DataHammer/scimrc huggingface dataset:
Question: Based on the paragraph below, please list out the 2 major causes supported by Kelly Slater.
Evidence: Slater is also a fundraiser and spokesperson for suicide prevention awareness. He has surfed in celebrity events for Surfers Against Suicide, telling sports website 'Athletes Talk': "I've lost a couple of friends myself to suicide and it's just a horrible thing that can be prevented. People get in this dark place and they don't know what to do so it's always nice to see a non-profit that isn't turning into anything else other than just trying to help people."

Slater is passionate about preserving oceans globally and protecting temperate reefs in California through his relationship with Reef Check. Slater is on the Board of Advisors (the Ocean Advocacy Advisory Board) of ocean conservation organization Sea Shepherd Conservation Society.
Answer: Kelly Slater's main activism has been directed towards suicide prevention and ocean conservation.
Question: Classify each of the following as "Vegetarian Friendly" or "Not Vegetarian Friendly": chicken, peanuts, eggs, bacon, sausage, lettuce, ketchup
Answer: Chicken: Not Vegetarian Friendly
Peanuts: Vegetarian Friendly
Eggs: Vegetarian Friendly
Bacon: Not Vegetarian Friendly
Sausage: Not Vegetarian Friendly
Lettuce: Vegetarian Friendly
Ketchup: Vegetarian Friendly
Question: Classify the cities based on whether they are located in Belarus or Cayman Islands.
Vitsyebsk, George Town, Minsk, Homyel’
Answer: Belarus - Minsk, Homyel’, Vitsyebsk
Cayman Islands - George Town
Question: Who designed the St. John the Baptist, Blackrock church?
Evidence: St. John the Baptist, Blackrock is a Roman Catholic church in the parish of Blackrock, Ireland. The church is still in use and named after the Saint John the Baptist. It is located on Temple Road, Blackrock, County Dublin.

The church was designed by the architect Patrick Byrne, who was educated at the Dublin Society Schools. It is one of the finest examples of the Gothic revival style in Ireland and was the first to be built in the Dublin Archdiocese. It is said to be inspired by the ideas of Augustus Welby Pugin (1812-1852).

