DataHammer/scimrc huggingface dataset:
Question: What's the difference between metrics and logs?
Answer: Logs have finer granularity and are therefore slower to query. Metrics are generally aggregated and faster to query. For example logs could be: "server 1 is running", "server 2 is running", and the corresponding metric would be "there are 2 servers running".
Question: According to the passage below, what is Reinforcement Learning?
Evidence: Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
Answer: In machine learning, reinforcement learning (RL) is a domain concerned with how intelligent agents respond to maximize their total rewards. Alongside supervised and unsupervised learning, it is one of 3 types of machine learning. Unlike supervised learning, RL does not require labeled data.
Question: Why is the Pacific NorthWest so unique?
Answer: The Pacific NorthWest (PNW) is indeed a unique location for living. It combines several good qualities including job opportunities, outdoor activities and amazing scenery. 

The PNW is also home for a lot of transplants from other locations bringing together very diverse and rich cultures.
Question: What are good tips to drive in the snow?
Answer: Driving in the snow can be a fun experience but it can be equally dangerous. Always prepare a medical kit and a bag of supplies such as potable water bottles, imperishable items, and a radio in case of emergencies. Make sure your vehicle has four wheel traction, and if necessary, install snow chains on the tires. If you find yourself in a snowstorm, drive extra carefully, and drive slow and safe until you obtain better visibility.
Question: What are the strengths of the GluonCV/NLP toolkits?
Evidence: Deep learning, a sub-field of machine learning research, has driven the rapid progress in artificial intelligence research, leading to astonishing breakthroughs on long-standing problems in a plethora of fields such as computer vision and natural language processing. Tools powered by deep learning are changing the way movies are made, diseases are diagnosed, and play a growing role in understanding and communicating with humans.
Such development is made possible by deep learning frameworks, such as Caffe BIBREF0 , Chainer BIBREF1 , CNTK BIBREF2 , Apache (incubating) MXNet BIBREF3 , PyTorch BIBREF4 , TensorFlow BIBREF5 , and Theano BIBREF6 . These frameworks have been crucial in disseminating ideas in the field. Specifically, imperative tools, arguably spearheaded by Chainer, are easy to learn, read, and debug. Such benefits make imperative programming interface quickly adopted by the Gluon API of MXNet (while can be seamlessly switched to symbolic programming for high performance), PyTorch, and TensorFlow Eager.
Leveraging the imperative Gluon API of MXNet, we design and develop the GluonCV and GluonNLP (referred to as GluonCV/NLP hereinafter) toolkits for deep learning in computer vision and natural language processing. To the best of our knowledge, GluonCV/NLP are the first open source toolkits for deep learning in both computer vision and natural language processing that simultaneously i) provide modular APIs to allow customization by re-using efficient building blocks; ii) provide pre-trained state-of-the-art models, training scripts, and training logs to enable fast prototyping and promote reproducible research; iii) leverage the MXNet ecosystem so that models can be deployed in a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala.
Leveraging the imperative Gluon API of MXNet, we design and develop the GluonCV and GluonNLP (referred to as GluonCV/NLP hereinafter) toolkits for deep learning in computer vision and natural language processing. GluonCV/NLP simultaneously i) provide modular APIs to allow customization by re-using efficient building blocks; ii) provide pre-trained state-of-the-art models, training scripts, and training logs to enable fast prototyping and promote reproducible research; iii) provide models that can be deployed in a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala (via the MXNet ecosystem).
Answer: The toolkits:i) provide modular APIs to allow customization by re-using efficient building blocks; ii) provide pre-trained state-of-the-art models, training scripts, and training logs to enable fast prototyping and promote reproducible research; iii) leverage the MXNet ecosystem so that models can be deployed in a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, and Scala.
Question: What do Georgians like about their home state?
Answer: I like the abundant forests of Georgia. The city of Atlanta is quite tree-filled, has an interesting history, is less expensive than other cities of a similar size, and has the Chattahoochee River flowing through it. To the north of the state is the beginning of the Appalachian Mountain Range. While not particularly tall mountains, they offer impressive vistas, moderate day hikes, and many waterfalls. The coastal region of Georgia has some beaches and historic Savanah. Farther inland to the south is the Okeefenokee Swamp. Another appealing aspect is the climate. Summer is hot, but that makes for great swimming weather. Winter is fairly mild, but we get an occasional snowfall to keep things interesting. Fall and Spring are temperate and great for outdoor activities. Georgia also is home to top-tier universities, including one of the best values in higher education, Georgia Institute of Technology, and #1 party school University of Georgia. Finally, the state is politically moderately conservative, with an emphasis on freedom and an appealing economy for businesses.
Question: What is the difference between an alligator and a crocodile?
Answer: Alligators and crocodiles have many similarities, including both having short legs, powerful tails, and long snouts, but there are also a number of ways to tell them apart.  First, they are usually found in different locations, with alligators preferring freshwater areas and crocodiles being found in saltwater habitats.  Second, their snouts are shaped differently, with alligators having a broad, U-shaped snout, where the snout of crocodiles is more narrow and V-shaped.  You can often see some of the bottom teeth on a crocodile, even when its mouth is shut, but not on an alligator.  Finally, alligators usually appear to be more black or dark gray, with crocodiles appearing more tan or olive.
Question: Given these paragraphs about the Tesla bot, what is its expected height and weight?
Evidence: Optimus, also known as Tesla Bot, is a conceptual general-purpose robotic humanoid under development by Tesla, Inc. It was announced at the company's Artificial Intelligence (AI) Day event on August 19, 2021. CEO Elon Musk claimed during the event that Tesla would likely build a prototype by 2022. Musk is on record having said that he thinks Optimus "has the potential to be more significant than the vehicle business over time."

