DataHammer/scimrc huggingface dataset:
Question: What is Augusta National Golf Club?
Evidence: Augusta National Golf Club was founded by Bobby Jones and Clifford Roberts, the course was designed by Jones and Alister MacKenzie and opened for play in 1932. Since 1934, the club has played host to the annual Masters Tournament, one of the four men's major championships in professional golf, and the only major played each year at the same course. It was the top-ranked course in Golf Digest's 2009 list of America's 100 greatest courses and was the number ten-ranked course based on course architecture on Golfweek Magazine's 2011 list of best classic courses in the United States
Answer: Augusta National Golf Club, sometimes referred to as Augusta is a private Golf Club in Augusta Georgia that was opened in 1932.  Augusta is famously known for hosting the Masters Tournament which is one of the four men's major championships in professional golf, and the only major played each year at the same course.
Question: What problem does the experiment with using sub-word units with Transformer-XL cope with?
Evidence: Language modeling is a probabilistic description of language phenomenon. It provides essential context to distinguish words which sound similar and therefore has one of the most useful applications in Natural Language Processing (NLP) especially in downstreaming tasks like Automatic Speech Recognition (ASR). Recurrent Neural Networks (RNN) especially Long Short Term Memory (LSTM) networks BIBREF0 have been the typical solution to language modeling which do achieve strong results. In spite of these results, their fundamental sequential computation constraint has restricted their use in the modeling of long-term dependencies in sequential data. To address these issues Transformer architecture was introduced. Transformers relies completely on an attention mechanism to form global dependencies between input and output. It also offers more parallelization and has achieved SOTA results in language modeling outperforming LSTM models BIBREF1.
In recent years,we have seen a lot of development based on this standard transformer models particularly on unsupervised pre-training(BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7 which have set state-of-the art results on multiple NLP benchmarks. One such model architecture has been the Bidirectional Encoder Representations from Transformers (BERT) model which uses a deep bidirectional transformer architecture.
Another architecture of interest would be the Transformer-XL, which introduces the notion of recurrence in a self-attention model.
The primary research focus though has been mostly on English language for which abundant data is present. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English.
In this project, we explore the implementation of Transformer-based models (BERT and Transformer-XL) in language modeling for Finnish. We will use the same training data as in BIBREF8 so that we can do fair comparisons with the performance of the LSTM models. Also, as the BERT model is a bi-directional transformer, we will have to approximate the conditional probabilities given a sequence of words. We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language. With smaller units, the modeled sequences are longer, and we hope that the recursive XL architecture can allow us to still model long term effects. To the best of our knowledge this is the first work with the Finnish language to use the following:
Approximation of perplexity using a BERT architecture
Using Transformer-XL architecture with sub-word units.
Comparison of Transformer and LSTM models as language models in the same comparable settings with an agglutinative language.
We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language.
Answer: To cope with the large vocabulary problems associated with the Finnish Language.
Question: How old was Octavia Butler when she died?
Evidence: Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author and a multiple recipient of the Hugo and Nebula awards. In 1995, Butler became the first science-fiction writer to receive a MacArthur Fellowship. Born in Pasadena, California, Butler was raised by her widowed mother. Extremely shy as a child, Butler found an outlet at the library reading fantasy, and in writing. She began writing science fiction as a teenager. She attended community college during the Black Power movement. While participating in a local writer's workshop, she was encouraged to attend the Clarion Workshop, then held in Pennsylvania, which focused on science fiction.
Answer: At the time of her death in 2006, Octavia Butler was 58 years old.
Question: What is the state-of-the art?
Evidence: Very recently, the success of deep neural networks in many natural language processing tasks ( BIBREF20 ) has inspired new work in abstractive summarization . BIBREF2 propose a neural attention model with a convolutional encoder to solve this task. BIBREF3 build a large dataset for Chinese text summarization and propose to feed all hidden states from the encoder into the decoder. More recently, BIBREF4 extended BIBREF2 's work with an RNN decoder, and BIBREF8 proposed an RNN encoder-decoder architecture for summarization. Both techniques are currently the state-of-the-art on the DUC competition. However, the encoders exploited in these methods lack the ability to encode each word condition on the whole text, as an RNN encodes a word into a hidden vector by taking into account only the words up to that time step. In contrast, in this work we propose a `Read-Again' encoder-decoder architecture, which enables the encoder to understand each input word after reading the whole sentence. Our encoder first reads the text, and the results from the first read help represent the text in the second pass over the source text. Our second contribution is a simple copy mechanism that allows us to significantly reduce the decoder vocabulary size resulting in much faster inference times. Furthermore our copy mechanism allows us to handle out-of-vocabulary words in a principled manner. Finally our experiments show state-of-the-art performance on the DUC competition.
Very recently, the success of deep neural networks in many natural language processing tasks ( BIBREF20 ) has inspired new work in abstractive summarization . BIBREF2 propose a neural attention model with a convolutional encoder to solve this task. 
More recently, BIBREF4 extended BIBREF2 's work with an RNN decoder, and BIBREF8 proposed an RNN encoder-decoder architecture for summarization. Both techniques are currently the state-of-the-art on the DUC competition.
Answer: neural attention model with a convolutional encoder with an RNN decoder and RNN encoder-decoder
Question: What does IIT stand for in India?
Evidence: The Indian Institutes of Technology (IITs) are prestigious central government owned public technical institutes located across India. Known for their excellence in education, they are under the ownership of the Ministry of Education of the Government of India. They are governed by the Institutes of Technology Act, 1961, declaring them as Institutes of National Importance and laying down their powers, duties, and framework for governance as the country's premier institutions in the field of technology. The act currently lists twenty-three IITs. Each IIT has autonomy and is linked to others through a common council called the IIT Council, which oversees their administration. The Minister of Education of India is the ex officio Chairperson of the IIT Council. Currently, there are 23 IITs across the country.
Answer: IIT stands for Indian Institutes of Technology. They are government owned technical institutes known for their excellence in education. Currently, there are 23 institutes across India.
Question: What is XML?
Answer: Extensible Markup Language (XML) is a markup language and file format for storing, transmitting, and reconstructing arbitrary data. It defines a set of rules for encoding documents in a format that is both human-readable and machine-readable.
Question: How is CAC 40 selected?
Evidence: The CAC 40 index composition is reviewed quarterly by an independent Index Steering Committee (French: Conseil Scientifique). If any changes are made, they are effected a minimum of two weeks after the review meeting. At each review date, the companies listed on Euronext Paris are ranked according to free float market capitalization and share turnover over the prior 12 months. From the top 100 companies in this ranking, forty are chosen to enter the CAC 40 such that it is "a relevant benchmark for portfolio management" and "a suitable underlying asset for derivatives products". If a company has more than one class of shares traded on the exchange, only the most actively traded of these will be accepted into the index (generally this will be the ordinary share)
Answer: CAC 40 is selected from top 100 of the companies listed on Euronext Paris are benchmarked and selected by Conseil Scientifique. Considering the weighted average of stock prices over a certain period and some indicators, only the most actively traded of these will be accepted into the index.
Question: Where does this company produce its products?
Evidence: Sweet Tree Holdings is a Vermont, United States, company that produces maple products from a sugarbush and processing facility in Island Pond.
Answer: Island Pond, Vermont
Question: Given a reference text about a former President of India, Dr. A. P. J. Abdul Kalam, tell me when he was born, where he was raised and the subjects he specialised in studies. Why was Abdul Kalam known as the Missile Man of India?
Evidence: Avul Pakir Jainulabdeen Abdul Kalam BR (/ˈɑːbdəl kəˈlɑːm/ (listen); 15 October 1931 – 27 July 2015) was an Indian aerospace scientist and statesman who served as the 11th president of India from 2002 to 2007. He was born and raised in Rameswaram, Tamil Nadu and studied physics and aerospace engineering. He spent the next four decades as a scientist and science administrator, mainly at the Defence Research and Development Organisation (DRDO) and Indian Space Research Organisation (ISRO) and was intimately involved in India's civilian space programme and military missile development efforts. He thus came to be known as the Missile Man of India for his work on the development of ballistic missile and launch vehicle technology. He also played a pivotal organisational, technical, and political role in India's Pokhran-II nuclear tests in 1998, the first since the original nuclear test by India in 1974.