The initial building consisted of the nave, chancel and bell tower. To the rear of the church, two carved heads can be seen up high on either side of the rose window. The head on the left depicts St. John the Baptist, while the head on the right depicts the Archbishop Daniel Murray.
Answer: Patrick Byrne
Question: What towns make up Cape Ann?
Answer: Cape Ann is a rocky peninsula in northeastern Massachusetts on the Atlantic Ocean. It is about 30 miles (48 km) northeast of Boston and marks the northern limit of Massachusetts Bay. Cape Ann includes the city of Gloucester and the towns of Essex, Manchester-by-the-Sea and Rockport.
Question: How does the author compare his method with the other two?
Evidence: We compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions.
In the first one Wang et al. BIBREF21 downloaded over 5M tweets which included one of 131 emotional hashtags based on Parrott's three-level categorization of emotions in seven categories: joy, sadness, anger, love, fear, thankfulness, surprise. To assess the quality of using hashtags as labels, the sampled 400 tweets randomly and after comparing human annotations by hashtag labels they came up with simple heuristics to increase the quality of labeling by ignoring tweets with quotations and URLs and only keeping tweets with 5 terms or more that have the emotional hashtags at the end of the tweets. Using these rules they extracted around 2.5M tweets. After sampling another 400 random tweets and comparing it to human annotation the saw that hashtags can classify the tweets with 95% precision. They did some pre-processing by making all words lower-case, replaced user mentions with @user, replaced letters/punctuation that is repeated more than twice with the same two letters/punctuation (e.g., ooooh INLINEFORM0 ooh, !!!!! INLINEFORM1 !!); normalized some frequently used informal expressions (e.g., ll → will, dnt INLINEFORM2 do not); and stripped hash symbols. They used a sub-sample of their dataset to figure out the best approaches for classification, and after trying two different classifiers (multinomial Naive Bayes and LIBLINEAR) and 12 different feature sets, they got their best results using logistic regression branch for LIBLINEAR classifier and a feature set consist of n-gram(n=1,2), LIWC and MPQA lexicons, WordNet-Affect and POS tags.
In the second one, the reported results are from a paper by BIBREF33 in which they used maximum entropy classifier with bag of words model to classify various emotional datasets. Here we only report part of their result for CrowdFlower dataset that can be mapped to one of our seven labels.
We compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions.
Answer: The first one uses almost the same tweet data as they use for training, and the second one is the CrowdFlower dataset annotated for emotions.
Question: What should I do if my laptop has been hacked?
Answer: If your laptop has been hacked, you can be in a big trouble. Attackers may have stolen all your confidential data and passwords. It is better to ask for helps from security experts. It is also important to reset all your passwords. Do not turn on your laptop and if it is a personal laptop make sure to re-install your operating system. Check your recent bank account activities to ensure that you have not lost any money.
Question: Give me a bulleted list of the last 10 winners of The Masters in order starting from 2022
Answer: - Scottie Scheffler
- Hideki Matsuyama
- Dustin Johnson
- Tiger Woods
- Patrick Reed
- Sergio Garcia
- Jordan Spieth
- Bubba Watson
- Adam Scott
Question: Are the following colours considered primary colours or not: red, orange, yellow, green, blue, indigo, violet
Answer: red - primary
orange - not primary
yellow - primary
green - not primary
blue - primary
indigo - not primary
violet - not primary
Question: According to Greek mythology, who was the first woman on earth?
Answer: Pandora
Question: When was the 8088 processor released?
Evidence: The 8086 (also called iAPX 86) is a 16-bit microprocessor chip designed by Intel between early 1976 and June 8, 1978, when it was released. The Intel 8088, released July 1, 1979, is a slightly modified chip with an external 8-bit data bus (allowing the use of cheaper and fewer supporting ICs),[note 1] and is notable as the processor used in the original IBM PC design.
Answer: The Intel 8088 processor was released July 1, 1979.
Question: Name a few famous Dutch artists, and the respective movements for which they are known.
Answer: 1. Rembrandt - Baroque, or the Dutch Golden Age. Known for his masterful use of light and shadow, and his expressive portraits.

2. Vermeer - Baroque, or the Dutch Golden Age. Known for the interplay of colors and light, and his depictions of ordinary domestic scenes.

