DataHammer/scimrc huggingface dataset:
Question: Who are some students at Hogwarts in Ravenclaw house that were in the same year as Harry Potter?
Answer: Hogwarts is the fictitious school in the Harry Potter book series. The books mentions several students in Harry Potter's year from the Ravenclaw house, such as Su Li, Lisa Turpin, Padma Patil, Terry Boot, and Anthony Goldstein.
Question: Is the dataset used to train the model multilingual or not？
Evidence: We use the WikiBio dataset introduced by lebret2016neural. It consists of INLINEFORM0 biography articles from English Wikipedia. A biography article corresponds to a person (sportsman, politician, historical figure, actor, etc.). Each Wikipedia article has an accompanying infobox which serves as the structured input and the task is to generate the first sentence of the article (which typically is a one-line description of the person). We used the same train, valid and test sets which were made publicly available by lebret2016neural.
We also introduce two new biography datasets, one in French and one in German. These datasets were created and pre-processed using the same procedure as outlined in lebret2016neural. Specifically, we extracted the infoboxes and the first sentence from the corresponding Wikipedia article. As with the English dataset, we split the French and German datasets randomly into train (80%), test (10%) and valid (10%). The French and German datasets extracted by us has been made publicly available. The number of examples was 170K and 50K and the vocabulary size was 297K and 143K for French and German respectively. Although in this work we focus only on generating descriptions in one language, we hope that this dataset will also be useful for developing models which jointly learn to generate descriptions from structured data in multiple languages.
We use the WikiBio dataset introduced by lebret2016neural. It consists of INLINEFORM0 biography articles from English Wikipedia. A biography article corresponds to a person (sportsman, politician, historical figure, actor, etc.). Each Wikipedia article has an accompanying infobox which serves as the structured input and the task is to generate the first sentence of the article (which typically is a one-line description of the person). We used the same train, valid and test sets which were made publicly available by lebret2016neural.
We also introduce two new biography datasets, one in French and one in German. These datasets were created and pre-processed using the same procedure as outlined in lebret2016neural. Specifically, we extracted the infoboxes and the first sentence from the corresponding Wikipedia article. As with the English dataset, we split the French and German datasets randomly into train (80%), test (10%) and valid (10%). The French and German datasets extracted by us has been made publicly available. The number of examples was 170K and 50K and the vocabulary size was 297K and 143K for French and German respectively. Although in this work we focus only on generating descriptions in one language, we hope that this dataset will also be useful for developing models which jointly learn to generate descriptions from structured data in multiple languages.
We use the WikiBio dataset introduced by Lebret et al. (2016).****We also introduce two new biography datasets, one in French and one in German.
Answer: Yes, they used datasets in English, French, and German.
Question: did they use a crowdsourcing platform for annotations?
Evidence: In this study, we constructed the first Japanese word similarity dataset. It contains various parts of speech and includes rare words in addition to common words. Crowdsourced annotators assigned similarity to word pairs during the word similarity task. We gave examples of similarity in the task request sent to annotators, so that we reduced the variance of each word pair. However, we did not restrict the attributes of words, such as the level of feeling, during annotation. Error analysis revealed that the notion of similarity should be carefully defined when constructing a similarity dataset.
Crowdsourced annotators assigned similarity to word pairs during the word similarity task. 
Answer: Yes.
Question: How is Tesla organized as a company?
Answer: Tesla has a functional organizational structure that is flat as well. Tesla’s organizational structure is designed in a way that is easy to manage and built for maximum efficiency. Tesla can still be considered as a startup in the automotive industry as its competitors are typically 75+ years old. As a startup, Tesla is designed for efficiency, to begin with as it must be easier to group and manage similar tasks. 

 

Some of the key functional groups of Tesla is as below: 

 

Materials Engineering 

Artificial Intelligence for Auto Pilot 

Hardware design engineering 

Energy operations 

Global communications 

Global Environment health and safety 

Global security 

Chief of staff 

Information operations 

 

All the functional groups report to the CEO and Tesla has a heavy flat organization structure. Materials engineering is responsible for material research and development, design for both Tesla and SpaceX. Tesla separated out the autopilot unit from its technology and information operations as it needs a dedicated focus for Tesla to sustain its competitive advantage in this space.  

