DataHammer/scimrc huggingface dataset:
Question: Why do people like to ski?
Answer: Skiing is a great way to get out into the open air in the winter months.  It also provides a great way to stay active, challenge yourself, be with family members, and enjoy the great outdoors.
Question: Why should I use toothpaste
Answer: Toothpaste is a paste or gel dentifrice used with a toothbrush to clean and maintain the aesthetics and health of teeth. Toothpaste is used to promote oral hygiene: it is an abrasive that aids in removing dental plaque and food from the teeth, assists in suppressing halitosis, and delivers active ingredients (most commonly fluoride) to help prevent tooth decay (dental caries) and gum disease (gingivitis). Owing to differences in composition and fluoride content, not all toothpastes are equally effective in maintaining oral health. The decline of tooth decay during the 20th century has been attributed to the introduction and regular use of fluoride-containing toothpastes worldwide. Large amounts of swallowed toothpaste can be toxic. Common colors for toothpaste include white (sometimes with colored stripes or green tint) and blue.

Usefulness
Toothpastes are generally useful to maintain dental health. Toothpastes containing fluoride are effective at preventing tooth decay. Toothpastes may also help to control and remove plaque build-up, promoting healthy gums. A 2016 systematic review indicated that using toothpaste when brushing the teeth does not necessarily impact the level of plaque removal. However, the active ingredients in toothpastes are able to prevent dental diseases with regular use.

Ingredients
Toothpastes are derived from a variety of components, the three main ones being abrasives, fluoride, and detergent.
Question: Classify each item as being at a playground or a gym: slide, swing, dumbbell, squat rack, seesaw
Answer: Slide: Playground
Swing: Playground
Dumbbell: Gym
Squat Rack: Gym
Seesaw: Playground
Question: Write a press release declaring the availability of IronChef, a cooking robot that can create various dishes in 5 cuisines. Be sure to mention some of its advanced technological features.
Answer: IronChef, a new cooking robot designed for automated food preparation, is now available for pre-order! With its advanced technological features, it will be great way to shorten the time spent preparing meals for the family. 

Here are some of its key features:
1. Robot arms that can pick up, rinse, chop, cook, and plate various dishes
2. Current cuisines supported: traditional European, Chinese, Thai, Korean, and Japanese with other cuisine types planned for later release
3. Currently can boil, steam, microwave, bake, air-fry and stir-fry, with other cooking methods planned for later release
4. Detect any food spoilage and take appropriate action
5. Substitute ingredients when the ingredients contained in the recipe are not available
6. Allow users to customize recipes
7. Inform owner when ingredients run low
8. Software and hardware are upgradable
9. Optimized parallel dish cooking, meaning not all dishes will be cooked sequentially, and if there is no overlap, it can cook multiple dishes at the same time.

