DataHammer/scimrc huggingface dataset:
Question: Tell me why people like the Skate video game series in one sentence
Evidence: The Skate series distinguishes itself from other skateboarding video games with its unique control system, called "Flick-it", in which players execute tricks with brief, rapid movements of either or both analog sticks of a twin-stick gamepad. Players assume the role of a customizable skater and freely explore a fictional city in an open world setting, completing diverse and increasingly difficult challenges while being filmed by cameraman Giovanni Reda to unlock new areas, obtain new cosmetics and eventually rise to stardom. Along the way, they can encounter, compete with and even play as famous real-life skaters, including Danny Way, Terry Kennedy and Rob Dyrdek, and must eventually choose from among several authentic licensed skateboard gear firms to strike an endorsement with for exclusive skateboard parts and gear. Players can make use of session markers to quickly travel to or return to a location of their choosing. A special injury system called the "Hall of Meat" tracks injuries sustained during wipeouts, particularly broken bones, which is key to completing certain challenges.
Answer: People like the Skate series because of its unique "Flick-it" controls, authenticity, and similarity to real world skateboarding.
Question: What is a derivative in finance?
Answer: In finance, a derivative is a contract that derives its value from the performance of an underlying entity. This underlying entity can be an asset, index, or interest rate, and is often simply called the "underlying". Derivatives can be used for a number of purposes, including insuring against price movements (hedging), increasing exposure to price movements for speculation, or getting access to otherwise hard-to-trade assets or markets.
Question: What were the evaluation metrics?
Evidence: Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance. The experimental results are illustrated in Table TABREF30.
We provide human evaluation on our framework and the compared models. These responses are based on distinct dialogue history. We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5. In each judgment, the expert is presented with the dialogue history, an output of a system with the name anonymized, and the gold response.
Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance. 
We provide human evaluation on our framework and the compared models. 
We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5.
Answer: The answers are shown as follows:
* BLEU
* Micro Entity F1
* quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5
Question: How do electric cars work?
Answer: An electric car, battery electric car, or all-electric car is an automobile that is propelled by one or more electric motors, using only energy stored in batteries. Compared to internal combustion engine (ICE) vehicles, electric cars are quieter, have no exhaust emissions, and lower emissions overall. In the United States and the European Union, as of 2020, the total cost of ownership of recent electric vehicles is cheaper than that of equivalent ICE cars, due to lower fueling and maintenance costs. Charging an electric car can be done at a variety of charging stations; these charging stations can be installed in both houses and public areas.
Question: Why is sleep training your baby important?
Answer: Sleep training your baby is important because it teaches your child the fundamental skill of self-soothing themselves to sleep. If your baby is not sleep trained, it will likely result in them waking up more often at night and needing the parent's help to soothe or rock them back to sleep. There are several methods you can use to sleep train your baby including the "cry it out" method, which works by allowing your child to cry while checking in on them incrementally to make sure they know you're there. When using the cry it out method, you need to ensure that all of your baby's needs have been met. Make sure your child is fed, changed, burped, and is overall comfortable. You can use words like "it's sleepy time" to signal your baby to sleep. This method takes about 4-7 days to work and has been proven to be the most effective and completely safe to use on your child.
Question: What does the paper investigate?
Evidence: With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
(1) We investigate the application of deep learning methods for the task of hate speech detection.
Answer: The application of deep learning methods for the task of hate speech detection.
Question: Given a reference text that describes LVMH, what brands are part of LVMH?
Evidence: Wines and spirits
Ao Yun
Ardbeg
Belvedere
Bodega Numanthia
Cape Mentelle
Chandon
Château d'Esclans
Château Cheval Blanc
Château d'Yquem
Cheval des Andes
Clos des Lambrays
Cloudy Bay
Colgin Cellars
Dom Pérignon
Glenmorangie
Hennessy
Krug
Mercier
Moët & Chandon
Newton Vineyard
Ruinart
Terrazas de los Andes
Veuve Clicquot
Volcan de mi Tierra
Woodinville

