DataHammer/scimrc huggingface dataset:
Question: List the languages in which Lata Mangeshkar has recorded songs.
Evidence: Lata Mangeshkar ([ləˈt̪aː məŋˈɡeːʃkər] born Hema Mangeshkar; 28 September 1929 – 6 February 2022) was an Indian playback singer and occasional music composer. She is widely considered to have been one of the greatest and most influential singers in India and one of the world's greatest singers, according to BBC.[failed verification] Her contribution to the Indian music industry in a career spanning eight decades gained her honorific titles such as the "Queen of Melody", "Nightingale of India", and "Voice of the Millennium".
Lata recorded songs in over thirty-six Indian languages and a few foreign languages, though primarily in Marathi, Hindi, and Bengali. Her foreign languages included English, Indonesian, Russian, Dutch, Nepali, and Swahili. She received several accolades and honors throughout her career. In 1989, the Dadasaheb Phalke Award was bestowed on her by the Government of India. In 2001, in recognition of her contributions to the nation, she was awarded the Bharat Ratna, India's highest civilian honour; she is only the second female singer, after M. S. Subbulakshmi, to receive this honour. France conferred on her its highest civilian award, Officer of the National Order of the Legion of Honour, in 2007.
She was the recipient of three National Film Awards, 15 Bengal Film Journalists' Association Awards, four Filmfare Best Female Playback Awards, two Filmfare Special Awards, the Filmfare Lifetime Achievement Award and many more. In 1974, she was the first Indian playback singer to perform at the Royal Albert Hall in London, England. Her last recorded song was "Saugandh Mujhe Is Mitti ki" released on 30 March 2019 as a tribute to the Indian Army and the Nation.
Answer: Lata Mangeshkar has recorded songs in Marathi, Hindi, Bengali, English, Indonesian, Russian, Dutch, Nepali, and Swahili.
Question: What are the main impacts to the earth as a result of climate change.
Evidence: Due to climate change, deserts are expanding, while heat waves and wildfires are becoming more common.Increased warming in the Arctic has contributed to melting permafrost, glacial retreat and sea ice loss. Higher temperatures are also causing more intense storms, droughts, and other weather extremes. Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct.Even if efforts to minimise future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise.
Climate change threatens people with increased flooding, extreme heat, increased food and water scarcity, more disease, and economic loss. Human migration and conflict can also be a result.
Answer: More intense storms and droughts; Heat waves and wild fires are more common; Extinction of animal species; Increased food and water scarcity
Question: Who is Sachin Tendulkar?
Answer: Sachin Ramesh Tendulkar is an Indian former international cricketer who captained the Indian national team. He is regarded as one of the greatest batsmen in the history of cricket. He is the all-time highest run-scorer in both ODI and Test cricket with more than 18,000 runs and 15,000 runs, respectively. He also holds the record for receiving the most man-of-the-match awards in international cricket.[
Question: Do they build one model per topic or on all topics?
Evidence: The turkers are asked to indicate their preference for system A or B based on the semantic resemblance to the human summary on a 5-Likert scale (`Strongly preferred A', `Slightly preferred A', `No preference', `Slightly preferred B', `Strongly preferred B'). They are rewarded $0.04 per task. We use two strategies to control the quality of the human evaluation. First, we require the turkers to have a HIT approval rate of 90% or above. Second, we insert some quality checkpoints by asking the turkers to compare two summaries of same text content but in different sentence orders. Turkers who did not pass these tests are filtered out. Due to budget constraints, we conduct pairwise comparisons for three systems. The total number of comparisons is 3 system-system pairs INLINEFORM0 5 turkers INLINEFORM1 (36 tasks INLINEFORM2 1 human summaries for Eng + 44 INLINEFORM3 2 for Stat2015 + 48 INLINEFORM4 2 for Stat2016 + 46 INLINEFORM5 2 for CS2016 + 3 INLINEFORM6 8 for camera + 3 INLINEFORM7 5 for movie + 3 INLINEFORM8 2 for peer + 50 INLINEFORM9 4 for DUC04) = 8,355. The number of tasks for each corpus is shown in Table TABREF14 . To elaborate as an example, for Stat2015, there are 22 lectures and 2 prompts for each lecture. Therefore, there are 44 tasks (22 INLINEFORM10 2) in total. In addition, there are 2 human summaries for each task. We selected three competitive systems (SumBasic, ILP, and ILP+MC) and therefore we have 3 system-system pairs (ILP+MC vs. ILP, ILP+MC vs. SumBasic, and ILP vs. SumBasic) for each task and each human summary. Therefore, we have 44 INLINEFORM11 2 INLINEFORM12 3=264 HITs for Stat2015. Each HIT will be done by 5 different turkers, resulting in 264 INLINEFORM13 5=1,320 comparisons. In total, 306 unique turkers were recruited and on average 27.3 of HITs were completed by one turker. The distribution of the human preference scores is shown in Fig. FIGREF34 .