Tesla Bot is planned to measure 5 ft 8 in (173 cm) tall and weigh 125 lb (57 kg). According to the presentation made during the first AI Day event, a Tesla Bot will be "controlled by the same AI system Tesla is developing for the advanced driver-assistance system used in its cars" and have a carrying capacity of 45 lb (20 kg). Proposed tasks for the product are ones that are "dangerous, repetitive and boring", such as providing manufacturing assistance.

https://en.wikipedia.org/wiki/Optimus_(robot)
Answer: Tesla Bot is planned to measure 5 ft 8 in (173 cm) tall and weigh 125 lb (57 kg).
Question: Classify the movies into Romance and Drama genres.

Pyaar Diwana Hota Hai, Bas Itna Sa Khwaab Hai, Paagalpan, Uljhan, Yeh Zindagi Ka Safar
Answer: Romance - Pyaar Diwana Hota Hai, Bas Itna Sa Khwaab Hai, Paagalpan
Drama - Uljhan, Yeh Zindagi Ka Safar
Question: Given this paragraph about the dimensions of a volleyball court, is there a difference between the hight of the net for men's competitions vs women's competitions?
Evidence: A volleyball court is 9 m × 18 m (29.5 ft × 59.1 ft), divided into equal square halves by a net with a width of one meter (39.4 in). The top of the net is 2.43 m (7 ft 11+11⁄16 in) above the centre of the court for men's competition, and 2.24 m (7 ft 4+3⁄16 in) for women's competition, varied for veterans and junior competitions.
Answer: The net is 19 cm higher for men's competitions (243 cm) than for women's competitions (224 cm).
Question: Is butter required to make cookies?
Answer: Butter is not required to make cookies. Vegetable oils can be a good alternative, especially for people who want to reduce the amount of saturated fat in their diet. There are also an increasing number of plant based butter-like alternatives.
Question: What do you use to calculate word/sub-word embeddings
Evidence: Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best. We sample and analyze the category attributes and find that many self-filled attributes contain misspellings. The FastText algorithm represents words by a sum of its character n-grams and it is much robust against problems like misspellings. In summary, FastText has greater advantages in dealing with natural language corpus usually with spelling mistakes.
Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best.
Answer: The answers are shown as follows:
* FastText
Question: Choose the word which is different from the rest: Swan, Chicken, Crocodile, Frog, Snake.
Answer: The options provided include multiple reptiles and birds. Reptiles are air-breathing, cold-blooded vertebrates that have scaly bodies rather than hair or feathers. Birds are warm-blooded egg-laying vertebrate distinguished by the possession of feathers, wings, and a beak. Since there are multiple reptiles and birds listed the answer lies in their habitat. The correct answer is chicken as all others listed can live in water.
Question: What is the difference between Malayalam and Malayalaee?
Answer: Malayalam is a Dravidian language spoken in the southern state of Kerala, India. it is closely related to Tamil though the origins of the language themselves are still disputed among scholars. Malayalaee (or Malayali), on the other hand, is a term used to denote a person originating from Kerala. The term Keralite is also often used as an alternative term to denote someone hailing from Kerala.
Question: What are the emotion detection tools used for audio and face input?
