DataHammer/scimrc huggingface dataset:
Question: What improvements they did for DMN?
Evidence: We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.
In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 .
the new DMN+ model does not require that supporting facts
In addition, we introduce a new input module to represent images.
Answer: The answers are shown as follows:
* the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training.
* In addition, we introduce a new input module to represent images.
Question: In chess, why is the queen valued at nine points instead of eight?
Answer: In chess, typically chess pieces are valued based on their importance. 
For example, 
Pawn - 1 point 
Knight - 3 points
Bishop - 3 points
Rook - 5 points
Queen - 9 points

Since the queen can move like the bishop and the rook, simple math would suggest that it is valued at 8 points (3 + 5). 
However, the game of chess, involves complex scenarios and strategies and having a single piece that can move in all directions gives it a distinct advantage during various phases of the chess game. 
This is why the queen is given a higher point valuation.
Question: Where is the corporate headquarters of Adobe Systems?
Evidence: The Adobe World Headquarters is the corporate headquarters of Adobe Systems, located in San Jose, California.

The buildings are known for their green design. The West Tower is listed as an Energy Star labeled building by the U.S. Environmental Protection Agency. In 2006, all three towers were awarded Leadership in Energy and Environmental Design (LEED) Platinum certification by the United States Green Building Council for environmental sustainability.
Answer: San Jose, California
Question: What was Selective Service Act?
Answer: The Selective Service Act was passed in 1917 (May). It enabled the country to enlist more men to the military
Question: What is a non fungible token?
Answer: A non fungible token (NFT) is another form of digital asset. This asset can be exchanged with other NFT token that are similar.  Similar NFTs could be art, music, or photography.  The idea is that you can monetize your craft by putting it into a block chain.  This ensure that the original author and original say art is one of a kind.  NFT art can be worth a lot of money and usually the intrinsic value of NFT art is tied to the popularity of the artist who produces it and how well known they are.
Question: What is an Enzyme inhibitor
Answer: An enzyme inhibitor is a molecule that binds to an enzyme and blocks its activity. Enzymes are proteins that speed up chemical reactions necessary for life, in which substrate molecules are converted into products. An enzyme facilitates a specific chemical reaction by binding the substrate to its active site, a specialized area on the enzyme that accelerates the most difficult step of the reaction.
Question: What should I do if I'm feeling depressed?
Answer: When you're feeling depressed, it's important to communicate your feelings to the outside world instead of keeping everything to yourself. You can talk to friends, family, or a therapist. Even if you don't have someone to talk to or are not ready to share your feelings, you can seek answers from books, online forums, etc. The important thing is to keep an open mind and be able to listen to or accept other perspectives, instead of being stuck in your own mindset and not willing to hear other opinions.
Question: How are odds different from probability?
Answer: Both are measurements for how likely it is for an event to occur. Odds are commonly used in gambling, which probability is a measure that is commonly used in mathematics/statistics. In statistics, probability measures the number of outcomes in which a particular event occurred divided by the total number of possible outcomes. For example, the probability of rain on a given day would be based on, historically with similar conditions, the number of rainy days divided by a total number of days. The odds are measured as the number of events divided by the number of non-events. To contrast, probability would be the number of events divided by the sum of events and non-events.
Question: Identify each of the following whether it is a type of food: noodle, grass, pizza, tree, rice, rose, steak.
Answer: noodle: Yes 
grass: No
pizza: Yes,
tree: No
rice: Yes
rose: No 
steak: Yes
Question: Is YamCha9 an opened source SVM library?
Evidence: Support Vector Machines (SVM) is a supervised machine learning method which considers dataset as a set of vectors and tries to classify them into specific classes. Basically, SVM is a binary classifier. however, most classification tasks are multi-class classifiers. When applying SVMs, the method has been extended to classify three or more classes. Particular NLP tasks, like word segmentation and Part-of-speech task, each token/word in documents will be used as a feature vector. For the word segmentation task, each token and its features are considered as a vector for the whole document, and the SVM model will classify this vector into one of the three tags (B-IO).
This technique is applied for Vietnamese word segmentation in several studies BIBREF7 , BIBREF24 . Nguyen et al. applied on a segmented corpus of 8,000 sentences and got the result at 94.05% while Ngo et al. used it with 45,531 segmented sentences and get the result at 97.2%. It is worth to mention that general SVM libraries (such as LIBSVM, LIBLINEAR, SVMlight, Node-SVM, and TreeSVM ), YamCha is an opened source SVM library that serves several NLP tasks: POS tagging, Named Entity Recognition, base NP chunking, Text Chunking, Text Classification and event Word Segmentation.
It is worth to mention that general SVM libraries (such as LIBSVM4, LIBLINEAR5, SVMlight6, Node-SVM7, and TreeSVM8 ), YamCha9 is an opened source SVM library that serves several NLP tasks: POS tagging, Named Entity Recognition, base NP chunking, Text Chunking, Text Classification and event Word Segmentation.
Answer: Yes.
Question: Why is March Madness so popular in the United States?
Answer: March Madness is an annual college basketball tournament played in March.  It consists of 68 teams that play in a single elimination tournament until a winner is determined.  There are both men's and women's tournaments, though the men's tournament has been taking place for longer, since 1939.  Interestingly, the first tournament had only 8 teams.

