DataHammer/scimrc huggingface dataset:
Question: How did Caesar come to power in Rome?
Answer: Gaius Julius Caesar was a Roman general and statesman born around 100 B.C. He was born in a patrician family (senatorial class) called the Julii that claimed lineage from the Goddess Venus and the legendary Prince Aeneas of Troy. His birth in this family had already put him ahead of most others on the road to power.

Caesar was smart, hard working and charming and made many friends and connections that would help him later in life. He married a relative of Gaius Marius, the dictator of Rome at the time and furthered his political connections. Though he suffered a setback when Sulla defeated Marius, Caesar was quickly able to use that to his advantage to escape a posting that required him to remain in Rome. It was important for any aspiring Consul (leader of Rome) to be seen as an effective military general who had conquered lands for Rome.

Caesar's next significant position was that of Aedile where one of his primary duties were to organize games. Caesar borrowed heavily and organized lavish games so that the people of Rome would know him. Unlike other patricians, Caesar had grown up in the Subura where, at the time, the plebeians lived and knew the importance and power that could be garnered from the love of the people.

Through multiple appointments, Caesar displayed his military genius and was eventually elected Consul. He formed an alliance with two other leading figures of the time: Pompey (a decorated general) and Crassus (richest man in Rome) known now as the "First Triumvirate". 

After his consulship, Caesar become Governor of Gaul and eventually won a lot of territory for Rome. However, his political opponents in Rome readied a case for prosecuting him and stripping of power. Unwilling to be subject to that, Caesar gathered one of his legions and marched on Rome. His political opponents performed a tactical retreat from Rome leaving him in nominal control. He consolidated his power by winning multiple wars in Greece, Spain and Egypt eventually solidifying his hold on Rome and getting himself elected Dictator
Question: Who was the quarterback for the Denver Broncos when they won their first Super Bowl and what was his number?
Answer: His name was John Elway and he wore #7
Question: Give this paragraph about TCP/IP, what are the four abstraction laters?
Evidence: The Internet protocol suite, commonly known as TCP/IP, is a framework for organizing the set of communication protocols used in the Internet and similar computer networks according to functional criteria. The foundational protocols in the suite are the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and the Internet Protocol (IP). Early versions of this networking model were known as the Department of Defense (DoD) model because the research and development were funded by the United States Department of Defense through DARPA.

The Internet protocol suite provides end-to-end data communication specifying how data should be packetized, addressed, transmitted, routed, and received. This functionality is organized into four abstraction layers, which classify all related protocols according to each protocol's scope of networking. An implementation of the layers for a particular application forms a protocol stack. From lowest to highest, the layers are the link layer, containing communication methods for data that remains within a single network segment (link); the internet layer, providing internetworking between independent networks; the transport layer, handling host-to-host communication; and the application layer, providing process-to-process data exchange for applications.

The technical standards underlying the Internet protocol suite and its constituent protocols are maintained by the Internet Engineering Task Force (IETF). The Internet protocol suite predates the OSI model, a more comprehensive reference framework for general networking systems.
Answer: From lowest to highest, the layers are:
- link layer, containing communication methods for data that remains within a single network segment (link)
- the internet layer, providing internetworking between independent networks; 
- the transport layer, handling host-to-host communication;
- the application layer, providing process-to-process data exchange for applications.
Question: What domains are detected in this paper?
Evidence: Experimental Setup
Experimental Setup
Answer: Answer with content missing: (Experimental setup not properly rendered) In our experiments we used seven target domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW),
“Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). Exceptionally, GEN does
not have a natural root category.
Question: Who are the best artists throughout history?
Answer: There are many artists throughout history and across different mediums such as painting, sculpture and photography among others.  Some of the most well known painters are Leonardo da Vinci, Rembrandt, Claude Monet and Salvador Dali.  Donatello is one of the most famous sculptors working with materials such as marble, bronze and wood.  Michelangelo is a famous artist known for both sculpture and painting.  There are also many famous photographers throughout history including Ansel Adams who is knows for his portrayal of nature.
Question: Is this library implemented into Torch or is framework agnostic?
Evidence: With this challenge in mind, we introduce Torch-Struct with three specific contributions:
Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework.
Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python.
Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization.
With this challenge in mind, we introduce Torch-Struct with three specific contributions:

Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework.

Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python.

Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization.
Answer: It uses deep learning framework (pytorch)
Question: What is the difference between Oculus Quest 2 and Meta Quest 2 VR headsets?
Answer: They are exactly the same in terms of technical specifications and capabilities. The only differences between them are their logos and names.
Question: Can you think of some different cities on the American West coast?
Answer: Seattle, Portland, San Francisco, Los Angeles, San Diego
Question: Does using microwave oven cause cancer?
Answer: No, it is a myth with no scientific validation to this hypothesis. Microwaves are a form of electromagnetic radiation which only heats the water molecules inside your food. As the water heats up the food heats up. Microwave ovens has been in use safely for more than 70 years
Question: Give me a list of the seven best beach honeymoon locations for 2024.
Answer: The Maldives
Santorini
Maui
Bora Bora
Bali
Jamaica
Seychelles
Question: What is increase in percentage of humor contained in headlines generated with TitleStylist method (w.r.t. baselines)?
Evidence: The human evaluation is to have a comprehensive measurement of the performances. We conduct experiments on four criteria, relevance, attraction, fluency, and style strength. We summarize the human evaluation results on the first three criteria in Table TABREF51, and the last criteria in Table TABREF57. Note that through automatic evaluation, the baselines NST, Fine-tuned, and Gigaword-MASS perform poorer than other methods (in Section SECREF58), thereby we removed them in human evaluation to save unnecessary work for human raters.
FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset.
FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset.
We summarize the human evaluation results on the first three criteria in Table TABREF51, and the last criteria in Table TABREF57.
FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset.
FLOAT SELECTED: Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset.
Answer: Humor in headlines (TitleStylist vs Multitask baseline):
Relevance: +6.53% (5.87 vs 5.51)
Attraction: +3.72% (8.93 vs 8.61)
Fluency: 1,98% (9.29 vs 9.11)
Question: What is Categorization and Sentiment Analysis
Answer: Categorization and Sentiment Analysis is typically referred in the Customer Experience space where customer interaction data is analyzed to get insights into categories of topics and sentiment associated with those interactions. Some of the use cases that benefit from these insights include Customer Representative/Agent Performance and Training, Workload Management, Product Performance et al
Question: Wat is goede vrijdag?
Evidence: Goede Vrijdag is de vrijdag voor Pasen. Op deze dag herdenken christenen de kruisiging en dood van Jezus. Jezus werd volgens de Bijbel veroordeeld tot de kruisdood door de Romeinse stadhouder Pontius Pilatus, op aandrang van het sanhedrin. Deze straf werd voltrokken op de heuvel Golgotha nabij de stad Jeruzalem. Goede Vrijdag volgt op Witte Donderdag en gaat vooraf aan Stille Zaterdag. Daarop volgt Pasen.
Answer: De dag dat Jezus gekruisigd werd
Question: What kind of model do they explore?
Evidence: Word embeddings have been widely used for natural language processing (NLP) tasks BIBREF0 . However, the large word vocabulary makes word embeddings expensive to train. Some people argue that we can model languages at the character-level BIBREF1 . For alphabetic languages such as English, where the characters are much fewer than the words, the character embeddings achieved the state-of-the-art results with much fewer parameters.
Unfortunately, for the other languages that use non-alphabetic systems, the character vocabulary can be also large. Moreover, Chinese and Japanese, two of the most widely used non-alphabetic languages, especially contain large numbers of ideographs: hanzi of Chinese and kanji of Japanese. The character vocabulary can be as scalable as the word vocabulary (e.g., see the datasets introduced in Section SECREF11 ). Hence the conventional character embedding-based method is not able to give us a slim vocabulary on Chinese and Japanese.
For convenience, let us collectively call hanzi and kanji as Chinese characters. Chinese characters are ideographs composed with semantic and phonetic radicals, both of which are available for character recognition, and the semantic information may be embedded in Chinese characters by the semantic radicals BIBREF2 . Besides, though the character vocabulary is huge, the number of the radicals is much fewer. Accordingly, we explored a model that represents the Chinese characters by the sequence of the radicals. We applied our proposed model to sentiment classification tasks on Chinese and Japanese and achieved the follows:
Accordingly, we explored a model that represents the Chinese characters by the sequence of the radicals.
Answer: A model that represents the Chinese characters by the sequence of the radicals.
Question: How is data engineering different from data science?
Answer: Data Engineering occurs before data science in the data lifecycle. Data can be generated from various sources and this raw data may be structured, semi structured or even unstructured. To make sense of this data, data has to be ingested and transformed. This is when data engineering comes into picture. Data engineering includes the process of extracting the raw data, ingesting data into a system like a data lake, transforming the data for business needs and finally loading it into a table. This is commonly known as ELT (Extract-Load-Transform). ETL (Extract-Transform-Load) is also possible where transformation happens before the loading stage.