Kalam was elected as the 11th president of India in 2002 with the support of both the ruling Bharatiya Janata Party and the then-opposition Indian National Congress. Widely referred to as the "People's President", he returned to his civilian life of education, writing and public service after a single term. He was a recipient of several prestigious awards, including the Bharat Ratna, India's highest civilian honour.
Answer: Avul Pakir Jainulabdeen Abdul Kalam BR, referred to as APJ Abdul Kalam in short, was born on 15th October 1931. He was born and raised in Rameswaram in Tamil Nadu, South India. APJ Abdul Kalam specialised in Physics and Aerospace Engineering as part of his studies. 
He had a career of around 4 decades as a scientist as well as an administrator with the organisations such as Defence Research and Development Organisation (DRDO) and Indian Space Research Organisation (ISRO). In these roles, APJ Abdul Kalam played a key role in India's civilian space program and military missile development activities. He also played a vital role in the development of ballistic missile and launch vehicle technology. As a result of his above mentioned contributions, Abdul Kalam was known as the Missile Man of India.
Question: Who was Henry Ford?
Answer: Henry Ford (July 30, 1863 – April 7, 1947) was an American industrialist and business magnate. He was the founder of Ford Motor Company, and chief developer of the assembly line technique of mass production. Ford created the first automobile that middle-class Americans could afford, and his conversion of the automobile from an expensive luxury into an accessible conveyance profoundly impacted the landscape of the 20th century.