3. Van Gogh - Post-Impression. Known for his vibrant and bold colors, and emotional portraits of daily life.

4. Mondrian - De Stijl. A pioneer of the De Stijl movement, which is known for its strict use of geometry and colors, he is well known for his abstract grid paintings.
Question: What are the names of the social insects that are mentioned?
Evidence: Solitary bees, such as leafcutters, do not form colonies. Unlike social insects (ants, yellow jackets, honeybees), leafcutters work alone building isolated nests. Similar to honeybees, female bees perform nearly all essential tasks of brood rearing. These native insects perform essential tasks, pollinating wild plants. The alfalfa leaf cutter bee (Megachile rotundata), native to Europe, has been semi-domesticated for crop pollination. In North America, the species was deliberately imported to assist in the pollination of food crops, but has now become feral and widespread.
Answer: ants, yellow jackets, honeybees
Question: What criteria should I be looking at when buying a new motorcycle?
Answer: There are a number of criteria you should be looking at:
- Price: How does it fit in your budget?
- Engine type: Do you want a traditional combustion engine, or go for an electric one?
- Number of cylinders: If you go for combustion engine, how many cylinders do you choose? Less cylinders are usually cheaper, make the motorcycle more lightweight and thus easier to maneuver. More cylinders make the engine feel smoother, also have more power, but are heavier and more expensive.
- Usage: What do you use it for? Just for fun, commuting to work, general transportation? Travel? Offroad? Racing?
- Assistance systems: Modern motorcycles have a range of assistance systems like ABS, Cruise Control, Distance sensors, electronic driving modes for different weather conditions etc. They can make driving safer , but also add weight and make the bike more expensive. Which ones do you need?  
- Style: There are different styles like naked bike, cruiser, chopper, dual sport, race bike , scrambler etc. Different styles can be more or less adequate depending on your usage, or you can simply pick the one that appeals to you.
- Size: The two most important aspects in terms of size are seat height and weight. Both have a huge impact on how well you are in control when not driving, for example when you need to stop at a traffic light or going slowly in stop & go traffic.
Seat height is important , so your feet can reach the ground when stopping. However the width of the seat and general width dimensions of the bike also play a role here. So two bikes with same seat height might feel very differently. Best is to try it out
Question: Identify which instrument is string or percussion: Metallophone, Doshpuluur
Answer: Doshpuluur is string, Metallophone is percussion.
Question: What kind of car was The Zuk
Evidence: The Żuk (pl. beetle) was a van and light truck produced in Lublin, Poland, between 1958 and 1998 by FSC. It was based on FSO Warszawa, which in turn was licensed from the Soviet passenger car GAZ-M20 Pobeda. The chassis, suspension and engine from FSO Warszawa formed the basis of the Żuk and the Nysa light vans designed in the late 1950s. About 587818 were manufactured.

The Żuk was mainly sold to state organizations, but also to individuals. After 1989, with the liberalization of the Polish economy, the Żuk was able to maintain sales to the traditional markets and expand the number sold to individual consumers. The final few years of production was in parallel to its successor, the Lublin van, as a cheaper alternative.
Answer: The Zuk (pl. beetle) was a van and light truck produced in Lublin, Poland, between 1958 and 1998 by FSC. It was based on FSO Warszawa, which in turn was licensed from the Soviet passenger car GAZ-M20 Pobeda. The chassis, suspension and engine from FSO Warszawa formed the basis of the Zuk and the Nysa light vans designed in the late 1950s. About 587818 were manufactured.

The Zuk was mainly sold to state organizations, but also to individuals. After 1989, with the liberalization of the Polish economy, the Zuk was able to maintain sales to the traditional markets and expand the number sold to individual consumers. The final few years of production was in parallel to its successor, the Lublin van, as a cheaper alternative.
Question: Does API provide ability to connect to models written in some other deep learning framework?
Evidence: The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29. For each structured model in the library, we define a conditional random field (CRF) distribution object. From a user's standpoint, this object provides all necessary distributional properties. Given log-potentials (scores) output from a deep network $\ell $, the user can request samples $z \sim \textsc {CRF}(\ell )$, probabilities $\textsc {CRF}(z;\ell )$, modes $\arg \max _z \textsc {CRF}(\ell )$, or other distributional properties such as $\mathbb {H}(\textsc {CRF}(\ell ))$. The library is agnostic to how these are utilized, and when possible, they allow for backpropagation to update the input network. The same distributional object can be used for standard output prediction as for more complex operations like attention or reinforcement learning.
The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29.
Answer: Yes.
Question: What position did Manis Lamond play?
Evidence: Manis Lamond (born 14 September 1966) is a retired soccer player from Papua New Guinea. An international who played as a striker, Lamond played professionally in Australia for Canberra City, Sydney United, A.P.I.A. Leichhardt Tigers, Wollongong City, Marconi Stallions and Newcastle United and in Singapore for Sembawang Rangers.
Answer: Manis Lamond was a striker
Question: What is the method that the authors described in this paper?
Evidence: Writing errors can occur in many different forms – from relatively simple punctuation and determiner errors, to mistakes including word tense and form, incorrect collocations and erroneous idioms. Automatically identifying all of these errors is a challenging task, especially as the amount of available annotated data is very limited. Rei2016 showed that while some error detection algorithms perform better than others, it is additional training data that has the biggest impact on improving performance.
Being able to generate realistic artificial data would allow for any grammatically correct text to be transformed into annotated examples containing writing errors, producing large amounts of additional training examples. Supervised error generation systems would also provide an efficient method for anonymising the source corpus – error statistics from a private corpus can be aggregated and applied to a different target text, obscuring sensitive information in the original examination scripts. However, the task of creating incorrect data is somewhat more difficult than might initially appear – naive methods for error generation can create data that does not resemble natural errors, thereby making downstream systems learn misleading or uninformative patterns.
Previous work on artificial error generation (AEG) has focused on specific error types, such as prepositions and determiners BIBREF0 , BIBREF1 , or noun number errors BIBREF2 . Felice2014a investigated the use of linguistic information when generating artificial data for error correction, but also restricting the approach to only five error types. There has been very limited research on generating artificial data for all types, which is important for general-purpose error detection systems. For example, the error types investigated by Felice2014a cover only 35.74% of all errors present in the CoNLL 2014 training dataset, providing no additional information for the majority of errors.
In this paper, we investigate two supervised approaches for generating all types of artificial errors. We propose a framework for generating errors based on statistical machine translation (SMT), training a model to translate from correct into incorrect sentences. In addition, we describe a method for learning error patterns from an annotated corpus and transplanting them into error-free text. We evaluate the effect of introducing artificial data on two error detection benchmarks. Our results show that each method provides significant improvements over using only the available training set, and a combination of both gives an absolute improvement of 4.3% in INLINEFORM0 , without requiring any additional annotated data.
 