The tournament is popular with basketball fans and non-fans alike.  Some people enjoy the tournament because their college or university's team is participating in the tournament.  Other fans enjoy the unpredictability of the results and cheer for a team that they adopt for the tournament.  Many people enjoy rooting for an underdog; a team that is ranked lower overall but can gain a lot of attention by winning against teams ranked higher.  This is one reason it is called 'March Madness.'
Question: What's the meaning of N-gram?
Evidence: Training Language Models (LMs) is a straightforward way to collect a set of rules by utilizing the fact that words do not appear in an arbitrary order; we in fact can gain useful information about a word by knowing the company it keeps BIBREF7 . A statistical language model estimates the probability of a sequence of words or an upcoming word. An N-gram is a contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence. For example, in the tweet
tears in Ramen #SingleLifeIn3Words
“tears”, “in”, “Ramen” and “#SingleLifeIn3Words” are unigrams; “tears in”, “in Ramen” and “Ramen #SingleLifeIn3Words” are bigrams and “tears in Ramen” and “in Ramen #SingleLifeIn3Words” are trigrams.
An N-gram model can predict the next word from a sequence of N-1 previous words. A trigram Language Model (LM) predicts the conditional probability of the next word using the following approximation: DISPLAYFORM0 
The assumption that the probability of a word depends only on a small number of previous words is called a Markov assumption BIBREF8 . Given this assumption the probability of a sentence can be estimated as follows: DISPLAYFORM0 
In a study on how phrasing affects memorability, BIBREF9 take a language model approach to measure the distinctiveness of memorable movie quotes. They do this by evaluating a quote with respect to a “common language” model built from the newswire sections of the Brown corpus BIBREF10 . They find that movie quotes which are less like “common language” are more distinctive and therefore more memorable. The intuition behind our approach is that humor should in some way be memorable or distinct, and so tweets that diverge from a “common language” model would be expected to be funnier.
In order to evaluate how funny a tweet is, we train language models on two datasets: the tweet data and the news data. Tweets that are more probable according to the tweet data language model are ranked as being funnier. However, tweets that have a lower probability according to the news language model are considered the funnier since they are the least like the (unfunny) news corpus. We relied on both bigrams and trigrams when training our models.
We use KenLM BIBREF11 as our language modeling tool. Language models are estimated using modified Kneser-Ney smoothing without pruning. KenLM also implements a back-off technique so if an N-gram is not found, KenLM applies the lower order N-gram's probability along with its back-off weights.
An N-gram is a contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence.
Answer: A contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence.
Question: Are this techniques used in training multilingual models, on what languages?
Evidence: We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely. Words are split into subwords units with a joint vocabulary of 32K tokens. BLEU scores are computed on the tokenized output with multi-bleu.perl from Moses BIBREF10.
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. 
Answer: English to French and English to German
Question: Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas.
Answer: Instead of jelly, try one of the following with peanut butter in a sandwich:
1. Honey
2. Raisins
3. Fruit preserves
4. Bananas
5. Sliced apples
Question: How about the instability of the word embeddings they find?
Evidence: Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. Recently, they have gained tremendous popularity in Natural Language Processing (NLP) and have been used in tasks as diverse as text similarity BIBREF0 , part-of-speech tagging BIBREF1 , sentiment analysis BIBREF2 , and machine translation BIBREF3 . Although word embeddings are widely used across NLP, their stability has not yet been fully evaluated and understood. In this paper, we explore the factors that play a role in the stability of word embeddings, including properties of the data, properties of the algorithm, and properties of the words. We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks.
Using the overlap between nearest neighbors in an embedding space as a measure of stability (see sec:definingStability below for more information), we observe that many common embedding spaces have large amounts of instability. For example, Figure FIGREF1 shows the instability of the embeddings obtained by training word2vec on the Penn Treebank (PTB) BIBREF4 . As expected, lower frequency words have lower stability and higher frequency words have higher stability. What is surprising however about this graph is the medium-frequency words, which show huge variance in stability. This cannot be explained by frequency, so there must be other factors contributing to their instability.
In the following experiments, we explore which factors affect stability, as well as how this stability affects downstream tasks that word embeddings are commonly used for. To our knowledge, this is the first study comprehensively examining the factors behind instability.
We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks.
Answer: Word embeddings exhibit substantial instabilities.
Question: The final of Duleep Trophy was held in
Answer: SNR College, Coimbatore, India
Question: What are some cardio machines at the gym?
Answer: Gyms often had a variety of cardio machines, including the treadmill, elliptical, rowing machine, stair climber, and spin bike.
Question: From the passage provided, extract the most recent stable version of C#
Evidence: C# (pronounced C sharp) is a general-purpose high-level programming language supporting multiple paradigms. C# encompasses static typing, strong typing, lexically scoped, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines.

