DataHammer/scimrc huggingface dataset:
Question: What is a typical Dutch tradition when a baby is born?
Answer: When a baby is born in the Netherlands, the typical traditional Dutch snack you can expect to be served when visiting the proud new parents is "Beschuit met muisjes". It consists of beschuit, which is a round, toasted, dry, and crunchy rusk,  smeared butter, covered in "muisjes" (literal translation is little mice), which are sugar-coated aniseeds. The colour of the mice will pink if the baby is a girl, and blue for a boy.
Question: what is the mascot of Garfield High School in Seattle?
Answer: Bulldogs
Question: As per the passage which languages did Ram Mohan Roy know?
Evidence: Ram Mohan Roy was born in Radhanagar, Hooghly District, Bengal Presidency. His great grandfather Krishnakanta Bandyopadhyay was a Rarhi Kulin (noble) Brahmin. Among Kulin Brahmins – descendants of the six families of Brahmins imported from Kannauj by Ballal Sen in the 12th century – those from the Rarhi district of West Bengal were notorious in the 19th century for living off dowries by marrying several women. Kulinism was a synonym for polygamy and the dowry system, both of which Rammohan campaigned against. His father, Ramkanta, was a Vaishnavite, while his mother, Tarini Devi, was from a Shaivite family. He was a great scholar of Sanskrit, Persian and English languages and also knew Arabic, Latin and Greek. One parent prepared him for the occupation of a scholar, the Shastri, while the other secured for him all the worldly advantages needed to launch a career in the laukik or worldly sphere of public administration.[citation needed] Torn between these two parental ideals from early childhood, Ram Mohan vacillated between the two for the rest of his life.During his childhood Ram Mohan Roy witnessed death of his sister in law through sati. The seventeen year old girl was dragged towards the pyre where Ram Mohan Roy witnessed her terrified state. He tried to protest but to no avail. She was burned alive. The people chanted "Maha Sati! Maha Sati! Maha Sati!" (great wife) over her painful screams. 
Answer: Sanskrit, Persian, English, Arabic, Latin and Greek.
Question: Who is Sasha? How does her life compare to Becky?
Evidence: A lonely twentysomething, Becky Green, becomes obsessed with the suicide of her estranged childhood friend Chloe and assumes a new identity as Sasha to engineer a "chance" meeting with Chloe's best friend, Livia, at an art event, followed by another run-in at a yoga class.

Sasha then infiltrates Chloe's group of close-knit friends. She also gains a job outside her genuine work experience through Livia's contacts as she attempts to discover why Chloe died. Through her new alter-ego, she leads a life that is far more exciting than her life as Becky.

A series of flashbacks reveals that Becky had two missed calls from Chloe minutes before her death. As Sasha, Becky eventually uncovers the truth of Chloe’s clandestine activities she has hidden from her husband and friends.

Becky starts an affair with Chloe's widowed husband, Elliot; whereas the rest of the group including Elliot's mother disapproves of their relationship. Elliot decides to run for election, Becky and Livia run his campaign along with Elliot's friend Nish.
Answer: Sasha is a new identity assumed by Becky in order to engineer a "chance" meeting with Chloe's best friend, Livia, at an art event, followed by another run-in at a yoga class. Becky continues to live life as both Becky and Sasha. Sasha infiltrates Chloe's group of close-knit friends and ends up gaining a job outside her genuine work experience. Sasha leads a life that is far more exciting than her life as Becky.
Question: What are the common area of property maintenance that a home owner must deal with?
Answer: Maintenance of a home involves addressing the functioning of its main systems: structure, roofing and windows, electrical, plumbing, HVAC (heating, ventilation, and air conditioning). 

Structure refers to the exterior and interior walls, many of which are "load bearing," meaning they support something above them, like a ceiling or a roof. Roofing and windows protect the interior of a home from the exterior conditions, keeping out rain, wind, and insects. Doors fit into this category, too. Electrical systems connect electricity provided by an outside source, such as a power company, into the home for use by appliances, lights, entertainment, and other items powered by electricity. Plumbing systems bring water into, then out of, the home for use in the kitchen and bathrooms. HVAC systems control the interior climate, managing temperature and humidity to make the home comfortable.
Question: Give me some good stock investing tips.
Answer: Based on some quotes from Legendary investors like Barren Buffet, Charlie Munger, Peter Lynch, etc, one must keep the following things in mind. Please bear in mind that this is for informational purpose and user should do their own research before investing in stock market.

