DataHammer/scimrc huggingface dataset:
Question: Write the synopsis for a spy thriller book.  The story should a team of experts in many various domains of science and technology, all working together to solve a potential global crisis.  Leave the synopsis with a cliff-hanger question.
Answer: "The Shadow Experts" tells a story that brings together the world of spying and a group of experts whose goal it is to help "just causes." The heroine, a very secretive individual with a European aristocratic cover, founded a network of specialists whose expertise encompasses a vast swath of current scientific knowledge. It ranges from highly technical fields such as nuclear physics or bio-medical research to others, more qualitative, such as psychology, art and criminology. Each of the members of the network is sworn to secrecy and only rarely meets either the heroine or other members. This network is known as "The Shadow Experts." They all have their own known and public activities, allowing them to stay at the leading edge of their specialty.
Here, The Shadow Experts are hired by the Mossad to help trace a deadly virus which seems to have hit a few geographical pockets with, on the surface, no apparent relationship with one another.
Using novel and at times even experimental equipment, the heroine, with the help of colleagues from "The Shadow Experts" and Mossad agents, works to find the source of the virus, the reasons for its introduction in selective geographies and the best approach to contain its spread. The hunt for the source takes her and members of her informal team around the world. In the process, she discovers a parallel network of criminals linked to a large and dangerous organization and at least one, possibly two countries and their secret services. Will the project prevent the catastrophe that is being planned?
Question: What are the experiment scenarios to evaluate the performances of their model?
Evidence: As Table TABREF35 shows, we select five benchmark datasets for text classification and design three experiment scenarios to evaluate the performances of our model.
Multi-Cardinality Movie review datasets with different average lengths and class numbers, including SST-1 BIBREF14 , SST-2 and IMDB BIBREF15 .
Multi-Domain Product review datasets on different domains from Multi-Domain Sentiment Dataset BIBREF16 , including Books, DVDs, Electronics and Kitchen.
Multi-Objective Classification datasets with different objectives, including IMDB, RN BIBREF17 and QC BIBREF18 .
As Table 1 shows, we select five benchmark datasets for text classification and design three experiment scenarios to evaluate the performances of our model. • Multi-Cardinality Movie review datasets with different average lengths and class numbers, including SST1 [Socher et al., 2013], SST-2 and IMDB [Maas et al., 2011]. • Multi-Domain Product review datasets on different domains from Multi-Domain Sentiment Dataset [Blitzer et al., 2007], including Books, DVDs, Electronics and Kitchen. • Multi-Objective Classification datasets with different objectives, including IMDB, RN [Apte´ et al., 1994] and QC [Li and Roth, 2002].
Answer: • Multi-Cardinality Movie review datasets with different average lengths and class numbers. • Multi-Domain Product review datasets on different domains from Multi-Domain Sentiment Dataset. • Multi-Objective Classification datasets with different objectives.
Question: What architecture did Nematus base on?
Evidence: Neural Machine Translation (NMT) BIBREF0 , BIBREF1 has recently established itself as a new state-of-the art in machine translation. We present Nematus, a new toolkit for Neural Machine Translation.
Nematus has its roots in the dl4mt-tutorial. We found the codebase of the tutorial to be compact, simple and easy to extend, while also producing high translation quality. These characteristics make it a good starting point for research in NMT. Nematus has been extended to include new functionality based on recent research, and has been used to build top-performing systems to last year's shared translation tasks at WMT BIBREF2 and IWSLT BIBREF3 .
Nematus is implemented in Python, and based on the Theano framework BIBREF4 . It implements an attentional encoder–decoder architecture similar to DBLP:journals/corr/BahdanauCB14. Our neural network architecture differs in some aspect from theirs, and we will discuss differences in more detail. We will also describe additional functionality, aimed to enhance usability and performance, which has been implemented in Nematus.
Nematus is implemented in Python, and based on the Theano framework .
Answer: The Theano framework.
