DataHammer/scimrc huggingface dataset:
Question: Where is Mount Everest?
Evidence: Mount Everest (Nepali: सगरमाथा, romanized: Sagarmāthā; Tibetan: Chomolungma ཇོ་མོ་གླང་མ; Chinese: 珠穆朗玛峰; pinyin: Zhūmùlǎngmǎ Fēng) is Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas. The China–Nepal border runs across its summit point. Its elevation (snow height) of 8,848.86 m (29,031 ft 8+1⁄2 in) was most recently established in 2020 by the Chinese and Nepali authorities.
Answer: Mount Everest is located in the Himalayan mountain range and runs across the China-Nepal border.
Question: Do you really need mobile phone?
Answer: We do not really need a mobile phone to live.
Question: Identify the bird from the list: Mouse, Balearic shearwater, Giraffe
Answer: Balearic shearwater
Question: Given a reference text about Jen Shah, what is she famous from, when was she criminally charged, what was the verdict, and when was she sentenced.
Evidence: Shah starred on the reality television series The Real Housewives of Salt Lake City, which premiered in November 2020. In March 2021, she was criminally charged with conspiracy to commit money laundering and wire fraud, pleading not guilty to both charges days later. Her legal case was chronicled in the documentary film Housewife and the Shah Shocker, which premiered on Hulu in November that same year. In July 2022, Shah pleaded guilty to conspiracy to commit wire fraud. In January 2023, Shah was sentenced to six-and-a-half years in prison. Shah began her prison sentence the following month.
Answer: Jen Shah came into the spotlight in November 2020 when The Real Housewives of Salt Lake City aired. In March 2021 she was charged with conspiracy to commit money laundering as well as wire fraud. She pled not guilty, but then come July 2022 she plead guilty to commit wire fraud. January of 2023 she was sentenced to 6.5 years in prison and her sentence began in February.
Question: Which team was relegated from the league at the conclusion of the 1991 Season?
Evidence: Waterside Karori were relegated at the end of the 1990 season, to be replaced by the winner of a play-off series between teams from the northern, central, and southern leagues (Mount Albert-Ponsonby, Nelson United, and Burndale United respectively). Nelson United won the series to gain promotion, but there is some controversy about their participation in the play-offs as they had only finished second in the central regional league, which was won by Petone.  

Gisborne City were relegated at the end of the 1991 season
Answer: Gisborne City were relegated at the end of the 1991 season.
Question: How to pre-train the universal encoder?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
Pre-train a universal encoder with source/pivot monolingual or source↔pivot bilingual data.
Answer: Pre-train a universal encoder with source/pivot monolingual or source↔pivot bilingual data.
Question: Classify each country as "African" or "European": Nigeria, Rwanda, Portugal, Romania, Ghana, Ukraine
Answer: Nigeria: African
Rwanda: African
Portugal: European
Romania: European
Ghana: African
Ukraine: European
Question: Do you know how many episodes of Game of Thrones are there?
Answer: Yes - there are 73 episodes for Game of Thrones that are spread over 8 seasons. The first season was released April 17, 2011 by HBO and season 8 concluded on May 19, 2019. The entire series is 70 hours and 14 minutes long and would take you just under 3 full days if you watched every episode back to back.
Question: Name some fruits that have yellow or orange skin.
Answer: Bananas, Lemons, Oranges, Nectarines and Apricots all have yellow or orange skin.
Question: What coloring of cat is almost always male?
Answer: Orange cats are generally male.
Question: What are some of the most efficient programing languages?
Answer: C, C++, Rust are very efficient, while Java and Scala are easier to develop with and have lower barrier of entry but are not as efficient
Question: From the passage provided, extract the acquisition details of Tableau.
