DataHammer/scimrc huggingface dataset:
Question: How large is the dataset for the training of the model？
Evidence:  This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified.
While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:
The SemEval-2010 benchmark dataset (Kim et al., 2010) is composed of 244 scientific articles collected from the ACM Digital Library (conference and workshop papers). The input papers ranged from 6 to 8 pages and were converted from PDF format to plain text using an off-the-shelf tool3. The only preprocessing applied is a systematic dehyphenation at line breaks4 and removal of author-assigned keyphrases. Scientific articles were selected from four different research areas as defined in the ACM classification, and were equally distributed into training (144 articles) and test (100 articles) sets.
Answer: 144 articles ranged from 6 to 8 pages.
Question: Tell me whether you drive on the right or left side of the road in these countries: USA, Mexico, Spain, England, New Zealand, Japan
Answer: USA: Right Side
Mexico: Right Side
Spain: Right Side
England: Left Side
New Zealand: Left Side
Japan: Left Side
Question: Given this paragraph about Saffron, where does this name originally come from?
Evidence: A degree of uncertainty surrounds the origin of the English word "saffron". It might stem from the 12th-century Old French term safran, which comes from the Latin word safranum, from the Arabic (زَعْفَرَان), za'farān, which comes from the Persian word zarparan meaning "gold strung" (implying either the golden stamens of the flower or the golden color it creates when used as flavor).
Answer: It comes from the Persian word zarparan
Question: What are some good steps to getting an AWS cloud certification and getting a cloud job?
Answer: Getting an AWS certification is a great way to get started with learning about cloud and potentially starting a career in it! Here are some good first steps:
1. Start with the free AWS Cloud Practitioner course and overview: https://aws.amazon.com/training/digital/aws-cloud-practitioner-essentials/?cp=sec&sec=prep
2. Pass the AWS cloud practitioner exam
3. Decide which specialization path to go down — options could include the architecture path, more of a data specialist, or a Security specialization, with multiple other options
4. If you choose the architecture path, Stephane Marek’s courses (https://www.udemy.com/user/stephane-maarek/) are excellent, frequently updated, and inexpensive. 
5. The next step is the AWS Solution Architect—Associate exam, which is difficult but the above courses prepare you well (combined with practice tests, note below on that)
6. To get hands on experience here is a list of interesting cloud sample projects, some easy and some hard: https://www.reddit.com/r/sysadmin/comments/8inzn5/so_you_want_to_learn_aws_aka_how_do_i_learn_to_be/
7. In preparing for the exams, especially the more advanced ones, Jon Bonso has excellent practice question banks: https://tutorialsdojo.com/courses/aws-certified-solutions-architect-associate-practice-exams/.
8. Once you have the Solution Architect—Associate certification and some sample projects complete you can likely start looking for a job in the cloud space. Companies are looking for people with those skills + cert + some hands-on experience, so good luck!
Question: What two different sets of features are used to train the informative prior?
Evidence: We used the Mboshi5K corpus BIBREF13 as a test set for all the experiments reported here. Mboshi (Bantu C25) is a typical Bantu language spoken in Congo-Brazzaville. It is one of the languages documented by the BULB (Breaking the Unwritten Language Barrier) project BIBREF14 . This speech dataset was collected following a real language documentation scenario, using Lig_Aikuma, a mobile app specifically dedicated to fieldwork language documentation, which works both on android powered smartphones and tablets BIBREF15 . The corpus is multilingual (5130 Mboshi speech utterances aligned to French text) and contains linguists' transcriptions in Mboshi (in the form of a non-standard graphemic form close to the language phonology). It is also enriched with automatic forced-alignment between speech and transcriptions. The dataset is made available to the research community. More details on this corpus can be found in BIBREF13 .
TIMIT is also used as an extra speech corpus to train the informative prior. We used two different set of features: the mean normalized MFCC + INLINEFORM0 + INLINEFORM1 generated by HTK and the Multilingual BottleNeck (MBN) features BIBREF16 trained on the Czech, German, Portuguese, Russian, Spanish, Turkish and Vietnamese data of the Global Phone database.
TIMIT is also used as an extra speech corpus to train the informative prior. We used two different set of features: the mean normalized MFCC + ∆ + ∆∆ generated by HTK and the Multilingual BottleNeck (MBN) features [17] trained on the Czech, German, Portuguese, Russian, Spanish, Turkish and Vietnamese data of the Global Phone database.
