DataHammer/scimrc huggingface dataset:
Question: What does electronics deal with?
Evidence: Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, sensors and integrated circuits, and associated passive interconnection technologies. The nonlinear behaviour of active components and their ability to control electron flows makes digital switching possible, and electronics is widely used in information processing, telecommunications, and signal processing. Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
Today, most electronic devices use semiconductor components to perform electron control. The underlying principles that explain how semiconductors work are studied in solid state physics, whereas the design and construction of electronic circuits to solve practical problems are part of electronics engineering.
Answer: Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, sensors and integrated circuits, and associated passive interconnection technologies.
Question: Group these animals into 2-legged and 4-legged categories:  kangaroo, dog, rhino, lion, giraffe, penguin, chimpanzee and cat.
Answer: Penguin, chimpanzee and kangaroo have 2 legs. Dog, cat, lion, rhino and giraffe have 4 legs.
Question: Classify each of the following as inclined or flat: stairs, beach, mountain, lake, escalator, pond, ramp
Answer: Stairs: Inclined
Beach: Flat
Mountain: Inclined
Lake: Flat
Escalator: Inclined
Pond: Flat
Ramp: Inclined
Question: What dataset is used?
Evidence: To evaluate the effectiveness of our model, and demonstrate its flexibility at handling heavy data-structure made of several types of entities, we used the RotoWire dataset BIBREF10. It includes basketball games statistical tables paired with journalistic descriptions of the games, as can be seen in the example of Figure FIGREF1. The descriptions are professionally written and average 337 words with a vocabulary size of $11.3$K. There are 39 different record keys, and the average number of records (resp. entities) in a single data-structure is 628 (resp. 28). Entities are of two types, either team or player, and player descriptions depend on their involvement in the game. We followed the data partitions introduced with the dataset and used a train/validation/test sets of respectively $3,398$/727/728 (data-structure, description) pairs.
To evaluate the effectiveness of our model, and demonstrate its flexibility at handling heavy data-structure made of several types of entities, we used the RotoWire dataset
Answer: RotoWire dataset.
Question: Identify which instrument is string or percussion: Paiban, Fegereng
Answer: Fegereng is string, Paiban is percussion.
Question: Based on this passage, what is the average number of people per family in Zangisheh?
Evidence: Zangisheh (Persian: زنگيشه, also Romanized as Zangīsheh) is a village in Qarah Su Rural District, in the Central District of Kermanshah County, Kermanshah Province, Iran. At the 2006 census, its population was 153, in 34 families.
Answer: I can only tell you what it was in 2006, because this is when the survey data provided is from. The average number of people per family at that time is the population divided by the number of families, or 153 / 34.
Question: How good as a pianist is Yeol Eum Son
Evidence: Yeol Eum Son (born May 2, 1986, in Wonju, South Korea) is a South Korean classical pianist. She first drew international attention in October 2004 at age 18 when she appeared as a soloist performing Liszt Piano Concerto No. 1 with the New York Philharmonic under the baton of Lorin Maazel on their Asia tour in Seoul, Daejeon, and Tokyo. Son again performed with Maazel and the New York Philharmonic when they returned to the Seoul Arts Center in February 2008, this time as soloist for Beethoven Piano Concerto No. 2.

Her awards include Silver Medals at both the Thirteenth Van Cliburn International Piano Competition (2009) and 2011 International Tchaikovsky Competition, where she also received the Best Chamber Concerto Performance and the Best Performance of the Commissioned Work prizes. She also won the Bronze Medal at the Arthur Rubinstein International Piano Master Competition (2005) 
Answer: Yeol Eum Son (born May 2, 1986, in Wonju, South Korea) is a South Korean classical pianist. She first drew international attention in October 2004 at age 18 when she appeared as a soloist performing Liszt Piano Concerto No. 1 with the New York Philharmonic under the baton of Lorin Maazel on their Asia tour in Seoul, Daejeon, and Tokyo. Son again performed with Maazel and the New York Philharmonic when they returned to the Seoul Arts Center in February 2008, this time as soloist for Beethoven Piano Concerto No. 2.