Fashion and leather goods
Berluti
Birkenstock
Celine
Christian Dior
Emilio Pucci
Fendi
Givenchy
JW Anderson
Kenzo
Loewe
Loro Piana
Louis Vuitton
Marc Jacobs
Moynat
Off-White
Patou
Phoebe Philo
Rimowa
Stella McCartney

Perfumes and cosmetics
Acqua di Parma
Benefit Cosmetics
BITE Beauty
Cha Ling
Fenty Beauty by Rihanna
Fresh Beauty
Parfums Givenchy
Guerlain
Kenzo Parfums
KVD Vegan Beauty
Maison Francis Kurkdjian
Make Up For Ever
Marc Jacobs Beauty
Officine Universelle Buly
Ole Henriksen
Parfums Christian Dior
Perfumes Loewe
Sephora

Watches and jewelry
Bulgari
Chaumet
Daniel Roth
Fred
Hublot
Gerald Genta
Repossi
TAG Heuer
Tiffany & Co.
Zenith

Selective retailing
DFS
La Grande Epicerie
La Samaritaine
Le Bon Marché
Starboard Cruise Services

Other activities
Belmond
Maisons Cheval Blanc
Connaissance des Arts
Cova
Investir
Jardin d'Acclimatation
Le Parisien
Les Echos
Radio Classique
Royal Van Lent
Answer: U.S. LVMH controls around 60 subsidiaries that manage 75 prestigious brands. These include Tiffany & Co., Christian Dior, Fendi, Givenchy, Marc Jacobs, Stella McCartney, Loewe, Loro Piana, Kenzo, Celine, Sephora, Princess Yachts, TAG Heuer, and Bulgari.
Question: What is the plot of Mob Psycho 100?
Evidence: Shigeo Kageyama is an average middle school-aged boy, nicknamed Mob. Although he looks like an inconspicuous person, he is in fact a powerful esper with immense psychic power. To keep from losing control of this power, he constantly lives a life under an emotional shackle. In order to help learn how to control his abilities, Mob works as an assistant to con-man Arataka Reigen, a self-proclaimed spirit medium. Mob wants to live a normal life just like those around him, but a barrage of trouble keeps coming after him. With his suppressed emotions growing inside Mob little by little, his power threatens to break through its limits as he eventually encounters other espers like the Claws.
Answer: Mob Psycho 100 is a manga and anime about a boy named Shigeo Kageyama, nicknamed Mob. He looks like a normal boy but has powerful psychic abilities that he struggles to control. He tries to live an emotionless life to avoid triggering his power and learn from who he believes is a Psychic master, but is actually a con-man named Arataka Reigen. Mob's desire to live a normal life keeps getting interrupted by trouble and enemies who come after him, which increases his suppressed emotions bit by bit. When the internal counter reaches 100, he explodes in a powerful emotion.
Question: Give me a bulleted list of ways to make my girlfriend happy on valentines day
Answer: Here are some ways to make your girlfriend happy on valentines day:
1. Give her breakfast in bed
2. Take her out to a romantic dinner
3. Surprise her with flowers
4. Write her a thoughtful letter
5. Buy her a heart shaped box of chocolates
Question: How is the Mars 2020 mission different from other missions?
Answer: The Perseverance rover pioneers a drill that can collect core samples of the most promising rocks and soils, and set them aside in a "cache" on the surface of Mars. A future mission could potentially return these samples to Earth.
Question: Give me the list of books related with the series 'The Lord of the Rings', in order of publication
Answer: Here is the list of books related with the series 'The Lord of the Rings', in order of publication:

1. The Hobbit 
2. The Fellowship of the Ring 
3. The Two Towers 
4. The Return of the King 
5. The Silmarillion 
6. Unfinished Tales of Numenor and Middle-earth 
7. The Children of Hurin 
8. Beren and Luthien 
9. The Fall of Gondolin
Question: Extract from the text who invented the Optophone.
Evidence: The optophone is a device, used by the blind, that scans text and generates time-varying chords of tones to identify letters. It is one of the earliest known applications of sonification. Dr. Edmund Fournier d'Albe of Birmingham University invented the optophone in 1913, which used selenium photosensors to detect black print and convert it into an audible output which could be interpreted by a blind person. The Glasgow company, Barr and Stroud, participated in improving the resolution and usability of the instrument.
Answer: Dr. Edmund Fournier d'Albe
Question: Give step by step instructions on how to make a Long Island Ice Tea.
Answer: Ingredients:
3/4 oz vodka
3/4 oz rum
3/4 oz gin
3/4 oz tequila
3/4 oz triple sec
3/4 oz simple syrup
3/4 oz lemon juice
Cola to fill
Lemon wedge to garnish

Steps:
1. Add the vodka, rum, gin, tequila, triple sec, simple syrup and lemon juice to a glass filled with ice. 
2. Fill the glass with cola and stir.
3. Garnish with the lemon wedge and serve.
Question: Classify each of the following as something that should be kept private or shared publicly: credit card number, social security number, local news, weather forecast, bank account details
Answer: Credit Card Number: Kept Private
Social Security Number: Kept Private
Local News: Shared Publicly
Weather Forecast: Shared Publicly
Bank Account Details: Kept Private
Question: What encoder module do they apply to the shared private extractor of the model?
Evidence: Shared-private feature extractor is mainly used for extracting shared features and private features among different tasks. In this paper, we apply the encoder module of transformer BIBREF16 (henceforth, transformer encoder) to the shared-private extractor of our model. Specially, we employ two transformer encoders to encode the input embeddings of the two tasks as their respective private features. A transformer encoder is used to encode simultaneously the input embeddings of the two tasks as shared features of both tasks. This process is illustrated by the shared-private layer of Figure FIGREF6. The red box in the middle denotes the extraction of shared features and the left and right boxes represent the extraction of private features of two tasks. Next, we take the extraction of the private feature of fake news detection as an example to elaborate on the process of transformer encoder.
The kernel of transformer encoder is the scaled dot-product attention, which is a special case of attention mechanism. It can be precisely described as follows:
where ${\rm \textbf {Q}} \in \mathbb {R}^{l \times (d_p+d_w)}$, ${\rm \textbf {K}} \in \mathbb {R}^{l \times (d_p+d_w)}$, and ${\rm \textbf {V}} \in \mathbb {R}^{l \times (d_p+d_w)}$ are query matrix, key matrix, and value matrix, respectively. In our setting, the query ${\rm \textbf {Q}}$ stems from the inputs itself, i.e., ${\rm \textbf {Q}}={\rm \textbf {K}}={\rm \textbf {V}}={\rm \textbf {E}}$.
To explore the high parallelizability of attention, transformer encoder designs a multi-head attention mechanism based on the scaled dot-product attention. More concretely, multi-head attention first linearly projects the queries, keys and values $h$ times by using different linear projections. Then $h$ projections perform the scaled dot-product attention in parallel. Finally, these results of attention are concatenated and once again projected to get the new representation. Formally, the multi-head attention can be formulated as follows:
where ${\rm \textbf {W}}_i^Q \in \mathbb {R}^{(d_p+d_w) \times d_k}$, ${\rm \textbf {W}}_i^K \in \mathbb {R}^{(d_p+d_w) \times d_k}$, ${\rm \textbf {W}}_i^V \in \mathbb {R}^{(d_p+d_w) \times d_k}$ are trainable projection parameters. $d_k$ is $(d_p+d_w)/h$, $h$ is the number of heads. In Eq.(DISPLAY_FORM11), ${\rm \textbf {W}}^o \in \mathbb {R}^{(d_p+d_w) \times (d_p+d_w)}$ is also trainable parameter.
Shared-private feature extractor is mainly used for extracting shared features and private features among different tasks. In this paper, we apply the encoder module of transformer (Vaswani et al., 2017) (henceforth, transformer encoder) to the sharedprivate extractor of our model. Specially, we employ two transformer encoders to encode the input embeddings of the two tasks as their respective private features.
Answer: The encoder module of transformer.
Question: Identify and briefly discuss four “commandments” for effective presidential relations with the Congress. Do you think a president needs a good relationship with Congress to be an effective president?
Answer: There are ten commandments for effective presidential relationals.  I will discuss four of these commandments. The first that I will discuss is that the president should respect Congress as an equal institution. President Obama said, “If Congress doesn’t act, I will.”  He went on to use executive orders to enable the policy that he was trying to enact.  The problem with this method is that Trump was then able to revoke those acts, undoing all the work that Obama accomplished.  The second is bi-partisan support for Presidential programs.  If both sides take ownership of an issue, it tends to make it easier to enact policy.  Third, the President needs to be able to compromise.  If he can’t compromise, he won’t be able to effectively negotiate a bill with Congress.  Fourth is the President needs a sound program.  If the program has no chance of success, then Congress won’t want to back it.
If we define a President as being effective when he creates lasting policy, then he obviously needs to have a good relationship with Congress.  If he’s not able to work with Congress to pass legislation, then his only other option would be to use executive orders to enact policy, which will typically not live past his Presidency.
Question: From the passage provided, extract the name of the last song recorded by Kishore Kumar
Evidence: By September 1987, Kishore Kumar had decided to retire as he was unhappy with kind of songs and tunes being made by music directors and was planning to return to his birthplace Khandwa.