Ford was born on a farm in Michigan's Springwells Township to a Belgian family, leaving home at age 16 to work in Detroit. It was a few years before this time that Ford first experienced automobiles, and throughout the later half of the 1880s, Ford began repairing and later constructing engines, and through the 1890s worked with a division of Edison Electric. He officially founded Ford Motor Company in 1903, after prior failures in business but success in constructing automobiles.

Ford's 1908 introduction of the Model T automobile revolutionized both transportation and American industry. As the Ford Motor Company sole owner, he became one of the richest and best-known people in the world. He is credited with "Fordism", the mass production of inexpensive goods coupled with high wages for workers. Ford was also among the pioneers of the five-day work week. Ford had a global vision, with consumerism as the key to peace. His intense commitment to systematically lowering costs resulted in many technical and business innovations, including a franchise system that put dealerships throughout North America and major cities on six continents.

Ford was widely known for his pacifism during the first years of World War I. In the 1920s Ford promoted antisemitic content through his newspaper The Dearborn Independent, and the book, The International Jew. After his son Edsel died in 1943, Ford resumed control of the company but was too frail to make decisions and quickly came under the control of subordinates. He turned over the company to his grandson Henry Ford II in 1945. He died in 1947 after leaving most of his wealth to the Ford Foundation, and control of the company to his family.
Question: What is Prometheus?
Answer: Prometheus is an open-source system for monitoring services and alerts based on a time series data model. Prometheus collects data and metrics from different services and stores them according to a unique identifier—the metric name—and a time stamp.
Question: Classify the following foods as Mexican or Italian: Burrito, Pizza, Pasta, Quesadilla, Taco, Gelato
Answer: Burrito: Mexican
Pizza: Italian
Pasta: Italian
Quesadilla: Mexican
Taco: Mexican
Gelato: Italian
Question: What factors should I considering when selecting a tennis racquet?
Answer: There are a series of factors when considering to purchase a tennis racquets.  Primary factors include head size, length, weight, stiffness, and swingweight  

Head size - Power is closely correlated to head size - the larger the head size, the more power can be generated, all other factors being equal.  A larger head size also provides larger hitting area and 'sweetspot'.  Smaller head sizes tend to equate to more control and maneuverability. 