Evidence: We preprocess the visual data by extracting the sequence of images for each interaction from the point where the agent's or the co-driver's question was completely uttered until the driver's response stops. The average length is 16.3 seconds, with the minimum at 2.2s and the maximum at 54.7s. We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions). It delivers frame-by-frame scores ($\in [0;100]$) for discrete emotional states of joy, anger and fear. While joy corresponds directly to our annotation, we map anger to our label annoyance and fear to our label insecurity. The maximal average score across all frames constitutes the overall classification for the video sequence. Frames where the software is not able to detect the face are ignored.
We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions).
Answer: The answers are shown as follows:
* cannot be disclosed due to licensing restrictions
Question: Given a reference text about Alberto Michán Halbinger, in what olympics did he compete?
Evidence: Alberto Michán Halbinger (born 2 December 1978) is an Israeli-Mexican Olympic horse rider. Born in Mexico, he competes for Israel. He qualified to represent Israel at the 2020 Summer Olympics in Tokyo.

Biography
Michan was born in Mexico; his family comes from Israel.

He was a finalist at the FEI World Cup Jumping in 2006 and 2007.

He competed in the 2006 and 2014 World Equestrian Games.

He competed for Mexico at the 2008 Summer Olympics in Beijing, both in individual jumping (coming in 29th) and in team jumping (coming in 8th).

He earned a bronze medal in Team Jumping at the 2011 Pan American Games in Guadalajara, Mexico.

At the 2012 Summer Olympics, he tied with Scott Brash and Nick Skelton, both of the team-gold medal-winning United Kingdom, for 5th in individual jumping. He was part of the Mexican team for team jumping, which finished in fifth place.

He qualified to represent Israel at the 2020 Summer Olympics in Tokyo.
Answer: Alberto Michán Halbinger competed in the 2008 and 2012 Summer Olympics.
Question: Based on the reference text about coffee, tell me where coffee plants are primarily grown and the most common bean types.
Evidence: Coffee is a beverage prepared from roasted coffee beans. Darkly colored, bitter, and slightly acidic, coffee has a stimulating effect on humans, primarily due to its caffeine content. It has the highest sales in the world market for hot drinks.

Seeds of the Coffea plant's fruits are separated to produce unroasted green coffee beans. The beans are roasted and then ground into fine particles that are typically steeped in hot water before being filtered out, producing a cup of coffee. It is usually served hot, although chilled or iced coffee is common. Coffee can be prepared and presented in a variety of ways (e.g., espresso, French press, caffè latte, or already-brewed canned coffee). Sugar, sugar substitutes, milk, and cream are often used to mask the bitter taste or enhance the flavor.

Though coffee is now a global commodity, it has a long history tied closely to food traditions around the Red Sea. The earliest credible evidence of coffee drinking in the form of the modern beverage appears in modern-day Yemen from the mid-15th century in Sufi shrines, where coffee seeds were first roasted and brewed in a manner similar to current methods. The Yemenis procured the coffee beans from the Ethiopian Highlands via coastal Somali intermediaries and began cultivation. By the 16th century, the drink had reached the rest of the Middle East and North Africa, later spreading to Europe. In the 20th century, coffee became a global commodity, creating different coffee cultures around the world.

