DataHammer/scimrc huggingface dataset:
Question: what are the mentioned cues?
Evidence: FLOAT SELECTED: Figure 2: The overall structure of our RLEL model. It contains three parts: Local Encoder, Global Encoder and Entity Selector. In this framework, (Vmt ,Vekt ) denotes the concatenation of the mention context vector Vmt and one candidate entity vector Vekt . The policy network selects one entity from the candidate set, and Vat denotes the concatenation of the mention context vector Vmt and the selected entity vector Ve∗t . ht represents the hidden status of Vat , and it will be fed into St+1.
Where $\oplus $ indicates vector concatenation. The $V_{m_i}^t$ and $V_{e_i}^t$ respectively denote the vector of $m_i$ and $e_i$ at time $t$ . For each mention, there are multiple candidate entities correspond to it. With the purpose of comparing the semantic relevance between the mention and each candidate entity at the same time, we copy multiple copies of the mention vector. Formally, we extend $V_{m_i}^t \in \mathbb {R}^{1\times {n}}$ to $V_{m_i}^t{^{\prime }} \in \mathbb {R}^{k\times {n}}$ and then combine it with $V_{e_i}^t \in \mathbb {R}^{k\times {n}}$ . Since $V_{m_i}^t$ and $V_{m_i}^t$0 are mainly to represent semantic information, we add feature vector $V_{m_i}^t$1 to enrich lexical and statistical features. These features mainly include the popularity of the entity, the edit distance between the entity description and the mention context, the number of identical words in the entity description and the mention context etc. After getting these feature values, we combine them into a vector and add it to the current state. In addition, the global vector $V_{m_i}^t$2 is also added to $V_{m_i}^t$3 . As mentioned in global encoder module, $V_{m_i}^t$4 is the output of global LSTM network at time $V_{m_i}^t$5 , which encodes the mention context and target entity information from $V_{m_i}^t$6 to $V_{m_i}^t$7 . Thus, the state $V_{m_i}^t$8 contains current information and previous decisions, while also covering the semantic representations and a variety of statistical features. Next, the concatenated vector will be fed into the policy network to generate action.
FLOAT SELECTED: Figure 2: The overall structure of our RLEL model. It contains three parts: Local Encoder, Global Encoder and Entity Selector. In this framework, (Vmt ,Vekt ) denotes the concatenation of the mention context vector Vmt and one candidate entity vector Vekt . The policy network selects one entity from the candidate set, and Vat denotes the concatenation of the mention context vector Vmt and the selected entity vector Ve∗t . ht represents the hidden status of Vat , and it will be fed into St+1.
As mentioned in global encoder module, $V_{m_i}^t$4 is the output of global LSTM network at time $V_{m_i}^t$5 , which encodes the mention context and target entity information from $V_{m_i}^t$6 to $V_{m_i}^t$7 .
Answer: The answers are shown as follows:
* output of global LSTM network at time $V_{m_i}^t$5 , which encodes the mention context and target entity information from $V_{m_i}^t$6 to $V_{m_i}^t$7
Question: Do the experiments confirm the importance of contextual information?
Evidence: In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new dataset of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
Answer: Yes, they do.
Question: What is a short hiking trail in Tiburon?
Answer: One short hiking trail in Tiburon, California is off Gilmartin Drive. It's easy to spot but unmarked! The trail is a local favorite and takes you to an overlook with views of San Francisco and the Golden Gate Bridge. You can check out the views while swinging from Hippie Tree, the giant eucalyptus tree.
Question: Extract from the text the various names that the Six Nations Championship has had throughout history with the beginning and end dates for each name and list them using bullet points ordered by beginning dates in the format {Beginning Date} / {End Date} - {Name}
Evidence: The Six Nations is the successor to the Home Nations Championship (1883–1909 and 1932–39), played between teams from England, Ireland, Scotland, and Wales, which was the first international rugby union tournament. With the addition of France, this became the Five Nations Championship (1910–31 and 1947–99), which in turn became the Six Nations Championship with the addition of Italy in 2000.
Answer: * 1883 / 1909 - Home Nations Championship 
* 1910 / 1931 - Five Nations Championship
* 1932 / 1939 - Home Nations Championship
* 1947 / 1999 - Five Nations Championship
* 2000 / present - Six Nations Championship
Question: How is a circular economy different than the USA's economy?
Evidence: A circular economy (also referred to as circularity and CE) is a model of production and consumption, which involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products for as long as possible. CE aims to tackle global challenges such as climate change, biodiversity loss, waste, and pollution by emphasizing the design-based implementation of the three base principles of the model. The three principles required for the transformation to a circular economy are: eliminating waste and pollution, circulating products and materials, and the regeneration of nature. CE is defined in contradistinction to the traditional linear economy. The idea and concepts of circular economy (CE) have been studied extensively in academia, business, and government over the past ten years. CE has been gaining popularity because it helps to minimize emissions and consumption of raw materials, open up new market prospects and principally, increase the sustainability of consumption and improve resource efficiency.