Question: Tell me whether each state touches the ocean or not: Florida, North Carolina, Nebraska, Oklahoma, Oregon, Texas
Answer: Florida: Touches The Ocean 
North Carolina: Touches The Ocean
Nebraska: Does Not Touch The Ocean
Oklahoma: Does Not Touch The Ocean
Oregon: Touches The Ocean
Texas: Touches The Ocean
Question: What baselines is the proposed model compared against?
Evidence: Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.
FLOAT SELECTED: TABLE III COMPARATIVE RESULTS BETWEEN BERT AND OUR PROPOSED MODEL
Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. 
FLOAT SELECTED: TABLE III COMPARATIVE RESULTS BETWEEN BERT AND OUR PROPOSED MODEL
Answer: The answers are shown as follows:
* QANet BIBREF39
* BERT-Base BIBREF26
Question: What are the words of House Targaryen?
Answer: "Fire and Blood"
Question: What are the characteristics of these 18 techniques?
Evidence: Propaganda uses psychological and rhetorical techniques to achieve its objective. Such techniques include the use of logical fallacies and appeal to emotions. For the shared task, we use 18 techniques that can be found in news articles and can be judged intrinsically, without the need to retrieve supporting information from external resources. We refer the reader to BIBREF10 for more details on the propaganda techniques; below we report the list of techniques:
For the shared task, we use 18 techniques that can be found in news articles and can be judged intrinsically, without the need to retrieve supporting information from external resources.
Answer: Can be found in news articles and can be judged intrinsically, without the need to retrieve supporting information from external resources.
Question: What would the fantasy city look like
Answer: An ideal city is a city by the water, full of bike lanes, art galleries, art workshops, coffee shops, wine bars and outdoor courtyards where people linger for hours as the times stays still. An ideal city has no notion of time other than the sun going up and going down.
Question: what was their system's f1 performance?
Evidence: FLOAT SELECTED: Table 2: F1 Results3
The approach we have developed establishes a new state of the art for classifying hate speech, outperforming previous results by as much as 12 F1 points. Table TABREF10 illustrates the robustness of our method, which often outperform previous results, measured by weighted F1.
FLOAT SELECTED: Table 2: F1 Results3
Table TABREF10 illustrates the robustness of our method, which often outperform previous results, measured by weighted F1.
Answer: Proposed model achieves 0.86, 0.924, 0.71 F1 score on SR, HATE, HAR datasets respectively.
Question: Give me a summary about Michael Jackson
Evidence: Michael Joseph Jackson (August 29, 1958 – June 25, 2009) was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is regarded as one of the most significant cultural figures of the 20th century. Over a four-decade career, his contributions to music, dance, and fashion, along with his publicized personal life, made him a global figure in popular culture. Jackson influenced artists across many music genres; through stage and video performances, he popularized complicated dance moves such as the moonwalk, to which he gave the name, as well as the robot.

The eighth child of the Jackson family, Jackson made his public debut in 1964 with his older brothers Jackie, Tito, Jermaine, and Marlon as a member of the Jackson 5 (later known as the Jacksons). Jackson began his solo career in 1971 while at Motown Records. He became a solo star with his 1979 album Off the Wall. His music videos, including those for "Beat It", "Billie Jean", and "Thriller" from his 1982 album Thriller, are credited with breaking racial barriers and transforming the medium into an artform and promotional tool. He helped propel the success of MTV and continued to innovate with videos for the albums Bad (1987), Dangerous (1991), HIStory: Past, Present and Future, Book I (1995), and Invincible (2001). Thriller became the best-selling album of all time, while Bad was the first album to produce five US Billboard Hot 100 number-one singles.[nb 1]
Answer: Michael Joseph Jackson (August 29, 1958 – June 25, 2009) was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is regarded as one of the most significant cultural figures of the 20th century. Over a four-decade career, his contributions to music, dance, and fashion, along with his publicized personal life, made him a global figure in popular culture. Jackson influenced artists across many music genres; through stage and video performances, he popularized complicated dance moves such as the moonwalk, to which he gave the name, as well as the robot.