The two most commonly grown coffee bean types are C. arabica and C. robusta. Coffee plants are cultivated in over 70 countries, primarily in the equatorial regions of the Americas, Southeast Asia, the Indian subcontinent, and Africa. As of 2018, Brazil was the leading grower of coffee beans, producing 35% of the world's total. Green, unroasted coffee is traded as an agricultural commodity. Despite sales of coffee reaching billions of dollars worldwide, farmers producing coffee beans disproportionately live in poverty. Critics of the coffee industry have also pointed to its negative impact on the environment and the clearing of land for coffee-growing and water use
Answer: Coffee plants are cultivated mainly in the equatorial regions of the Americas, Southeast Asia, the Indian subcontinent, and Africa. The most common types of coffee beans are arabica and robusta.
Question: What are fun activities to do in New Orleans?
Answer: New Orleans is an exciting place to visit. Many people go to New Orleans for Mardi Gras parades, with creative floats and beads thrown to onlookers, but this event is just once a year. No matter what time of year you go, make time to appreciate some live music. Jazz and blues are standouts around Frenchman street. The food and beverage scene is also quite compelling, with classics like red beans and rice, fried chicken, delicious seafood, and beignets. Two weekends in the spring combine the music and the food in a giant festival called Jazz Fest, with many stages and vendors spread out for an open-air celebration of the best the city has to offer. There are also comprehensive culture and history museums, including the famous World War 2 museum, because the boats used to storm the beaches at Normandy were inspired by watercraft common to Louisiana.
Question: From the passage provided, extract supported consumers for Spark streaming. Separate them with a comma.
Evidence: Spark Streaming uses Spark Core's fast scheduling capability to perform streaming analytics. It ingests data in mini-batches and performs RDD transformations on those mini-batches of data. This design enables the same set of application code written for batch analytics to be used in streaming analytics, thus facilitating easy implementation of lambda architecture. However, this convenience comes with the penalty of latency equal to the mini-batch duration. Other streaming data engines that process event by event rather than in mini-batches include Storm and the streaming component of Flink. Spark Streaming has support built-in to consume from Kafka, Flume, Twitter, ZeroMQ, Kinesis, and TCP/IP sockets.

In Spark 2.x, a separate technology based on Datasets, called Structured Streaming, that has a higher-level interface is also provided to support streaming.

Spark can be deployed in a traditional on-premises data center as well as in the cloud.
Answer: Kafka, Flume, Twitter, ZeroMQ, Kinesis, TCP/IP sockets
Question: Types of Treaking ?
Answer: 1) Tea house trek, known as lodge trekking, is the most common form of trekking. Tea houses are for eating and resting during hikes or treks. Several popular trek routes like the Everest circuit, Annapurna circuit, and Langtang circuit have tea houses in different areas at regular intervals contributing trekkers to pack fewer supplies and food. 


2) Camping Trekking

Unlike tea house hiking, camping trekking in Nepal is less well-known. All trekking areas provide the option of doing a camping trek. Team members include a guide, an assistant guide, a porter, a chef, a kitchen assistant, and a camp helper.