On 13 October 1987, his brother Ashok Kumar's 76th birthday, he died of a heart attack in Bombay at 4:45 pm. His body was taken to Khandwa for cremation. Kishore had recorded his last song, "Guru Guru", a duet with Asha Bhosle for the film Waqt Ki Awaz (1988) composed by Bappi Lahiri for Mithun Chakraborty and Sridevi, the day before he died.

Recalling the day of Kishore Kumar's death and his last lines before he took his last breath, his wife Leena Chandavarkar was quoted as saying: "On the morning of October 13 (the day Kishore Kumar passed away in 1987), he looked pale and as though in deep sleep. As I went near him, he woke up and asked, ‘Did you get scared? Today is my holiday’.” That day he had several meetings at home. During lunch he told me that we’d watch the film River of No Return in the evening. A little later, I heard him move furniture in the next room. When I went to see what was happening, I saw him lying on the bed. Nervously he said, ‘I’m feeling weak’. I ran to call the doctor. He got angry and said, ‘If you call the doctor, I’ll get a heart-attack’. Those were his last lines. His eyes were wide open and he was breathing out. I thought he was fooling as usual but that was the end."
Answer: Guru Guru
Question: will AI replace my jobs
Answer: AI is growing rapidly nowadays. It reduces repetitive work and improves productivity in a lot of industries. For sure, some labor-intensive work will be eliminated by AI. Some trends we can see for example are: AI-assisted surveillance monitoring can reduce the working hours for security guards; AI-generated images and renderings can be used for commercials or portraits; etc. But it will generate new job families too like prompt engineers who tests and tunes AI systems by sending prompts. AI application developers would be another example.
Question: Name the academic institutions which John Cena attended.
Evidence: Cena was born in West Newbury, Massachusetts on April 23, 1977, to Carol (née Lupien) and John Joseph Cena. His mother is of English and French-Canadian descent, while his father, a former ring announcer for Chaotic Wrestling, has Italian ancestry. His maternal grandfather was baseball player Tony Lupien, while his maternal great-grandfather was businessman Ulysses J. Lupien. He is the second oldest of five brothers: Dan, Matt, Steve, and Sean. A fan of wrestling growing up, he would create championship belts out of cardboard for himself and his brothers. He is a cousin of computer scientist Natalie Enright Jerger and was raised Roman Catholic. Cena was teased and beaten up while in school, and asked for a weightlifting bench at the age of 12.