The eighth child of the Jackson family, Jackson made his public debut in 1964 with his older brothers Jackie, Tito, Jermaine, and Marlon as a member of the Jackson 5 (later known as the Jacksons). Jackson began his solo career in 1971 while at Motown Records. He became a solo star with his 1979 album Off the Wall. His music videos, including those for "Beat It", "Billie Jean", and "Thriller" from his 1982 album Thriller, are credited with breaking racial barriers and transforming the medium into an artform and promotional tool. He helped propel the success of MTV and continued to innovate with videos for the albums Bad (1987), Dangerous (1991), HIStory: Past, Present and Future, Book I (1995), and Invincible (2001). Thriller became the best-selling album of all time, while Bad was the first album to produce five US Billboard Hot 100 number-one singles.
Question: Who is the lead singer of Korn?
Answer: Jonathan Houseman Davis is the lead singer of nu-metal band, Korn.
Question: What is on the DVD Our Favorite Things
Evidence: Our Favorite Things is a compilation DVD by the band Negativland, released on November 27, 2007. The original release date on October 23 was skipped due to a pressing error in the DVDs and the bonus CD, a collection of Negativland covers done by an a cappella doo-wop group. It contains 20 of the band's greatest "hits" animated by 20 different artists from all over the United States. The cover parodies the artwork of the film The Sound of Music as well as title of the film's song "My Favorite Things". The front features a "review" from the character Goofy saying, "It's goofy". The people on the cover are dolls.
Answer: Our Favorite Things is a compilation DVD by the band Negativland, released on November 27, 2007. The original release date on October 23 was skipped due to a pressing error in the DVDs and the bonus CD, a collection of Negativland covers done by an a cappella doo-wop group. 

It contains 20 of the band's greatest "hits" animated by 20 different artists from all over the United States. The cover parodies the artwork of the film The Sound of Music as well as title of the film's song "My Favorite Things". The front features a "review" from the character Goofy saying, "It's goofy". The people on the cover are dolls.
Question: How do airplanes land?
Answer: The pilot reduces engine power which lowers thrust. This reduces the lift and the plane descends.
Question: What is a digital twin?
Answer: A digital twin is a virtual representation of an object, product, piece of equipment, person, process, or even a complete manufacturing ecosystem. It is created using data derived from sensors (often IoT or IIoT) that are attached to or embedded in the original object. This data allows for both structural and operational views of what happens to the object in real-time, allowing engineers to monitor systems and model systems dynamics. Adjustments can be made to the digital twin to assess the impact of real-world changes before making any changes to the original system.

For a discrete or process manufacturing, a digital twin gathers process and system data with the help of various IoT sensors (both IT and OT) to form a virtual model which is then used to run simulations, study performance issues, and generate new insights.
Question: Where does the name of the UK county 'Essex' originate from?
Evidence: The name Essex originates in the Anglo-Saxon period of the Early Middle Ages and has its root in the Anglo-Saxon (Old English) name Ēastseaxe ("East Saxons"), the eastern kingdom of the Saxons who had come from the continent and settled in Britain. Excavations at Mucking have demonstrated the presence of Anglo-Saxon settlers in the early fifth century, however the way in which these settlers became ascendent in the territory of the Trinovantes is not known. Studies suggest a pattern of typically peaceful co-existence, with the structure of the Romano-British landscape being maintained, and with the Saxon settlers believed to have been in the minority.
Answer: The name Essex originates in the Anglo-Saxon period of the Early Middle Ages and has its root in the Anglo-Saxon (Old English) name Ēastseaxe ("East Saxons").
Question: What singer has had a Billboard No. 1 hit in each of the last four decades?
Answer: Mariah Carey
Question: Write a short poem about the relevance of AI in the workplace in the style of Edgar Allan Poe's The Raven.
Answer: Once upon a work night late, as I laboured, pondered, flailed,
Over such an email that management might adore —
While I nodded, nearly napping, suddenly there came a tapping,
As of something gently rapping, rapping in my browser's core.
"'Tis the little AI helper, tapping in my browsers core—
            Volunteering to do my chore."
Question: Which word embeddings do they use to represent medical visits?
Evidence: Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. We compute two separate embeddings, because we want to catch the similarity between terms in their specific context, i.e. words similar in the interview may not be similar in the examination description (for example we computed that the nearest words to cough in interview descriptions was runny nose, sore throat, fever, dry cough but in examination description it was rash, sunny, laryngeal, dry cough).
