DataHammer/scimrc huggingface dataset:
Question: What are the Pros and Cons of social media?
Answer: Social media are interactive technologies that facilitate the creation and sharing of information, ideas, interests, and other forms of expression through virtual communities and networks. Social media are great tools for making new friends or connect with old ones, can bring people together that are interested in the same topic and allow reaching a wide audience in a matter of seconds. However, the nature of social media bring in a range of potential issues. Too much time spent on social media can lead to social anxiety, depression and exposure to content that is not appropriate for the age of the user. Another potential issue is about cyberbullying, a form of bullying or harassment using electronic means which has become increasingly common, especially among teenagers, as the digital sphere has expanded.
Question: Name a song The Mo is best known for that is not named after a dairy product.
Evidence: The Mo, also known simply as Mo, was a Dutch pop band best remembered for the 1980s hits "Fred Astaire" and "Cheese." The band was known to be an experimental pop band during the new wave era.
Answer: Fred Astaire
Question: What has NLP focused its attention on so far?
Evidence: Recent studies have demonstrated that the systems trained on the human-written texts learn human-like biases BIBREF1 , BIBREF6 . In general, any predictive model built on historical data may inadvertently inherit human biases based on gender, ethnicity, race, or religion BIBREF7 , BIBREF8 . Discrimination-aware data mining focuses on measuring discrimination in data as well as on evaluating performance of discrimination-aware predictive models BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 .
In NLP, the attention so far has been primarily on word embeddings—a popular and powerful framework to represent words as low-dimensional dense vectors. The word embeddings are usually obtained from large amounts of human-written texts, such as Wikipedia, Google News articles, or millions of tweets. Bias in sentiment analysis systems has only been explored in simple systems that make use of pre-computed word embeddings BIBREF13 . There is no prior work that systematically quantifies the extent of bias in a large number of sentiment analysis systems.
This paper does not examine the differences in accuracies of systems on text produced by different races or genders, as was done by hovy2015demographic,blodgett2016demographic,jurgens2017incorporating,buolamwini2018gender. Approaches on how to mitigate inappropriate biases BIBREF14 , BIBREF1 , BIBREF15 , BIBREF16 , BIBREF13 , BIBREF17 , BIBREF18 are also beyond the scope of this paper. See also the position paper by hovy2016social, which identifies socio-ethical implications of the NLP systems in general.
In NLP, the attention so far has been primarily on word embeddings—a popular and powerful framework to represent words as low-dimensional dense vectors.
Answer: In NLP, the attention so far has been primarily on word embeddings—a popular and powerful framework to represent words as low-dimensional dense vectors.
Question: Decide the sentiment of the following user comments. Classify each of them as "Positive", "Neutral", "Mixed", or "Negative".

1. the latest update removed all the best features!! how could they do this to such a good app!!
2. There's not much to say; it checks emails, has a slick UI, and lets me focus on what I need to do.
3. It's pretty good.
4. The app's UI is amazing, but it doesn't seem to notify me consistently which really sucks. Not sure what's up with that but if they could get that working then it'd be amazing.
5. I can't believe how terrible this app is; it literally won't do anything I ask it to.
6. wow, im impressed
Answer: 1. Negative
2. Positive
3. Neutral
4. Mixed
5. Negative
6. Positive
Question: Given this paragraph, what are some examples of long short-term memory (LSTM) use cases?
Evidence: Long short-term memory (LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process not only single data points (such as images), but also entire sequences of data (such as speech or video). This characteristic makes LSTM networks ideal for processing and predicting data. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, speech activity detection, robot control, video games, and healthcare.
Answer: Long short-term memory  is used for handwriting recognition,  speech recognition, machine translation, speech activity detection, robot control, video games and healthcare.
Question: What two large datasets are used for evaluation?
Evidence: In this paper, we try to fulfill the above insufficiency via an exploratory study on real-world semantic parsing in context. Concretely, we present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods. Our best model achieves state-of-the-art (SOTA) performances on both datasets with significant improvements. Furthermore, we summarize and generalize the most frequent contextual phenomena, with a fine-grained analysis on representative models. Through the analysis, we obtain some interesting findings, which may benefit the community on the potential research directions. We will open-source our code and materials to facilitate future work upon acceptance.
Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods. 
Answer: The answers are shown as follows:
* SParC BIBREF2 and CoSQL BIBREF6
Question: What is a garden center?
Evidence: A garden centre (Commonwealth English spelling; U.S. nursery or garden center) is a retail operation that sells plants and related products for the domestic garden as its primary business.