In addition, we describe a method for learning error patterns from an annotated corpus and transplanting them into error-free text.
Answer: A method for learning error patterns from an annotated corpus and transplanting them into error-free text.
Question: What parameter does the x-axis represent?
Evidence: Finally, Figure FIGREF28 shows INLINEFORM0 correlation of different variants of Sera with pyramid based on selection of different cut-off points ( INLINEFORM1 and INLINEFORM2 correlations result in very similar graphs). When the cut-off point increases, more documents are retrieved for the candidate and the gold summaries, and therefore the final Sera score is more fine-grained. A general observation is that as the search cut-off point increases, the correlation with pyramid scores decreases. This is because when the retrieved result list becomes larger, the probability of including less related documents increases which negatively affects correct estimation of the similarity of the candidate and gold summaries. The most accurate estimations are for metrics with cut-off points of 5 and 10 which are included in the reported results of all variants in Table TABREF23 .
Figure 1: ρ correlation of SERA with pyramid based on different cut-off points. The x-axis shows the cut-off point parameter. DIS: Discounted variant of SERA; NP: Query reformulation with Noun Phrases; KW: Query reformulation with Keywords.
Answer: The cut-off point parameter.
Question: Does the proposed model outperform the previous best model?
Evidence: In this work, we incorporate a domain discriminator to align the mismatched domains between ASR hypotheses and reference transcriptions. The adversarial learning allows the end-to-end QA model to learn domain-invariant features and improve the robustness to ASR errors. The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score. 
The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
Answer: Yes, the proposed model outperforms the previous best model by 2% EM score and over 1.5% F1 score.
Question: How are healthcare organizations thinking about their strategy to improve healthcare delivery?
Answer: One popular model that many healthcare organizations use to set goals and measure their progress is known as the "Quadruple Aim." This framework looks to achieve four simultaneous and interconnected goals: Improving the patient experience, Improving healthcare outcomes, reducing cost of care, and improving the working experience of healthcare providers.
Question: What are 6 natural resources of Africa?
Answer: Gold, Oil, Natural Gas,Copper, Silver,Diamonds
Question: Classify each of these vehicles, based on whether they are two wheel vehicles, or four wheels vehicles: car, Gyrocar, van, motorbike, scooter, all-terrain vehicle (ATV), bicycle and Segway.
Answer: Vehicles with two wheels include: bicycle, motorbike, scooter, Gyrocar, and Segway.
Vehicles with four wheels include: car, van, all-terrain vehicle (ATV)
Question: What is Kotlin?
Answer: Kotlin is a cross-platform, statically typed, general-purpose high-level programming language with type inference. Kotlin is designed to interoperate fully with Java, and the JVM version of Kotlin's standard library depends on the Java Class Library, but type inference allows its syntax to be more concise.
Question: What foods are commonly eaten with ketchup?
Evidence: Ketchup or catsup is a table condiment with a sweet and sour flavor. The unmodified term ("ketchup") now typically refers to tomato ketchup, although early recipes used egg whites, mushrooms, oysters, grapes, mussels, or walnuts, among other ingredients.