IronChef will providing many families that have neither the time or the energy to prepare meals, as well as families that have a repertoire of favorite meals that needs to be prepared regularly. This will allow more quality family time while enjoying consistent, safe and delicious meals at home.
Question: Where are good locations to scuba dive?
Answer: Belize, Hondurus, US Virgin Islands, British Virgin Islands, Antartica, Cozumel, Hawaii, Galapagos Islands, Philippines
Question: What is the difference between a road bike and a mountain bike?
Answer: Road bikes are built to be ridden on asphalt and cement surfaces and have thin tires, whereas mountain bikes are built to be ridden on dirt and have wider tires. Road bikes also have more aerodynamic handle bars while mountain bike handle bars a built for less responsive steering while bouncing around off the road.
Question: What are some steps I can take to switch into a career in technology or computer programming?
Answer: There are many different ways to break into a career in tech, even if you do not have a degree in computer science:
- enroll in a coding bootcamp
- see if anyone in your network is hiring
- practice coding interview questions on LeetCode
- go back to school for a master's degree in computer science or information science
- watch Youtube tutorials on how to code your own website
Question: How close do clusters match to ground truth tone categories?
Evidence: To test this hypothesis, we evaluate the model on only the first syllable of every word, which eliminates carry-over and declination effects (Table TABREF14). In both Mandarin and Cantonese, the clustering is more accurate when using only the first syllables, compared to using all of the syllables.
FLOAT SELECTED: Table 3. Normalized mutual information (NMI) between cluster assignments and ground truth tones, considering only the first syllable of each word, or all syllables.
To test this hypothesis, we evaluate the model on only the first syllable of every word, which eliminates carry-over and declination effects (Table TABREF14). In both Mandarin and Cantonese, the clustering is more accurate when using only the first syllables, compared to using all of the syllables.
FLOAT SELECTED: Table 3. Normalized mutual information (NMI) between cluster assignments and ground truth tones, considering only the first syllable of each word, or all syllables.
Answer: NMI between cluster assignments and ground truth tones for all sylables is:
Mandarin: 0.641
Cantonese: 0.464
Question: Given the following paragraph, where did the L'Aquila earth occur?
Evidence: The 2009 L'Aquila earthquake occurred in the region of Abruzzo, in central Italy. The main shock occurred at 03:32 CEST (01:32 UTC) on 6 April 2009, and was rated 5.8 or 5.9 on the Richter magnitude scale and 6.3 on the moment magnitude scale; its epicentre was near L'Aquila, the capital of Abruzzo, which together with surrounding villages suffered the most damage. There have been several thousand foreshocks and aftershocks since December 2008, more than thirty of which had a Richter magnitude greater than 3.5.
Answer: The 2009 L'Aquila earthquake occurred in the region of Abruzzo, in central Italy.
Question: What is a persona?
Answer: A persona is a fictional character that embodies the characteristics and traits of a particular group. Personas can be leveraged to efficiently identify and target a stakeholder's unique needs, common tasks they perform, and challenges they face.
Question: What is the difference between a fried egg prepared "sunny-side-up" and an egg prepared "over-easy"?
Answer: "Sunny-side-up" refers to an egg that is fried only on one side and then removed from the pan. "Over-easy" refers to a fried egg that is briefly flipped and cooked on the other side, but not so long that the yolk becomes hardened.
Question: What is kindergarten?
Answer: Kindergarten is the first year of public school education, for children who are 5 or 6 years old. The curriculum helps children learn their colors, shapes, numbers, letters, and letter sounds with the goal of helping children to read independently by the end of the school year.
Question: Highlight relevant facts related to Heidelberg
Evidence: Heidelberg (German: [ˈhaɪdl̩bɛʁk] (listen); Palatine German: Heidlberg) is a city in the German state of Baden-Württemberg, situated on the river Neckar in south-west Germany. As of the 2016 census, its population was 159,914, of which roughly a quarter consisted of students.

Located about 78 km (48 mi) south of Frankfurt, Heidelberg is the fifth-largest city in Baden-Württemberg. Heidelberg is part of the densely populated Rhine-Neckar Metropolitan Region.

Heidelberg University, founded in 1386, is Germany's oldest and one of Europe's most reputable universities. Heidelberg is a scientific hub in Germany and home to several internationally renowned research facilities adjacent to its university, including the European Molecular Biology Laboratory and four Max Planck Institutes. The city has also been a hub for the arts, especially literature, throughout the centuries, and it was designated a "City of Literature" by the UNESCO Creative Cities Network.

Heidelberg was a seat of government of the former Electorate of the Palatinate and is a popular tourist destination due to its romantic cityscape, including Heidelberg Castle, the Philosophers' Walk, and the Baroque old town.
Answer: Heidelberg has the oldest University in Germany. The city is a scientific hub and home to several internationally renowned research facilities. It was designated a "City of Literature" by the UNESCO Creative Cities Network.
Question: Identify which animal species is alive or extinct: Platybelodon, Lion
Answer: Lion is alive, Platybelodon is extinct.
Question: Who is Jon Bon Jovi
Evidence: John Francis Bongiovi Jr. (born March 2, 1962), known professionally as Jon Bon Jovi, is an American singer, songwriter, guitarist, and actor. He is best known as the founder and frontman of the rock band Bon Jovi, which was formed in 1983. He has released 15 studio albums with his band as well as two solo albums.

In the 1990s, Bon Jovi started an acting career, starring in the films Moonlight and Valentino and U-571 and appearing on television in Sex and the City, Ally McBeal, and The West Wing.

As a songwriter, Bon Jovi was inducted into the Songwriters Hall of Fame in 2009. In 2012, he ranked number 50 on the list of Billboard magazine's "Power 100", a ranking of "The Most Powerful and Influential People in the Music Business". In 1996, People magazine named him one of the "50 Most Beautiful People in the World". In 2000, People awarded him the title "Sexiest Rock Star".

Bon Jovi was a founder and former majority owner of the Arena Football League team, the Philadelphia Soul. He is the founder of The Jon Bon Jovi Soul Foundation, founded in 2006.
Answer: John Francis Bongiovi Jr. (born March 2, 1962), known professionally as Jon Bon Jovi, is an American singer, songwriter, guitarist, and actor. He is best known as the founder and frontman of the rock band Bon Jovi, which was formed in 1983. He has released 15 studio albums with his band as well as two solo albums.
Question: Is the paper the first to develop a Greek POS tagger with morphological features?
Evidence: This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed.
The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models.
Answer: Yes.
Question: Given a reference text about Chadwick, Illinois, tell me median family income and the percent of families below the poverty line.
Evidence: Chadwick is a village in Carroll County, Illinois, United States. The population was 481 at the 2020 census, down from 551 at the 2010 census.

