DataHammer/scimrc huggingface dataset:
Question: For what was Luigi Giura reknowned known.
Evidence: Giura was the Inspector of the Corps of Bridges and Roads of the Kingdom of the Two Sicilies. Giura is most famous for the design and construction of the second suspension bridge in Europe, the first in continental Europe, in 1832.
Answer: Luigi Giura was an an Italian engineer and architected who lived from 1795 to 1865 and was famous for the design and construction of the second suspension bridge in Europe, which was the first suspension bridge within continental Europe.

Giura was born in Masschito, a small town within the Vulture-Melfi geographical region within the Basilicata administrative region in Southern Italy.  He later was an inaugural student of the School of Bridges and Roads.  After his graduation in 1814, he was admitted to the Corps of Bridges and Roads.  During his time with the Corps of Bridges and Roads, he was able to undertake a series of study tours in Britain and France within which he became familiarized with their new perspectives and industrial developments.

The Corps of Bridges and Roads completed many projects while Giura was engaged, including channels for the flow of water in critical mountain areas.  However, Luigi Guira is most famous for his suspension bridge designs including the Bridge Real Ferdinando sul Garigliano--the first suspension bridge in continental Europe-- and the  Maria Cristina Bridge on the River Calore.  He later returned as the director of the School of Bridges and Roads in 1860 and was sought as a public advisor to regional governments within Italy.
Question: List the sectoral weightage allocation in Nifty 50 as mentioned in the above passage. Use the format ‘Sector Name - Percentage’ in a numbered list to depict the results and also sort the result based on highest allocation percentage to the lowest.
Evidence: The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly owned subsidiary of the NSE Strategic Investment Corporation Limited. NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index has shaped up to be the largest single financial product in India, with an ecosystem consisting of exchange-traded funds (onshore and offshore), and futures and options at NSE and SGX. NIFTY 50 is the world's most actively traded contract. WFE, IOM and FIA surveys endorse NSE's leadership position. Between 2008 & 2012, the NIFTY 50 index's share of NSE market fell from 65% to 29% due to the rise of sectoral indices like NIFTY Bank, NIFTY IT, NIFTY Pharma, and NIFTY Next 50.

The NIFTY 50 index covers 13 sectors of the Indian economy and offers investment managers exposure to the Indian market in one portfolio. As of January 2023, NIFTY 50 gives a weightage of 36.81% to financial services including banking, 14.70% to IT, 12.17% to oil and gas, 9.02% to consumer goods, and 5.84% to automobiles.
Answer: 1. financial services - 36.81% 
2. IT - 14.70%
3. oil and gas - 12.17%
4. consumer goods - 9.02% 
5. automobiles - 5.84%
Question: How they indentify conceptual neighbours?
Evidence: We now consider the following problem: given two BabelNet categories $A$ and $B$, predict whether they are likely to be conceptual neighbors based on the sentences from a text corpus in which they are both mentioned. To train such a classifier, we use the distant supervision labels from Section SECREF8 as training data. Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known.
To find sentences in which both $A$ and $B$ are mentioned, we rely on a disambiguated text corpus in which mentions of BabelNet categories are explicitly tagged. Such a disambiguated corpus can be automatically constructed, using methods such as the one proposed by BIBREF30 mancini-etal-2017-embedding, for instance. For each pair of candidate categories, we thus retrieve all sentences where they co-occur. Next, we represent each extracted sentence as a vector. To this end, we considered two possible strategies:
We now consider the following problem: given two BabelNet categories $A$ and $B$, predict whether they are likely to be conceptual neighbors based on the sentences from a text corpus in which they are both mentioned. To train such a classifier, we use the distant supervision labels from Section SECREF8 as training data. Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known.
To find sentences in which both $A$ and $B$ are mentioned, we rely on a disambiguated text corpus in which mentions of BabelNet categories are explicitly tagged. Such a disambiguated corpus can be automatically constructed, using methods such as the one proposed by BIBREF30 mancini-etal-2017-embedding, for instance. For each pair of candidate categories, we thus retrieve all sentences where they co-occur. Next, we represent each extracted sentence as a vector. 
Answer: The answers are shown as follows:
* Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known.
Question: Give me a list of the 3 best activities to do with your kids when the weather is bad outside
Answer: Build a fort in the living room with blankets, pillows and chairs. Watch nostalgic movies like The Goonies, Sandlot and Honey, I Shrunk the kids. Build a lego castle.
Question: What do The Matrix, Point Break, Constantine, and John Wick have in common?
Answer: They are all Keanu Reeves movies.
Question: Give me a comma separated list of factors that can cause Insulin oscillation.