At a government level, CE is viewed as means of combating global warming as well as a facilitator of long-term growth. CE may geographically connect actors and resources to stop material loops at the regional level. In its core principle, the European Parliament defines CE as, “a model of production and consumption, which involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products as long as possible. In this way, the life cycle of products is extended.”

In a linear economy, natural resources are turned into products that are ultimately destined to become waste because of the way they have been designed and manufactured. This process is often summarized by "take, make, waste". By contrast, a circular economy employs reuse, sharing, repair, refurbishment, remanufacturing and recycling to create a closed-loop system, reducing the use of resource inputs and the creation of waste, pollution and carbon emissions. The circular economy aims to keep products, materials, equipment and infrastructure in use for longer, thus improving the productivity of these resources. Waste materials and energy should become input for other processes through waste valorization: either as a component for another industrial process or as regenerative resources for nature (e.g., compost). The Ellen MacArthur Foundation (EMF) defines the circular economy as an industrial economy that is restorative or regenerative by value and design.
Answer: A circular economy focuses on the principles of eliminating waste and pollution, circulating products and materials, and the regeneration of nature. In contrast, most capitalistic economies are called "linear" in that raw materials are turned into products which are eventually discarded. While a circular economy focuses on sustainable consumption on an Earth with limited resources, linear economies like the USA's implicitly assume a never-ending supply of raw material inputs.
Question: From the passage mention the name of the kids of Nita Ambani. Display the results in comma separated format.
Evidence: He married Nita Ambani in 1985 and they have two sons, Akash and Anant, and a daughter, Isha, who is Akash's twin. They met after his father attended a dance performance which Nita took part in and thought of the idea of arranging a marriage between the two.

They live in Antilia, a private 27-storey building in Mumbai, which was valued at US$1 billion and was the most expensive private residence in the world at the time it was built. The building requires a staff of 600 for maintenance, and it includes three helipads, a 160-car garage, private movie theater, swimming pool, and fitness center.

In 2007, Ambani gifted his wife a $60 million Airbus A319 for her 44th birthday. The Airbus, which has a capacity of 180 passengers, has been custom-fitted to include a living room, bedroom, satellite television, WiFi, sky bar, Jacuzzi, and an office.

Ambani was titled "The World's Richest Sports Team Owner" after his purchase of the IPL cricket team Mumbai Indians for $111.9 million in 2008.

Mukesh Ambani is a strict vegetarian and teetotaler.

During the fiscal year ending 31 March 2012, he reportedly decided to forgo nearly ₹240 million from his annual pay as chief of Reliance Industries Ltd (RIL). He elected to do this even as RIL's total remuneration packages to its top management personnel increased during that fiscal year. Mukesh Ambani holds a 50.4% stake in the company. This move kept his salary capped at ₹150 million for the fourth year in a row.