Length - The standard length is a 27 inches, but can range from 26.5 inches to 29 inches.  A longer racquets provides more reach and often results in more power.  Similar to head size, longer racquets may limit maneuverability and control. 

Weight - The heavier the racquet, the more power you will generate (increasing the 'swingweight') of the racquet.  The lighter the racquet, the more maneuverable and easier it will be for players to get the racket to the right swing position.  The balance of the racquet is also critical as part of weight.  A more head light racket (more weight in the handle) provides increased maneuverability, especially when using a heavier racquet. A more balanced or head heavy racket are often reserved for lighter racquets to maintain the mass behind the ball

Stiffness - Generally speaking, the stiffer the racket, the less energy is lost as the ball is redirected with your racquet and thus, creating more power.  More flexible racquets allow for more feel and control.  Flexible racquets provide more comfort as less shock and impact is transferred to the arm and elbow.

Swingweight - Swingweight measures how heavy a racquet feels when you swing it and the amount of power you can transfer to the ball.  Higher swingweight will also provide more stability in the racquet but will sacrifice maneuverability.
Question: What evaluation metrics did they use?
Evidence: Given the mapped embedding spaces, the translations are retrieved using a distance metric, with Cross-Lingual Similarity Scaling BIBREF12 as the most common and best performing in the literature. Intuitively, CSLS decreases the scores of pairs that lie in dense areas, increasing the scores of rarer words (which are harder to align). The retrieved pairs are compared to the gold standard and evaluated using precision at $k$ (P@$k$, evaluating how often the correct translation is within the $k$ retrieved nearest neighbours of the query). Throughout this work we report P@1, which is equivalent to accuracy, but we also provide results with P@5 and P@10 in the Appendix.
The retrieved pairs are compared to the gold standard and evaluated using precision at $k$ (P@$k$, evaluating how often the correct translation is within the $k$ retrieved nearest neighbours of the query). Throughout this work we report P@1, which is equivalent to accuracy, but we also provide results with P@5 and P@10 in the Appendix.
Answer: The answers are shown as follows:
* we report P@1, which is equivalent to accuracy
* we also provide results with P@5 and P@10 in the Appendix
Question: What does the term "tulip mania" mean?
Evidence: Tulip mania (Dutch: tulpenmanie) was a period during the Dutch Golden Age when contract prices for some bulbs of the recently introduced and fashionable tulip reached extraordinarily high levels. The major acceleration started in 1634 and then dramatically collapsed in February 1637. It is generally considered to have been the first recorded speculative bubble or asset bubble in history. In many ways, the tulip mania was more of a then-unknown socio-economic phenomenon than a significant economic crisis. It had no critical influence on the prosperity of the Dutch Republic, which was one of the world's leading economic and financial powers in the 17th century, with the highest per capita income in the world from about 1600 to about 1720. The term "tulip mania" is now often used metaphorically to refer to any large economic bubble when asset prices deviate from intrinsic values.
Answer: The term "tulip mania" is now often used metaphorically to refer to any large economic bubble when asset prices deviate from intrinsic values.
Question: What large corpus is used for experiments?
Evidence: The goal of this part is to highlight the differences present in the excitation when a given speaker produces different voice qualities. The De7 database used for this study was designed by Marc Schroeder as one of the first attempts of creating diphone databases for expressive speech synthesis BIBREF2. The database contains three voice qualities (modal, soft and loud) uttered by a German female speaker, with about 50 minutes of speech available for each voice quality. In Section SECREF1, the glottal flow estimation method and glottal flow parametrization used in this work are briefly presented. The harmonicity of speech is studied via the maximum voiced frequency in Section SECREF3. As an important perceptual charactersitic, spectral tilt is analyzed in Section SECREF4. Section SECREF6 compares the so-called eigenresiduals BIBREF3 of the different voice qualities. Finally Section SECREF8 quantifies the separability between the three voice qualities for the extracted excitation features.
The De7 database used for this study was designed by Marc Schroeder as one of the first attempts of creating diphone databases for expressive speech synthesis BIBREF2. The database contains three voice qualities (modal, soft and loud) uttered by a German female speaker, with about 50 minutes of speech available for each voice quality.
Answer: The answers are shown as follows:
* The De7 database
Question: Which GAN do they use?
Evidence: Suppose we have a number of different corpora INLINEFORM0 , which for example can be based on different categories or sentiments of text documents. We suppose that INLINEFORM1 , INLINEFORM2 , where each INLINEFORM3 represents a document. The words in all corpora are collected in a dictionary, and indexed from 1 to INLINEFORM4 . We name the GAN model to train cross-corpus word embeddings as “weGAN,” where “we” stands for “word embeddings,” and the GAN model to generate document embeddings for multiple corpora as “deGAN,” where “de” stands for “document embeddings.”
We name the GAN model to train cross-corpus word embeddings as “weGAN,” where “we” stands for “word embeddings,” and the GAN model to generate document embeddings for multiple corpora as “deGAN,” where “de” stands for “document embeddings.”
Answer: The answers are shown as follows:
* weGAN
* deGAN
Question: What is the name of the first ten amendments to the Constitution?
Answer: the bill of rights
Question: How is the expectation regularization loss defined?
Evidence: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0
Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0
Answer: The answers are shown as follows:
* DISPLAYFORM0
Question: From the given text, list some of the accomplishments of pitcher Mariano Rivera in his baseball career, in a comma-separated list.
Evidence: Mariano Rivera (born November 29, 1969) is a Panamanian-American former professional baseball pitcher who played 19 seasons in Major League Baseball (MLB) for the New York Yankees, from 1995 to 2013. Nicknamed "Mo" and "Sandman", he spent most of his career as a relief pitcher and served as the Yankees' closer for 17 seasons. A thirteen-time All-Star and five-time World Series champion, he is MLB's career leader in saves (652) and games finished (952). Rivera won five American League (AL) Rolaids Relief Man Awards and three Delivery Man of the Year Awards, and he finished in the top three in voting for the AL Cy Young Award four times. He was inducted into the Baseball Hall of Fame as part of its class of 2019 in his first year of eligibility, and was the first and to date only player ever to be elected unanimously by the Baseball Writers' Association of America (BBWAA).

