DataHammer/scimrc huggingface dataset:
Question: Are there any baseline models?
Evidence:  This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified.
While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:
In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing.
Answer: No.
Question: What languages does the corpus cover?
Evidence: The Reuters Corpus Volume 2 BIBREF2 , in short RCV2, is a multilingual corpus with a collection of 487,000 news stories. Each news story was manually classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). Topic codes were assigned to capture the major subject of the news story. The entire corpus covers thirteen languages, i.e. Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish, written by local reporters in each language. The news stories are not parallel. Single-label stories, i.e. those labeled with only one topic out of the four top categories, are often used for evaluations. However, the class distributions vary significantly across all the thirteen languages (see Table 1 ). Therefore, using random samples to extract evaluation corpora may lead to very imbalanced test sets, i.e. undesired and misleading variability among the languages when the main focus is to evaluate cross-lingual transfer.
The entire corpus covers thirteen languages, i.e. Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish, written by local reporters in each language.
Answer: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish
Question: Which of the following films came out in the 40s?
Evidence: La buenaventura (1934)
Grand Canary (1934)
Tripping Through the Tropics (1934)
Tres Amores (1934)
El cantante de Napoles (1935)
Julieta Buys a Son (1935)
Rosa de Francia (1935)
El crimen de media noche (1936)
El capitan Tormenta (1936)
Song of the Gringo (1936)
Fiesta (1941)
The Face of Marble (1946)
Gilda (1946)
Two Years Before the Mast (1946)
Secret Beyond the Door... (1947)
Secret of the Incas (1954)
The Rose Tattoo (1955)
The Bottom of the Bottle (1956)
Lawman (1959 episode "The Outsider") as Mrs. Lebeau
Answer: Fiesta
The Face of Marble
Gilda
Two Years Before the Mast
Secret Beyond the Door...
Question: What is the Family of the orchid Bulbophyllum concatenatum?
Evidence: Bulbophyllum concatenatum is a species of orchid in the genus Bulbophyllum. 

Kingdom: 	Plantae
Clade: 	Tracheophytes
Clade: 	Angiosperms
Clade: 	Monocots
Order: 	Asparagales
Family: 	Orchidaceae
Subfamily: 	Epidendroideae
Tribe: 	Dendrobieae
Genus: 	Bulbophyllum
Species: 	B. concatenatum
Answer: Orchidaceae
Question: What idea do they develop in their method?
Evidence: In our approach we develop the idea of BIBREF16 that proposed to construct similarity sets between ngram phrases between each other and single words. Phrases and words are included in the same similarity set if they have the same component word, for example, weapon – nuclear weapon – weapon of mass destruction; discrimination – racial discrimination. It was supposed that if expressions from the same similarity set co-occur in the same document then their contribution into the document's topics is really more than it is presented with their frequencies, therefore their frequencies should be increased. In such an approach, the algorithm can "see" similarities between different multiword expressions with the same component word.
In our approach, at first, we include related single words and phrases from a thesaurus such as WordNet or EuroVoc in these similarity sets. Then, we add preliminarily extracted ngrams into these sets and, this way, we use two different sources of external knowledge. We use the same LDA-SIM algorithm as described in BIBREF16 but study what types of semantic relations can be introduced into such similarity sets and be useful for improving topic models. The pseudocode of LDA-SIM algorithm is presented in Algorithm SECREF3 , where INLINEFORM0 is a similarity set, expressions in similarity sets can comprise single words, thesaurus phrases or generated noun compounds.
We can compare this approach with the approaches applying the generalized Polya urn model BIBREF8 , BIBREF9 , BIBREF10 . To add prior knowledge, those approaches change topic distributions for related words globally in the collection. We modify topic probabilities for related words and phrases locally, in specific texts, only when related words (phrases) co-occur in these texts.
[ht!] collection INLINEFORM0 , vocabulary INLINEFORM1 , number of topics INLINEFORM2 , initial INLINEFORM3 and INLINEFORM4 , sets of similar expressions INLINEFORM5 , hyperparameters INLINEFORM6 and INLINEFORM7 , INLINEFORM8 is the frequency of INLINEFORM9 in the document INLINEFORM10 distributions INLINEFORM11 and INLINEFORM12 not meet the stop criterion INLINEFORM13 INLINEFORM14 
 INLINEFORM0 INLINEFORM1 
 INLINEFORM0 
 INLINEFORM0 
 LDA-SIM algorithm
In our approach we develop the idea of [17] that proposed to construct similarity sets between ngram phrases between each other and single words.
Answer: To construct similarity sets between ngram phrases between each other and single words.
Question: What language is easy to learn?
Answer: English is usually considered as an easier language to learn than Chinese, Russian, Arabic and so on. The grammar has less exceptions, and the pronunciations are easy for people from different places in the world to pronounce. 

