DataHammer/scimrc huggingface dataset:
Question: What is an string instrument that is similar to a Violin, but larger in size and with a lower and deeper sound?
Evidence: The viola (/viˈoʊlə/ vee-OH-lə, also UK: /vaɪˈoʊlə/ vy-OH-lə, Italian: [ˈvjɔːla, viˈɔːla]) is a string instrument that is bowed, plucked, or played with varying techniques. Slightly larger than a violin, it has a lower and deeper sound. Since the 18th century, it has been the middle or alto voice of the violin family, between the violin (which is tuned a perfect fifth above) and the cello (which is tuned an octave below). The strings from low to high are typically tuned to C3, G3, D4, and A4.
Answer: The instrument you are looking for is a Viola.
Question: Which coreference phenomena are analyzed?
Evidence: Therefore, in our analysis, we focus on the chain features related to the phenomena of shining through and explicitation. These features include number of mentions, number of chains, average chain length and the longest chain size. Machine-translated texts are compared to their sources and the corresponding human translations in terms of these features. We expect to find shining through and explicitation effects in automatic translations.
Therefore, in our analysis, we focus on the chain features related to the phenomena of shining through and explicitation.
Answer: The answers are shown as follows:
* shining through
* explicitation
Question: Given the information provided about hottest chili pepper, which is the hottest naturally grown chili pepper in the world?
Evidence: Especially among growers in the US, the UK, and Australia, there has been a competition since the 1990s to grow the hottest chili pepper. Chili pepper species and cultivars registering over 1,000,000 Scoville Heat units (SHU) are called "super-hots". Past Guinness World Record holders (in increasing order of hotness) include the ghost pepper, Infinity chili, Trinidad Moruga scorpion, Naga Viper pepper, and Trinidad Scorpion Butch T. The current record holder, declared in 2017, is the Carolina Reaper, at more than 1.6 million SHU.
Answer: Although Carolina Reaper currently holds the Guinness World Record for the hottest chili pepper, it is not the hottest chili pepper that is cultivated naturally. The Ghost Pepper, also known as Bhut Jholokia, is the spiciest chili pepper in the world that is grown naturally. It is most commonly found in Bangladesh and northeastern India. This pepper is frequently referred to as the "ghost pepper" in the West since "bhut" means "ghost" in the Assamese language. These peppers have thin, easily tearing, dented skin.

A ghost pepper and an Ed Currie-bred Carolina Reaper can be compared because both are extremely fiery and have a sweet, fruity flavor. The Carolina Reaper has more than twice the heat of the ghost pepper when it is at its fiercest, despite the fact that both peppers are extremely spicy. Ghost peppers have a maximum SHU of 1,041,427, while the Carolina Reaper has a maximum SHU of 2.2 million.

