DataHammer/scimrc huggingface dataset:
Question: What is the oldest written complaint?
Answer: The complaint tablet to Ea-nasir was a clay tablet written in Akkadian cuneiform is considered the oldest written complaint. This tablet was written in ancient Ur in the year 1750 BCE. This tablet can be currently seen in the British Museum and has become famous in modern times by being the subject of an Internet Meme. The subject of the complaint is substandard copper ingots being sold by Ea-nasir to Nanni. Nanni sent is servant with money to complete the sale, however on arrival the copper was considered substandard and was not accepted. The complaint also states that Nanni's servant was treated rudely.
Question: What are the linguistic and structural features that the "actionable model" focuses on?
Evidence: We apply a multi-class classification approach to label the articles of the sampled dataset into the six WikiProject quality classes. In order to have a baseline, we first apply the state of the art model proposed in BIBREF7 to the dataset.
The “actionable model" in BIBREF7 focuses on five linguistic and structural features and it weighs them as follows:
where
In order to evaluate such model over our dataset, we have extracted from our the dataset the above mentioned features. We have measured ArticleLength, NumWikilinks and NumBrokenWikilinks as suggested in BIBREF11 .
As shown in Figure 3 , the actionable model features have been extracted applying simple scripts mainly based on regular expressions (the “regexp extractor" block), which process the whole HTML code of an article (free text plus media wiki tags). The regexp extractor relies on Python BeautifulSoup for extracting HTML structures and excerpts of the textual content within the MediaWiki tags and on nltk libraries for basic NLP analysis. In details, nltk has been used for computing the InfoNoise feature, whose computation includes the stopwords removal, following the Porter Stopwords Corpus available through nltk BIBREF12 .
The classification results according to the baseline model are reported in Section "Experiments and results" .
The “actionable model” in [28] focuses on five linguistic and structural features and it weighs them as follows: 1. Completeness = 0.4*NumBrokenWikilinks + 0.4*NumWikilinks 2. Informativeness = 0.6*InfoNoise + 0.3*NumImages 3. NumHeadings 4. ArticleLength 5. NumReferences/ArticleLength
Answer: 1. Completeness = 0.4*NumBrokenWikilinks + 0.4*NumWikilinks 2. Informativeness = 0.6*InfoNoise + 0.3*NumImages 3. NumHeadings 4. ArticleLength 5. NumReferences/ArticleLength
Question: Why do we watch television?
Answer: Television are in almost every home in the United States today.  Currently 97% of all US households have an average of 2-3 televisions per home.  We use TV as a communication tool to learn what is going on around us in our communities, outside of our communities, and all around the world.  It is a way for us to stay connected on current events and get prepared and be safe due to forecasted natural disasters.  We use television to even find missing persons, learn who to donate to for good causes, and stay up to date with what is going on in the political arena.
Question: How did YouTube exit?
Evidence: YouTube is an American global online video sharing and social media platform headquartered in San Bruno, California, United States. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users, who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

In October 2006, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube also approved creators to participate in Google's AdSense program, which seeks to generate more revenue for both parties. YouTube reported revenue of $29.2 billion in 2022. In 2021, YouTube's annual advertising revenue increased to $28.8 billion, an increase in revenue of 9 billion from the previous year.

Since its purchase by Google, YouTube has expanded beyond the core website into mobile apps, network television, and the ability to link with other platforms. Video categories on YouTube include music videos, video clips, news, short films, feature films, songs, documentaries, movie trailers, teasers, live streams, vlogs, and more. Most content is generated by individuals, including collaborations between YouTubers and corporate sponsors. Established media corporations such as Disney, Paramount, NBCUniversal, and Warner Bros. Discovery have also created and expanded their corporate YouTube channels to advertise to a larger audience.