Also, since English is widely used in today's world, you may naturally find more TV programs, commercials, films, classes etc. that could help you quickly learn English as well.
Question: Write a concise and somewhat funny email about making a betting pool for the 2023 Masters golf tournament
Answer: From: Me
To: Friend
Subject: 2023 Masters Betting Pool

Hi Closest Friends,

April has arrived, the Easter Bunny is coming and although the azaleas have all died from blooming too early and then freezing, the Masters will go on! 

It should be an eventful year with Tiger (aka the GOAT) making his return, Koepka coming off a huge win on the LIV tour, the incredible golf program that is University of Florida (aka Billy Horschel) looking for their first Masters champion, and many more. We plan on having the 101st annual Master's draft on Tuesday around 10PM EST.  

See you there. Cheers!

- Me
Question: What is the only supervision during training?
Evidence: Neural language models BIBREF0 , BIBREF1 , BIBREF2 have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning. They have also become a common benchmarking application in machine learning research on recurrent neural networks (RNN), because producing an accurate probabilistic model of human language is a very challenging task which requires all levels of linguistic analysis, from pragmatics to phonology, to be taken into account.
A typical language model is trained on text in a single language, and if one needs to model multiple languages the standard solution is to train a separate model for each language. This presupposes large quantities of monolingual data in each of the languages that needs to be covered and each model with its parameters is completely independent of any of the other models.
We propose instead to use a single model with real-valued vectors to indicate the language used, and to train this model with a large number of languages. We thus get a language model whose predictive distribution INLINEFORM0 is a continuous function of the language vector INLINEFORM1 , a property that is trivially extended to other neural NLP models. In this paper, we explore the “language space” containing these vectors, and in particular explore what happens when we move beyond the points representing the languages of the training corpus.
The motivation of combining languages into one single model is at least two-fold: First of all, languages are related and share many features and properties, a fact that is ignored when using independent models. The second motivation is data sparseness, an issue that heavily influences the reliability of data-driven models. Resources are scarce for most languages in the world (and also for most domains in otherwise well-supported languages), which makes it hard to train reasonable parameters. By combining data from many languages, we hope to mitigate this issue.
In contrast to related work, we focus on massively multilingual data sets to cover for the first time a substantial amount of the linguistic diversity in the world in a project related to data-driven language modeling. We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding. From that and the actual training examples, the system learns dense vector representations for each language included in our data set along with the character-level RNN parameters of the language model itself.
We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding.
Answer: A language identifier as a one-hot encoding.
Question: Who is the greatest musical theatre composer of all time?
Answer: While art is subjective, there are a few luminary composers who have made significant contributions to musical theatre and whose work has stood the test of time. One could make a case for any of the following. Jerome Kern wrote more than 700 songs and wrote the music to "Showboat", one of the first Broadway musicals with a serious plot and themes. The songwriting team of Rogers & Hammerstein was the first to use songs to further the plot vs. break the plot for a song and dance. Their musicals are still enormously popular and performed constantly. They include "Oklahoma", "The King & I", and "The Sound of Music". Leonard Bernstein, while