 

Certain functional units like global security and global environmental health and safety span across all the Tesla markets. Again, Tesla must have been designed to increase efficiency without creating complex divisional structures and duplicate efforts.
Question: Write a letter to your Mom and Dad asking whether or not I can study abroad during my Junior year in college.
Answer: I have a huge desire to study abroad so that I can experience life in a different culture.  I would learn how to navigate different cultures, work with diverse peers, and even learn to better communicate in other languages.  It would give me a more diverse view of our world, experience new perspectives and even provide me with a competitive advantage over my peers.  While I might get homesick, thanks to modern technology, I will be able to communicate with all of my loved ones back home, very easily.  Help me create lifelong connections by allowing me the opportunity to study abroad.
Question: Is their method better than detox?
Evidence: News portals often allow their readers to comment on articles, in order to get feedback, engage their readers, and build customer loyalty. User comments, however, can also be abusive (e.g., bullying, profanity, hate speech), damaging the reputation of news portals, making them liable to fines (e.g., when hosting comments encouraging illegal actions), and putting off readers. Large news portals often employ moderators, who are frequently overwhelmed by the volume and abusiveness of comments. Readers are disappointed when non-abusive comments do not appear quickly online because of moderation delays. Smaller news portals may be unable to employ moderators, and some are forced to shut down their comments.
In previous work BIBREF0 , we introduced a new dataset of approx. 1.6M manually moderated user comments from a Greek sports news portal, called Gazzetta, which we made publicly available. Experimenting on that dataset and the datasets of Wulczyn et al. Wulczyn2017, which contain moderated English Wikipedia comments, we showed that a method based on a Recurrent Neural Network (rnn) outperforms detox BIBREF1 , the previous state of the art in automatic user content moderation. Our previous work, however, considered only the texts of the comments, ignoring user-specific information (e.g., number of previously accepted or rejected comments of each user). Here we add user embeddings or user type embeddings to our rnn-based method, i.e., dense vectors that represent individual users or user types, similarly to word embeddings that represent words BIBREF2 , BIBREF3 . Experiments on Gazzetta comments show that both user embeddings and user type embeddings improve the performance of our rnn-based method, with user embeddings helping more. User-specific or user-type-specific scalar biases also help to a lesser extent.
Experimenting on that dataset and the datasets of Wulczyn et al. Wulczyn2017, which contain moderated English Wikipedia comments, we showed that a method based on a Recurrent Neural Network (rnn) outperforms detox, the previous state of the art in automatic user content moderation.
Answer: Yes.
Question: What features do they extract?
Evidence: Satirical news is not based on or does not aim to state the fact. Rather, it uses parody or humor to make statement, criticisms, or just amusements. In order to achieve such effect, contradictions are greatly utilized. Therefore, inconsistencies significantly exist in different parts of a satirical news tweet. In addition, there is a lack of entity or inconsistency between entities in news satire. We extracted these features at semantic level from different sub-structures of the news tweet. Different structural parts of the sentence are derived by part-of-speech tagging and named entity recognition by Flair. The inconsistencies in different structures are measured by cosine similarity of word phrases where words are represented by Glove word vectors. We explored three different aspects of inconsistency and designed metrics for their measurements. A word level feature using tf-idf BIBREF22 is added for robustness.
We explored three different aspects of inconsistency and designed metrics for their measurements. 
A word level feature using tf-idf BIBREF22 is added for robustness.
Answer: The answers are shown as follows:
* Inconsistency in Noun Phrase Structures
*  Inconsistency Between Clauses
* Inconsistency Between Named Entities and Noun Phrases
* Word Level Feature Using TF-IDF
Question: What is gRPC?