The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.
Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 .
Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately.
The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.

Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 .
Answer: The answers are shown as follows:
* GloVe
* concatenation of average embeddings calculated separately for the interview and for the medical examination
Question: In the series A Song of Ice and Fire, who is the founder of House Bulwer?
Answer: Bors the Breaker
Question: How big is dataset for this challenge?
Evidence: We use the VisDial v1.0 BIBREF0 dataset to train our models, where one example has an image with its caption, 9 question-answer pairs, and follow-up questions and candidate answers for each round. At round $r$, the caption and the previous question-answer pairs become conversational context. The whole dataset is split into 123,287/2,000/8,000 images for train/validation/test, respectively. Unlike the images in the train and validation sets, the images in the test set have only one follow-up question and candidate answers and their corresponding conversational context.
We use the VisDial v1.0 BIBREF0 dataset to train our models, where one example has an image with its caption, 9 question-answer pairs, and follow-up questions and candidate answers for each round. At round $r$, the caption and the previous question-answer pairs become conversational context. The whole dataset is split into 123,287/2,000/8,000 images for train/validation/test, respectively. Unlike the images in the train and validation sets, the images in the test set have only one follow-up question and candidate answers and their corresponding conversational context.
Answer: 133,287 images
Question: What is a byte?
Evidence: The byte is a unit of digital information that most commonly consists of eight bits. Historically, the byte was the number of bits used to encode a single character of text in a computer and for this reason it is the smallest addressable unit of memory in many computer architectures. To disambiguate arbitrarily sized bytes from the common 8-bit definition, network protocol documents such as the Internet Protocol (RFC 791) refer to an 8-bit byte as an octet. Those bits in an octet are usually counted with numbering from 0 to 7 or 7 to 0 depending on the bit endianness. The first bit is number 0, making the eighth bit number 7.
Answer: A byte is a unit of digital information consisting of eight bits (typically). Historically, a byte was the number of bits needed to encode a single character of text in a computer. Because of this, a byte is the smallest unit of. memory in many computer architectures.
Question: What did the baseline use?
Evidence: While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.
After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.
Answer: The baseline used keywords and word unigrams before.
Question: Correct the typos in this sentence
Evidence: Robert Ralph Furman is brn on August 21, 1915, in Trenton, New Jersey, on of five son of William and Leila Ficht Furman.
Answer: Robert Ralph Furman was born on August 21, 1915, in Trenton, New Jersey, one of five sons of William and Leila Ficht Furman.
Question: What are the most important figures in the passage provided about Singapore? Please provide in bullet points.
Evidence: Singapore (/ˈsɪŋ(ɡ)əpɔːr/), officially the Republic of Singapore, is an island country and city-state in maritime Southeast Asia. It lies about one degree of latitude (137 kilometres or 85 miles) north of the equator, off the southern tip of the Malay Peninsula, bordering the Strait of Malacca to the west, the Singapore Strait to the south, the South China Sea to the east, and the Straits of Johor to the north. The country's territory is composed of one main island, 63 satellite islands and islets, and one outlying islet; the combined area of these has increased by 25% since the country's independence as a result of extensive land reclamation projects. It has the third highest population density in the world. With a multicultural population and recognising the need to respect cultural identities of the major ethnic groups within the nation, Singapore has four official languages: English, Malay, Mandarin, and Tamil. English is the lingua franca and numerous public services are available only in English. Multi-racialism is enshrined in the constitution and continues to shape national policies in education, housing, and politics.
Answer: - Geographic position to equator: 137 kilometres or 85 miles north of equator
- Singapore has 1 main island, 63 smaller islands and 1 outlying islet
- Area has increased of 25% since country independence
- Has the 3rd densest population in the world
- Singapore has 4 official languages: English, Malay, Mandarin, and Tamil
Question: What is the most popular sport in Brazil?
Answer: Football is the most popular sport in Brazil. It is a widespread sport practiced in schools, streets and clubs by millions of people. It is considered a national passion in the country.