building our bilingual language models, named RAMEN3 for the ease of discussion.
Answer: RAMEN.
Question: How different is the dataset size of source and target?
Evidence: The procedure of transfer learning in this work is straightforward and includes two steps. The first step is to pre-train the model on one MCQA dataset referred to as the source task, which usually contains abundant training data. The second step is to fine-tune the same model on the other MCQA dataset, which is referred to as the target task, that we actually care about, but that usually contains much less training data. The effectiveness of transfer learning is evaluated by the model's performance on the target task.
The first step is to pre-train the model on one MCQA dataset referred to as the source task, which usually contains abundant training data. The second step is to fine-tune the same model on the other MCQA dataset, which is referred to as the target task, that we actually care about, but that usually contains much less training data. 
Answer: the training dataset is large while the target dataset is usually much smaller
Question: What is the best bidding system in duplicate bridge?
Answer: Bidding systems in bridge allow the partnership to communicate the strength and various characteristics of their hand to arrive at an appropriate contract. A good bidding system should be descriptive, accommodate many different hand types, and account for interference from the opponents. There is no clear best bidding system as they all have advantages and disadvantages but 2/1 (two-over-one) is accepted to be the standard that people use. The reason is that it can describe many hand types efficiently through natural bidding, it is easy to learn and understand, and handle interference from opponents.
Question: Which episode did Daenerys Targaryen lose her dragon, Viserion, to a White Walker's spear?
Answer: The Night King, the leader of the White Walkers, threw a spear at Viserion, which killed him.
Question: Make a list of different reasons that a kite might not be able to be flown
Answer: You might not be able to fly a kite if there isn't any wind, if the strings are tangled, if the kite is broken or if the kite gets caught up in a tree
Question: How do they create the combined model?
Evidence: Finally, we created a combined temporal-linguistic model. Since both the linguistic and the temporal models were built using the same framework, it was fairly simple to combine the two models. The combined model was created by merging the linguistic and temporal corpora and vocabularies. (Recall that we treated temporal features as words). We then experimented with the same four methods as in the last two sections to create our combined models.
Table 4 shows the performance of each of these models. Across the board, the combined models outperformed their corresponding linguistic and temporal models, though the difference with the linguistic models were not as great. These results suggest that at some level the temporal and the linguistic "styles" of users provide non-overlapping cues about the identity of said users. Also, note that as with the linguistic and temporal models, our combined confusion model outperformed the other combined models.
Another way to evaluate the performance of the different combined models is through the rank-statistics plot. This is shown in Figure 2 . The figure shows the distribution of the ranks of the $5,612$ users for different combined models. The x-axis is the rank percentile (divided into bins of $5\%$ ), the y-axis is the percentage of the users that fall in each bin. For example, for the confusion model, $69\%$ (3880) of the $5,612$ users were correctly linked between Twitter and Facebook when looking at the top $5\%$ (281) of the predictions by the model. From the figure, you can clearly see that the confusion model is superior to the other models, with TF-IDF a close second. You can also see from the figure that the rank plot for the random baseline is a horizontal line, with each rank percentile bin having $5\%$ of the users ( $5\%$ because the rank percentiles were divided into bins of $5\%$ ).
The combined model was created by merging the linguistic and temporal corpora and vocabularies.
Answer: By merging the linguistic and temporal corpora and vocabularies.
Question: Pick a few months with 31 days.
Answer: January, March, May, July, August, October and December have 31 days.
Question: How does an umpire signal that a batter has been dismissed in a cricket match ?
Evidence: The match details, including runs and dismissals, are recorded by two official scorers, one representing each team. The scorers are directed by the hand signals of an umpire (see image, right). For example, the umpire raises a forefinger to signal that the batter is out (has been dismissed); he raises both arms above his head if the batter has hit the ball for six runs. The scorers are required by the Laws to record all runs scored, wickets taken and overs bowled; in practice, they also note significant amounts of additional data relating to the game
Answer: An umpire signals that a batter has been dismissed by raising a forefinger.
Question: How is neural planning component trained?
Evidence: At training time, we use the plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller, choosing which action to perform at each step. At test time, we use the controller to guide the truncated DFS process. This mechanism is inspired by transition based parsing BIBREF5. The action set at each stage is dynamic. During traversal, it includes the available children at each stage and pop. Before traversals, it includes a choose-i action for each available node $n_i$. We assign a score to each action, normalize with softmax, and train to choose the desired one using cross-entropy loss. At test time, we either greedily choose the best action, or we can sample plans by sampling actions according to their assigned probabilities.
At training time, we use the plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller, choosing which action to perform at each step.
Answer: The answers are shown as follows:
* plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller
Question: Which human rights organisation founded 1961 got Nobel 1977
Answer: Amnesty International
Question: How many provinces are in Canada?
Answer: Canada has 10 provinces and 3 territories. The most populous provinces are Ontario, Quebec, and British Columbia.
Question: Who are the best chess players of all time?
Answer: It's difficult to determine who the best chess players of all time are as there are a variety of factors to consider when determining chess strength. For example, you might consider things like chess rating, number of tournament wins, time spent as world chess champion, etc. It's especially difficult to compare players of different eras, as more modern chess players will have learned from past Grandmasters and have more advanced technology available to facilitate their understanding of the game.