Therefore, there are 44 tasks (22 INLINEFORM10 2) in total. In addition, there are 2 human summaries for each task. We selected three competitive systems (SumBasic, ILP, and ILP+MC) and therefore we have 3 system-system pairs (ILP+MC vs. ILP, ILP+MC vs. SumBasic, and ILP vs. SumBasic) for each task and each human summary.
Answer: One model per topic.
Question: What experience do the authors present in the paper?
Evidence: We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus. Our data is a collection of Russian and Ukrainian academic texts, for which topics are their academic fields. In order to build language-independent semantic representations of these documents, we train neural distributional models on monolingual corpora and learn the optimal linear transformation of vectors from one language to another. The resulting vectors are then used to produce `semantic fingerprints' of documents, serving as input to a clustering algorithm. The presented method is compared to several baselines including `orthographic translation' with Levenshtein edit distance and outperforms them by a large margin. We also show that language-independent `semantic fingerprints' are superior to multi-lingual clustering algorithms proposed in the previous work, at the same time requiring less linguistic resources.
We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus.
Answer: Experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus.
Question: Where Is The Charging Port On A Tesla Model Y?
Answer: The Tesla Model Y charging port is located on the left side of the car near the rear light cluster. It's straightforward to charge the Tesla Model Y. All you need to do is plug the CCS 2 into your charging inlet, and you're ready to charge. Be aware that many public charging networks require you to have RFID cards or download their apps before you can use them.
Question: What are some uses for a buff?
Answer: A buff can be used to keep the neck warm, cover the head, wrap around the forehead like a headband, it can be used as a snot rag for runny noses, as a napkin during meals, and as a wash cloth or dish rag when camping.
Question: You've bought a condominium in the city, your first home, with more rooms than your previous apartments. It doesn't have a garage or any yard. Which of the following items would be handy? A lawnmower, a bus pass, a security system, more bathroom towels, wall art, gardening tools, a washer and dryer set, a car
Answer: You would want to get a bus pass so that you can get around the city, since you don't have a car. Though a lot might be walkable, there is always something that is far enough away.
You would want to buy a home security system; your condominium has fewer inherent security features than a large apartment complex, so it would be helpful to have a bit more peace of mind.
You would want to buy more bathroom towels since your condominium is larger and likely has more bathrooms that need towels.
You would want a washer and dryer set if you don't already have one. You need a way to wash your clothes at home now that you aren't in an apartment, and it would be hard to carry your laundry on the bus.
You might want some wall art; now that you own your own home, you can hang things on the wall without worrying about your lease agreement.
You don't need gardening tools or a lawnmower, since you don't have a yard.
You don't need a car, since you don't have a garage, and you live in the city.
Question: What is a music chord?
Answer: A chord in music is any harmony of two or more unique notes. In western music, we tend to focus on chords comprised of three unique notes characterized as a triad. The three notes are the root note plus the third and fifth intervals above the root in a diatonic scale of the key of the desired chord. For instance, if we are creating a C Major Chord, we will use the root (C), the third interval (E), and the fifth interval (G) in the C Major scale.
Question: How do I become a pilot?
Answer: In the U.S. are several requirements to become a pilot. These vary based on what type of pilot you want to become (e.g., private or commercial), what aircraft you want to fly, and under what conditions. Generally speaking, all pilots must get a medical certificate to prove they're in good enough health to fly, pass the FAA written exam, complete sufficient training hours, then pass a check ride. Do all this, and congratulations, you're now a pilot!
Question: Did Thomas Attewell bat with a right or left hand?
Evidence: Thomas Attewell (7 November 1869 – 6 July 1937) was an English first-class cricketer. Attewell was a right-handed batsman who bowled right-arm medium pace. He was born at Keyworth, Nottinghamshire.

His brother William played first-class cricket for Nottinghamshire and the Marylebone Cricket Club, as well as Test cricket for England. His cousin Walter Attewell played first-class cricket for Nottinghamshire.
Answer: right hand
Question: How is the data automatically generated?
Evidence: To create minimal pairs exemplifying a wide array of linguistic contrasts, it is necessary to artificially generate all datasets. This ensures both that we have sufficient unacceptable examples, and that the data is fully controlled, allowing for repeated isolation of a single linguistic phenomenon in each paradigm BIBREF30. The data generation scripts use a basic template to create each paradigm, pulling from a vocabulary of over 3000 words annotated for morphological, syntactic, and semantic features needed to create grammatical and semantically felicitous sentences. Examples SECREF6 and SECREF6 show one such template for the `acceptable' and `unacceptable' sentences within a pair: the sole difference between them is the underlined word, which differs only in whether the anaphor agrees in number with its antecedent. Our generation codebase and scripts are freely available.