1. Bear in mind that investing is always associated with risk. So please put your money accordance to your investing style.
2. Invest in companies that ideally should be in your circle of competence and the ones that understand. Don't gamble with your money.
3. Always do your own research and not buy or sell a stock based on influencers, your relatives, friends, etc. Investment is an active decision, and like any other decision, you are responsible for your own judgements.
4. Warren Buffet famously said something along these lines, "Rule number 1 of investing is Never Lose Money. And second rule is to never forget rule number 1". So message is clear.
5. Think about long term and don't let the short term fluctuations cloud your stock investment judgement.
Question: Given the paragraph around Star Trek starships, which is the fastest.
Evidence: The warp drive of the Prometheus-class made the ships of this class faster than any other starship in the fleet at the time in the mid 2370s. The warp drive was capable of maintaining speeds exceeding warp 9.9. (VOY: "Message in a Bottle")
Answer: The fastest starship in the Star Trek universe is the USS Prometheus
Question: Identify which animal species is alive or extinct: Troodon, African Elephant
Answer: Troodon is extinct, African Elephant is alive.
Question: What constitutes Jasper’s architecture?
Evidence: Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words. Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system.
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6 . We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers. Liptchinsky et al. BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout.
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks. Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10 . We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR. As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs.
It is possible to increase the capacity of the Jasper model by stacking these operations. Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters). We use residual connections to enable this level of depth. We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR).
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2.95% WER and SOTA results among end-to-end models on LibriSpeech test-other. We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S). Using only greedy decoding without a language model we achieve 3.86% WER on LibriSpeech test-clean.
This paper makes the following contributions:
As a result, Jasper’s architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs.
Answer: 1D convolution, batch normalization, ReLU, and dropout layers.
Question: Given a reference text about the Salt River, tell me how long the river is and where it starts and finishes.
Evidence: The Salt River is a formerly navigable hanging channel of the Eel River which flowed about 9 miles (14 km) from near Fortuna and Waddington, California, to the estuary at the Pacific Ocean, until siltation from logging and agricultural practices essentially closed the channel. It was historically an important navigation route until the early 20th century. It now intercepts and drains tributaries from the Wildcat Hills along the south side of the Eel River floodplain. Efforts to restore the river began in 1987, permits and construction began in 2012, and water first flowed in the restored channel in October 2013.
Answer: The Salt River is about 9 miles long and starts near Fortuna and Waddington California and flows into the Pacific Ocean.
Question: What is Humanism?
Answer: Humanism is a belief system that emphasizes the power of the human individual rather than any god or other divinity. Humanists believe in the innate goodness of human beings, the inherent capacity of humans to care about each other, and it stresses the rational in explaining the world around us.  It is a belief system for those who cannot embrace the supernatural and prefer instead to reside in the actual world and try to understand it with scientific explanations. Humanists use facts, reasoning, and empathy to make decisions. Some others call Humanism 'Good without god'. Humanists aren't openly hostile or antagonistic towards organized religions, but instead they respect that everyone has the right to their own beliefs.
Question: What do they extract entities from?
Evidence: Entities play a central role in the interplay between social media and online news BIBREF0 . Everyday millions of tweets are generated about local and global news, including people reactions and opinions regarding the events displayed on those news stories. Trending personalities, organizations, companies or geographic locations are building blocks of news stories and their comments. We propose to extract entities from tweets and their associated context in order to understand what is being said on Twitter about those entities and consequently to create a picture of people reactions to recent events.
With this in mind and using text mining techniques, this work explores and evaluates ways to characterize given entities by finding: (a) the main terms that define that entity and (b) the sentiment associated with it. To accomplish these goals we use topic modeling BIBREF1 to extract topics and relevant terms and phrases of daily entity-tweets aggregations, as well as, sentiment analysis BIBREF2 to extract polarity of frequent subjective terms associated with the entities. Since public opinion is, in most cases, not constant through time, this analysis is performed on a daily basis. Finally we create a data visualization of topics and sentiment that aims to display these two dimensions in an unified and intelligible way.
The combination of Topic Modeling and Sentiment Analysis has been attempted before: one example is a model called TSM - Topic-Sentiment Mixture Model BIBREF3 that can be applied to any Weblog to determine a correlation between topic and sentiment. Another similar model has been proposed proposed BIBREF4 in which the topic extraction is achieved using LDA, similarly to the model that will be presented. Our work distinguishes from previous work by relying on daily entity-centric aggregations of tweets to create a meta-document which will be used as input for topic modeling and sentiment analysis.
We propose to extract entities from tweets and their associated context in order to understand what is being said on Twitter about those entities and consequently to create a picture of people reactions to recent events.
Answer: Tweets and their associated context.
Question: How do they measure model size?
Evidence: Furthermore, as SIM has no slot-specific neural network structures, its model size is much smaller than previous models. Table TABREF20 shows that, on WoZ and DSTC2 datasets, SIM model has the same number of parameters, which is only 23% and 19% of that in GLAD model.
Table TABREF20 shows that, on WoZ and DSTC2 datasets, SIM model has the same number of parameters, which is only 23% and 19% of that in GLAD model.
Answer: By the number of parameters.
Question: Which is an imperial or a metric measurement: inch, millimetres
Answer: inch is imperial, millimetres is metric
Question: Based on the passage list the four companies apart from Apple which are part of Big Five American information technology companies. Present the result in a comma separated manner.
Evidence: Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Apple is the largest technology company by revenue, totaling US$394.3 billion in 2022. As of March 2023, Apple is the world's biggest company by market capitalization. As of June 2022, Apple is the fourth-largest personal computer vendor by unit sales and second-largest mobile phone manufacturer. It is one of the Big Five American information technology companies, alongside Alphabet (known for Google), Amazon, Meta (known for Facebook), and Microsoft.