Answer: gRPC (gRPC Remote Procedure Calls) is a cross-platform open source high performance remote procedure call (RPC) framework. gRPC was initially created by Google, which used a single general-purpose RPC infrastructure called Stubby to connect the large number of microservices running within and across its data centers from about 2001.
Question: Classify each of the following as either cities in Europe or in the United States: Sevilla, Boston, Miami, Dallas, New York, Paris, Frankfurt, Seattle, Milan
Answer: Sevilla, Paris, Frankfurt and Milan are cities in Europe.
Boston, Miami, Dallas, New York and Seattle are cities in the United States.
Question: What should I check when planning a trip?
Answer: In addition to the usual items like air tickets, accommodation, sightseeing spots, visa requirements, travel restrictions, ... you should also check some of these depending on where you’re heading:
- Monsoon season if for example traveling to Southeast Asia. It tends to rain more during this season which may impact the activities you can do or the road conditions. Keep in mind that the monsoon season varies across countries and even across regions of the same country.
- Major holidays: Including public holidays, festivals, school holidays. These impact the opening hours and the crowd at sightseeing spots. Check the major holidays in neighbouring countries as well. For example, long holidays in Australia may affect the crowd in Bali, Indonesia as a popular travel destination, or the Golden week in Japan means that a lot of locals would travel around Japan.
- Major events: Including popular festivals and major sport events. You are either interested in some of these events so you may want to plan your trip to attend those or you may want to avoid them as finding accommodation and air tickets can be more difficult and more expensive. For example, it is more difficult to travel to Melbourne during the Australian Open.
- Natural phenomena: Such as the cyclone season in Fiji or the jellyfish season in Cairns, Australia.
Question: How is this problem evaluated?
Evidence: Approach ::: Method: Training and Testing ::: Experiment 1: Representation
Some of the problems encountered by prior approaches seem to be attributable to the use of infix notation. In this experiment, we compare translation BLEU-2 scores to spot the differences in representation interpretability. Traditionally, a BLEU score is a metric of translation quality BIBREF24. Our presented BLEU scores represent an average of scores a given model received over each of the target test sets. We use a standard bi-gram weight to show how accurate translations are within a window of two adjacent terms. After testing translations, we calculate an average BLEU-2 score per test set, which is related to the success over that data. An average of the scores for each dataset become the presented value.
Approach ::: Method: Training and Testing ::: Experiment 2: State-of-the-art
This experiment compares our networks to recent previous work. We count a given test score by a simple “correct versus incorrect" method. The answer to an expression directly ties to all of the translation terms being correct, which is why we do not consider partial precision. We compare average accuracies over 3 test trials on different randomly sampled test sets from each MWP dataset. This calculation more accurately depicts the generalization of our networks.
Approach ::: Method: Training and Testing ::: Experiment 1: Representation
Some of the problems encountered by prior approaches seem to be attributable to the use of infix notation. In this experiment, we compare translation BLEU-2 scores to spot the differences in representation interpretability.
Approach ::: Method: Training and Testing ::: Experiment 2: State-of-the-art
This experiment compares our networks to recent previous work. We count a given test score by a simple “correct versus incorrect" method. The answer to an expression directly ties to all of the translation terms being correct, which is why we do not consider partial precision. We compare average accuracies over 3 test trials on different randomly sampled test sets from each MWP dataset.
Answer: The answers are shown as follows:
* BLEU-2
* average accuracies over 3 test trials on different randomly sampled test sets
Question: Who created the musical genre metal?
Answer: The creation of metal is often attributed to the band Black Sabbath. Specifically, the feel and sound of metal come from their guitarist, Tony Iommi. War Pigs is a great song of theirs which shows the feel and sound of early metal. 