This generation procedure is not without limitations, and despite the very detailed vocabulary we use, implausible sentences are occasionally generated (e.g., `Sam ran around some glaciers'). In these cases, though, both the acceptable and unacceptable sentences will be equally implausible given world knowledge, so any difference in the probability assigned to them is still due to the intended grammatical contrast.
To create minimal pairs exemplifying a wide array of linguistic contrasts, it is necessary to artificially generate all datasets. This ensures both that we have sufficient unacceptable examples, and that the data is fully controlled, allowing for repeated isolation of a single linguistic phenomenon in each paradigm BIBREF30. The data generation scripts use a basic template to create each paradigm, pulling from a vocabulary of over 3000 words annotated for morphological, syntactic, and semantic features needed to create grammatical and semantically felicitous sentences. Examples SECREF6 and SECREF6 show one such template for the `acceptable' and `unacceptable' sentences within a pair: the sole difference between them is the underlined word, which differs only in whether the anaphor agrees in number with its antecedent. Our generation codebase and scripts are freely available.
This generation procedure is not without limitations, and despite the very detailed vocabulary we use, implausible sentences are occasionally generated (e.g., `Sam ran around some glaciers'). In these cases, though, both the acceptable and unacceptable sentences will be equally implausible given world knowledge, so any difference in the probability assigned to them is still due to the intended grammatical contrast.
Answer: The answers are shown as follows:
*  The data generation scripts use a basic template to create each paradigm, pulling from a vocabulary of over 3000 words annotated for morphological, syntactic, and semantic features needed to create grammatical and semantically felicitous sentences.
Question: Name some of the bravolebrities from The Real Housewives of Dubai
Answer: Caroline Brooks, Caroline Stanbury, Chanel Ayan, Dr. Sara Al Madani, Lesa Milan Hall and Nina Ali
Question: What is the triple crown of baseball?
Answer: The triple crown is a rare feat accomplished in baseball when an individual player finishes the season with the American or National league lead in Home Runs, RBIs (runs batted in) and Batting Average.  There have been 14 triple crown winners all time in baseball.
Question: Who were Jaehaerys I Targaryen's children?
Answer: Prince Aegon, Princess Daenerys, Prince Aemon, Prince Baelon, Princess Alyssa, Septa Maegelle, Archmaester Vaegon, Princess Daella, Princess Saera, Princess Viserra, Prince Gaemon, Prince Valerion, Princess Gael
Question: What is EDM?
Answer: EDM is short for electronic dance music. It is a genre that is made up or electronic musical melodies and is sometimes referred to as club or dance music.
Question: What is a pineapple?
Answer: The pineapple is a fruit. It is native to South America, Central America and the Caribbean. The word "pineapple" came from European explorers, who thought the fruit looked similar to a pine cone. The pineapple is also a tropical plant and it must not be kept under 10 degrees. It takes a long time for the pineapple to grow. It mainly depends on the location and the growing conditions it gets. If the temperature is warm, the pineapple will grow faster. On average, it takes about two to three years to grow. If the fruit travels by boat, the fruit must be picked before ripening, since the trip takes a long time. Sometimes, pineapple has to travel by boat to be accessible to all. It is different for air travel. The pineapple can be picked when it is grown, since travel time is short. In most countries, they call the pineapple ananas.
Question: What is their future plan?
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.
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.
Answer: 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.
Question: Tell me whether each of these ski resorts are in Colorado or California: Big Bear, Mammoth, North Star, Vail, Aspen, Telluride
Answer: Big Bear: California
Mammoth: California
North Star: California
Vail: Colorado
Aspen: Colorado
Telluride: Colorado
Question: How do you juggle?
Answer: Juggling is the act of continually throwing and catching three or more objects, usually balls. Humans juggle for entertainment, recreation, exercise, and fun. Some jugglers are professionally hired to perform at birthday parties, festivals, and events. You juggle by throwing balls from hand to hand, releasing a ball before catching another.
Question: From the below text about MS Dhoni, tell me what they were most famous for?
Evidence: MS Dhoni, is a former Indian cricketer and captain of the Indian national team in limited-overs formats from 2007 to 2017 and in Test cricket from 2008 to 2014, who plays as a Wicket-keeper-Batsman. He is also the current captain of Chennai Super Kings in the Indian Premier League. Under his captaincy, India won the 2007 ICC World Twenty20, the 2011 Cricket World Cup, and the 2013 ICC Champions Trophy, the most by any captain. He also led India to victory in the 2010 and 2016 Asia Cup. Additionally, under his leadership, India won the 2010 and 2011 ICC Test Mace and 2013 ICC ODI Championship. Dhoni is a right-handed wicket-keeper batsman known for his calm captaincy and his ability to finish matches in tight situations. He has scored 17,266 runs in International Cricket (including 10,000 plus runs in ODI Internationals) and is considered one of the best finishers in the game, as well as one of the greatest wicket-keepers and captains in the history of cricket.