Ghost peppers have a potent, sweet-fruity chili flavor. The first 30 to 45 seconds do not experience any heat. After the heat sets in, anticipate to experience shortness of breath, hiccups, perspiration, and watery eyes. The burning typically worsens over 10 to 15 minutes before going away after 30 to 40 minutes.
Question: How can I become a private pilot?
Answer: First, determine what type of pilot you want to become. There are different requirements for private and commercial pilots. Next, enroll in a flight training school or find a certified instructor. You'll also need to find a medical professional who can issue you a 1st or 2nd class medical certificate. You'll be up in the air quickly after that but must pass the FAA written exam, accumulate enough training hours, and pass a final check ride before you earn your pilot certificate.
Question: What's the function of using sub-word units with Transformer-XL?
Evidence: Language modeling is a probabilistic description of language phenomenon. It provides essential context to distinguish words which sound similar and therefore has one of the most useful applications in Natural Language Processing (NLP) especially in downstreaming tasks like Automatic Speech Recognition (ASR). Recurrent Neural Networks (RNN) especially Long Short Term Memory (LSTM) networks BIBREF0 have been the typical solution to language modeling which do achieve strong results. In spite of these results, their fundamental sequential computation constraint has restricted their use in the modeling of long-term dependencies in sequential data. To address these issues Transformer architecture was introduced. Transformers relies completely on an attention mechanism to form global dependencies between input and output. It also offers more parallelization and has achieved SOTA results in language modeling outperforming LSTM models BIBREF1.
In recent years,we have seen a lot of development based on this standard transformer models particularly on unsupervised pre-training(BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7 which have set state-of-the art results on multiple NLP benchmarks. One such model architecture has been the Bidirectional Encoder Representations from Transformers (BERT) model which uses a deep bidirectional transformer architecture.
Another architecture of interest would be the Transformer-XL, which introduces the notion of recurrence in a self-attention model.
The primary research focus though has been mostly on English language for which abundant data is present. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English.
In this project, we explore the implementation of Transformer-based models (BERT and Transformer-XL) in language modeling for Finnish. We will use the same training data as in BIBREF8 so that we can do fair comparisons with the performance of the LSTM models. Also, as the BERT model is a bi-directional transformer, we will have to approximate the conditional probabilities given a sequence of words. We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language. With smaller units, the modeled sequences are longer, and we hope that the recursive XL architecture can allow us to still model long term effects. To the best of our knowledge this is the first work with the Finnish language to use the following:
Approximation of perplexity using a BERT architecture
Using Transformer-XL architecture with sub-word units.
Comparison of Transformer and LSTM models as language models in the same comparable settings with an agglutinative language.
We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language.
Answer: To cope with the large vocabulary problems associated with the Finnish Language.
Question: What's the advantage of the model in the paper?
Evidence: The Transformer architecture BIBREF0 for deep neural networks has quickly risen to prominence in NLP through its efficiency and performance, leading to improvements in the state of the art of Neural Machine Translation BIBREF1, BIBREF2, as well as inspiring other powerful general-purpose models like BERT BIBREF3 and GPT-2 BIBREF4. At the heart of the Transformer lie multi-head attention mechanisms: each word is represented by multiple different weighted averages of its relevant context. As suggested by recent works on interpreting attention head roles, separate attention heads may learn to look for various relationships between tokens BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9.
The attention distribution of each head is predicted typically using the softmax normalizing transform. As a result, all context words have non-zero attention weight. Recent work on single attention architectures suggest that using sparse normalizing transforms in attention mechanisms such as sparsemax – which can yield exactly zero probabilities for irrelevant words – may improve performance and interpretability BIBREF12, BIBREF13, BIBREF14. Qualitative analysis of attention heads BIBREF0 suggests that, depending on what phenomena they capture, heads tend to favor flatter or more peaked distributions.
Recent works have proposed sparse Transformers BIBREF10 and adaptive span Transformers BIBREF11. However, the “sparsity" of those models only limits the attention to a contiguous span of past tokens, while in this work we propose a highly adaptive Transformer model that is capable of attending to a sparse set of words that are not necessarily contiguous. Figure FIGREF1 shows the relationship of these methods with ours.
Our contributions are the following:
We introduce sparse attention into the Transformer architecture, showing that it eases interpretability and leads to slight accuracy gains.
We propose an adaptive version of sparse attention, where the shape of each attention head is learnable and can vary continuously and dynamically between the dense limit case of softmax and the sparse, piecewise-linear sparsemax case.
We make an extensive analysis of the added interpretability of these models, identifying both crisper examples of attention head behavior observed in previous work, as well as novel behaviors unraveled thanks to the sparsity and adaptivity of our proposed model.
We introduce sparse attention into the Transformer architecture, showing that it eases interpretability and leads to slight accuracy gains.
Answer: It eases interpretability and leads to slight accuracy gains.
Question: How much of the earth is covered in land?
Evidence: Earth is the third planet from the Sun and the only place known in the universe where life has originated and found habitability. While Earth may not contain the largest volumes of water in the Solar System, only Earth sustains liquid surface water, extending over 70.8% of the Earth with its ocean, making Earth an ocean world. Earth's polar regions currently retain most of all other water with large sheets of ice covering ocean and land, dwarfing Earth's groundwater, lakes, rivers and atmospheric water. Land, consisting of continents and islands, extends over 29.2% of the Earth and is widely covered by vegetation. Below Earth's surface material lies Earth's crust consisting of several slowly moving tectonic plates, which interact to produce mountain ranges, volcanoes, and earthquakes. Earth's liquid outer core generates a magnetic field that shapes the magnetosphere of Earth, largely deflecting destructive solar winds and cosmic radiation.
Answer: 29.2% of the earth's surface is covered in land.
Question: Some people describe braising as "transformative". Why is this so?
Answer: Braising is a common cooking technique where some primary ingredient (typically a cut of tough meat) cooks for a long time under low temperature and with lots of liquid. The main advantage of braising is that it causes the collagen in the meat fibers to break down into gelatin, which makes the meat fibers more separated and thus the meat more tender. In addition, the gelatin mixes with the remaining cooking liquids to create a scrumptious sauce. Thus, the overall process transforms meat from tough into tender, along with a tasty sauce that can be served alongside the meat. Braising is also convenient in that the cook does not have to attend frequently to the food during the cooking process. These two reasons, namely, rendering  tough cuts of meat (which also tend to be cheaper) more tender and requiring less effort from the cook, have made braising a common cooking technique among several cultures.
Question: What are five totally distinct, creative ways to describe the usefulness of a child's imagination?
Answer: 1. A child's imagination is a powerful tool, allowing them to create anything they can dream up.
2. A child's imagination can be a source of great joy and wonder.
3. A child's imagination is a source of endless creativity.
4. A child's imagination can be used to develop problem-solving skills.
5. A child's imagination can help them understand the world around them.
Question: Are these places in the United States or Spain? California, New Mexico, Andalusia, Catalonia, Utah, Madrid, Texas
Answer: California: US, New Mexico: US, Andalusia: Spain, Catalonia: Spain, Utah: US, Madrid: Spain, Texas: US
Question: Please let me know the details of 1MDB saga and scandal.
Evidence: The 1Malaysia Development Berhad scandal, often referred to as the 1MDB scandal or just 1MDB, describes a corruption, bribery and money laundering conspiracy in which the Malaysian sovereign wealth fund 1Malaysia Development Berhad (1MDB) was systematically embezzled, with assets diverted globally by the perpetrators of the scheme. It had a global scope, implicated institutions and individuals in politics, banking, and entertainment, and sparked criminal investigations across a number of nations. The 1MDB scandal has been described as "one of the world's greatest financial scandals" and declared by the United States Department of Justice as the "largest kleptocracy case to date" in 2016.
Answer: Originally 1MDB stands for of 1Malaysia Development Berhad was a Malaysian state fund, set up in 2009 to promote development through foreign investment and partnerships. The then prime minister, Najib Razak, was its chairman.