The C# programming language was designed by Anders Hejlsberg from Microsoft in 2000 and was later approved as an international standard by Ecma (ECMA-334) in 2002 and ISO/IEC (ISO/IEC 23270) in 2003. Microsoft introduced C# along with .NET Framework and Visual Studio, both of which were closed-source. At the time, Microsoft had no open-source products. Four years later, in 2004, a free and open-source project called Mono began, providing a cross-platform compiler and runtime environment for the C# programming language. A decade later, Microsoft released Visual Studio Code (code editor), Roslyn (compiler), and the unified .NET platform (software framework), all of which support C# and are free, open-source, and cross-platform. Mono also joined Microsoft but was not merged into .NET.

As of November 2022, the most recent stable version of the language is C# 11.0, which was released in 2022 in .NET 7.0.
Answer: As of November 2022, the most recent stable version of the language is C# 11.0
Question: What is the unsupervised task in the final layer?
Evidence: In our model we represent linguistically motivated hierarchies in a multi-task Bi-Directional Recurrent Neural Network where junior tasks in the hierarchy are supervised at lower layers.This architecture builds upon sogaard2016deep, but is adapted in two ways: first, we add an unsupervised sequence labeling task (Language Modeling), second, we add a low-dimensional embedding layer between tasks in the hierarchy to learn dense representations of label tags. In addition to sogaard2016deep.
This architecture builds upon sogaard2016deep, but is adapted in two ways: first, we add an unsupervised sequence labeling task (Language Modeling), second, we add a low-dimensional embedding layer between tasks in the hierarchy to learn dense representations of label tags.
Answer: The answers are shown as follows:
* Language Modeling
Question: From the paragraph about Space Needle, tell me where is located, when it was built, how tall the monument is and one more tourist fact about it.
Evidence: The Space Needle is an observation tower in Seattle, Washington, United States. Considered to be an icon of the city, it has been designated a Seattle landmark. Located in the Lower Queen Anne neighborhood, it was built in the Seattle Center for the 1962 World's Fair, which drew over 2.3 million visitors.

The Space Needle was once the tallest structure west of the Mississippi River, standing at 605 ft (184 m). The tower is 138 ft (42 m) wide, weighs 9,550 short tons (8,660 metric tons), and is built to withstand winds of up to 200 mph (320 km/h) and earthquakes of up to 9.0 magnitude, as strong as the 1700 Cascadia earthquake.

The Space Needle features an observation deck 520 ft (160 m) above ground, providing views of the downtown Seattle skyline, the Olympic and Cascade Mountains, Mount Rainier, Mount Baker, Elliott Bay, and various islands in Puget Sound. Visitors can reach the top of the Space Needle by elevators, which take 41 seconds. On April 19, 1999, the city's Landmarks Preservation Board designated the tower a historic landmark.
Answer: The Space Needle was built in 1962 and it is located in Lower Queen Anne area in the city of Seattle. The tower has a height of 605 feet and it takes 41 seconds for visitors to reach the top of it with an elevator.
Question: Tell me whether these are European countries or Asian countries: Spain, France, Norway, the Czech Republic, Denmark, Portugal, Brazil, South Africa, Canada, China, Japan, Malaysia, Singapore, Sri Lanka, The Philippines
Answer: Not all of them are European countries or Asian countries! Brazil, South Africa, Canada are not part of Europe or Asia. 