Tomato ketchup is made from tomatoes, sugar, and vinegar, with seasonings and spices. The spices and flavors vary, but commonly include onions, allspice, coriander, cloves, cumin, garlic, and mustard, and sometimes include celery, cinnamon, or ginger.[citation needed] The market leader in the United States (60% market share) and the United Kingdom (82%) is Heinz Tomato Ketchup. Tomato ketchup is most often used as a condiment to dishes that are usually served hot and are fried or greasy: french fries and other potato dishes, hamburgers, hot dogs, chicken tenders, hot sandwiches, meat pies, cooked eggs, and grilled or fried meat. Ketchup is sometimes used as the basis for, or as one ingredient in, other sauces and dressings, and the flavor may be replicated as an additive flavoring for snacks, such as potato chips.
Answer: Ketchup is often used with french fries and other potato dishes, hamburgers, hot dogs, chicken tenders, hot sandwiches, meat pies, cooked eggs, grilled or fried meat and as an ingredient for salad dressing and sauces.
Question: What is the show Lizzie McGuire about?
Evidence: Lizzie McGuire is an American comedy television series created by Terri Minsky that premiered on Disney Channel on January 12, 2001. The series stars Hilary Duff as the titular character who learns to navigate the personal and social issues of her teenage years. Duff also voices an animated version of Lizzie that performs soliloquies to express the character's inner thoughts and emotions. The series also stars Lalaine, Adam Lamberg, Jake Thomas, Hallie Todd and Robert Carradine. The series concluded on February 14, 2004, after a total of 65 episodes were produced. A feature film based on the series, The Lizzie McGuire Movie, was released in 2003.
Answer: Lizzie McGuire is a show premiered on Disney Channel about a character (Lizzie McGuire) navigating the personal and social issues of her teenage years.
Question: What evaluations did the authors use on their system?
Evidence: FLOAT SELECTED: Table 1: Experiment results on the NIST Chinese-English translation tasks. [+Cd] is the proposed model with the dynamic cache. [+Cd,Ct] is the proposed model with both the dynamic and topic cache. The BLEU scores are case-insensitive. Avg means the average BLEU score on all test sets.
FLOAT SELECTED: Table 3: The average number of words in translations of beginning sentences of documents that are also in the topic cache. Reference represents the average number of words in four human translations that are also in the topic cache.
FLOAT SELECTED: Table 6: The average cosine similarity of adjacent sentences (coherence) on all test sets.
FLOAT SELECTED: Table 1: Experiment results on the NIST Chinese-English translation tasks. [+Cd] is the proposed model with the dynamic cache. [+Cd,Ct] is the proposed model with both the dynamic and topic cache. The BLEU scores are case-insensitive. Avg means the average BLEU score on all test sets.
FLOAT SELECTED: Table 3: The average number of words in translations of beginning sentences of documents that are also in the topic cache. Reference represents the average number of words in four human translations that are also in the topic cache.
FLOAT SELECTED: Table 6: The average cosine similarity of adjacent sentences (coherence) on all test sets.
Answer: BLEU scores, exact matches of words in both translations and topic cache, and cosine similarities of adjacent sentences for coherence.