Evidence: The insulin concentration in blood increases after meals and gradually returns to basal levels during the next 1–2 hours. However, the basal insulin level is not stable. It oscillates with a regular period of 3-6 min. After a meal the amplitude of these oscillations increases but the periodicity remains constant. The oscillations are believed to be important for insulin sensitivity by preventing downregulation of insulin receptors in target cells. Such downregulation underlies insulin resistance, which is common in type 2 diabetes. It would therefore be advantageous to administer insulin to diabetic patients in a manner mimicking the natural oscillations. The insulin oscillations are generated by pulsatile release of the hormone from the pancreas. Insulin originates from beta cells located in the islets of Langerhans. Since each islet contains up to 2000 beta cells and there are one million islets in the pancreas it is apparent that pulsatile secretion requires sophisticated synchronization both within and among the islets of Langerhans.
Answer: age, insulin resistance, glycemic level
Question: Which of these are indoor and outdoor games: badminton, cricket, basketball, football, kho-kho, golf
Answer: Outdoor: koi-kho, football, cricket, golf
Indoor: badminton, basketball
Question: What does the Finnish text data used for the language modeling task consist of?
Evidence: The Finnish text data used for the language modeling task is provided by BIBREF13. The dataset consists mainly of newspapers and books of around 144 million word tokens and 4.2 million unique tokens. We use a Morfessor 2.0 BIBREF14 using the basic unsupervised Morfessor Baseline algorithm BIBREF15 with a corpus weight parameter ($\alpha $) of 0.001. We have a vocabulary of 34K subword tokens for the left+right-marked (+m+) markings and 19K subword tokens for the left-marked (+m) markings. We also pre-process the data to remove any punctuation marks such that we can use the same data with an ASR system. The input is one sentence per line and we shuffle the sentences at each epoch. The data is randomly divided into- training dataset and a validation dataset. The test dataset consists of 2850 Finnish news articles obtained from the Finnish national broadcaster YLE.
The Finnish text data used for the language modeling task is provided by [14]. The dataset consists mainly of newspapers and books of around 144 million word tokens and 4.2 million unique tokens.
Answer: It consists mainly of newspapers and books of around 144 million word tokens and 4.2 million unique tokens.
Question: What problems can expansive soil cause?
Answer: Expansive soil can cause foundation problems for homes built on such sites.  The soil expands or swells with moisture and shrinks in dry weather.  This causes movement in the foundation year in and year out.  This can result in foundation cracks and sinking of floors.  This can also cause, as a secondary effect, cracks in the walls and sticking or jamming of doors.
Question: What does it mean for something to be compostable?
Answer: An item that is compostable will "decompose" over a short duration of time, back into natural materials such as compost. This is in contrast to materials like plastic or aluminum that require thousands or even millions of years to degrade and transform into something found naturally. The benefit of compostable materials is that they do not generate any trash to take up space in landfills, and use more naturally-derived inputs in production processes (in contrast to fossil fuels).
Question: What is their model?
Evidence: Most annotated corpus based NE recognition tasks can benefit a great deal from a known NE dictionary, as NEs are those words which carry common sense knowledge quite differ from the rest ones in any language vocabulary. This work will focus on the NE recognition from plain text instead of corpus based NE recognition. For a purpose of learning from limited annotated linguistic resources, our preliminary discovery shows that it is possible to build a geometric space projection between embedding spaces to help cross-lingual NE recognition. Our study contains two main steps: First, we explore the NE distribution in monolingual case. Next, we learn a hypersphere mapping between embedding spaces of languages with minimal supervision.
For a purpose of learning from limited annotated linguistic resources, our preliminary discovery shows that it is possible to build a geometric space projection between embedding spaces to help cross-lingual NE recognition.
Answer: The answers are shown as follows:
* cross-lingual NE recognition
Question: Given this paragraph about cat's senses, what light level can domestic house cats see at in comparison to human sight?
Evidence: Cats have excellent night vision and can see at only one-sixth the light level required for human vision.: 43  This is partly the result of cat eyes having a tapetum lucidum, which reflects any light that passes through the retina back into the eye, thereby increasing the eye's sensitivity to dim light. Large pupils are an adaptation to dim light. The domestic cat has slit pupils, which allow it to focus bright light without chromatic aberration. At low light, a cat's pupils expand to cover most of the exposed surface of its eyes. The domestic cat has rather poor color vision and only two types of cone cells, optimized for sensitivity to blue and yellowish green; its ability to distinguish between red and green is limited. A response to middle wavelengths from a system other than the rod cells might be due to a third type of cone. This appears to be an adaptation to low light levels rather than representing true trichromatic vision. Cats also have a nictitating membrane, allowing them to blink without hindering their vision.
Answer: Domestic cats are able to see at one-sixth the light level required for human vision.
Question: Will immunity increase when you have pets like Dogs/Cats?
Answer: Mostly yes! immunity increases of humans with pets excluding the people with pet dander allergy. Thoughts to ponder for growing up kids who are exposed to pets build a higher level of immunity and resilience. Added to the unconditional LOVE the pet offers.