Data Science and Analytics is the next step in the data lifecycle. Once the data needed for specific business need is available, data scientists use this data to run machine learning models to find the most accurate model. This data is available to the data scientists in the form of tables. Data analysts also use this data to do some exploratory analysis and create dashboards. 
In essence, the data lifecycle would look as follows:
Raw data from different sources -> Data Engineering -> Data Science and Analytics
Question: What is the biggest planet from the solar system?
Evidence: Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass more than two and a half times that of all the other planets in the Solar System combined, and slightly less than one one-thousandth the mass of the Sun. Jupiter is the third brightest natural object in the Earth's night sky after the Moon and Venus, and it has been observed since prehistoric times. It was named after Jupiter, the chief deity of ancient Roman religion.
Answer: Jupiter is the largest plant in the Solar system with a surface of 120 times the earth
Question: What are the main components of an ordinary neural language generator？
Evidence: The recurrent language generator proposed in this paper is based on a neural language generator BIBREF8 , which consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the semantic elements, and (iii) an RNN Decoder that generates output sentences. The generator architecture is shown in Figure 1 . The Encoder first encodes the MR into input semantic elements which are then aggregated and selected by utilizing an attention-based mechanism by the Aligner. The input to the RNN Decoder at each time step is a 1-hot encoding of a token $\textbf {w}_{t}$ and an attentive DA representation $\textbf {d}_{t}$ . At each time step $t$ , RNN Decoder also computes how much the feature value vector $\textbf {s}_{t-1}$ retained for the next computational steps, and adds this information to the RNN output which represents the probability distribution of the next token $\textbf {w}_{t+1}$ . At generation time, we can sample from this conditional distribution to obtain the next token in a generated sentence, and feed it as the next input to the RNN Decoder. This process finishes when an end sign is generated BIBREF17 , or some constraints are reached BIBREF16 . The model can produce a sequence of tokens which can finally be lexicalized to form the required utterance.
The recurrent language generator proposed in this paper is based on a neural language generator (Wen et al., 2016b), which consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the semantic elements, and (iii) an RNN Decoder that  generates output sentences.
Answer: It consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the semantic elements, and (iii) an RNN Decoder that  generates output sentences.
Question: What dataset did they use?
Evidence: Data Collection: We are using the disaster data from BIBREF5. It contains various dataset including the CrisiLexT6 dataset which contains six crisis events related to English tweets in 2012 and 2013, labeled by relatedness (on-topic and off-topic) of respective crisis. Each crisis event tweets contain almost 10,000 labeled tweets but we are only focused on flood-related tweets thus, we experimented with only two flood event i.e. Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada and relabeled all on-topic tweets as Related and Off-topic as Unrelated for implicit class labels understanding in this case. The data collection process and duration of CrisisLex data is described in BIBREF5 details.
We are using the disaster data from BIBREF5. It contains various dataset including the CrisiLexT6 dataset which contains six crisis events related to English tweets in 2012 and 2013, labeled by relatedness (on-topic and off-topic) of respective crisis. Each crisis event tweets contain almost 10,000 labeled tweets but we are only focused on flood-related tweets thus, we experimented with only two flood event i.e. Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada and relabeled all on-topic tweets as Related and Off-topic as Unrelated for implicit class labels understanding in this case.
Answer: The answers are shown as follows:
*  disaster data from BIBREF5
* Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada
Question: How many James Bond movies did Daniel Craig star in?
Answer: Daniel Craig starred in 5 James Bond movies
Question: Is switchgrass a good biofuel source?
Answer: Switchgrass is an excellent biofuel source for many reasons.  Switchgrass grows natively throughout the United States, Mexico, and Canada.  It has a dramatically higher energy yield than corn.  It requires almost no pesticides to grow and no fertilizer.  Switchgrass actually pulls carbon out of the air and puts it into the ground through it's large root system, thus enriching the soil it grows in rather than depleting it.  Once a switchgrass plant is established it grows back naturally every year for 18 to 20 years.
Question: Which soccer player has the most number of career goals?
Answer: Cristiano Ronaldo has the highest number of career goals. His current tally is 834 goals. He is still active as a professional soccer player. Lionel Messi is currently in third place with 803 goals and is also active.
Question: Can cats communicate?
Evidence: Cats need to communicate with each other for bonding, and relating with each other; they need to collaborate, play, and share resources. When they communicate with people, they do so to get what they need or want, such as food, affection, or play.