YouTube has had unprecedented social impact, influencing popular culture, internet trends, and creating multimillionaire celebrities. Despite all its growth and success, YouTube has been widely criticized. Criticism of YouTube includes the website being used to facilitate the spread of misinformation, copyright issues, routine violations of its users' privacy, enabling censorship, their guidelines and how they are implemented, and endangering child safety and wellbeing.
Answer: YouTube was bought by Google for $1.65 billion in October 2006.
Question: What kind of information do the HMMs learn that the LSTMs don't?
Evidence: We interpret the HMM and LSTM states in the hybrid algorithm with 10 LSTM state dimensions and 10 HMM states in Figures 3 and 3 , showing which features are identified by the HMM and LSTM components. In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters. The HMM and LSTM states pick up on spaces, indentation, and special characters in the data (such as comment symbols in Linux data). We see some examples where the HMM and LSTM complement each other, such as learning different things about spaces and comments on Linux data, or punctuation on the Shakespeare data. In Figure 2 , we see that some individual LSTM hidden state dimensions identify similar features, such as comment symbols in the Linux data.
FLOAT SELECTED: Figure 2: Visualizing HMM and LSTM states on Linux data for the hybrid with 10 LSTM state dimensions and 10 HMM states. The HMM and LSTM components learn some complementary features in the text related to spaces and comments.
We see some examples where the HMM and LSTM complement each other, such as learning different things about spaces and comments on Linux data, or punctuation on the Shakespeare data.
FLOAT SELECTED: Figure 2: Visualizing HMM and LSTM states on Linux data for the hybrid with 10 LSTM state dimensions and 10 HMM states. The HMM and LSTM components learn some complementary features in the text related to spaces and comments.
Answer: The HMM can identify punctuation or pick up on vowels.
Question: Best place to visit Sikkim for 2 days
Answer: 1. Go to Gangtok
2. Night go to Marg road
3. Next Day go Nathula pass and stay over night
Question: Why do people like traveling for leisure?
Answer: Moving around the world seems to be part of human beings nature. Traveling represents a great opportunity. It is a relaxing and educational experience at the same time. Traveling is a great leaning opportunity.
Question: For what linguistic phenomena, the additional context has been proved important?
Evidence: In addition to using the Multi30K dataset as is (standard setup), we probe the ability of our models to address the three linguistic phenomena where additional context has been proved important (Section ): ambiguities, gender-neutral words and noisy input. In a controlled experiment where we aim to remove the influence of frequency biases, we degrade the source sentences by masking words through three strategies to replace words by a placeholder: random source words, ambiguous source words and gender unmarked source words. The procedure is applied to the train, validation and test sets. For the resulting dataset generated for each setting, we compare models having access to text-only context versus additional text and multimodal contexts. We seek to get insights into the contribution of each type of context to address each type of degradation.
In this setting (RND) we simulate erroneous source words by randomly dropping source content words. We first tag the entire source sentences using the spacy toolkit BIBREF26 and then drop nouns, verbs, adjectives and adverbs and replace these with a default BLANK token. By focusing on content words, we differ from previous work that suggests that neural machine translation is robust to non-content word noise in the source BIBREF27 .
In this setting (AMB), we rely on the MLT dataset BIBREF11 which provides a list of source words with multiple translations in the Multi30k training set. We replace ambiguous words with the BLANK token in the source language, which results in two language-specific datasets.
In this setting (PERS), we use the Flickr Entities dataset BIBREF28 to identify all the words that were annotated by humans as corresponding to the category person. We then replace such source words with the BLANK token.
The statistics of the resulting datasets for the three degradation strategies are shown in Table TABREF10 . We note that RND and PERS are the same for language pairs as the degradation only depends on the source side, while for AMB the words replaced depend on the target language.
In addition to using the Multi30K dataset as is (standard setup), we probe the ability of our models to address the three linguistic phenomena where additional context has been proved important (Section 1): ambiguities, gender-neutral words and noisy input.
Answer: Ambiguities, gender-neutral words and noisy input.