Evidence: Tableau Software (/tæbˈloʊ/ tab-LOH) is an American interactive data visualization software company focused on business intelligence. It was founded in 2003 in Mountain View, California, and is currently headquartered in Seattle, Washington. In 2019 the company was acquired by Salesforce for $15.7 billion. At the time, this was the largest acquisition by Salesforce (a leader in the CRM field) since its foundation. It was later surpassed by Salesforce's acquisition of Slack.

The company's founders, Christian Chabot, Pat Hanrahan and Chris Stolte, were researchers at the Department of Computer Science at Stanford University. They specialized in visualization techniques for exploring and analyzing relational databases and data cubes, and started the company as a commercial outlet for research at Stanford from 1999 to 2002.

Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations. The software can also extract, store, and retrieve data from an in-memory data engine.
Answer: Tableau was acquired in 2019 by Salesforce for $15.7 billion.
Question: Write some important points regarding Family Justice Council using given text as a reference
Evidence: The Family Justice Council, established in 2004, is an advisory, non-statutory, non-departmental public body sponsored by the Judicial Office of England and Wales. It provides independent expert advice, from an inter-disciplinary perspective, on the operation and reform of the family justice system to the Family Justice Board (jointly chaired by ministers from the Ministry of Justice and Department for Education) and Government. It is chaired by the President of the Family Division, and in August 2012 became part of the President's office.
Answer: 1. The Judicial Office of England and Wales sponsors the Family Justice Council, an advisory, non-statutory, non-departmental public organisation founded in 2004.
2. It offers independent professional advice to the Family law Board, which is jointly chaired by ministers from the Ministry of Justice and Department for Education, as well as the Government on how to run and reform the family law system.
3. It is presided over by the Family Division President and joined the President's office in August 2012.
Question: What is the name of the first dog that went to space?
Answer: Laika
Question: Who is Ted Lasso in the eponymous TV show?
Evidence: Ted Lasso is an American sports comedy-drama television series developed by Jason Sudeikis, Bill Lawrence, Brendan Hunt and Joe Kelly, based on a character Sudeikis first portrayed in a series of promos for NBC Sports' coverage of England's Premier League. The show follows Ted Lasso, an American college football coach who is hired to coach an English soccer team with the secret intention that his inexperience will lead it to failure, but whose folksy, optimistic leadership proves unexpectedly successful.
Answer: The show follows Ted Lasso, an American college football coach who is hired to coach an English soccer team with the secret intention that his inexperience will lead it to failure,
Question: What are state of the art methods MMM is compared to?
Evidence: FLOAT SELECTED: Table 3: Accuracy on the DREAM dataset. Performance marked by ? is reported by (Sun et al. 2019). Numbers in parentheses indicate the accuracy increased by MMM compared to the baselines.
FLOAT SELECTED: Table 3: Accuracy on the DREAM dataset. Performance marked by ? is reported by (Sun et al. 2019). Numbers in parentheses indicate the accuracy increased by MMM compared to the baselines.
Answer: FTLM++, BERT-large, XLNet
Question: What is Heineken Evergreen?
Answer: Evergreen is the company strategy of Heineken. The goal of Evergreen is to future proof the business and adapt to external dynamics.
Question: Make a list of different fruits you could put in a fruit salad with at least 5 ingredients
Answer: Five fruits you could use are bananas, strawberries, apples, pears and blueberries
Question: How do I make great Neapolitan pizza dough at home?
Answer: To make great Neapolitan pizza you need 4 ingredients. Water, yeast, flour and salt. For best results you should use typo "00" flour. When using such finely grained flour go for a 65% hydration dough, 3% salt, and 0.5% dry yeast. This means for every 1000 parts flour, put 650 parts water.

Great pizza dough takes time to ferment. There are few ways to do it but the easiest one for the home is to pre-ferment a small portion of equal amounts of water and flour.