Fans of metal are often referred to as metalheads. Though its appearance and lyrics can be dark, metal actually has some of the more happiest fans of any musical genres. There have been studies on this proving this to be true; often citing the catharsis that is achieved via the expression of negative emotion.
Question: What is a web browser?
Answer: A web browser is an application graphical user interface window that allows you to interact with content from the internet.  It is used for accessing websites and there are many web browsers available today like mozilla firefox, google chrome, microsoft edge, and safari.  When a webpage is requested by the web browser files and data are retrieved from the web server and  then rendered as a webpage through the web browser.
Question: Who is country singer Jordan Davis
Evidence: Jordan Carl Wheeler Davis (born March 30, 1988) is an American country pop singer and songwriter. He is signed to Universal Music Group Nashville's MCA Nashville division, for which he has released one album and two extended plays.

Jordan Carl Wheeler Davis was born in Shreveport, Louisiana, to mother Luwanna and father Ricky. He has a brother, Jacob Davis (who is also a country singer), and a sister, Jentry. His uncle, Stan Paul Davis, wrote Tracy Lawrence's hit singles "Today's Lonely Fool" and "Better Man, Better Off". He attended C. E. Byrd High School before graduating college with an Environmental Science degree from Louisiana State University.

After graduation, he moved to Nashville in 2012, and secured a recording contract with Universal Music Group Nashville in 2016.

Davis's debut single, "Singles You Up", came out in mid-2017. He wrote the song with Steven Dale Jones and Justin Ebach. It hit number one on the Billboard Country Airplay chart in April 2018. The corresponding debut album, Home State, was released on March 23, 2018. Paul DiGiovanni, guitarist for Boys Like Girls, produced the album. "Take It from Me" was released to country radio on May 7, 2018; it was the second single from the album. It reached number two on the Country Airplay chart in March 2019. The album's third single, "Slow Dance in a Parking Lot" released to country radio on April 22, 2019, and also topped Country Airplay.

In May 2020, Davis announced the release of his self-titled extended play, which was produced by Paul DiGiovanni. Davis said "I can't tell y'all how pumped I am for y'all to hear these songs. I'm proud of this one." The EP was preceded by the release of "Cool Anymore", "Detours", and "Almost Maybes". Davis is nominated for ACM Song of the Year and Single of the Year with "Buy Dirt" featuring Luke Bryan. They won Song of the Year. Davis co-wrote the single "Broken Umbrella" by Jojo Mason.
Answer: Jordan Davis is a country music singer, born in Shreveport, Louisiana on March 30, 1988.    He is married and has 2 children with his wife Kristen O'Conner.  They have 2 children.   Jordan Davis was nominated and won for ACM Song of the year and Single of the year with "Buy Dirt" featuring Luke Bryan.   In 2023 he released "Next Thing You Know" which is considered a huge hit.
Question: What food is served at a Mexican restaurant?
Answer: Mexican restaurants often serve a variety of dishes using similar set of basic ingrients, including: flour tortillas, corn tortillas, beans, rice, beef, chicken, cheese, peppers, and salsa.  Some of the most common Mexican dishes are burritos, enchiladas, fajitas, and nachos.  In the United States authentic Mexican cuisine is sometimes considered separate from the more common "Tex-Mex" variation.
Question: Classify each of these as team sports, individual sports, or neither: soccer, tennis, swimming, lacrosse, golf, checkers, potatoes.
Answer: Soccer is a team sport. It is played with 11 players on each side that must work together and cooperate to try to score goals against the opposing team. The team that scores more goals during regulation time wins the game. A typical score would be 2-0. Each player will hold a different position (offense, mid-field, defense, etc.) where they must work together to move the soccer ball up the field and score into the goal. The team must also defend against the other team scoring.

Tennis is usually and individual sport. Tennis is also played in pairs called doubles. A single person or a team of doubles will face off against another solo player or pair. They play games that add up to matches, and whoever wins 6 games in a match wins the match. Ties can lead to tiebreakers.

Swimming is usually a individual sport. Swimmers compete against other swimmers. The swimmer who gets the fastest time in a heat wins. There are also relay swim events where once one swimmer completes their task, another swimmer begins. The team of swimmers time is finished once all of the swimmers have completed their swims. The team with the lowest total time wins.

Lacrosse is a team sport. Similar to soccer, each player plays their own position and must work together to pass the ball and shoot and score. The team with the most points wins.

Golf is an individual sport usually. But often played in a foursome.

Checkers is not considered a sport. There is no real physical activity that is required in the game. But it is a solo game that one plays without a team.