3) GAP Trekking
GAP stands for Guide, Accommodation, and Porters. Trekking guides and porters are on hand to accompany travellers on their highland trek. Those prepared to travel with just a guide and porter to unique adventure locations like snowy mountains, endless cultural variety, and stunning surroundings may get more out of their trek.
Question: What is used to train the PV-DBOW model?
Evidence: Our framework combines text-embedding, geometric graph construction and multi-resolution community detection to identify, rather than impose, content-based clusters from free, unstructured text in an unsupervised manner.
Figure 1 shows a summary of our pipeline. First, we pre-process each document to transform text into consecutive word tokens, where words are in their most normalised forms, and some words are removed if they have no distinctive meaning when used out of context BIBREF5 , BIBREF6 . We then train a paragraph vector model using the Document to Vector (Doc2Vec) framework BIBREF7 on the whole set (13 million) of preprocessed text records, although training on smaller sets (1 million) also produces good results. This training step is only done once. This Doc2Vec model is subsequently used to infer high-dimensional vector descriptions for the text of each of the 3229 documents in our target analysis set. We then compute a matrix containing pairwise similarities between any pair of document vectors, as inferred with Doc2Vec. This matrix can be thought of as a full, weighted graph with documents as nodes and edges weighted by their similarity. We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF8 , a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The derived MST-kNN graph is analysed with Markov Stability BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , a multi-resolution dynamics-based graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. MS uses a diffusive process on the graph to reveal the multiscale organisation at different resolutions without the need for choosing a priori the number of clusters, scale or organisation. To analyse a posteriori the different partitions across levels of resolution, we use both visualisations and quantitative scores. The visualisations include word clouds to summarise the main content, graph layouts, as well as Sankey diagrams and contingency tables that capture the correspondences across levels of resolution and relationships to the hand-coded classifications. The partitions are also evaluated quantitatively to score: (i) their intrinsic topic coherence (using pairwise mutual information BIBREF13 , BIBREF14 ), and (ii) their similarity to the operator hand-coded categories (using normalised mutual information BIBREF15 ). We now expand on the steps of the computational framework.
Here, we use the Gensim Python library [24] to train the PV-DBOW model.
Answer: Gensim Python library.
Question: How much does this system outperform prior work?
Evidence: FLOAT SELECTED: Table 1: Accuracy on the ListOps dataset. All models have 128 dimensions. Results for models with * are taken from Nangia and Bowman (2018).
FLOAT SELECTED: Table 1: Accuracy on the ListOps dataset. All models have 128 dimensions. Results for models with * are taken from Nangia and Bowman (2018).
Answer: The system outperforms by 27.7% the LSTM model, 38.5% the RL-SPINN model and 41.6% the Gumbel Tree-LSTM
Question: Identify which instrument is string or percussion: Taal, Akonting
Answer: Akonting is string, Taal is percussion.
Question: How were dialogue act labels defined?
Evidence: There have been many taxonomies for dialogue acts: speech acts BIBREF14 refer to the utterance, not only to present information but to the action at is performed. Speech acts were later modified into five classes (Assertive, Directive, Commissive, Expressive, Declarative) BIBREF15. There are many such standard taxonomies and schemes to annotate conversational data, and most of them follow the discourse compositionality. These schemes have proven their importance for discourse or conversational analysis BIBREF16. During the increased development of dialogue systems and discourse analysis, the standard taxonomy was introduced in recent decades, called Dialogue Act Markup in Several Layers (DAMSL) tag set. According to DAMSL, each DA has a forward-looking function (such as Statement, Info-request, Thanking) and a backwards-looking function (such as Accept, Reject, Answer) BIBREF17.
There have been many taxonomies for dialogue acts: speech acts BIBREF14 refer to the utterance, not only to present information but to the action at is performed. Speech acts were later modified into five classes (Assertive, Directive, Commissive, Expressive, Declarative) BIBREF15. There are many such standard taxonomies and schemes to annotate conversational data, and most of them follow the discourse compositionality. These schemes have proven their importance for discourse or conversational analysis BIBREF16. During the increased development of dialogue systems and discourse analysis, the standard taxonomy was introduced in recent decades, called Dialogue Act Markup in Several Layers (DAMSL) tag set. According to DAMSL, each DA has a forward-looking function (such as Statement, Info-request, Thanking) and a backwards-looking function (such as Accept, Reject, Answer) BIBREF17.
Answer: The answers are shown as follows:
* Dialogue Act Markup in Several Layers (DAMSL) tag set
Question: What's are common synonyms for an enterprise sales person?
Answer: salesman, saleswoman, rep, sales rep, account exec, seller, AE, account rep