It is a development from the concept of the retail plant nursery but with a wider range of outdoor products and on-site facilities. It is now usual for garden centres to obtain their stock from plants which have been propagated elsewhere, such as by specialist nurseries or wholesalers.
Answer: A garden center sells plants and products for domestic gardens. Plants are usually obtained from other specialist nurseries or wholesalers. It is the addition of outdoor products that make it different from the plant nurseries of the past.
Question: What type of mistakes is commonly seen in NMT results？
Evidence: Neural machine translation (NMT) emerged in the last few years as a very successful paradigm BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . While NMT is generally more fluent than previous statistical systems, adequacy is still a major concern BIBREF4 : common mistakes include dropping source words and repeating words in the generated translation.
Previous work has attempted to mitigate this problem in various ways. BIBREF5 incorporate coverage and length penalties during beam search—a simple yet limited solution, since it only affects the scores of translation hypotheses that are already in the beam. Other approaches involve architectural changes: providing coverage vectors to track the attention history BIBREF6 , BIBREF7 , using gating architectures and adaptive attention to control the amount of source context provided BIBREF8 , BIBREF9 , or adding a reconstruction loss BIBREF10 . BIBREF11 also use the notion of fertility implicitly in their proposed model. Their “fertility conditioned decoder” uses a coverage vector and an “extract gate” which are incorporated in the decoding recurrent unit, increasing the number of parameters.
In this paper, we propose a different solution that does not change the overall architecture, but only the attention transformation. Namely, we replace the traditional softmax by other recently proposed transformations that either promote attention sparsity BIBREF12 or upper bound the amount of attention a word can receive BIBREF13 . The bounds are determined by the fertility values of the source words. While these transformations have given encouraging results in various NLP problems, they have never been applied to NMT, to the best of our knowledge. Furthermore, we combine these two ideas and propose a novel attention transformation, constrained sparsemax, which produces both sparse and bounded attention weights, yielding a compact and interpretable set of alignments. While being in-between soft and hard alignments (Figure FIGREF20 ), the constrained sparsemax transformation is end-to-end differentiable, hence amenable for training with gradient backpropagation.
To sum up, our contributions are as follows:
While NMT is generally more fluent than previous sta-tistical systems, adequacy is still a major concern (Koehn and Knowles, 2017): common mis-takes include dropping source words and repeating words in the generated translation.
Answer: Dropping source words and repeating words in the generated translation.
Question: What’s a recipe for chimichurri?
Answer: Start with parsley as the foundation of the sauce, add salt, pepper, garlic powder, oregano, paprika and a touch of black garlic, finally, add olive oil mixing all spices throughly.
Question: Is it faster to take the subway or drive a car?
Answer: Sometimes it is faster to take the subway and sometimes it is faster to drive a car. The answer for any particular trip primarily depends on the origin and destination of the trip. If the origin and destination are both near a subway station, then it is often faster to take the subway, especially if there is a lot of automobile traffic on the roads and you do not need to connect between subway lines. But if either the origin or destination, or both, are far from a subway station, then the total time for the trip may be dominated by the time it takes to get to and from the subway station on either or both ends of the journey; this may add enough time to the time spent on the subway so that the total trip end-to-end takes longer than driving the whole way.
Question: What is Cisco's formal company name, where is their headquarters, and how many employees are there in the company?
Evidence: Cisco Systems, Inc., commonly known as Cisco, is an American-based multinational digital communications technology conglomerate corporation headquartered in San Jose, California. Cisco develops, manufactures, and sells networking hardware, software, telecommunications equipment and other high-technology services and products. Cisco specializes in specific tech markets, such as the Internet of Things (IoT), domain security, videoconferencing, and energy management with leading products including Webex, OpenDNS, Jabber, Duo Security, and Jasper. Cisco is one of the largest technology companies in the world ranking 74 on the Fortune 100 with over $51 billion in revenue and nearly 80,000 employees.
Answer: Cisco Systems, Inc. is located in San Jose, California and has nearly 80,000 employees.
Question: How do you train for a half marathon?
Answer: To train for a half marathon, build up your running volume over the course of a months. At first, start with just a couple of 2-3 mile runs per week. Then increase mileage no more than 10% per week up to 15-20 miles total per week. 