Question: Extract out some details about Hair of the Dog Brewing Company brewery from given paragraph
Evidence: Hair of the Dog Brewing Company is a brewery in Portland, Oregon. Several of its beers are bottle conditioned. The tasting room in southeast Portland's Buckman neighborhood closed on June 26, 2022.

Alan Sprints is the owner and brewer with a few helpers, including his sister and his three sons. He graduated from Le Cordon Bleu culinary school. He was president of the home brewers club Oregon Brew Crew, prior to founding Hair of the Dog in 1993.

The brewery's biggest claim to fame was its barley wine, Dave. Its Eve beer was frozen distilled three times. The brewery's Adam beer is a recreation of an Adambier, a historic brew that was originally made in Dortmund, Germany.
Answer: 1. Hair of the Dog Brewing Company is a brewery in Portland, Oregon.
2. Several of its beers are bottle conditioned. The tasting room in southeast Portland's Buckman neighborhood closed on June 26, 2022.
3. The tasting room in southeast Portland's Buckman neighborhood closed on June 26, 2022.
4. Alan Sprints is the owner and brewer with a few helpers, including his sister and his three sons.
5. He graduated from Le Cordon Bleu culinary school.
6. He was president of the home brewers club Oregon Brew Crew, prior to founding Hair of the Dog in 1993.
7. The brewery's biggest claim to fame was its barley wine, Dave.
8. Its Eve beer was frozen distilled three times.
9. The brewery's Adam beer is a recreation of an Adambier, a historic brew that was originally made in Dortmund, Germany.
Question: Classify each of the following universities as public or private: Duke, Wake Forest, University of Florida, Florida Atlantic University, University of Alabama, Harvard, Yale
Answer: Duke: Private
Wake Forest: Private
University of Florida: Public
Florida Atlantic University: Public
University of Alabama: Public
Harvard: Private
Yale: Private
Question: What were their performance results?
Evidence: FLOAT SELECTED: Table 6: Comparison of ACC of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
FLOAT SELECTED: Table 7: Comparison of NMI of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
FLOAT SELECTED: Table 6: Comparison of ACC of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
FLOAT SELECTED: Table 7: Comparison of NMI of our proposed methods and some other non-biased models on three datasets. For LPI, we project the text under the best dimension as described in Section 4.3. For both bi-LSTM and bi-GRU based clustering methods, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models.
Answer: On SearchSnippets dataset ACC 77.01%, NMI 62.94%, on StackOverflow dataset ACC 51.14%, NMI 49.08%, on Biomedical dataset ACC 43.00%, NMI 38.18%
Question: Identify which instrument is string or percussion: Thattai, Tanpura
Answer: Tanpura is string, Thattai is percussion.
Question: Which is the most sparsely populates nation?
Evidence: Mongolia (/mɒŋˈɡoʊliə/ (listen)) is a landlocked country in East Asia, bordered by Russia to the north and China to the south. It covers an area of 1,564,116 square kilometres (603,909 square miles), with a population of just 3.3 million, making it the world's most sparsely populated sovereign nation. Mongolia is the world's largest landlocked country that does not border a closed sea, and much of its area is covered by grassy steppe, with mountains to the north and west and the Gobi Desert to the south. Ulaanbaatar, the capital and largest city, is home to roughly half of the country's population.
Answer: Mongolia
Question: What different approaches of encoding syntactic information authors present?
Evidence: The most intuitive way to represent syntactic information is to use individual dependency relations directly, like dependency head and dependency relation label, denoted as Dep and Rel for short.
In order to preserve the structural information of dependency trees as much as possible, we take the syntactic path between candidate arguments and predicates in dependency trees as linguistic knowledge. Referring to BIBREF9, we use the Tree-based Position Feature (TPF) as Dependency Path (DepPath) and use the Shortest Dependency Path (SDP) as Relation Path (RelPath).
The most intuitive way to represent syntactic information is to use individual dependency relations directly, like dependency head and dependency relation label, denoted as Dep and Rel for short.