These are the European countries: Spain, France, Norway, the Czech Republic, Denmark, Portugal

And these are Asian countries: China, Japan, Malaysia, Singapore, Sri Lanka, The Philippines
Question: What baseline model is used?
Evidence: FLOAT SELECTED: Table 5: Performance of human evaluators and our classifiers (trained on all features), for Dataset-H as the test set
FLOAT SELECTED: Table 5: Performance of human evaluators and our classifiers (trained on all features), for Dataset-H as the test set
Answer: Human evaluators
Question: From the passage note down the various themes on which Harry Potter series is based upon. List the results in comma separated format.
Evidence: Harry Potter is a series of seven fantasy novels written by British author J. K. Rowling. The novels chronicle the lives of a young wizard, Harry Potter, and his friends Hermione Granger and Ron Weasley, all of whom are students at Hogwarts School of Witchcraft and Wizardry. The main story arc concerns Harry's conflict with Lord Voldemort, a dark wizard who intends to become immortal, overthrow the wizard governing body known as the Ministry of Magic and subjugate all wizards and Muggles (non-magical people).

The series was originally published in English by Bloomsbury in the United Kingdom and Scholastic Press in the United States. All versions around the world are printed by Grafica Veneta in Italy. A series of many genres, including fantasy, drama, coming-of-age fiction, and the British school story (which includes elements of mystery, thriller, adventure, horror, and romance), the world of Harry Potter explores numerous themes and includes many cultural meanings and references. According to Rowling, the main theme is death. Other major themes in the series include prejudice, corruption, and madness.

Since the release of the first novel, Harry Potter and the Philosopher's Stone, on 26 June 1997, the books have found immense popularity, positive reviews, and commercial success worldwide. They have attracted a wide adult audience as well as younger readers and are widely considered cornerstones of modern literature. As of February 2023, the books have sold more than 600 million copies worldwide, making them the best-selling book series in history, and have been available in 85 languages. The last four books consecutively set records as the fastest-selling books in history, with the final instalment selling roughly 2.7 million copies in the United Kingdom and 8.3 million copies in the United States within twenty-four hours of its release.
Answer: death, prejudice, corruption, madness
Question: When does the authors' approach show the accuracy gains?
Evidence: In this section, we present the results of out models for two zero-shot cross lingual transfer tasks: XNLI and dependency parsing.
Our approach shows the accuracy gains when adapting from a better pretrained model.
Answer: When adapting from a better pretrained model.
Question: How do you generate power in a roundhouse kick in Muay Thai?
Answer: The power comes from a coordinated full body motion. You pivot on the ball of your foot,  turn your hips and torso, and throw your leg forward in a sideways motion resembling the swinging of a baseball bat.
Question: what are the evaluation datasets?
Evidence: We evaluate our approach on two well benchmarked sequence tagging tasks, the CoNLL 2003 NER task BIBREF13 and the CoNLL 2000 Chunking task BIBREF14 . We report the official evaluation metric (micro-averaged INLINEFORM0 ). In both cases, we use the BIOES labeling scheme for the output tags, following previous work which showed it outperforms other options BIBREF15 . Following BIBREF8 , we use the Senna word embeddings BIBREF2 and pre-processed the text by lowercasing all tokens and replacing all digits with 0.
We evaluate our approach on two well benchmarked sequence tagging tasks, the CoNLL 2003 NER task BIBREF13 and the CoNLL 2000 Chunking task BIBREF14 . 
Answer: The answers are shown as follows:
* CoNLL 2003
* CoNLL 2000
Question: Does platonic love hurt?
Answer: Friendship breakups truly are not talked about enough. I feel like there's plenty of advice for how to deal with exes, but nearly none for how to recover from a very important friendship in your life ending. It hurts like hell and if you were really attached to the person, it hurts just as much--if not even more. Platonic love is no less profound than romantic love.
Question: How big is dataset of pathology reports collected from Ruijing Hospital?
Evidence: Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs.
Answer: The answers are shown as follows:
* 17,833 sentences, 826,987 characters and 2,714 question-answer pairs