To reduce injury risk, do 80% of your running at slow pace. This will allow you to build an aerobic base without putting too much load on your body. On the remaining 20%, go hard and push your limits.

On the race day, make sure to get enough rest and eat a good breakfast. Try to do a warm up mile before the race even starts. Then during the race, find the pacer that is running at your speed, and stick with the group for most of the race. In the last half mile, sprint to the finish!
Question: What genre did Taylor Swift begin her career in?
Answer: Taylor Swift began her career as a country artist
Question: What things could a high school graduate do in a gap year between high school and college?
Answer: Go to community college, take different classes and see which subjects he likes.

Travel / backpacking in a very different part of the world.

Get a job, earn some money and get work experience.

Take on a passion project and try to make it work.

Volunteer at a non-profit in an area he might want to pursue as a career.
Question: Categorize the following data types into personal or business data: e-mail address, financials, government identifiers, system designs, marketing materials, credit card numbers
Answer: E-mail Address: Personal Data
Financials: Business Data
Government Identifiers: Personal Data
System Designs: Business Data
Marketing Materials: Business Data
Credit Card Numbers: Personal Data
Question: Identify which car manufacturer is German or American: Tesla, Elektron Motors
Answer: Tesla is American, Elektron Motors is German
Question: In the series A Song of Ice and Fire, who is the founder of House Redbeard?
Answer: Gerrick Kingsblood was a free folk raider who founded House Redbeard.
Question: Jot down some points about University of Liverpool from given text
Evidence: The University of Liverpool (abbreviated UOL) is a public research university based in the city of Liverpool, England. Founded as a college in 1881, it gained its Royal Charter in 1903 with the ability to award degrees, and is also known to be one of the six 'red brick' civic universities, the first to be referred to as The Original Red Brick. It comprises three faculties organised into 35 departments and schools. It is a founding member of the Russell Group, the N8 Group for research collaboration and the university management school is triple crown accredited.
Answer: 1. The institution of Liverpool is a public research institution with its main campus in Liverpool, England (abbreviated UOL).
2. The Original Red Brick was the first of the six civic universities known as "red bricks," and it was established in 1881 as a college before receiving its Royal Charter in 1903, enabling it to grant degrees.
3. It has 35 departments and schools divided into three faculties.
4. The university management school has earned the triple crown of accreditation, and it is a founding member of the Russell Group and the N8 Group for research collaboration.
Question: How is performance measured?
Evidence: To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA. A high performance on this benchmark shows that the mapping has been successful in placing semantically similar terms near to each other whereas dissimilar terms are relatively far apart in the space. We repeat the computation for each pair in the reverse direction.
To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA. A high performance on this benchmark shows that the mapping has been successful in placing semantically similar terms near to each other whereas dissimilar terms are relatively far apart in the space. We repeat the computation for each pair in the reverse direction.
Answer: The answers are shown as follows:
* To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA.
Question: Could anyone access the MIMIC-III database?
Evidence: As this corpus of annotated patient notes comprises original healthcare data which contains protected health information (PHI) per The Health Information Portability and Accountability Act of 1996 (HIPAA) BIBREF16 and can be joined to the MIMIC-III database, individuals who wish to access to the data must satisfy all requirements to access the data contained within MIMIC-III. To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC” BIBREF17. This corpus can also be accessed on GitHub after completing all of the above requirements.
To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC”1 (Moseley et al., 2020).
Answer: No, they couldn't.
Question: What domains are considered that have such large vocabularies?
Evidence: More recently, novel applications of word2vec involving unconventional generalized “words” and training corpuses have been proposed. These powerful ideas from the NLP community have been adapted by researchers from other domains to tasks beyond representation of words, including relational entities BIBREF1 , BIBREF2 , general text-based attributes BIBREF3 , descriptive text of images BIBREF4 , nodes in graph structure of networks BIBREF5 , and queries BIBREF6 , to name a few.
These powerful ideas from the NLP community have been adapted by researchers from other domains to tasks beyond representation of words, including relational entities BIBREF1 , BIBREF2 , general text-based attributes BIBREF3 , descriptive text of images BIBREF4 , nodes in graph structure of networks BIBREF5 , and queries BIBREF6 , to name a few.
Answer: The answers are shown as follows:
* relational entities
* general text-based attributes
* descriptive text of images
* nodes in graph structure of networks
* queries
Question: What is electric current and how is it measured?
Evidence: The movement of electric charge is known as an electric current, the intensity of which is usually measured in amperes. Current can consist of any moving charged particles; most commonly these are electrons, but any charge in motion constitutes a current. Electric current can flow through some things, electrical conductors, but will not flow through an electrical insulator.
By historical convention, a positive current is defined as having the same direction of flow as any positive charge it contains, or to flow from the most positive part of a circuit to the most negative part. Current defined in this manner is called conventional current. The motion of negatively charged electrons around an electric circuit, one of the most familiar forms of current, is thus deemed positive in the opposite direction to that of the electrons. However, depending on the conditions, an electric current can consist of a flow of charged particles in either direction, or even in both directions at once. The positive-to-negative convention is widely used to simplify this situation.
The process by which electric current passes through a material is termed electrical conduction, and its nature varies with that of the charged particles and the material through which they are travelling. Examples of electric currents include metallic conduction, where electrons flow through a conductor such as metal, and electrolysis, where ions (charged atoms) flow through liquids, or through plasmas such as electrical sparks. While the particles themselves can move quite slowly, sometimes with an average drift velocity only fractions of a millimetre per second, the electric field that drives them itself propagates at close to the speed of light, enabling electrical signals to pass rapidly along wires.
In engineering or household applications, current is often described as being either direct current (DC) or alternating current (AC). These terms refer to how the current varies in time. Direct current, as produced by example from a battery and required by most electronic devices, is a unidirectional flow from the positive part of a circuit to the negative.  If, as is most common, this flow is carried by electrons, they will be travelling in the opposite direction. Alternating current is any current that reverses direction repeatedly; almost always this takes the form of a sine wave. Alternating current thus pulses back and forth within a conductor without the charge moving any net distance over time. The time-averaged value of an alternating current is zero, but it delivers energy in first one direction, and then the reverse. Alternating current is affected by electrical properties that are not observed under steady state direct current, such as inductance and capacitance. These properties however can become important when circuitry is subjected to transients, such as when first energised.
Answer: The movement of electric charge is known as an electric current, the intensity of which is usually measured in amperes.
Question: List as many types of craft beer as you can
Answer: IPA, pale ale, wheat beer, lager, pilsner, stout, porter, brown ale, sour, saison, red ale, imperial stout, mild ale, english special bitter, milkshake IPA, sour IPA, cream ale
Question: Extract the names from the cast of the film Replikator, and separate them with a comma.
Evidence: Replikator is a 1994 science fiction film directed by G. Philip Jackson and starring Michael St. Gerard, Brigitte Bako, Ned Beatty and Ilona Staller.