In order to preserve the structural information of dependency trees as much as possible, we take the syntactic path between candidate arguments and predicates in dependency trees as linguistic knowledge. Referring to BIBREF9, we use the Tree-based Position Feature (TPF) as Dependency Path (DepPath) and use the Shortest Dependency Path (SDP) as Relation Path (RelPath).
Answer: The answers are shown as follows:
* dependency head and dependency relation label, denoted as Dep and Rel for short
* Tree-based Position Feature (TPF) as Dependency Path (DepPath)
* Shortest Dependency Path (SDP) as Relation Path (RelPath)
Question: What is Microsoft Teams?
Answer: Microsoft Teams is communication platform that has functionality for email, video conferencing, peer chatting, file sharing, conversation and file search, and integration with Sharepoint and Onenote to name a few.  Their closest competitor is Slack owned by SalesForce.  All of these platforms mentioned above operate in real-time as well.
Question: What is the difference between a Narrow Transformation and Wide Transformation
Answer: + Narrow Transformation: In Narrow transformation, all the elements that are required to compute the records in single partition live in the single partition of parent RDD. Ex:- Select, Filter, Union,
+ Wide Transformation: Wide transformation, all the elements that are required to compute the records in the single partition may live in many partitions of parent RDD. The partition may live in many partitions of parent RDD. Involves a network shuffle and are split between stages. Ex:- GroupBy, Repartition, Sorts
Question: Name some television shows that were popular on Nickelodeon in the 1990s.
Answer: Rugrats, Clarissa Explains it All, Are You Afraid of the Dark?, Aaahh!!! Real Monsters, Hey Dude, The Wild Thornberries, Rocko's Modern Life
Question: Is it safe for dogs to eat chocolate?
Answer: No, chocolate is considered dangerous for dogs to consume.
Question: Who amongst these are cricketers: Virat Kohli, Roger Federer, Rafael Nadal, 
Novak Djokovic, Serena Williams, Andre Agassi, Sachin Tendulkar, Ricky Ponting, Anil Kumble, Harmanpreet Kaur, Mithali Raj, Sarah Taylor.
Answer: Virat Kohli, Sachin Tendulkar, Ricky Ponting, Anil Kumble, Harmanpreet Kaur, Mithali Raj, Sarah Taylor are cricketers.
Question: What does "16-bit" mean when referring to digital audio?
Evidence: A PCM signal is a sequence of digital audio samples containing the data providing the necessary information to reconstruct the original analog signal. Each sample represents the amplitude of the signal at a specific point in time, and the samples are uniformly spaced in time. The amplitude is the only information explicitly stored in the sample, and it is typically stored as either an integer or a floating point number, encoded as a binary number with a fixed number of digits: the sample's bit depth, also referred to as word length or word size.

The resolution indicates the number of discrete values that can be represented over the range of analog values. The resolution of binary integers increases exponentially as the word length increases. Adding one bit doubles the resolution, adding two quadruples it and so on. The number of possible values that can be represented by an integer bit depth can be calculated by using 2^n, where n is the bit depth. Thus, a 16-bit system has a resolution of 65,536 (2^16) possible values.
Answer: Converting sound into a digital format requires taking samples of the original sound wave at regular points in time. These samples numerically represent the relative strength, or amplitude, of the analog wave as it varies over time. For digital audio, these numbers are recorded in binary notation. When digital audio is advertised as 16-bit, we mean that the numerical samples of the artist's recording were recorded as 16 digit-long binary numbers.
Question: From the passage provided, extract the distance between Mercury and Sun in AU when it is closest to the sun in its orbit.