Geography
Chadwick is located at 42°0′51″N 89°53′21″W (42.014081, -89.889078).

According to the 2021 census gazetteer files, Chadwick has a total area of 0.31 square miles (0.80 km2), all land.

Demographics
As of the 2020 census there were 481 people, 256 households, and 163 families residing in the village. The population density was 1,546.62 inhabitants per square mile (597.15/km2). There were 246 housing units at an average density of 791.00 per square mile (305.41/km2). The racial makeup of the village was 96.67% White, 0.42% African American, 0.62% Asian, and 2.29% from two or more races. Hispanic or Latino of any race were 2.49% of the population.

There were 256 households, out of which 30.86% had children under the age of 18 living with them, 51.95% were married couples living together, 5.08% had a female householder with no husband present, and 36.33% were non-families. 31.25% of all households were made up of individuals, and 10.55% had someone living alone who was 65 years of age or older. The average household size was 2.61 and the average family size was 2.07.

The village's age distribution consisted of 15.5% under the age of 18, 6.0% from 18 to 24, 19.2% from 25 to 44, 35.5% from 45 to 64, and 23.6% who were 65 years of age or older. The median age was 50.4 years. For every 100 females, there were 128.0 males. For every 100 females age 18 and over, there were 125.8 males.

The median income for a household in the village was $50,000, and the median income for a family was $57,039. Males had a median income of $36,908 versus $25,652 for females. The per capita income for the village was $27,324. About 3.7% of families and 4.9% of the population were below the poverty line, including 11.0% of those under age 18 and 3.2% of those age 65 or over.
Answer: The median family income in Chadwick, Illinois is $57,039 and about 3.7% of families live below the poverty line.
Question: What dictionaries are used for automatic extraction of PIEs?
Evidence: We evaluate the quality of three idiom dictionaries by comparing them to each other and to three idiom corpora. Before we report on the comparison we first describe why we select and how we prepare these resources. We investigate the following six idiom resources:
Wiktionary;
the Oxford Dictionary of English Idioms (ODEI, BIBREF31);
UsingEnglish.com (UE);
the Sporleder corpus BIBREF10;
the VNC dataset BIBREF9;
There are four sizeable sense-annotated PIE corpora for English: the VNC-Tokens Dataset BIBREF9, the Gigaword dataset BIBREF14, the IDIX Corpus BIBREF10, and the SemEval-2013 Task 5 dataset BIBREF15. An overview of these corpora is presented in Table TABREF7.
We investigate the following six idiom resources:

Wiktionary;

the Oxford Dictionary of English Idioms (ODEI, BIBREF31);

UsingEnglish.com (UE);

the Sporleder corpus BIBREF10;

the VNC dataset BIBREF9;