The fund has since been at the heart of one of the biggest corruption scandals in the world. The US justice department believes more than $4.5bn was stolen by Jho Low and the resulting scandal has been responsible for the toppling of a government this year and the arrest of Najib, his wife, Rosmah Mansor, and a growing number of close associates.
Question: What are the five best places to hike in the San Francisco Bay Area and why?
Answer: 1. Alamere Fall includes a waterfall spilling into the Pacific Ocean
2. Tomales Point includes views of open water in three directions along with close encounters with the local elk population.
3. Land's End is a beautiful cliffside trail right in the city of San Francisco.
4. Mount Tamalpais is a Marin County peak with views of the ocean and the city.
5. The trail at Grey Whale Cove runs south to Montara State Beach, and in the spring and fall seasons is the perfect place to spot migrating grey whales.
Question: What genres are covered?
Evidence: FLOAT SELECTED: Table 1. The distribution over genres.
FLOAT SELECTED: Table 1. The distribution over genres.
Answer: genre, entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement
Question: How long did the Roman Empire last
Evidence: In modern historiography, Ancient Rome refers to Roman civilisation from the founding of the Italian city of Rome in the 8th century BC to the collapse of the Western Roman Empire in the 5th century AD. It encompasses the Roman Kingdom (753–509 BC), Roman Republic (509–27 BC) and Roman Empire (27 BC–476 AD) until the fall of the western empire.
Answer: 1229 years
Question: Is a Hot dog a sandwich?
Answer: Technically a sandwich is defined as being made from 2 slices of some sort of bread or a split in half roll. By that definition, yes a hotdog is a sandwich. 

This is however an extremely contentious subject that many dispute.
Question: Which supervised learning algorithms are used in the experiments?
Evidence: To build the classifiers we used three different learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM). We used version 0.17.1 of scikit-learn machine learning library for Python to implement the classifiers. An open source tool of Python, Gensim BIBREF19 was used to generate the word embeddings. We compare our results with the two best performing systems reported in BIBREF9 which are the two state-of-the-art models for identifying gang members in Twitter. Both baseline models are built from a random forest classifier trained over term frequencies for unigrams in tweet text, emoji, profile data, YouTube video data and image tags. Baseline Model(1) considers all 3,285 gang and non-gang member profiles in our dataset. Baseline Model(2) considers all Twitter profiles that contain every feature type discussed in Section SECREF2 . Because a Twitter profile may not have every feature type, baseline Model(1) represents a practical scenario where not every Twitter profile contains every type of feature. However, we compare our results to both baseline models and report the improvements.
To build the classifiers we used three different learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM).
Answer: The answers are shown as follows:
* Logistic Regression (LR)
* Random Forest (RF)
* Support Vector Machines (SVM)
Question: What are some of the most popular fantasy books from the last ten years?
Answer: Some of the most popular fantasy books from the last ten years include The Stormlight Archives series by Brandon Sanderson, the first of which is The Way of Kings, as well as the series A Song of Ice and Fire from George R.R. Martin, popularly known as A Game of Thrones due to both the first book in the series as well as the popular TV show based on the series. Other popular fantasy books include The Name of the Wind by Patrick Rothfuss, and, while certainly not written in the last ten years, The Lord of the Rings by J.R.R. Tolkien remains extremely popular.
Question: What are The Benefits of Trekking?
Answer: 1) It Benefits of Sports in General , Like all physical activities, trekking is beneficial for your health: develop your bones, reduce the risk of osteoporosis, increase your cardiovascular and respiratory capacities, strengthen your immune system, improve your muscular capacity
2) Disconnection from everyday life really helps to take a step back from mundane worries and connect with yourself.
3) You work both the front and the back of your body. Finally, if you have poles, you work both up and down your body.
Question: What is wholesale banking?
Answer: Wholesale banking is the provision of services by banks to larger customers or organizations such as mortgage brokers, large corporate clients, mid-sized companies, real estate developers and investors, international trade finance businesses, institutional customers (such as pension funds and government entities/agencies), and services offered to other banks or other financial institutions.