For example, start with separating out the total ingredients: 1000g flour, 650g water, 30g salt, 5g dry yeast. This will result in 1680g of dough enough for ~8 medium pizzas. Mix 200g of water, flour and the yeast and let it sit on counter for an hour. Then move to the refrigerator for slow fermentation for ~24h. Take this out and add the remaining ingredients. Kneed until smooth, and let it rest on the counter for 1h. Cover with olive oil to avoid drying out. Cut balls and place in a tray. Lightly cover with olive oil again and cling film. After 1-2h the balls double in size and will be ready for some amazing pizza.
Question: Think up of a bunch of different dishes you could make with a pound of potatoes
Answer: Mashed potatoes, hashbrowns, french fries, baked potatoes, roasted potatoes, potato and leek soup, latkes
Question: Tell me why your dog likes dog daycare.
Answer: My dog has a lot of energy and is an extrovert with both people and other dogs. He gets so excited in the car on the way there! I like the fact that the dogs' play is supervised by trained dog specialists, and that dogs who are getting too aggressive are placed in a timeout in a crate to settle down. I like that it's indoors so it can continue in the winter months and rain or shine. I like that they have a nap in the middle of the day so they can re-charge for the afternoon play session. While my dog doesn't got there for more than 9 hours, it is convenient that the daycare is open from 7am to 7pm in case I needed him to stay there longer. I like that they have two separate playrooms at our daycare, one for smaller breeds/older dogs/timid dogs, and the other for larger dogs/younger dogs who may want to roughhouse more. Another great benefit is that my daycare offers spa services for my dog like a bath, nail trimming, ear cleaning, teeth brushing, and I do take advantage of that every other month or so. Last, if we are going out of town, and my son can't come over to watch our dog, the dog daycare center does offer boarding services. I would highly recommend that you check it out!
Question: How do I know if I am in love?
Answer: Love is different for everyone. And there are different stages to the journey of love of a partner. Love starts heavy and passionate and then subsides. And when it subsides you have to make a decision. You have to work out whether your roots have become so entwined together that it is inconceivable that you should ever part. Because this is what love is. Love is not breathlessness, it is not excitement, it is not the promulgation of promises of eternal passion. That is just being "in love" which any of us can convince ourselves we are. Love itself is what is left over when being in love has burned away, and this is both an art and a fortunate accident.
Question: Where are the documents originate from?
Evidence: Legal documents are a rather heterogeneous class, which also manifests in their linguistic properties, including the use of named entities and references. Their type and frequency varies significantly, depending on the text type. Texts belonging to specific text type, which are to be selected for inclusion in a corpus must contain enough different named entities and references and they need to be freely available. When comparing legal documents such as laws, court decisions or administrative regulations, decisions are the best option. In laws and administrative regulations, the frequencies of person, location and organization are not high enough for NER experiments. Court decisions, on the other hand, include person, location, organization, references to law, other decision and regulation.
Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
From the table of contents, 107 documents from each court were selected (see Table ). The data was collected from the XML documents, i. e., it was extracted from the XML elements Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel. The metadata at the beginning of the documents (name of court, date of decision, file number, European Case Law Identifier, document type, laws) and those that belonged to previous legal proceedings was deleted. Paragraph numbers were removed. The extracted data was split into sentences, tokenised using SoMaJo BIBREF16 and manually annotated in WebAnno BIBREF17.
The annotated documents are available in CoNNL-2002. The information originally represented by and through the XML markup was lost in the conversion process. We decided to use CoNNL-2002 because our primary focus was on the NER task and experiments. CoNNL is one of the best practice formats for NER datasets. All relevant tools support CoNNL, including WebAnno for manual annotation. Nevertheless, it is possible, of course, to re-insert the annotated information back into the XML documents.
The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
Answer: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
Question: Does the performance necessarily drop when more control is desired?