Question: What mechanism does LiLi use to solve the problem of limitation in user knowledge?
Evidence: We design LiLi as a combination of two interconnected models: (1) a RL model that learns to formulate a query-specific inference strategy for performing the OKBC task, and (2) a lifelong prediction model to predict whether a triple should be in the KB, which is invoked by an action while executing the inference strategy and is learned for each relation as in C-PR. The framework improves its performance over time through user interaction and knowledge retention. Compared to the existing KB inference methods, LiLi overcomes the following three challenges for OKBC:
1. Mapping open-world to close-world. Being a closed-world method, C-PR cannot extract path features and learn a prediction model when any of INLINEFORM0 , INLINEFORM1 or INLINEFORM2 is unknown. LiLi solves this problem through interactive knowledge acquisition. If INLINEFORM3 is unknown, LiLi asks the user to provide a clue (an example of INLINEFORM4 ). And if INLINEFORM5 or INLINEFORM6 is unknown, LiLi asks the user to provide a link (relation) to connect the unknown entity with an existing entity (automatically selected) in the KB. We refer to such a query as a connecting link query (CLQ). The acquired knowledge reduces OKBC to KBC and makes the inference task feasible.
2. Spareseness of KB. A main issue of all PR methods like C-PR is the connectivity of the KB graph. If there is no path connecting INLINEFORM0 and INLINEFORM1 in the graph, path enumeration of C-PR gets stuck and inference becomes infeasible. In such cases, LiLi uses a template relation (“@-?-@") as the missing link marker to connect entity-pairs and continues feature extraction. A path containing “@-?-@" is called an incomplete path. Thus, the extracted feature set contains both complete (no missing link) and incomplete paths. Next, LiLi selects an incomplete path from the feature set and asks the user to provide a link for path completion. We refer to such a query as missing link query (MLQ).
3. Limitation in user knowledge. If the user is unable to respond to MLQs or CLQs, LiLi uses a guessing mechanism (discussed later) to fill the gap. This enables LiLi to continue its inference even if the user cannot answer a system question.
3. Limitation in user knowledge. If the user is unable to respond to MLQs or CLQs, LiLi uses a guessing mechanism (discussed later) to fill the gap. This enables LiLi to continue its inference even if the user cannot answer a system question.
Answer: A guessing mechanism.
Question: which are the must-visit places in India?
Answer: Every state in India is famous for its heritage and diverse culture and tradition and thus has many things which make it a must-visit place. Very popular is the Taj Mahal in Agra.
Question: Extract the names of the teams that Michael Jordan has been a part of from the text below.
Evidence: Michael Jeffrey Jordan (born February 17, 1963), also known by his initials MJ, is an American former professional basketball player and businessman. His biography on the official National Basketball Association (NBA) website states: "By acclamation, Michael Jordan is the greatest basketball player of all time." He played fifteen seasons in the NBA, winning six NBA championships with the Chicago Bulls. Jordan is the principal owner and chairman of the Charlotte Hornets of the NBA and of 23XI Racing in the NASCAR Cup Series. He was integral in popularizing the basketball sport and the NBA around the world in the 1980s and 1990s, becoming a global cultural icon.

Jordan played college basketball for three seasons under coach Dean Smith with the North Carolina Tar Heels. As a freshman, he was a member of the Tar Heels' national championship team in 1982. Jordan joined the Bulls in 1984 as the third overall draft pick, and quickly emerged as a league star, entertaining crowds with his prolific scoring while gaining a reputation as one of the game's best defensive players. His leaping ability, demonstrated by performing slam dunks from the free-throw line in Slam Dunk Contests, earned him the nicknames "Air Jordan" and "His Airness". Jordan won his first NBA title with the Bulls in 1991, and followed that achievement with titles in 1992 and 1993, securing a three-peat. Jordan abruptly retired from basketball before the 1993–94 NBA season to play Minor League Baseball but returned to the Bulls in March 1995 and led them to three more championships in 1996, 1997, and 1998, as well as a then-record 72 regular season wins in the 1995–96 NBA season. He retired for the second time in January 1999 but returned for two more NBA seasons from 2001 to 2003 as a member of the Washington Wizards. During the course of his professional career he was also selected to play for the United States national team, winning four gold medals (at the 1983 Pan American Games, 1984 Summer Olympics, 1992 Tournament of the Americas and 1992 Summer Olympics), while also being undefeated.
Answer: Michael Jordan played basketball for the North Carolina Tar Heels, Chicago Bulls and Washington Wizards. He is the principal owner of the Charlotte Hornets of the NBA and 23XI Racing in the NASCAR Cup Series. He also played for the United States national team, winning gold medals at the 1983 Pan American Games, 1984 Summer Olympics, 1992 Tournament of the Americas and 1992 Summer Olympics. In addition, he played Minor League Baseball between stints on the Chicago Bulls between 1994 and 1995.