Answer: Theauthors used two different set of features: the mean normalized MFCC + ∆ + ∆∆ generated by HTK and the Multilingual BottleNeck (MBN) features trained on the Czech, German, Portuguese, Russian, Spanish, Turkish and Vietnamese data of the Global Phone database.
Question: Classify each artist as "country" or "rap": Brad Paisley, Morgan Wallen, 50 Cent, Thomas Rhett, 21 Savage, Gucci Mane
Answer: Brad Paisley: Country
Morgan Wallen: Country
50 Cent: Rap
Thomas Rhett: Country
21 Savage: Rap
Gucci Mane: Rap
Question: Why are plants green?
Answer: Parts of a plant that are green, such as leaves, are green because they contain chlorophyll.  Chlorophyll absorbs light energy (but less so green light than red or blue) and helps convert that energy into food for the plant as part of photosynthesis.

A more whimsical answer, which is not scientifically accurate, is that plants need both sunlight (which is yellow) and water (which is blue) to live and yellow plus blue makes green.
Question: What factors contribute to interpretive biases according to this research?
Evidence: While the choice of wording helps to convey bias, just as crucial is the way that the reporters portray the march as being related to other events. Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march are crucial factors in conveying bias. Townhall's bias against the March of Science expressed in the argument that it politicizes science cannot be traced back to negative opinion words; it relies on a comparison between the March for Science and the Women's March, which is portrayed as a political, anti-Trump event. Newsbusters takes a different track: the opening paragraph conveys an overall negative perspective on the March for Science, despite its neutral language, but it achieves this by contrasting general interest in the march with a claimed negative view of the march by many “actual scientists.” On the other hand, the New York Times points to an important and presumably positive outcome of the march, despite its controversiality: a renewed look into the role of science in public life and politics. Like Newsbusters, it lacks any explicit evaluative language and relies on the structural relations between events to convey an overall positive perspective; it contrasts the controversy surrounding the march with a claim that the march has triggered an important discussion, which is in turn buttressed by the reporter's mentioning of the responses of the Times' readership.
Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march are crucial factors in conveying bias.
Answer: The answers are shown as follows:
* Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march
Question: What does an east coast oyster taste like?
Answer: East Coast oysters tend to have high salinity and a lot of minerality. Predominant flavors include brine, copper, seaweed, or vegetal tastes. The meat, or texture, is often chewy, distinctly more solid than a West Coast oyster.
Question: How can I get from San Francisco airport to downtown San Francisco?
Answer: To travel from San Francisco airport to downtown San Francisco, there are multiple different options. You can travel by car or rideshare which normally takes around 20-60 minutes depending on traffic. Another option is to take public transit using BART which has a direct link inside the airport. This can take up to an hour depending on the time of day and weather.
Question: Why the Kings will beat the Warriors in the Playoffs
Answer: The Kings and Warriors both have high octane offenses and are both weak on defense. Both teams have superior shooting but where Kings lack in playoff experience, they gain in youth. Warriors star players are all over the age of 31 and are on their last legs. Where Kings will thrive is in the paint, the warriors have no one to stop Sabonis. He will dominated Draymond Green in the paint and force double teams which will lead to another Sabonis skill, passing.
Question: Name a winner of the Fields Medal in mathematics.
Evidence: Maryam Mirzakhani was an Iranian mathematician and a professor of mathematics at Stanford University. Her research topics included Teichmüller theory, hyperbolic geometry, ergodic theory, and symplectic geometry. In 2005, as a result of her research, she was honored in Popular Science's fourth annual "Brilliant 10" in which she was acknowledged as one of the top 10 young minds who have pushed their fields in innovative directions. Mirzakhani was honored with the Fields Medal, the most prestigious award in mathematics, becoming the first Iranian to be honored with the award and the first of only two women to date. The award committee cited her work in "the dynamics and geometry of Riemann surfaces and their moduli spaces". Mirzakhani died of breast cancer at the age of 40 in 2017.
Answer: Maryam Mirzakhani, an Iranian mathematician, received the Fields Medal in mathematics in 2014 before her untimely death from breast cancer in 2017.
Question: Why is drinking coffee better before sport?