Evidence: Performance INLINEFORM0 Trade-off: To see if the selector affects performance, we also ask human annotators to judge the text fluency. The fluency score is computed as the average number of text being judged as fluent. We include generations from the standard Enc-Dec model. Table TABREF32 shows the best fluency is achieved for Enc-Dec. Imposing a content selector always affects the fluency a bit. The main reason is that when the controllability is strong, the change of selection will directly affect the text realization so that a tiny error of content selection might lead to unrealistic text. If the selector is not perfectly trained, the fluency will inevitably be influenced. When the controllability is weaker, like in RS, the fluency is more stable because it will not be affected much by the selection mask. For SS and Bo.Up, the drop of fluency is significant because of the gap of soft approximation and the independent training procedure. In general, VRS does properly decouple content selection from the enc-dec architecture, with only tiny degrade on the fluency.
The main reason is that when the controllability is strong, the change of selection will directly affect the text realization so that a tiny error of content selection might lead to unrealistic text. If the selector is not perfectly trained, the fluency will inevitably be influenced. When the controllability is weaker, like in RS, the fluency is more stable because it will not be affected much by the selection mask.
Answer: Yes.
Question: Which of these Disney cartoons were remade as a live action movie? Lion King, Toy Story, Aladdin, Jungle Book, Fox and the Hound, Shrek
Answer: Disney made a live action version of The Lion King, Aladdin, and The Jungle Book. Disney has not announced plans to remake Toy Story or The Fox and the Hound as live action movies. Shrek is not a Disney movie and there is not a live action version.
Question: What causes a spinning top to stop?
Answer: Friction.  If using a frictionless environment, a top could spin indefinitely
Question: Where is data in their dataset from?
Evidence: News portals often allow their readers to comment on articles, in order to get feedback, engage their readers, and build customer loyalty. User comments, however, can also be abusive (e.g., bullying, profanity, hate speech), damaging the reputation of news portals, making them liable to fines (e.g., when hosting comments encouraging illegal actions), and putting off readers. Large news portals often employ moderators, who are frequently overwhelmed by the volume and abusiveness of comments. Readers are disappointed when non-abusive comments do not appear quickly online because of moderation delays. Smaller news portals may be unable to employ moderators, and some are forced to shut down their comments.
In previous work BIBREF0 , we introduced a new dataset of approx. 1.6M manually moderated user comments from a Greek sports news portal, called Gazzetta, which we made publicly available. Experimenting on that dataset and the datasets of Wulczyn et al. Wulczyn2017, which contain moderated English Wikipedia comments, we showed that a method based on a Recurrent Neural Network (rnn) outperforms detox BIBREF1 , the previous state of the art in automatic user content moderation. Our previous work, however, considered only the texts of the comments, ignoring user-specific information (e.g., number of previously accepted or rejected comments of each user). Here we add user embeddings or user type embeddings to our rnn-based method, i.e., dense vectors that represent individual users or user types, similarly to word embeddings that represent words BIBREF2 , BIBREF3 . Experiments on Gazzetta comments show that both user embeddings and user type embeddings improve the performance of our rnn-based method, with user embeddings helping more. User-specific or user-type-specific scalar biases also help to a lesser extent.
In previous work, we introduced a new dataset of approx. 1.6M manually moderated user comments from a Greek sports news portal, called Gazzetta, which we made publicly available.
Answer: A Greek sports news portal called Gazzetta.
Question: Where can I ride roller coasters in the united states?
Answer: Here are some of the places you can ride roller coasters in the united states: Hersheypark in Hershey, Pennsylvania; Universal Studios in Orlando, Florida; Disneyland in Anaheim, California; Cedar Point in Sandusky, Ohio; Busch Gardens in Williamsburg, Virginia; Silver Dollar City, Branson, Missouri; Magic Mountain in Valencia, CA; Kings Island in Mason, Ohio; Dollywood in Pigeon Forge, Tennessee; Knoebels in Elysburg, Pennsylvania; Hersheypark in Hershey, Pennsylvania; Knott’s Berry Farm in Buena Park, California; Sea World in Orlado, Florida; Kennywood in West Mifflin, Pennsylvania;  Holiday World in Santa Claus, Indiana; or Carowinds in Charlotte, North Carolina.