Potatoes are not a sport or game. They are a vegetable.
Question: Given these paragraphs about the founding of Tesla, who else part from Elon Musk and J. B. Straubel are co-founders of Tesla?
Evidence: The company was incorporated as Tesla Motors, Inc. on July 1, 2003, by Martin Eberhard and Marc Tarpenning. Eberhard and Tarpenning served as CEO and CFO, respectively. Eberhard said he wanted to build "a car manufacturer that is also a technology company", with its core technologies as "the battery, the computer software, and the proprietary motor".

Ian Wright was Tesla's third employee, joining a few months later. In February 2004, the company raised $7.5 million in series A funding, including $6.5 million from Elon Musk, who had received $100 million from the sale of his interest in PayPal two years earlier. Musk became the chairman of the board of directors and the largest shareholder of Tesla. J. B. Straubel joined Tesla in May 2004 as chief technical officer.

A lawsuit settlement agreed to by Eberhard and Tesla in September 2009 allows all five – Eberhard, Tarpenning, Wright, Musk, and Straubel – to call themselves co-founders.
Answer: Eberhard, Tarpenning and Wright
Question: Can I see results of the method at least on CIFAR-10?
Evidence: On CIFAR-10’s preliminary results, the hard WTA and SoftHebb achieved an accuracy of 49.78% and 50.27% respectively.****Towards this, we implemented a first version of a convolutional
SoftHebb, with an added supervised classifier. In these early results, conv-SoftHebb achieved 98.63%
on MNIST, and 60.30% on CIFAR-10.
Answer: We now report the network’s accuracy on **CIFAR-10** (49.78%). In addition, **we have implemented a convolutional version** of SoftHebb, and our tests show that it is able to learn. It achieves in fact a much improved accuracy of 98.63% on MNIST and 60.30% on CIFAR-10.
Question: Which cities in China are self-governing cities? Beijing, Shanghai, Changsha, Shenzhen
Answer: The self-governing cities of the People's Republic of China are provincial-level administrative units. They are not provinces, but have the same rank as provinces and autonomous regions and report directly to the central government of the People's Republic of China.

There are four self-government cities in China. These are:

Beijing , the capital of China;
Tianjin , a port city 120 km south-east of Beijing;
Chongqing , by far the largest in terms of area - and thus also the largest in terms of population - of the four cities whose administrative area used to form the eastern seventh of Sichuan Province ;
Shanghai , the economic and financial metropolis at the mouth of the Yangtze River .

So only Beijing and Shanghai are the self-governing cities in your question.
Question: Where did the 1951 French legislative election take place
Evidence: Elections to the French National Assembly were held in French Somaliland on 17 June 1951 as part of the wider French parliamentary elections. Edmond Magendie was elected as the territory's MP, defeating the incumbent Jean-Carles Martine.
Answer: The elections to the French National Assembly were held in French Somaliland on 17 June 1951 as part of the wider French parliamentary elections. Edmond Magendie was elected as the territory's MP, defeating the incumbent Jean-Carles Martine.
Question: How can listening to music attentively influence you?
Answer: You'll likely become a better listener in general - not only in hearing new details in songs but being a better listener during conversations. Listening to music attentively will also make you more present in the moment and enjoy the flow instead of focusing on the future or the past too much.
Question: What is the difference between a sitar and a tanpura?
Answer: Both the sitar and tanpura are string music instruments.  The sitar has frets and is used for composing and playing complete melodies.  Sitar players are like guitar players who perform solo or as part of an ensemble band.  The tanpura on the other hand does not have any frets and is only an accompanying instrument.  Its strings provide a continued background rhythm to a music performance.   While the tanpura alone cannot be used for composing a melody, a music performance is incomplete without a tanpura.
Question: The Bacillus Calmette-Guerin (BCG) vaccine is administered to Indian children soon after birth to protect them against which disease?
Answer: The Bacillus Calmette-Guerin (BCG) vaccine is administered to Indian children soon after birth to protect them against Tuberculosis