most known for classical music made a major contribution to musicals with the groundbreaking "West Side Story", an updated Romeo and Juliet story with spectacular music, every song a classic. Bernstein collaborated with Stephen Sondheim on this musical. Sondheim ushered in the modern-day musical in which shows might explore a theme vs. a linear plot. While he is often criticized for not having "hummable" melodies, his genius is undeniable, and he did have one bonafide hit with "Send in the Clowns". Jules Styne composed what is often considered the greatest musical of all time, "Gypsy". The dynamite songwriting team of John Kander and Fred Ebb composed the longest-running American musical, "Chicago" and the dark yet entertaining "Cabaret". Finally, while often criticized by musical-theatre purists as blatantly commercial, the success of Andrew Lloyd Webber cannot be denied. He composed the enormously successful "Cats", the longest-running Broadway musical of all time "Phantom of the Opera" and even into his 80's is still composing, with the recently opened "Bad Cinderella" on Broadway.
Question: Missy Elliott references what infamous celebrity couple in "Let Me Fix My Weave?"
Answer: Ben Affleck and Jennifer Lopez
Question: Give me a list of golf ball options to use this weekend.
Answer: -Bridgestone Tour B X
-Titleist Pro V1
-Bridgestone Tour B XS
-Titleist Pro V1x
-Bridgestone Tour B RX
-Titleist Pro V1x Left Dash
-Bridgestone Tour B RXS
-Titleist AVX
-Bridgestone e12 Contact
-Callaway Chrome Soft
-Bridgestone e12 Long Drive
-Callaway Chrome Soft X
-Callaway Supersoft
Question: What is an "outie" in the context of this paragraph?
Evidence: Mark's innie awakens in Devon's home and finds himself hugging Cobel. While excusing himself to find Devon, he calls Cobel by name, alerting her that the overtime contingency has been activated. Cobel calls Milchick and has him check the security office. Mark privately reveals to Devon that he is in innie form; Devon tells him of Gemma's death, and learns that "Mrs. Selvig" is Mark's boss. Mark encourages Devon to report Lumon's misdeeds to the press as Lumon likely controls the police. Irving wakes up in his apartment, discovering his outie's paintings and background in the U.S. Navy, and finds a map and employee directory in the closet which he uses to locate Burt. Helly wakes up at a Lumon gala where she learns that her outie is Helena Eagan – daughter of Lumon CEO Jame Eagan – who underwent severance to build public support for legalizing the procedure. Cobel races to the gala and attempts to stop Helly from making a scheduled speech. Milchick reaches the security office and cuts his way through the makeshift restraints Dylan has placed on the door. Helly gets onstage and tells the crowd of the innies' subjugation and torment. Irving arrives at Burt's house only to find he is already in a relationship. Mark finds a photo confirming Ms. Casey to be Gemma. He rushes to tell Devon, but is only able to say "She's alive!" before Milchick tackles Dylan, deactivating the overtime contingency and reverting the three to their outie state.
Answer: Based on the provided paragraph, it's not clear what is an "outie". However, based on this sentence, "He rushes to tell Devon, but is only able to say "She's alive!" before Milchick tackles Dylan, deactivating the overtime contingency and reverting the three to their outie state.", it seems like an outie is a state that a person can be in and that the state can be switched on by deactivating the overtime contingency.
Question: Classify each instrument as a woodwind, string, brass, or percussion: trumpet, violin, oboe, french horn, bassoon, cymbal, cello, clarinet, trombone, triangle, piccolo, flute, harp, tuba, snare drum
Answer: A violin, cello, and harp are classified as a string instrument. An oboe, bassoon, clarinet, piccolo, and flute are classified as a woodwind instrument. A trumpet, french horn, trombone, and tuba are classified as a brass instrument. A triangle, cymbal, and snare drum are classified as a percussion instrument.
Question: Give me a bulleted list of the 5 oldest countries in the world:
Answer: Here are the 5 oldest countries in the world:

- Egypt
- Greece
- Hungary
- France
- Japan
Question: What are some of the highest-rated episodes of The X-Files, based on viewer's ratings?
Answer: According to IMDB, users on the site have ranked the following as the top ten episodes from The X-Files: Bad Blood, Clyde Bruckman's Final Repose, Anasazi, Dreamland, Musings of a Cigarette Smoking Man, Paper Clip, Redux II, The Erlenmeyer Flask, Jose Chung's From Outer Space, and Small Potatoes.
Question: Give me some movie/tv series to learn English
Answer: There are several tv series that can be used to learn English. 

1. "Friends" is an American television sitcom that lasts ten seasons from Sep 22, 1994 to May 6, 2004. The show revolves around six friends in their 20s and 30s who live in Manhattan, New York City. The language used in Friends is casual and not challenging to understand. Watching Friends to learn English will help you to build up your vocabulary. 

2. "The Big Bang Theory" is an American television sitcom about two physicists at Caltech who share an apartment and their friends. The show has some challenging English word that requires some domain knowledge to understand. This show is recommended to people who have an intermediate English level.
Question: What is the difference between guitar and ukulele?
Answer: Guitar and ukulele are similar instruments. But there are many differences between them:
- The biggest difference is that a ukulele is much smaller than a guitar.
- Second, the ukulele has four strings while the guitar has six strings. Ukulele is tuned to G-C-E-A, while the guitar is tuned differently as E-A-D-G-B-E.
- Next, the sound is different. The guitar sounds much fuller than the smaller-body ukulele.
- Ukulele usually comes with nylon string, while the acoustic guitar usually comes with steel ones.
- Scale length. The length from nut to the saddle is much longer in a guitar. 
- Difficult to play. The six strings make the guitar a little more difficult to play than a ukulele.
Question: What are the key components of Hadoop?
Answer: There are three key components of Hadoop:
- Hadoop HDFS (Hadoop Distributed File System) - the storage unit.
- Hadoop MapReduce - the processing unit.
- Hadoop YARN (Yet Another Resource Negotiator) - the resource management unit.
Question: Based on this paragraph, please list the symptoms of dementia separating them with bullets.
Evidence: Dementia is a disorder which manifests as a set of related symptoms, which usually surfaces when the brain is damaged by injury or disease. The symptoms involve progressive impairments in memory, thinking, and behavior, which negatively affects a person's ability to function and carry out everyday activities. Aside from memory impairment and a disruption in thought patterns, the most common symptoms include emotional problems, difficulties with language, and decreased motivation. The symptoms may be described as occurring in a continuum over several stages. Consciousness is not affected. Dementia ultimately has a significant effect on the individual, caregivers, and on social relationships in general. A diagnosis of dementia requires the observation of a change from a person's usual mental functioning and a greater cognitive decline than what is caused by normal aging.
Answer: -Progressive impairments in memory, thinking and behavior
-Emotional problems
-Difficulties with language
-Decreased motivation
Question: According to this paragraph, when was the university of virginia founded?
Evidence: The University of Virginia (UVA) is a public research university in Charlottesville, Virginia. It was founded in 1819 by Thomas Jefferson. Set within the Academical Village, a UNESCO World Heritage Site, the university is referred to as a "Public Ivy" for offering an academic experience similar to that of an Ivy League university. It is known in part for certain rare characteristics among public universities such as its historic foundations, student-run honor code, and secret societies.
Answer: The University of Virginia was founded in 1819.
Question: What is Sauvignon blanc?
Evidence: Sauvignon blanc is a green-skinned grape variety that originates from the city of Bordeaux in France. The grape most likely gets its name from the French words sauvage ("wild") and blanc ("white") due to its early origins as an indigenous grape in South West France. It is possibly a descendant of Savagnin. Sauvignon blanc is planted in many of the world's wine regions, producing a crisp, dry, and refreshing white varietal wine. The grape is also a component of the famous dessert wines from Sauternes and Barsac. Sauvignon blanc is widely cultivated in France, Chile, Romania, Canada, Australia, New Zealand, South Africa, Bulgaria, the states of Oregon, Washington, and California in the US. Some New World Sauvignon blancs, particularly from California, may also be called "Fumé Blanc", a marketing term coined by Robert Mondavi in reference to Pouilly-Fumé.

Depending on the climate, the flavor can range from aggressively grassy to sweetly tropical. In cooler climates, the grape has a tendency to produce wines with noticeable acidity and "green flavors" of grass, green bell peppers and nettles with some tropical fruit (such as passion fruit) and floral (such as elderflower) notes. In warmer climates, it can develop more tropical fruit notes but risks losing much aroma from over-ripeness, leaving only slight grapefruit and tree fruit (such as peach) notes.
Answer: Sauvignon blanc is a green-skinned grape variety that originates from the city of Bordeaux in France.  Sauvignon blanc is planted in many of the world's wine regions, producing a crisp, dry, and refreshing white varietal wine.
Question: Who is the primary female antagonist on "The X-Files"?
Answer: Special Agent Dana Katherine Scully
Question: How much wood could a woodchuck chuck if a woodchuck could chuck wood?
Answer: The answer to this depends on various factors.  If the wood if very heavy, a woodchuck may not be capable of chucking it at all.  If the pieces are smaller and the woodchuck was relatively young and spritely, then they could probably chuck at least 30 to 40lbs within a few hours.  An older woodchuck may no longer have dexterity in its hands or the sufficient strength needed to chuck any piece of wood regardless of its size or weight.
Question: What is the air speed velocity of an unladen swallow?
Answer: The European swallow, weighing on average 20 grams, flaps its wings 12 times every second, this results in an estimated air speed of 9 meters per second, which is roughly 20.1 miles per hour.