Question: What type of animal was: Hobbes, Snoopy, Garfield, and Dumbo.
Answer: Hobbes was a tiger. Snoopy was a dog. Garfield was a cat. Dumbo was an elephant.
Question: Inflation means that the general level of prices is going up, the opposite of deflation. More money will be needed to pay for goods (like a loaf of bread) and services (like getting a haircut at the hairdresser's).
Answer: Inflation means that the general level of prices is going up, the opposite of deflation. More money will be needed to pay for goods (like a loaf of bread) and services (like getting a haircut at the hairdresser's). Economists measure inflation regularly to know an economy's state. Inflation changes the ratio of money towards goods or services; more money is needed to get the same amount of a good or service, or the same amount of money will get a lower amount of a good or service.
Question: What is it like to live on the moon?
Answer: The moon is incredibly dark. You must quickly remember on Earth how much you rely on the sun. I miss it deeply. Also, I never thought I would use the word convenient to describe gravity. Life without is more challenging in so many ways. The biggest is the lack of order you have without gravity. Without gravity, the simplest things lose their order, so you have to strap, tape, or glue them down, which later becomes an inconvenience should you need to move them.
Question: How many minutes in a football match?
Answer: There are 90 minutes in a football match. This is played in two 45 minute halves where teams swap sides of the pitch at half-time. This is for football, also called soccer, which is one of the most popular sports in Europe.
Question: How does BLI measure alignment quality?
Evidence: Evaluation Task. Our task is bilingual lexicon induction (BLI). It has become the de facto standard evaluation for projection-based CLWEs BIBREF16 , BIBREF17 . In short, after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words. Its lightweight nature allows us to conduct a comprehensive evaluation across a large number of language pairs. Since BLI is cast as a ranking task, following glavas2019howto we use mean average precision (MAP) as the main evaluation metric: in our BLI setup with only one correct translation for each “query” word, MAP is equal to mean reciprocal rank (MRR).
after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words
retrieve target language translations for a test set of source language words
BLI is cast as a ranking task
in our BLI setup with only one correct translation for each “query” word, MAP is equal to mean reciprocal rank (MRR)
Answer: The answers are shown as follows:
* we use mean average precision (MAP) as the main evaluation metric
Question: What are the three best commuter trains in California?
Answer: 1. Capital Corridor (it connects multiple large metro areas and is very comfortable)
2. Caltrain (helps many commuters get to work along the San Francisco peninsula)
3. Coaster (for around $5, passengers get a beautiful view of the Pacific Ocean)
Question: What does the dataset bring out or include?
Evidence: In this paper, we showed how we can effectively leverage a prodigious blog dataset. Not only does the dataset bring out the extensive linguistic content reflected in the blog posts, but also includes location information and rich metadata. These data allow for the generation of maps that reflect the demographics of the population, variations in language use, and differences in psycholinguistic and semantic categories. These mappings can be valuable to both psychologists and linguists, as well as lexicographers. A prototype demo has been made available together with the code used to collect our dataset.
In this paper, we showed how we can effectively leverage a prodigious blog dataset. Not only does the dataset bring out the extensive linguistic content reflected in the blog posts, but also includes location information and rich metadata.
Answer: The extensive linguistic content reflected in the blog posts, location information, and rich metadata.
Question: What is the second variant?
Evidence: Out of nearly 7000 languages spoken worldwide, current speech (ASR, TTS, voice search, etc.) technologies barely address 200 of them. Broadening ASR technologies to ideally all possible languages is a challenge with very high stakes in many areas and is at the heart of several fundamental research problems ranging from psycholinguistic (how humans learn to recognize speech) to pure machine learning (how to extract knowledge from unlabeled data). The present work focuses on the narrow but important problem of unsupervised Acoustic Unit Discovery (AUD). It takes place as the continuation of an ongoing effort to develop a Bayesian model suitable for this task, which stems from the seminal work of BIBREF0 later refined and made scalable in BIBREF1 . This model, while rather crude, has shown that it can provide a clustering accurate enough to be used in topic identification of spoken document in unknown languages BIBREF2 . It was also shown that this model can be further improved by incorporating a Bayesian "phonotactic" language model learned jointly with the acoustic units BIBREF3 . Finally, following the work in BIBREF4 it has been combined successfully with variational auto-encoders leading to a model combining the potential of both deep neural networks and Bayesian models BIBREF5 . The contribution of this work is threefold:
The second variant, which we shall denote Structured Variational AutoEncoder (SVAE) AUD, is based upon the work of [5] and embeds the HMM model into the Variational AutoEncoder framework [10].
Answer: It is denoted as Structured Variational AutoEncoder (SVAE) AUD and embeds the HMM model into the Variational AutoEncoder framework.