Wholesale finance refers to financial services conducted between financial services companies and institutions such as banks, insurers, fund managers, and stockbrokers.

Modern wholesale banks engage in:

- Finance wholesaling
- Underwriting
- Market making
- Consultancy
- Mergers and acquisitions
- Fund management
Question: What are the corpora used for the task?
Evidence: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains in order to assess the performance of our framework with regard to the sentence splitting subtask. The results show that our proposed sentence splitting approach outperforms the state of the art in structural TS, returning fine-grained simplified sentences that achieve a high level of grammaticality and preserve the meaning of the input. The full evaluation methodology and detailed results are reported in niklaus-etal-2019-transforming. In addition, a comparative analysis with the annotations contained in the RST Discourse Treebank BIBREF6 demonstrates that we are able to capture the contextual hierarchy between the split sentences with a precision of almost 90% and reach an average precision of approximately 70% for the classification of the rhetorical relations that hold between them. The evaluation of the German version is in progress.
For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains 
For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains 
The evaluation of the German version is in progress.
Answer: The answers are shown as follows:
* For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains
* The evaluation of the German version is in progress.
Question: Give me a bulleted list of the seven more recent Indian Prime Ministers
Answer: - Narendra Modi (2014– )
- Manmohan Singh (2004–14)
- Atal Bihari Vajpayee (1998–2004; 2nd time)
- Inder K. Gujral (1997–98)
- H.D. Deve Gowda (1996–97)
- Atal Bihari Vajpayee (1996; 1st time)
- P.V. Narasimha Rao (1991–96)
Question: How big is the dataset?
Evidence: The measured precision with respect to PropBank is low for adjuncts due to the fact that our annotators were capturing many correct arguments not covered in PropBank. To examine this, we analyzed 100 false positive arguments. Only 32 of those were due to wrong or incomplete QA annotations in our gold, while most others were outside of PropBank's scope, capturing either implied arguments or roles not covered in PropBank. Extrapolating from this manual analysis estimates our true precision (on all roles) to be about 91%, which is consistent with the 88% precision figure in Table TABREF19. Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts. Overall, the comparison to PropBank reinforces the quality of our gold dataset and shows its better coverage relative to the 2015 dataset.
Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts. 
Answer: The answers are shown as follows:
* 1593 annotations
Question: Whether pre-training is conducted when training a model of forward translation and back-translation?
Evidence: The encoder-decoder-reconstructor is trained with likelihood of both the encoder-decoder and the reconstructor on a set of training datasets. tu2016neural trained a back-translation model from the hidden state of the decoder into the source sequence by reconstructor to enforce agreement between source and target sentences using Equation EQREF23 after training the forward translation in a manner similar to the conventional attention-based NMT using Equation EQREF13 .
In addition, we experiment to jointly train a model of forward translation and back-translation without pre-training. It may learn a globally optimal model compared to locally optimal model pre-trained using the forward translation.
In addition, we experiment to jointly train a model of forward translation and back-translation without pre-training.
Answer: No.
Question: What is a balloon?
Answer: A balloon is a latex rubber flexible bag that can be inflated with a gas or liquid.  If you want a balloon to float you can inflate it with helium.  It can also be filled with a liquid like water to make it a water balloon.  Other types of balloons are hot air balloons filled with flammable gas that go up thousands of feet in the sky and can carry people, or weather balloon which floats high into the atmosphere to measure different weather patterns.
Question: Which of these sports involve a ball: swimming, baseball, javelin, hockey, cycling, high jump, tennis, , netball, basketball, and handball.
Answer: Hockey, tennis, baseball, basketball, netball, and handball involve a ball. High jump, javelin, swimming, and cycling do not involve a ball.
Question: Identify which instrument is string or woodwind: Spinet, Caval
Answer: Caval is woodwind, Spinet is string.