Her awards include Silver Medals at both the Thirteenth Van Cliburn International Piano Competition (2009) and 2011 International Tchaikovsky Competition, where she also received the Best Chamber Concerto Performance and the Best Performance of the Commissioned Work prizes. She also won the Bronze Medal at the Arthur Rubinstein International Piano Master Competition (2005)
Question: Extract a list of rivalries with the Detroit Lions in bulleted form, sorted alphabetically.
Evidence: The Lions have had several division rivals in their existence. Their oldest rivals are the Chicago Bears and the Green Bay Packers, whom they have faced since 1930. The Minnesota Vikings have been in a division with Detroit ever since their inaugural season in 1961. Another notable longtime division opponent was the Tampa Bay Buccaneers (25 seasons from 1977 to 2001).

The Lions also have a preseason rivalry with the Cleveland Browns, dubbed the Great Lakes Classic. The two teams have been playing for The Barge Trophy since 2002. The Lions and Browns had a solid rivalry in the 1950s, when they met four times for the NFL championship (Detroit won three of the matchups); they have met much less frequently during the regular season since the 1970 AFL–NFL merger due to the Browns' move to the AFC.
Answer: Here is a list of rivalries with the Detroit Lions, sorted alphabetically:

* Chicago Bears
* Cleveland Browns
* Green Bay Packers
* Minnesota Vikings
* Tampa Bay Buccaneers
Question: Extract the total population and total area of California from the reference text
Evidence: California is a state in the Western United States, located along the Pacific Coast. With nearly 39.2 million residents across a total area of approximately 163,696 square miles (423,970 km2), it is the most populous U.S. state and the third-largest by area. It is also the most populated subnational entity in North America and the 34th most populous in the world. The Greater Los Angeles and San Francisco Bay areas are the nation's second and fifth most populous urban regions respectively, with the former having more than 18.7 million residents and the latter having over 9.6 million. Sacramento is the state's capital, while Los Angeles is the most populous city in the state and the second most populous city in the country. San Francisco is the second most densely populated major city in the country. Los Angeles County is the country's most populous, while San Bernardino County is the largest county by area in the country. California borders Oregon to the north, Nevada and Arizona to the east, the Mexican state of Baja California to the south; and it has a coastline along the Pacific Ocean to the west.
Answer: California has a population of 39.2 million residents and an area of 163,696 square miles.
Question: Who is the Greatest male Tennis player of all time?
Answer: Here are few things to consider to come up with the player. 
1. Player who has won most grand slams
2. Player who spent most time as world #1
3. Player who has won grand slams on all three surfaces (Hard, Clay, Grass)
4. Player who has won most year end ATP finals and also Olympics
5. Head to head wins between players