In early 2019, a court in Mumbai held his younger brother, Anil Ambani, in criminal contempt for non-payment of personally guaranteed debt Reliance Communications owed to Swedish gearmaker Ericsson. Instead of jail time, the court gave Anil a month to come up with the funds. At the end of the month, Mukesh bailed out his younger brother, paying the debt. In 2021, he was the subject of a bomb scare when a green Mahindra Scorpio SUV packed with explosives was found near a Mumbai skyscraper housing Ambani.
Answer: Akash, Anant, Isha
Question: If McDonald’s were to go out of business and disappear, would the world be a better or worse place?
Answer: McDonald’s has more than 36 thousand locations in 190 countries, and people still cannot believe that McDonald's would soon put a period to the honorable accomplishment they did in the last several years making 4.73 billion dollars in a year as a whole. I do believe McDonald’s disappearance would have some difference in some communities in the short term since there are 210,000 people working at McDonald’s. And also, it can have environmental, and medical benefits that can make the world a better place.
    The world is hugely affected by McDonald’s action, which makes the disappearance of McDonald’s could make the world a better place although it could have some negative influence on equality and education. Although McDonald's helps organizations that help people who lack education, suffer in poverty, or fight against discrimination, the positive impact of McDonald’s departure makes the world a better place. For example, since McDonald’s cut down the forest to build a cow farm, it can cause deforestation that is threatening the ecosystem and animal habitats. In addition, McDonald’s produces 2 million tons of packaging each year. This means that the retirement of McDonald’s could prevent habitat loss, deforestation, and even waste problems and have a huge, positive impact on the environment.
    However, the change in McDonald's retirement causes would vary between different stakeholders, but overall, it would only have a mild, or little difference between people’s lives. Although McDonald’s disappearance might shock many people, after a short term, the other fast-food restaurants would replace McDonald’s, so fast-food lovers could adapt to the new environment. Furthermore, although 210,000 people could lose their job, it is still possible for them to change to another job, or have an opportunity to change into another job that they have an interest in. Therefore, McDonald’s disappearance could have little or no difference at all to some people, and withdraw from all the honorable accomplishments.
    In conclusion, I think that the disappearance of McDonald’s can make the world a better place for environmental, and medical reasons although it could have negative effects in some communities, and that McDonald’s would not be forgotten by the joy and honorable achievements that they have achieved in the last few years.
Question: In what city was Titanium first discovered?
Evidence: Titanium was discovered in Cornwall, Great Britain, by William Gregor in 1791 and was named by Martin Heinrich Klaproth after the Titans of Greek mythology. The element occurs within a number of minerals, principally rutile and ilmenite, which are widely distributed in the Earth's crust and lithosphere; it is found in almost all living things, as well as bodies of water, rocks, and soils. The metal is extracted from its principal mineral ores by the Kroll and Hunter processes. The most common compound, titanium dioxide, is a popular photocatalyst and is used in the manufacture of white pigments. Other compounds include titanium tetrachloride (TiCl4), a component of smoke screens and catalysts; and titanium trichloride (TiCl3), which is used as a catalyst in the production of polypropylene.
Answer: Cornwall
Question: Tell me whether these are birds or bugs: termites, ants, cockroaches.
Answer: This is a list of bugs, not birds.
Question: How to plan a Lakehouse migration journey?
Answer: 1. The discovery phase: Ask internal questions.
The key to this step is to answer two questions: Where am I
now, and where do I need to go? Make sure that you collect
questionnaires from all your data teams, chief information
officers, and other relevant stakeholders. Be prepared for a
lot of new learning and self-discovery as teams test and
validate assumptions.

2. The assessment phase: Make a migration assessment.
Refine and evaluate the solutions on the table. Take an
inventory of all migration items and prioritize the use cases.
When you complete the migration assessment, you’ll have a
clearer sense of your timeline and alignment with your
original planned schedule.

3. The strategy phase: Conduct technical planning.
Think through your target architecture and make sure it
supports the business in the long term. You make crucial
decisions in this phase on your ingestion strategy and
technologies, extract, transform, load (ETL) patterns and
tools, data organization principles in the lakehouse, and
semantic and reporting layer architectural and tool choices.
4.The production pilot phase: Complete evaluation and
enablement.
Understand what your new platform has to offer. Conduct
targeted demos or plans to help vet your approach.