Question: Who is the visual of the kpop group Twice?
Answer: Tzuyu Chou is generally regarded as the visual of Twice, though some may disagree.
Question: Who was Paramahansa Yogananda?
Evidence: Paramahansa Yogananda (born Mukunda Lal Ghosh; January 5, 1893 – March 7, 1952) was an Indian Hindu monk, yogi and guru who introduced millions to the teachings of meditation and Kriya Yoga through his organization Self-Realization Fellowship (SRF) / Yogoda Satsanga Society (YSS) of India, and who lived his last 32 years in America. A chief disciple of the Bengali yoga guru Swami Sri Yukteswar Giri, he was sent by his lineage to spread the teachings of yoga to the West, to prove the unity between Eastern and Western religions and to preach a balance between Western material growth and Indian spirituality. His long-standing influence in the American yoga movement, and especially the yoga culture of Los Angeles, led him to be considered by yoga experts as the "Father of Yoga in the West."
Answer: Paramahansa Yogananda was an Indian Hindu monk who introduced large populations of people to the meditation and Kriya Yoga. He organized Self-Realization Fellowship (SRF) / Yogoda Satsanga Society (YSS) of India and was sent by his Guru Swami Sri Yukteswar Giri to the United States to speak the knowledge of Yoga to the West. He was known as  "Father of Yoga in the West."
Question: List the Hindi television series by Ekta Kapoor.
Evidence: Ektaa Kapoor started her career at the age of 17, interning with advertisement and feature filmmaker Kailash Surendranath. After obtaining financing from her father, she was a producer under her banner Balaji Telefilms. Her initial projects were failures, with six of her pilot episodes being rejected, leading to a total loss of ₹50 lakh (equivalent to ₹1.7 crore or US$210,000 in 2020). In 1995, Mano Ya Na Mano was selected by Zee TV and her music-based show Dhun Dhamaka was chosen by Doordarshan. Her 1995 sitcom, Hum Paanch was her first success.

In the 2000s, the letter 'K' became her lucky alphabet and she launched many shows with the first word of every title starting with the same letter, including Kyunki Saas Bhi Kabhi Bahu Thi, which broke all records to become the series to attract the highest TRPs in 2000. Other shows beginning with 'K' include Kahaani Ghar Ghar Ki, Kabhii Sautan Kabhii Sahelii, Kohi Apna Sa, Kaahin Kissii Roz, Kalash, Kasautii Zindagii Kay, Kahiin To Hoga and Kasamh Se. In 2001, she was awarded the title of Best Entrepreneur of the Year.

By July 2001, Kapoor was producing more than 30 hours of television shows per week. 20 of her 34 serials were listed as the most popular serials, across all major TV channels like Zee TV, Sony, Star Plus and Metro. Her projected turnover for 2000–2001 was listed as ₹35 crore (equivalent to ₹118 crore or US$15 million in 2020).

After establishing herself in Hindi TV serials, she began venturing into other regional languages, starting with the Tamil series, Kudumbum. The series became very popular and she then got it dubbed into Telugu and launched it in Hindi as Ghar Ek Mandir. She then ventured into Punjabi, Marathi, Gujarati and Bengali languages. As of 2001, Kapoor wrote her own scripts despite having writers working for her, taking an active interest in all stages of production, while her mother, Shobha, handled the accounts.
Answer: The Hindi TV series by Ekta Kapoor include Mano Ya Na Mano, Dhun Dhamaka, Hum Paanch, Kyunki Saas Bhi Kabhi Bahu Thi, Kahaani Ghar Ghar Ki, Kabhii Sautan Kabhii Sahelii, Kohi Apna Sa, Kaahin Kissii Roz, Kalash, Kasautii Zindagii Kay, Kahiin To Hoga and Kasamh Se.