Cats use a range of communication methods such as vocal, visual, tactile and olfactory. Cats mostly meow to communicate with people, rarely with other animals. As such, the cats' communication methods have been significantly affected by domestication. Up to 21 different cat vocalizations have been studied. It is now evident that domestic cats meow more than feral cats.
Answer: Cat vocalizations have been categorized according to a range of characteristics. Originally suggested by Mildred Moelk, cat sounds are often divided into three main classes:

sounds produced with the mouth closed (murmurs – purring, trilling)
sounds produced when the mouth is first opened and then gradually closed (meowing, howling, yowling)
sounds produced with the mouth held tensely open in the same position (growls, snarls, hisses, spits, chattering, and chirping).
In 1944, Moelk published the first phonetic study of cat sounds. She listened very carefully to her own cats and organized their sounds into 16 phonetic patterns divided into three main categories. She also used a phonetic alphabet to transcribe or write down the different sounds. She claimed that cats had six different forms of meows to represent friendliness, confidence, dissatisfaction, anger, fear and pain. Moelk classified eight other sounds involved in mating and fighting by listening to the animals in her yard and on the street.

Brown et al. categorized vocal responses of cats according to the behavioral context: during separation of kittens from mother cats, during food deprivation, during pain, prior to or during threat or attack behavior, as in disputes over territory or food, during a painful or acutely stressful experience, as in routine prophylactic injections and during kitten deprivation. Less commonly recorded calls from mature cats included purring, conspecific greeting calls or murmurs, extended vocal dialogues between cats in separate cages, "frustration" calls during training or extinction of conditioned responses.

Miller classified vocalizations into five categories according to the sound produced: the purr, chirr, call, meow and growl/snarl/hiss.

Owens et al. categorized cat vocalizations based on their acoustic structures. There are three categories: tonal sounds, pulse sounds, and broadband sounds. Tonal sounds are further categorized into groups of harmonically structured sounds or regular tonal sounds. Pulse vocalizations are separated into pulse bursts and hybrid pulse bursts with tonal endings. Broadband sounds are separated into four groups: non-tonal broadband sounds, broadband sounds with tonal beginnings, broadband sounds with short tonal elements, and broadband sounds with long tonal endings.
Question: Who was the winner of Top Chef Season 4?
Evidence: Stephanie Izard is an American chef and television personality best known as the first female chef to win Bravo's Top Chef, taking the title during its fourth season. She is the co-owner and executive chef of three award-winning Chicago restaurants, Girl and the Goat, Little Goat, and Duck Duck Goat, and opened her first restaurant, Scylla (now closed) as chef-owner at the age of 27. Izard received a James Beard Foundation Award for "Best Chef: Great Lakes" in 2013 for her work at Girl and the Goat. She has made a number of appearances on Top Chef since her win, both as a guest judge on subsequent seasons and as a participant in Top Chef Duels. In 2017, Izard competed in the Food Network series Iron Chef Gauntlet, where she overall defeated chefs Bobby Flay, Michael Symon, and Masaharu Morimoto to obtain the title of Iron Chef.
Answer: Stephanie Izard won the fourth season of Top Chef
Question: What colors are in the traditional style of American tattoos?
Answer: American traditional tattoos consist mostly of thick black lines with black shading and red, blue, yellow, and green. Sometimes you will see brown and white also used in American traditional tattoos.
Question: Did the authors evaluate their system output for coherence?
Evidence: We want to further study how the proposed cache-based neural model influence coherence in document translation. For this, we follow Lapata2005Automatic to measure coherence as sentence similarity. First, each sentence is represented by the mean of the distributed vectors of its words. Second, the similarity between two sentences is determined by the cosine of their means.
we follow Lapata2005Automatic to measure coherence as sentence similarity
Answer: Yes.
Question: Tell me what league each of the following teams play in:
Anaheim Ducks
Baltimore Ravens
St. Louis Cardinals
Atlanta Falcons
Toronto Raptors
New Orleans Pelicans
Arizona Cardinals
Atlanta Hawks
Baltimore Orioles
Seattle Seahawks
Pittsburgh Penguins
Philadelphia Eagles
Toronto Blue Jays
Answer: Here are the leagues the teams you mentioned play in:
Anaheim Ducks - NHL
Baltimore Ravens - NFL
St. Louis Cardinals - MLB
Atlanta Falcons - NFL
Toronto Raptors - NBA
New Orleans Pelicans - NBA
Arizona Cardinals - NFL
Atlanta Hawks - NBA
Baltimore Orioles - MLB
Seattle Seahawks - NFL
Pittsburgh Penguins - NHL
Philadelphia Eagles - NFL
Toronto Blue Jays - MLB
Question: Identify which animal species is alive or extinct: Thylacoleo, Skipjack Tuna
Answer: Thylacoleo is extinct, Skipjack Tuna is alive.