Cena attended Central Catholic High School in Lawrence, Massachusetts, before transferring to Cushing Academy, a private prep boarding school in Ashburnham, Massachusetts. He then attended Springfield College in Springfield, Massachusetts, where he was an NCAA Division III All-American center and captain on their college football team, wearing the number 54. He graduated from Springfield College in 1999 with a degree in exercise physiology and body movement, after which he pursued a bodybuilding career and worked as a limousine driver. Cena worked in the store area of Gold's Gym for $6 an hour.
Answer: Central Catholic High School, Cushing Academy, a private prep boarding school in Ashburnham, Massachusetts, Springfield College.
Question: Who are the top NFL Quarterbacks?
Answer: Tom Brady
Peyton Manning
Drew Brees
Aaron Rodgers
Josh Allen
Patrick Mahomes
Jalen Hurts
Lamar Jackson
Russell Wilson
Philip Rivers
Eli Manning
Justin Herbert
Question: Give a bulleted list of the amount of money the film "Everything Everywhere All at Once" earned outside of the US in the format {country}-{money earned in millions in USD}
Evidence: Outside of the United States, other top-earning territories as of July 31 were the United Kingdom ($6.2 million), Canada ($5.1 million), Australia ($4.5 million), Russia ($2.4 million), Taiwan ($2.3 million), Mexico ($2 million), Hong Kong ($1.7 million), Germany ($1.5 million), and the Netherlands ($1.1 million).
Answer: United Kingdom-$6.2
Canada-$5.1
Australia-$4.5
Russia-$2.4
Taiwan-$2.4
Mexico-$2
Hong Kong-$1.7
Germany-$1.5
Netherlands-$1.1
Question: Give me a bulleted list of cricketers by highest number of international centuries scored
Answer: Sachin Tendulkar - 100
Virat Kohli - 75
Ricky Ponting - 71
Kumar Sangakkara - 63
Jacques Kallis - 62
Hashim Amla - 55
Mahela Jayawardene - 54
Brian Lara - 53
Rahul Dravid - 48
AB de Villiers - 47
Question: Who was the first human to walk on the moon?
Answer: Neil Armstrong was the first human to set foot on the moon.
Question: Which stands out among other adjectives in the frequency?
Evidence: After finishing constructing UIT-ViIC dataset, we have a look in statistical analysis on our corpus in this section. UIT-ViIC covers 3,850 images described by 19,250 Vietnamese captions. Sticking strictly to our annotation guidelines, the majority of our captions are at the length of 10-15 tokens. We are using the term “tokens” here as a Vietnamese word can consist of one, two or even three tokens. Therefore, to apply Vietnamese properly to Image Captioning, we present a tokenization tool - PyVI BIBREF17, which is specialized for Vietnamese language tokenization, at words level. The sentence length using token-level tokenizer and word-level tokenizer are compared and illustrated in Fig. FIGREF23, we can see there are variances there. So that, we can suggest that the tokenizer performs well enough, and we can expect our Image Captioning models to perform better with Vietnamese sentences that are tokenized, as most models perform more efficiently with captions having fewer words.
Table TABREF24 summarizes top three most occuring words for each part-of-speech. Our dataset vocabulary size is 1,472 word classes, including 723 nouns, 567 verbs, and 182 adjectives. It is no surprise that as our dataset is about sports with balls, the noun “bóng” (meaning “ball") occurs most, followed by “sân” and "cầu thủ" (“pitch” and “athlete” respectively). We also found that the frequency of word “tennis” stands out among other adjectives, which specifies that the set covers the majority of tennis sport, followed by “bóng chày” (meaning “baseball”). Therefore, we expect our model to generate the best results for tennis images.
We also found that the frequency of word “tennis” stands out among other adjectives, which specifies that the set covers the majority of tennis sport, followed by “bóng chày” (meaning “baseball”).
Answer: The word 'tennis' stands out.
Question: Why you should have 2 incomes
Answer: Because you do not know what will happen after 2 weeks with you in near future. So one income will be always give you backup.