Cast

    Michael St. Gerard as Ludo Ludovic
    Brigitte Bako as Kathy Moscow
    Ned Beatty as Inspector Victor Valiant
    Ilona Staller as Miss Tina Show (as La Cicciolina)
    Lisa Howard as Lena
    Peter Outerbridge as John Cheever
    Ron Lea as Byron Scott
    David Hemblen as Police Chief
    Mackenzie Gray as Candor
    Frank Moore as Investigating Officer
Answer: Michael St. Gerard as Ludo Ludovic, Brigitte Bako as Kathy Moscow, Ned Beatty as Inspector Victor Valiant, Ilona Staller as Miss Tina Show (as La Cicciolina), Lisa Howard as Lena, Peter Outerbridge as John Cheever, Ron Lea as Byron Scott
, David Hemblen as Police Chief, Mackenzie Gray as Candor and Frank Moore as Investigating Officer
Question: What is a remote control used for?
Answer: A remote control is used for controlling electronic devices.  Some items you can use with a remote are a remote control car, television, stereo receiver, lights, home automation, fans, and kids toys.  Using a remote will take batteries or some sort of power source in order to make it operational.
Question: What makes Southwest Virginia special?
Answer: Southwest Virginia is a beautiful place.  The area is nestled in the Appalachian Mountains.  The perfect backdrop for incredible scenic views.  The climate is fantastic.  Springtime and Fall brings brisk weather and the welcoming or good bye of leaves.  Finally, the friendly people of Virginia are eager to share the beauty with visitors.