Answer: Consuming pre-workout caffeine may rev up your metabolism, suppress the effect of perceived exertion, improve microcirculation, and enhance your athletic performance
Question: Give me a list of great sci-fi
Answer: Sci-fi movies can be graded by many different dimensions to determine "the best".  The largest universe market cap for a sci fi universe is Star Wars created by George Lucas and all its canon. Star Wars at its core story is about an all binding energy called the force which bound the galaxy together which a few people could harness its power.  Its a classic story of good vs evil paired with amazing special effects to give the viewer an immersive believable universe.  One of the longest running Sci Fi Series with a cult following was Stargate SG-1 which aired for 10 seasons and had two movies created.    Stargate SG-1 is about a network of inter-planetary gates that allowed for anyone to instantaneously travel between entire different planets.  The saga is ripe with discovering new cultures in foreign planets, intermixed with long story arcs about formidable foes and a team with a very diverse set of people and aliens battling for good of the galaxy.  Stargate SG-1 had amazing character development over its 10 season course which made it a great fan favorite.  The 2004 remake of Battlestar Galactica is an epic story of artificial intelligence versus humanity.  Humanity is being hunted and on the brink of all out extinction.  Its a war-time classic that explores the factors that make us human, what a society is about, and survival.
Question: What is a good dad joke?
Answer: What do you call an okay factory?  A satisfactory.
Question: To which family belongs the Caltha scaposa
Answer: Caltha scaposa is a low, perennial herb with one or two yellow hermaphrodite saucer-shaped flowers. This marsh-marigold species belongs to the buttercup family, grows in moist alpine fields and is native to the eastern Himalayas and the mountains on the eastern margin of the Tibetan highland.
Question: Which six domains are covered in the dataset?
Evidence: To help solve the data problem we present Taskmaster-1, a dataset consisting of 13,215 dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations. For the spoken dialogs, we created a “Wizard of Oz” (WOz) system BIBREF12 to collect two-person, spoken conversations. Crowdsourced workers playing the “user" interacted with human operators playing the “digital assistant” using a web-based interface. In this way, users were led to believe they were interacting with an automated system while it was in fact a human, allowing them to express their turns in natural ways but in the context of an automated interface. We refer to this spoken dialog type as “two-person dialogs". For the written dialogs, we engaged crowdsourced workers to write the full conversation themselves based on scenarios outlined for each task, thereby playing roles of both the user and assistant. We refer to this written dialog type as “self-dialogs". In a departure from traditional annotation techniques BIBREF10, BIBREF8, BIBREF13, dialogs are labeled with simple API calls and arguments. This technique is much easier for annotators to learn and simpler to apply. As such it is more cost effective and, in addition, the same model can be used for multiple service providers.
To help solve the data problem we present Taskmaster-1, a dataset consisting of 13,215 dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations. 
Answer: The answers are shown as follows:
* ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations
Question: What is Redshift?
Answer: Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services. It is built on top of technology from the massive parallel processing data warehouse company ParAccel, to handle large scale data sets and database migrations.
Question: Which are used to embed the vectors to target sentences for the decoder part?
Evidence: Our main goal in this section is to see if Image Captioning models could learn well with Vietnamese language. To accomplish this task, we train and evaluate our dataset with two published Image Captioning models applying encoder-decoder architecture. The models we propose are Neural Image Captioning (NIC) model BIBREF14, Image Captioning model from the Pytorch-tutorial BIBREF15 by Yunjey.
Overall, CNN is first used for extracting image features for encoder part. The image features which are presented in vectors will be used as layers for decoding. For decoder part, RNN - LSTM are used to embed the vectors to target sentences using words/tokens provided in vocabulary.
For decoder part, RNN - LSTM are used to embed the vectors to target sentences using words/tokens provided in vocabulary.
Answer: RNN - LSTM are used.
Question: What are the two main parts of DOLORES?
Evidence: Given a knowledge graph $G=(E, R)$ where $E$ denotes the set of entities and $R$ denotes the set of relations among those entities, we seek to learn $d$ -dimensional embeddings of the entities and relations. In contrast to previous knowledge graph embedding methods like BIBREF2 , BIBREF9 , BIBREF4 , BIBREF10 , BIBREF5 which are based on shallow models and operates primarily on triples, our method Dolores uses a deep neural model to learn “deep” and “contextualized” knowledge graph embeddings.
Having formulated the problem, we now describe Dolores. Dolores consists of two main components:
Path Generator This component is responsible for generating a large set of entity-relation chains that reflect the varying contextual usages of entities and relations in the knowledge graph.