and the SemEval-2013 Task 5 dataset BIBREF15.
Answer: The answers are shown as follows:
* Wiktionary
* Oxford Dictionary of English Idioms
* UsingEnglish.com (UE)
* Sporleder corpus
* VNC dataset
* SemEval-2013 Task 5 dataset
Question: What are 3 novel fusion techniques that are proposed?
Evidence: Proposed Fusion Techniques ::: Step-Wise Decoder Fusion
Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. This differs from the usual practice of passing the visual feature only at the beginning of the decoding process BIBREF5.
Proposed Fusion Techniques ::: Multimodal Attention Modulation
Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Then, the true attention distribution $a_{t}(s)$ is computed as an interpolation between the visual and text based attention scores. The score function is a content based scoring mechanism as usual.
Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer
In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction. However, to our knowledge, visual-semantic supervision hasn't been much explored for multimodal translation in terms of loss functions.
Proposed Fusion Techniques ::: Step-Wise Decoder Fusion
Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process.
Proposed Fusion Techniques ::: Multimodal Attention Modulation
Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well.
Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer
In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction.
Answer: The answers are shown as follows:
* Step-Wise Decoder Fusion
* Multimodal Attention Modulation
* Visual-Semantic (VS) Regularizer
Question: What's the method used here?
Evidence: Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates. Formally, a single document consists of $n$ sentences $D=\lbrace s_1,s_2,\cdots ,s_n\rbrace $. We denote $i$-th sentence as $s_i=\lbrace w_{i1},w_{i2},\cdots ,w_{im}\rbrace $ where $w_{ij}$ is the $j$-th word in $s_i$. The extractor learns to pick out a subset of $D$ denoted as $\hat{D}=\lbrace \hat{s}_1,\hat{s}_2,\cdots ,\hat{s}_k|\hat{s}_i\in D\rbrace $ where $k$ sentences are selected. The abstractor rewrites each of the selected sentences to form a summary $S=\lbrace f(\hat{s}_1),f(\hat{s}_2),\cdots ,f(\hat{s}_k)\rbrace $, where $f$ is an abstracting function. And a gold summary consists of $l$ sentences $A=\lbrace a_1,a_2,\cdots ,a_l\rbrace $.
The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. BERT as the encoder maps the input sequence $D$ to sentence representation vectors $H=\lbrace h_1,h_2,\cdots ,h_n\rbrace $, where $h_i$ is for the $i$-th sentence in the document. Then, the decoder utilizes $H$ to extract $\hat{D}$ from $D$.
We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations. The decoder extracts sentences recurrently, producing a distribution over all of the remaining sentence representations excluding those already selected. Since we use the sequential model which selects one sentence at a time step, our decoder can consider the previously selected sentences. This property is needed to avoid selecting sentences that have overlapping information with the sentences extracted already.
The abstractor network approximates $f$, which compresses and paraphrases an extracted document sentence to a concise summary sentence. We use the standard attention based sequence-to-sequence (seq2seq) model BIBREF23, BIBREF24 with the copying mechanism BIBREF25 for handling out-of-vocabulary (OOV) words. Our abstractor is practically identical to the one proposed in BIBREF8.
Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates. 
The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers.
We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations.
 Our abstractor is practically identical to the one proposed in BIBREF8.
Answer: Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8.
Question: What is Assembler?
Answer: An assembler program creates object code by translating combinations of mnemonics and syntax for operations and addressing modes into their numerical equivalents. This representation typically includes an operation code ("opcode") as well as other control bits and data. The assembler also calculates constant expressions and resolves symbolic names for memory locations and other entities. The use of symbolic references is a key feature of assemblers, saving tedious calculations and manual address updates after program modifications. Most assemblers also include macro facilities for performing textual substitution – e.g., to generate common short sequences of instructions as inline, instead of called subroutines.

Some assemblers may also be able to perform some simple types of instruction set-specific optimizations. One concrete example of this may be the ubiquitous x86 assemblers from various vendors. Called jump-sizing, most of them are able to perform jump-instruction replacements (long jumps replaced by short or relative jumps) in any number of passes, on request. Others may even do simple rearrangement or insertion of instructions, such as some assemblers for RISC architectures that can help optimize a sensible instruction scheduling to exploit the CPU pipeline as efficiently as possible.
Question: Given this short summary of Giovan Battista Cavagna, how long did it take to build the second Monte di Pieta?
Evidence: In Naples, he worked in 1572–1577 at the church of San Gregorio Armeno together with Giovanni Vincenzo della Monica. He helped design the Bank in Naples, called the second Monte di Pietà, started in 1539 with goal, according to Sasso, of liberating citizens from the supposed usury of the Jews. By 1597, the building was completed and unified all the banks in the city. In this project, he was aided by Giovanni Giacomo Di Conforto and Giovanni Cola di Franco.
Answer: It took 58 years.  Started in 1539, completed in 1597.
Question: What is Power BI?
Answer: Power BI is a dashboard reporting tool that enables users and customers to interact with data using interactive and custom visualizations, custom calculations, and queries.  You can build data models in Power BI, and you can run Power BI in direct query or import mode.  Direct Query queries the data directly from the system of record it connects to and import mode makes a copy of that data locally in Power BI.  Depending on the size of the dataset this can affect performance.
Question: What are Salesforce's Inc current state and challenges?
Answer: Salesforce can be best described as being in the “Elaboration” stage within the organization's lifecycle. Salesforce, to date, has managed its growth very well. Despite growing to 17000 employees, Salesforce managed to retain its core culture. It also responded to external challenges, especially against the competition, as Salesforce today competes in the CRM industry and emerged as a viable alternative to AWS. Salesforce relies more heavily on bureaucratic control mechanisms than clan and market control. Salesforce divided itself into functional units, namely, Product, Customer, People, Strategy, and Revenue. It does have some core focus areas, like the public sector as an industry vertical. Even though Salesforce, when viewed through a rational lens, is a bureaucratic organization, it is designed for flexibility, i.e, organic design. The decision-making is pushed down to the business unit rather than being driven by the top management. Salesforce’s Tableau and Slack acquisitions are adaptive, quick, innovative, and much required in a changing and complex environment. With Slack and Tableau, Salesforce has an edge against other cloud providers in the platform space.