5. The execution phase: Execute your migration.
The rubber meets the road — make sure you get this
migration right the first time.
Question: Who won the 2022 SEC softball Season?
Evidence: The 2022 SEC softball season began play Thursday, February 10, and conference play began on Thursday, March 11. The 2022 Southeastern Conference softball tournament was May 10-14 at Katie Seashole Pressly Softball Stadium in Gainesville, Florida. Arkansas played Missouri in the tournament final, winning by a score of 4–0. Vanderbilt University is the only full member of the Southeastern Conference to not sponsor a softball program.
Answer: Arkansas won the SEC softball season beating Missouri by 4-0.
Question: Which U.S. wine company produces the most wine in the United States?
Answer: E&J Gallo is the largest wine producer in the United States, owning over 23,000 acres of vineyards and nineteen wineries.
Question: What resources must be available for users of the semi-supervised QA system at the very least?
Evidence: Deep learning systems have shown a lot of promise for extractive Question Answering (QA), with performance comparable to humans when large scale data is available. However, practitioners looking to build QA systems for specific applications may not have the resources to collect tens of thousands of questions on corpora of their choice. At the same time, state-of-the-art machine reading systems do not lend well to low-resource QA settings where the number of labeled question-answer pairs are limited (c.f. Table 2 ). Semi-supervised QA methods like BIBREF0 aim to improve this performance by leveraging unlabeled data which is easier to collect.
In this work, we present a semi-supervised QA system which requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents. Our proposed system consists of three stages. First, we construct cloze-style questions (predicting missing spans of text) from the unlabeled corpus; next, we use the generated clozes to pre-train a powerful neural network model for extractive QA BIBREF1 , BIBREF2 ; and finally, we fine-tune the model on the small set of provided QA pairs.
Our cloze construction process builds on a typical writing phenomenon and document structure: an introduction precedes and summarizes the main body of the article. Many large corpora follow such a structure, including Wikipedia, academic papers, and news articles. We hypothesize that we can benefit from the un-annotated corpora to better answer various questions – at least ones that are lexically similar to the content in base documents and directly require factual information.
We apply the proposed system on three datasets from different domains – SQuAD BIBREF3 , TriviaQA-Web BIBREF4 and the BioASQ challenge BIBREF5 . We observe significant improvements in a low-resource setting across all three datasets. For SQuAD and TriviaQA, we attain an F1 score of more than 50% by merely using 1% of the training data. Our system outperforms the approaches for semi-supervised QA presented in BIBREF0 , and a baseline which uses the same unlabeled data but with a language modeling objective for pretraining. In the BioASQ challenge, we outperform the best performing system from previous year's challenge, improving over a baseline which does transfer learning from the SQuAD dataset. Our analysis reveals that questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes.
In this work, we present a semi-supervised QA system which requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents.
Answer: It requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents.
Question: Why do cooks use kosher salt?
Answer: Many cooks prefer kosher salt because it has larger grains which makes it easier to pinch as well as to sprinkle more evenly across food.  Additionally when you purchase a particular brand of kosher salt it provides consistency and reliability to your recipe usage over time.
Question: Did automobiles exist prior to the invention of the ICE (internal combustion engine)?
Evidence: Development of the automobile started in 1672 with the invention of the first steam-powered vehicle, which led to the creation of the first steam-powered automobile capable of human transportation, built by Nicolas-Joseph Cugnot in 1769. Inventors began to branch out at the start of the 19th century, creating the de Rivas engine, one of the first internal combustion engines, and an early electric motor. Samuel Brown later tested the first industrially applied internal combustion engine in 1826.
Answer: The first engines that were capable of enabling human transportation were steam powered engines. While the development of steam powered vehicles capable of enabling human transportation was initiated in 1672, the first actual steam powered automobile was built by Nicolas-Joseph Cugnot in 1769.
Question: How many bins have the data for the number of posts been divided into?
Evidence: For the purposes of this paper, we focused on matching accounts between two of the largest social networks: Twitter and Facebook. In order to proceed with our study, we needed a sizeable (few thousand) number of English speaking users with accounts on both Twitter and Facebook. We also needed to know the precise matching between the Twitter and Facebook accounts for our ground truth.
To that end, we crawled publicly available, English-language, Google Plus accounts using the Google Plus API and scraped links to the users' other social media profiles. (Note that one of the reasons why we used Twitter and Facebook is that they were two of the most common sites linked to on Google Plus). We used a third party social media site (i.e., Google Plus), one that was not used in our analysis to compile our ground truth in order to limit selection bias in our data collection.
We discarded all users who did not link to an account for both Twitter and Facebook and those whose accounts on either of these sites were not public. We then used the APIs of Twitter and Facebook to collect posts made by the users on these sites. We only collected the linguistic content and the date and time at the which the posts were made. For technical and privacy reasons, we did not collect any information from the profile of the users, such as the location, screen name, or birthday.
Our analysis focused on activity of users for one whole year, from February 1st, 2014 to February 1st, 2015. Since we can not reliably model the behaviour patterns of users with scarce data, users with less than 20 posts in that time period on either site were discarded. Overall, we collected a dataset of $5,612$ users with each having a Facebook and Twitter account for a total of $11,224$ accounts.
Figure 1 shows the distribution of the number of posts per user for Twitter and Facebook for our collected dataset. In the figure, the data for the number of posts has been divided into 500 bins. For the Twitter data, each bin corresponds to 80 tweets, while for the Facebook data, it corresponds to 10 posts. Table 1 shows some statistics about the data collected, including the average number of posts per user for each of the sites.
Figure 1 shows the distribution of the number of posts per user for Twitter and Facebook for our collected dataset. In the figure, the data for the number of posts has been divided into 500 bins.
Answer: 500 bins.