Apple was founded as Apple Computer Company on April 1, 1976, by Steve Wozniak, Steve Jobs and Ronald Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and Wozniak as Apple Computer, Inc. in 1977. The company's second computer, the Apple II, became a best seller and one of the first mass-produced microcomputers. Apple went public in 1980 to instant financial success. The company developed computers featuring innovative graphical user interfaces, including the 1984 original Macintosh, announced that year in a critically acclaimed advertisement. By 1985, the high cost of its products, and power struggles between executives, caused problems. Wozniak stepped back from Apple amicably and pursued other ventures, while Jobs resigned bitterly and founded NeXT, taking some Apple employees with him.
Answer: Alphabet, Amazon, Meta, Microsoft
Question: How is an incoming claim used to retrieve similar factchecked claims?
Evidence: Traditional text clustering methods, using TFIDF and some clustering algorithm, are poorly suited to the problem of clustering and comparing short texts, as they can be semantically very similar but use different words. This is a manifestation of the the data sparsity problem with Bag-of-Words (BoW) models. BIBREF16 . Dimensionality reduction methods such as Latent Dirichlet Allocation (LDA) can help solve this problem by giving a dense approximation of this sparse representation BIBREF17 . More recently, efforts in this area have used text embedding-based systems in order to capture dense representation of the texts BIBREF18 . Much of this recent work has relied on the increase of focus in word and text embeddings. Text embeddings have been an increasingly popular tool in NLP since the introduction of Word2Vec BIBREF19 , and since then the number of different embeddings has exploded. While many focus on giving a vector representation of a word, an increasing number now exist that will give a vector representation of a entire sentence or text. Following on from this work, we seek to devise a system that can run online, performing text clustering on the embeddings of texts one at a time
While many focus on giving a vector representation of a word, an increasing number now exist that will give a vector representation of a entire sentence or text. Following on from this work, we seek to devise a system that can run online, performing text clustering on the embeddings of texts one at a time
Answer: The answers are shown as follows:
* text clustering on the embeddings of texts
Question: Where is Ecsenius tessera commonly found?
Evidence: Ecsenius tessera is a species of combtooth blenny in the genus Ecsenius. It is found in coral reefs in the western Pacific ocean, around Vanuatu. It can reach a maximum length of 4.5 centimetres. Blennies in this species feed primarily off of plants, including benthic algae and weeds.
Answer: Ecsenius tessera is commonly found in coral reefs in the western Pacific ocean, close to Vanuatu.
Question: Write a paragraph highlighting the rise of cyber crime, and the vulnerability of people  who are unaware of the risks (or careless), and end the note with a single line on suggested steps.
Answer: We live in a digital age. Whilebmost humans have used the internet or a digitally connected application atleast once in their lifetime, many people are perennially online. This includes scenarios of office workers working from home, or students attending courses and seminars online. People often blindly trust technology blindly, and consequently all their passwords, credit card information, social security and other personally identifiable information is available on their computers and mobile devices. This provides new opportunities for hackers, and cyber crime has become a major menace to the digital economy, as more rogue operators realise it is an easy way to make money.
Apart from having robust laws, we need to educate all users to be aware of the risks and take adequate precautions including using a virtual private network, using tough-to-guess passwords, and more.
Question: What do they create a taxonomy of speech acts for Twitter by?
Evidence: Speech act recognition is a multi-class classification problem. As with any other supervised classification problem, a large labelled dataset is needed. In order to create such a dataset we first created a taxonomy of speech acts for Twitter by identifying and defining a set of commonly occurring speech acts. Next, we manually annotated a large collection of tweets using our taxonomy. Our primary task was to use the expertly annotated dataset to analyse and select various syntactic and semantic features derived from tweets that are predictive of their corresponding speech acts. Using our labelled dataset and robust features we trained standard, off-the-shelf classifiers (such as SVMs, Naive Bayes, etc) for our speech act recognition task.
Using Searle's speech act taxonomy BIBREF3 , we established a list of six speech act categories that are commonly seen on Twitter: Assertion, Recommendation Expression, Question, Request, and Miscellaneous. Table TABREF1 shows an example tweet for each of these categories.
In order to create such a dataset we first created a taxonomy of speech acts for Twitter by identifying and defining a set of commonly occurring speech acts.
Answer: By identifying and defining a set of commonly occurring speech acts.
Question: What are the key features of a Galleon
Evidence: Galleons were large, multi-decked sailing ships first used as armed cargo carriers by European states from the 16th to 18th centuries during the age of sail and were the principal vessels drafted for use as warships until the Anglo-Dutch Wars of the mid-1600s. Galleons generally carried three or more masts with a lateen fore-and-aft rig on the rear masts, were carvel built with a prominent squared off raised stern, and used square-rigged sail plans on their fore-mast and main-masts.
Answer: Galleons can be identified by the large size, multiple decks and tall masts with big sails. They typically has big raised sterns as well.
Question: What is DNA sequence assembly?
Answer: In bioinformatics, sequence assembly refers to aligning and merging fragments from a longer DNA sequence in order to reconstruct the original sequence. This is needed as DNA sequencing technology might not be able to 'read' whole genomes in one go, but rather reads small pieces of between 20 and 30,000 bases, depending on the technology used. Typically, the short fragments (reads) result from shotgun sequencing genomic DNA, or gene transcript (ESTs).