Evidence: Mercury (0.307–0.588 AU (45.9–88.0 million km; 28.5–54.7 million mi) from the Sun) is the closest planet to the Sun. The smallest planet in the Solar System (0.055 MEarth), Mercury has no natural satellites. The dominant geological features are impact craters or basins with ejecta blankets, the remains of early volcanic activity including magma flows, and lobed ridges or rupes that were probably produced by a period of contraction early in the planet's history. Mercury's very tenuous atmosphere consists of solar-wind particles trapped by Mercury's magnetic field, as well as atoms blasted off its surface by the solar wind.Its relatively large iron core and thin mantle have not yet been adequately explained. Hypotheses include that its outer layers were stripped off by a giant impact, or that it was prevented from fully accreting by the young Sun's energy. There have been searches for "Vulcanoids", asteroids in stable orbits between Mercury and the Sun, but none have been discovered.
Answer: Mercury has egg shaped orbit. The distance between the Sun and the Mercury when Mercury is closest to the sun it is orbit is 0.307 AU.
Question: What is a lakehouse?
Answer: A lakehouse is a new, open architecture that combines the best elements of data lakes and data warehouses.
Question: Tell me what sport this professional athlete plays (NFL, NBA, MLB, etc.): Justin Herbert, LeBron James, Austin Ekeler, Joel Embiid, Juan Soto, Mookie Betts
Answer: Justin Herbert plays in the NFL, LeBron James plays in the NBA, Austin Ekeler plays in the NFL, Joel Embiid plays in the NBA, Juan Soto plays in the MLB, Mookie Betts plays in the MLB
Question: Who painted the Two Monkeys
Evidence: Two Monkeys or Two Chained Monkeys is a 1562 painting by Dutch and Flemish Renaissance artist Pieter Bruegel the Elder. The work is now in the Gemäldegalerie (Painting Gallery) of the Berlin State Museums.
Answer: The two Monkeys or Two Chained Monkeys is a 1562 painting by Dutch and Flemish Renaissance artist Pieter Bruegel the Elder. The work is now in the Gemaeldegalerie (Painting Gallery) of the Berlin State Museums.
Question: Give me a bulleted list of ten things cities I should visit in Spain
Answer: 10 cities you should visit in Spain are:
- Barcelona
- Madrid
- Seville
- Bilbao
- Valencia
- San Sebastián
- Toledo
- Vigo
- Granada
- Cordoba
Question: What are the five classes for which taggers are developed based on dictionary lookup, pattern-based rules, and statistical models?
Evidence: Until now, NER has not received a lot of attention in the legal domain, developed approaches are fragmented and inconsistent with regard to their respective methods, datasets and typologies used. Among the related work, there is no agreement regarding the selection of relevant semantic categories from the legal domain. In addition, corpora or datasets of legal documents with annotated named entities do not appear to exist, which is, obviously, a stumbling block for the development of data-driven NER classifiers.
dozier2010named describe five classes for which taggers are developed based on dictionary lookup, pattern-based rules, and statistical models. These are jurisdiction (a geographic area with legal authority), court, title (of a document), doctype (category of a document), and judge. The taggers were tested with documents such as US case law, depositions, pleadings etc. cardellino2017low develop an ontology of legal concepts, making use of NERC (6 classes), LKIF (69 classes) and YAGO (358 classes). On the NERC level, entities were divided in abstraction, act, document, organization, person, and non-entity. With regard to LKIF, company, corporation, contract, statute etc. are used. Unfortunately, the authors do not provide any details regarding the questions how the entities were categorised or if there is any correlations between the different levels. They work with Wikipedia articles and decisions of the European Court of Human Rights. glaser2017named use GermaNER BIBREF9 and DBpedia Spotlight BIBREF10, BIBREF11 for the recognition of person, location and organization entities. References are identified based on the rules described by landthaler2016unveiling. The authors created an evaluation dataset of 20 court decisions.
Dozier et al. (2010) describe five classes for which taggers are developed based on dictionary lookup, patternbased rules, and statistical models. These are jurisdiction (a geographic area with legal authority), court, title (of a document), doctype (category of a document), and judge.
Answer: These are jurisdiction (a geographic area with legal authority), court, title (of a document), doctype (category of a document), and judge.