Based on this criteria, the greatest male tennis player of all time is Novak Djokovic. There are two other male players who are in contention: Roger Federer and Rafael Nadal. Novak and Nadal both have won 22 grand slams each. All three have a favorite surface (Grass for Roger, Clay for Nadal and Hard for Djokovic) but they won grand slams on all surfaces. Djokovic has the record of spending most time as world #1. He also has a better head-to-head record over Nadal and Federer. Djokovic and Roger share the record of winning most year end ATP Finals. Roger Federer kickstarted the golden age of Tennis and he is extremely popular for his skill, talent and grace with which he used to play. Roger won Wimbledon the highest number of times among all players. Nadal won French Open 14 times which is a record that potentially may never ever be broken. History will remember all three players for their contribution to Tennis and a lot of tennis fans are lucky to have been part of the Golden age of tennis.
Question: Write the first paragraph of a blog post describing the appeal of gravel bikes in the bay area.
Answer: For many years, cycling has been a staple in the bay area. Its temperate climate, easy access to nature, and bike-friendly sentiment have fostered a steady growth of interest in the sport, especially with the rise of COVID creating a shift toward distanced outdoor activities in recent years. Traditionally, cycling has had three modalities: mountain biking, road biking, and cyclocross. Recently, a fourth modality, gravel biking, has had a rapid rise in popularity.
Question: What were the main results of applying statistical analysis to the English language?
Evidence: Information theory's fundamental contribution to natural language processing and computational linguistics was further established in 1951, in his article "Prediction and Entropy of Printed English", showing upper and lower bounds of entropy on the statistics of English – giving a statistical foundation to language analysis. In addition, he proved that treating whitespace as the 27th letter of the alphabet actually lowers uncertainty in written language, providing a clear quantifiable link between cultural practice and probabilistic cognition.
Answer: The main results of applying statistical analysis to the English language were establishing upper and lower bounds of entropy for it and that treating whitespace as a 27th letter of the alphabet lowers uncertainty.
Question: Who wins the point in tennis?
Evidence: Tennis is a racket sport that is played either individually against a single opponent (singles) or between two teams of two players each (doubles). Each player uses a tennis racket that is strung with cord to strike a hollow rubber ball covered with felt over or around a net and into the opponent's court. The object of the game is to manoeuvre the ball in such a way that the opponent is not able to play a valid return. The player who is unable to return the ball validly will not gain a point, while the opposite player will.
Answer: A player who can return the ball validly when the opposite player unable to return it.
Question: How to re-rated problems by experts in logic and/or linguistics?
Evidence: We have randomly selected 150 problems out of the RTE corpus which were marked as “YES” (i.e. entailment holds). The problems were not further selected nor doctored by us. The problems were then re-rated by experts in logic and/or linguistics. For each problem, three experts were consulted, and each expert rated 30 problems. More precisely, the experts were instructed to re-consider each problem and be especially wary of missing hypotheses. If they considered the entailment to hold, we still gave the instruction to optionally mention any additional implicit hypothesis that they would be using. Similarly, if they considered that there was no entailment in the problem, they were given prompted to (optionally) give an argument for their judgement.
In order to facilitate data collection, the experts were chosen from the network of contacts of the author. Despite this method, the process of data collection took nearly six months. The authors themselves were put to contribution in the data-collection process (taking one set of 30 problems each) in order to complete the survey.
Additionally, collecting all the inputs received and using our best judgments, we have put together a test set of 150 problems comprised of the original problems, a new judgement (“yes” or “no”), and added missing hypotheses (if “yes” is a reasonable option).
The problems were then re-rated by experts in logic and/or linguistics. For each problem, three experts were consulted, and each expert rated 30 problems.
Answer: For each problem, three experts were consulted, and each expert rated 30 problems.
Question: What was the home port of the Aquila?
Evidence: After completing her fitting out, Aquila got underway from Bremerton, Washington, on 15 July 1982 in company with Taurus (PHM-3) and Frederick (LST-1184) and shaped a course for her home port, Key West, Florida. Frederick escorted the two guided-missile hydrofoil gunboats as far as Rodman in the Canal Zone where El Paso (LKA-117) took over. Aquila and her traveling companions transited the Panama Canal on 8 August and arrived in Key West three days later. The warship spent the remainder of 1982 conducting shakedown and other training out of her base at the Trumbo Point Annex, Naval Air Station, Key West, Florida. During the first quarter of 1983, post-shakedown repairs occupied her time. That spring, Aquila embarked upon a schedule of normal training duties out of Key West that occupied her time through the summer and into the all of 1983. From 10 to 20 October, she joined Taurus and Spartanburg County (LST-1192) for special operations in the Caribbean Sea.
Answer: The Aquila home port was Trumbo Point Annex, Naval Air Station, Key West, Florida.
Question: How do I get to Juneau, Alaska?
Answer: There are no roads that lead directly to Juneau, Alaska. That leaves two options, by boat or by plane. Remote Alaskan villages and cities like Juneau are one of the reasons bush pilots are so prevalent in the state.
Question: Tell me whether these are found at the beach or on a mountains: ocean, ski resort, chair lift, shell, seaweed, hiking trail
Answer: Ocean: Beach
Ski Resort: Mountain
Chair Lift: Mountain
Shell: Beach
Seaweed: Beach
Hiking Trail: Mountain
Question: How are the different senses annotated/labeled? 
Evidence: The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.
The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9.
The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.