The problem of sequence assembly can be compared to taking many copies of a book, passing each of them through a shredder with a different cutter, and piecing the text of the book back together just by looking at the shredded pieces. Besides the obvious difficulty of this task, there are some extra practical issues: the original may have many repeated paragraphs, and some shreds may be modified during shredding to have typos. Excerpts from another book may also be added in, and some shreds may be completely unrecognizable.
Question: Which languages do they focus on?
Evidence: We investigate both methods, either in isolation or combined, on two translation directions (En-It and En-De) for which the length of the target is on average longer than the length of the source. Our ultimate goal is to generate translations whose length is not longer than that of the source string (see example in Table FIGREF1). While generating translations that are just a few words shorter might appear as a simple task, it actually implies good control of the target language. As the reported examples show, the network has to implicitly apply strategies such as choosing shorter rephrasing, avoiding redundant adverbs and adjectives, using different verb tenses, etc. We report MT performance results under two training data conditions, small and large, which show limited degradation in BLEU score and n-gram precision as we vary the target length ratio of our models. We also run a manual evaluation which shows for the En-It task a slight quality degradation in exchange of a statistically significant reduction in the average length ratio, from 1.05 to 1.01.
We investigate both methods, either in isolation or combined, on two translation directions (En-It and En-De) for which the length of the target is on average longer than the length of the source.
En-It, En-De in both directions
Answer: The answers are shown as follows:
* two translation directions (En-It and En-De)