Three chess players commonly discussed as possibly being the best of all time include: Bobby Fischer, Gary Kasparov, and Magnus Carlsen. Bobby Fischer is well-known for winning the "Game of the Century" at the age of 13 and, as an adult, won 20 consecutive games against some of the best chess players in the world. Gary Kasparov was the number one rated chess player for over 20 years and was World Chess Champion for 15 consecutive years. Magnus Carlsen achieved the highest chess rating ever in 2014 with a rating of 2882 and has been the number one rated chess player since 2011.
Question: Who was the first Grand Prix driver to used a safety belt in 1967
Answer: Jackie Stuart
Question: Please give me a bulleted list of men's Roland Garros champions of the last 5 years.
Answer: Sure! Here's the list of men's Roland Garros champions of the last 5 years:
* 2022 - Rafael Nadal
* 2021 - Novak Djokovic
* 2020 - Rafael Nadal
* 2019 - Rafael Nadal
* 2018 - Rafael Nadal
Question: Given a reference text about Margot Robbie, tell me when they were born and what they are famous for.
Evidence: Margot Elise Robbie (/ˈmɑːrɡoʊ ˈrɒbi/; born 2 July 1990) is an Australian actress and producer. Known for her work in both blockbuster and independent films, she has received various awards and nominations, including nominations for two Academy Awards, four Golden Globe Awards, and five British Academy Film Awards. Time magazine named her one of the 100 most influential people in the world in 2017 and she was ranked as one of the world's highest-paid actresses by Forbes in 2019.
Answer: Margot Robbie was born on July 2nd, 1990. She is famous for being an Australian actress and one of Time Magazine's 100 most influential people in the world in 2017.
Question: What is the most popular breed of dog in the US?
Answer: The Golden Retriever is consistently in the top 3 most popular dog breeds in the US in annual surveys.
Question: Write an introductory email to a student I will be academically coaching through Services for Students with Disabilities office (SSD).
Answer: Hi Naomi,

My name is Chandler, and I am your Academic Coach through the Services for Students with Disabilities office (SSD). 

I am looking forward to working with you during this time in your academic journey! As your academic coach, my role is to partner with you as we focus on your process of learning. 

During our time together we will examine your:
learning experience
habits of working
current difficulties or barriers to success

While examining these areas we will work together to:
Develop individualized learning strategies and study habits
Navigate campus resources
Promote self-advocacy

We will also focus on building and sustaining Executive Functioning skills such as:
Time Management
Organization
Self-regulation
Self-efficacy

I look forward to hearing back from you.
Question: Who was Luis Gonzalez de Ubieta
Evidence: Luis González de Ubieta y González del Campillo (1899 – 1950) was an admiral of the Spanish Republican Navy during the Spanish Civil War. He died in exile as the captain of the Panamanian merchant vessel Chiriqui, refusing to be rescued when the ship under his command sank in the Caribbean Sea not far from Barranquilla.
Answer: Luis Gonzalez de Ubieta y Gonzalez del Campillo (1899 – 1950) was an admiral of the Spanish Republican Navy during the Spanish Civil War. He died in exile as the captain of the Panamanian merchant vessel Chiriqui, refusing to be rescued when the ship under his command sank in the Caribbean Sea not far from Barranquilla.
Question: Why do the team use both the definition of the term and the definition of its related concepts?
Evidence: We obtain definitions for each of the two terms we wish to measure. Due to the sparsity and inconsistencies of the definitions in the UMLS, we not only use the definition of the term (CUI) but also include the definition of its related concepts. This follows the method proposed by PatwardhanP06 for general English and WordNet, and which was adapted for the UMLS and the medical domain by LiuMPMP12. In particular we add the definitions of any concepts connected via a parent (PAR), child (CHD), RB (broader than), RN (narrower than) or TERM (terms associated with CUI) relation. All of the definitions for a term are combined into a single super–gloss. At the end of this process we should have two super–glosses, one for each term to be measured for relatedness.
Next, we process each super–gloss as follows:
We extract a first–order co–occurrence vector for each term in the super–gloss from the co–occurrence matrix created previously.
We take the average of the first order co–occurrence vectors associated with the terms in a super–gloss and use that to represent the meaning of the term. This is a second–order co–occurrence vector.
After a second–order co–occurrence vector has been constructed for each term, then we calculate the cosine between these two vectors to measure the relatedness of the terms.
Due to the sparsity and inconsistencies of the definitions in the UMLS, we not only use the definition of the term (CUI) but also include the definition of its related concepts.
Answer: Because the definitions in the UMLS are featured with sparsity and inconsistencies.