Learner This component is a deep neural model that takes as input entity-relation chains and learns entity and relation embeddings which are weighted linear combination of internal states of the model thus capturing context dependence.
Both of the above components are motivated by recent advances in learning deep representations of words in language modeling. We motivate this below and also highlight key connections that enable us to build on these advances to learn Dolores knowledge graph embeddings.
DOLORES consists of two main components: 1. Path Generator This component is responsible for generating a large set of entity-relation chains that reflect the varying contextual usages of entities and relations in the knowledge graph. 2. Learner This component is a deep neural model that takes as input entity-relation chains and learns entity and relation embeddings which are weighted linear combination of internal states of the model thus capturing context dependence.
Answer: Path Generator and Learner.
Question: Based on the paragraph below, what are some key elements to consider in designing an OLTP system?
Evidence: Systems design
To build an OLTP system, a designer must know that the large number of concurrent users does not interfere with the system's performance. To increase the performance of an OLTP system, a designer must avoid excessive use of indexes and clusters.

The following elements are crucial for the performance of OLTP systems:

Rollback segments
Rollback segments are the portions of database that record the actions of transactions in the event that a transaction is rolled back. Rollback segments provide read consistency, rollback transactions, and recovery of the database.
Clusters
A cluster is a schema that contains one or more tables that have one or more columns in common. Clustering tables in a database improves the performance of join operations.
Discrete transactions
A discrete transaction defers all change to the data until the transaction is committed. It can improve the performance of short, non-distributed transactions.
Block size
The data block size should be a multiple of the operating system's block size within the maximum limit to avoid unnecessary I/O.
Buffer cache size
SQL statements should be tuned to use the database buffer cache to avoid unnecessary resource consumption.
Dynamic allocation of space to tables and rollback segments
Transaction processing monitors and the multi-threaded server
A transaction processing monitor is used for coordination of services. It is like an operating system and does the coordination at a high level of granularity and can span multiple computing devices.
Partition (database)
Partition use increases performance for sites that have regular transactions while still maintaining availability and security.
Database tuning
With database tuning, an OLTP system can maximize its performance as efficiently and rapidly as possible.
Answer: The designer of an OLTP system must ensure that the number of concurrent users does not interfere with the performance of the system. Some key elements include rollback segments (which record actions of transactions to enable rollback if needed); clusters (data schema which which improves performance of join operations); discrete transactions (ensures the data is not changed until the transaction is committed, which improves performance); block size (reduces unnecessary I/O by ensuring data block size is a multiple of operating system block size); buffer cache size (when SQL statements are tuned to use buffer cache, it avoids unnecessary consumption); dynamic allocation of space to tables and rollback segments; transaction processing monitors (used for coordination of services, acting as kind of an operating system that can span multiple computing devices); database partitions (using partitions increases performance while balancing availability and security); and database tuning (allowing the OLTP system to maximize its performance efficiently and rapidly).
Question: List the weapons of Goddess Durga in a comma separated manner.
Evidence: Durga is a warrior goddess, and she is depicted to express her martial skills. Her iconography typically resonates with these attributes, where she rides a lion or a tiger, has between eight and eighteen hands, each holding a weapon to destroy and create. She is often shown in the midst of her war with Mahishasura, the buffalo demon, at the time she victoriously kills the demonic force. Her icon shows her in action, yet her face is calm and serene. In Hindu arts, this tranquil attribute of Durga's face is traditionally derived from the belief that she is protective and violent not because of her hatred, egotism or getting pleasure in violence, but because she acts out of necessity, for the love of the good, for liberation of those who depend on her, and a mark of the beginning of soul's journey to creative freedom.

Durga traditionally holds the weapons of various male gods of Hindu mythology, which they give her to fight the evil forces because they feel that she is shakti (energy, power). These include the chakra, conch, bow, arrow, sword, javelin, trishula, shield, and a noose. These weapons are considered symbolic by Shakta Hindus, representing self-discipline, selfless service to others, self-examination, prayer, devotion, remembering her mantras, cheerfulness and meditation. Durga herself is viewed as the "Self" within and the divine mother of all creation. She has been revered by warriors, blessing their new weapons. Durga iconography has been flexible in the Hindu traditions, where for example some intellectuals place a pen or other writing implements in her hand since they consider their stylus as their weapon.
Answer: Chakra, conch, bow, arrow, sword, javelin, trishula, shield, a noose