The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9.
Answer: The answers are shown as follows:
* The contexts are manually labelled with WordNet senses of the target words
Question: What do they use for machine translation?
Evidence: Before training the neural network with the labeled tweets, it is necessary to apply the proper tokenization to every tweet. In this way, the text corpus is split into word elements, taking white spaces and the various punctuation symbols used in the language into account. This was done using the Moses package for machine translation.
We choose to limit the maximum size of each tweet to be considered during training to 30 words, and padded tweets of shorter size with zeros. Next, tweets are converted into vectors using word-based frequency, as described in Section SECREF5 . To feed the various classifiers in our evaluation, we attach the feature values onto every tweet vector.
In this work we experimented with various combinations of attached features INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 that express the user tendency. The details of each experiment, including the resulting size of each embedding can be found in Table TABREF10 , with the latter denoted `input dimension' in the table.
Before training the neural network with the labeled tweets, it is necessary to apply the proper tokenization to every tweet. In this way, the text corpus is split into word elements, taking white spaces and the various punctuation symbols used in the language into account. This was done using the Moses1 package for machine translation.
Answer: Moses package.
Question: How are the two different models trained?
Evidence: We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model.
We first investigate the impact of pre-training on BERT-BASE's performance. We then compare the performance of BERT-BASE with BERT-LARGE. For both, we vary the number of word-pieces from each article that are used in training. We perform tests with 100, 250 and 500 word pieces.
Next, we further explore the impact of sequence length using BERT-LARGE. The model took approximately 3 days to pre-train when using 4 NVIDIA GeForce GTX 1080 Ti. On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100. The model's training time scaled roughly linearly with sequence length. We did a grid search on sequence length and learning rate.
We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model.
We first investigate the impact of pre-training on BERT-BASE's performance. 
On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100. 
Answer: They pre-train the models using 600000 articles as an unsupervised dataset and then fine-tune the models on small training set.
Question: How large is the Earth?
Answer: The Earth's radius is 6378 kilometers, and its mass is 5.972 x 10^24 kilograms. Its surface area is about 510 million square kilometers.
Question: What is the capital city of Malaysia?
Answer: The capital city of Malaysia is Kuala Lumpur.
Question: What is the origin of ethnology?
Evidence: Sociocultural anthropology/ethnology:
This field can trace its roots to processes of European colonization and globalization, when European trade with other parts of the world and eventual political control of overseas territories offered scholars access to different cultures. Anthropology was the scientific discipline that searches to understand human diversity, both culturally and biologically. Originally anthropology focused on understanding groups of people then considered "primitive" or "simple" whereas sociology focused on modern urban societies in Europe and North America although more recently cultural anthropology looks at all cultures around the world, including those in developed countries. Over the years, sociocultural anthropology has influenced other disciplines like urban studies, gender studies, ethnic studies and has developed a number of sub-disciplines like medical anthropology, political anthropology, environmental anthropology, applied anthropology, psychological anthropology, economic anthropology and others have developed.
Answer: Ethnology can trace its roots to European colonization and globalization.
Question: When did Ivan Zhevakhov joined the military?
Evidence: Zhevakhov joined the military service as a cadet of the Ukrainian hussar regiment in 1775 and received his first major military experience in fighting with the Trans-Kubanian Circassians in 1777. He then fought in the Russo-Turkish War (1787–1792) and was wounded when storming Ochakov in 1789. Having fought in Poland in 1792, Zhevakhov was promoted to colonel in 1800 and then participated in the wars against Napoleonic France from 1805 to 1807.

During the 1812 French invasion of Russia, he commanded the Serpukhov dragoon regiment within the 3rd Army of the West and contributed to the Russian success in the July 1812 battles of Kobryn and Gorodechna, which halted the French advance toward Kiev. He then led cavalry regiments in Osten-Sacken’s corps and participated in the 1813-14 campaign against Napoleon. On 8 April 1813 he was promoted to the rank of major-general. Zhevakhov resigned due to health problems in 1817 and henceforth lived in Odessa where he died in 1837.
Answer: Ivan Zhevakhov joined the military service as a cadet of the Ukrainian hussar regiment in 1775 and received his first major military experience in fighting with the Trans-Kubanian Circassians in 1777.
Question: Why is the sky blue?
Answer: The earth's sky is blue due to how the gases in the earth's atmosphere scatter the short electromagnetic wavelengths of the sun's blue light while allowing red, orange and yellow light to pass through. The scientific name for this phenomenon is Rayleigh scattering.
Question: Why it is not easy to construct high-quality training data?
Evidence: The major advantage of our system is that we only leverage NLTK tokenization and a POS tagger to preprocess our training dataset. When implementing our neural network based clinical information extraction system, we found it is not easy to construct high quality training data due to the noisy format of clinical notes. Choosing the proper tokenizer is quite important for span identification. After several experiments, we found "RegexpTokenizer" can match our needs. This tokenizer can generate spans for each token via sophisticated regular expression like below,
The major advantage of our system is that we only leverage NLTK2 tokenization and a POS tagger to preprocess our training dataset. When implementing our neural network based clinical information extraction system, we found it is not easy to construct high quality training data due to the noisy format of clinical notes.
Answer: The authors found that it is not easy to construct high quality training data due to the noisy format of clinical notes.