Raised in the modest Panamanian fishing village of Puerto Caimito, Rivera was an amateur player until he was signed by the Yankees organization in 1990. He debuted in the major leagues in 1995 as a starting pitcher, before permanently converting to a relief pitcher late in his rookie year. After a breakthrough season in 1996 as a setup man, he became the Yankees' closer in 1997. In the following seasons, he established himself as one of baseball's top relievers, leading the major leagues in saves in 1999, 2001, and 2004. Rivera primarily threw a sharp-moving, mid-90s mile-per-hour cut fastball that frequently broke hitters' bats and earned a reputation as one of the league's toughest pitches to hit. With his presence at the end of games, signaled by his foreboding entrance song "Enter Sandman", Rivera was a key contributor to the Yankees' success in the late 1990s and early 2000s. An accomplished postseason performer, he was named the 1999 World Series Most Valuable Player (MVP) and the 2003 AL Championship Series MVP, and he holds several postseason records, including lowest earned run average (ERA) (0.70) and most saves (42).
Answer: All-Star, World Series champion, MLB's career leader in saves, MLB's career leader in games finished, AL Rolaids Relief Man Award, Delivery Man of the Year Award, Baseball Hall of Fame, World Series Most Valuable Player, AL Championship Series MVP, postseason lowest earned run average, postseason most saves