Answer: MS Dhoni is a former cricketer and captain of the Indian national team. He is most famously known for leading India to victory in 2007 ICC World Twenty20, the 2011 Cricket World Cup and the 2013 ICC Champions Trophy. He is also known for his calm captaincy and considered as one of the best finishers and greatest wicket-keepers in the history of the game.
Question: Write the introduction to a story about a rocket ship on its way to a distant galaxy
Answer: Deep space. It had been so long since they last had a reference point. How long? Was it days? Weeks? Months? The crew didn't know. They longed for a feature, some contact; a planet, even a small asteroid would do. Deep space is a lonely place. 

But the thought of the mission and the uniqueness of their experience kept them going. They had travelled further than any crew before. They had left the solar system and were well on their way to Andromeda. They would be the first, they would be the pioneers who discovered new lands for the human race. 

Days continued to pass - or was it weeks again? They started to hear strange sounds from the ship. Was it mechanical issues? Did they have a visitor? Or was it just their minds turning on them?
Question: In computer science, where did the term big-endian originate?
Evidence: Danny Cohen introduced the terms big-endian and little-endian into computer science for data ordering in an Internet Experiment Note published in 1980.

The adjective endian has its origin in the writings of 18th century Anglo-Irish writer Jonathan Swift. In the 1726 novel Gulliver's Travels, he portrays the conflict between sects of Lilliputians divided into those breaking the shell of a boiled egg from the big end or from the little end. Because the emperor's son had cut his finger while opening an egg from the big end, doing so was prohibited by an imperial edict; those who rebelled and did so were called "Big-Endians" (Swift did not use the term Little-Endians in the work). Cohen makes the connection to Gulliver's Travels explicit in the appendix to his 1980 note.
Answer: Danny Cohen introduced the terms big-endian in an Internet Experiment Note published in 1980. In the appendix to the 1980 note he connected the idea to Gulliver's Travels.
Question: What was the football team that played at the University of Oregon in 1949?
Evidence: The 1949 Oregon Webfoots football team represented the University of Oregon in the Pacific Coast Conference (PCC) during the 1949 college football season.  In their third season under head coach Jim Aiken, the Webfoots compiled a 4–6 record (2–5 against PCC opponents), finished in a tie for sixth place in the PCC, and outscored their opponents, 250 to 219. The team played its home games at Hayward Field in Eugene, Oregon.
Answer: The team that played football at the University of Oregon in 1949 was the Oregon Webfoots. The coach at the time was Jim Aiken, and the Webfoots held a 4-6 record (2-5 against PCC opponents), and finished with a tie for sixth place. Their home games were played at Hayward Field in Eugene, Oregon.
Question: What are five totally distinct, creative ways to describe the usefulness of empty time?
Answer: 1. "Empty time is like an open window, allowing the possibilities of the future to unfold." 
2. "Empty time is like a blank canvas, ready to be filled with new ideas and opportunities." 
3. "Empty time is like a fresh start, allowing us to make the most of the present moment." 
4. "Empty time is like a blank page, waiting to be written upon." 
5. "Empty time is like a vacuum, opening up a space for new ideas to come in."
Question: Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?
Evidence: We use two different unsupervised approaches for word sense disambiguation. The first, called `sparse model', uses a straightforward sparse vector space model, as widely used in Information Retrieval, to represent contexts and synsets. The second, called `dense model', represents synsets and contexts in a dense, low-dimensional space by averaging word embeddings.
In the synset embeddings model approach, we follow SenseGram BIBREF14 and apply it to the synsets induced from a graph of synonyms. We transform every synset into its dense vector representation by averaging the word embeddings corresponding to each constituent word: DISPLAYFORM0
We observe that the SenseGram-based approach for word sense disambiguation yields substantially better results in every case (Table TABREF25 ). The primary reason for that is the implicit handling of similar words due to the averaging of dense word vectors for semantically related words. Thus, we recommend using the dense approach in further studies. Although the AdaGram approach trained on a large text corpus showed better results according to the weighted average, this result does not transfer to languages with less available corpus size.
We use two different unsupervised approaches for word sense disambiguation. 
The second, called `dense model', represents synsets and contexts in a dense, low-dimensional space by averaging word embeddings.
In the synset embeddings model approach, we follow SenseGram BIBREF14 and apply it to the synsets induced from a graph of synonyms. 
We observe that the SenseGram-based approach for word sense disambiguation yields substantially better results in every case (Table TABREF25 ). The primary reason for that is the implicit handling of similar words due to the averaging of dense word vectors for semantically related words. 
Answer: Yes.