DataHammer/scimrc huggingface dataset:
Question: How to skip rope?
Evidence: A skipping rope or jump rope is a tool used in the sport of skipping/jump rope where one or more participants jump over a rope swung so that it passes under their feet and over their heads. There are multiple subsets of skipping/jump rope, including single freestyle, single speed, pairs, three-person speed (Double Dutch), and three-person freestyle (Double Dutch freestyle).

Rope skipping is commonly performed as an exercise or recreational activity, and there are also several major organizations that support jump rope as a competitive sport. Often separated by sex and age, events include hundreds of competitive teams all around the world. In the US, schools rarely have jump rope teams, and few states have sanctioned official events at the elementary school level. In freestyle events, jumpers use a variety of basic and advanced techniques in a routine of one minute, which is judged by a head judge, content judges, and performance judges. In speed events, a jumper alternates their feet with the rope going around the jumper every time one of their feet hits the ground for 30 seconds, one minute, or three minutes. The jumper is judged on the number of times the right foot touches the ground in those times.
Answer: A skipping rope is a fun form of exercise that is enjoyed by children and adults. 
While holding on to both ends of the rope, allow the middle of rope to rest on the ground in front of your feet, step over the rope so the rope is now behind your feet. 
Simultaneously, swing both arms over your head in a full swing so the rope is follows the motion of your arms and just before the rope reached the front of your feet, hop with both feet, avoiding the rope and continue the rhythm repeatedly.
Question: Tell me about the career of David Allen Laycock
Answer: David Allen Laycock (2 September 1947 – 16 September 2008) was an English professional cricketer. He played for Kent County Cricket Club between 1969 and 1973.

Laycock was born at Woolwich in east London in 1847. He first played for Kent's Second XI in 1966 and went on to make his first-class cricket debut in August 1969 against Leicestershire at Canterbury. After making five appearances for the First XI in 1969, including one in the John Player League, Laycock appeared infrequently for the side in the coming years. He made a total of 10 first-class[A] and five List A appearances for Kent between his debut and his final match in August 1973, playing as a batsman and occasional wicket-keeper in a strong Kent side which won the County Championship in 1970 and one-day trophies in each season between 1972 and 1974.

Despite playing 69 matches for the Second XI, including scoring runs during 1974,[B] Laycock was released by Kent at the endow the 1974 season. He became a postman, although he continued to coach cricket.

Laycock died at Eastbourne in Sussex in September 2008 after a long battle with brain cancer. He was 61.
Question: What advantages do the exsting event detection approaches have?
Evidence: Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant micro-posts containing the keyword -- referred to as the expectation of the distribution -- and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.
Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training.
Answer: They fail to reliably estimate the informativeness of a keyword and its expectation for model training.
Question: what kinds of shoes are there?
Answer: The different kinds of shoes you can buy are running shoes, hiking shoes, steel toe boots, sandals, high heels, walking shoes, dress shoes, slippers, construction boots, etc.  You should pick the pair of shoes you need based on the activity you will be engaging in.  Using the right pair of shoes for the right job will keep you safe, less prone to injury, and make it easier and more enjoyable to do the activity you are doing.
Question: What is the difference between a compact and economy-sized car?
Answer: These two terms are typically used at car rental agencies to refer to the size of the car, with economy the smallest available rental car and compact one size larger.
Question: What is ice hockey?
Answer: Ice hockey is a team sport which is popular primarily in North America and Europe. Gameplay consists of two teams attempting to propel a puck into the opposing team's net using hockey sticks. What makes ice hockey unique is that it's played on a sheet of ice and players must wear ice skates to maneuver around the playing surface. 

Ice hockey teams will generally have 6 players on the ice at any given time; 3 forwards whose objective it is to score goals, 2 defenseman whose main objective is to defend their own net from opposing players, and one goaltender who is tasked from preventing pucks from entering their team's net.

The most popular professional ice hockey league is the National Hockey League (NHL) which is based in the US and Canada. However, there are professional hockey leagues throughout the world including the Kontinental Hockey League in Russia, Swedish Hockey League in Sweden, and the Liiga based in Finalnd.
Question: What is love?
Answer: Love is a emotion to show empathy and compassion. However, for this question it could also refer to a song by the band Haddaway. Released in 1993, the song became popular when it was used in several SNL skits as well as the 1998 movie, One Night At The Roxbury
Question: What is used a baseline?
Evidence: As shown in TABLE TABREF24 , we report the micro average of precision, recall and F1-score for all 11 types of labels for a baseline as well as the proposed model. As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 . As presented in TABLE TABREF24 , the score obtained by the proposed model is clearly higher than the baseline. Here, as described in Section SECREF14 , the sizes of window and lower bounds of frequency for selecting contextual keywords are tuned as 4 and 7 throughout the evaluation of English dataset, and tuned as 3 and 4 throughout the evaluation of Chinese dataset. The number of extracted contextual keywords from the English dataset is 1,328, and from the Chinese dataset is 331.
As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 .
Answer: The answers are shown as follows:
* As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12
Question: What is a GPU?
Answer: A GPU (Graphics Processing Unit) is a specialized processor designed for processing graphical data. It is commonly found in video games consoles, computer monitors, and other graphics-intensive applications. GPUs are able to perform more complex operations than CPUs (Central Processing Units) and are more efficient at rendering and manipulating graphics.
Question: Identify which instrument is string or percussion: Madhalam, Erhu
Answer: Erhu is string, Madhalam is percussion.
Question: Given this paragraph about Siedlce, tell me how many people live there and how far away from Warsaw it is.
Evidence: Siedlce [Polish pronunciation: ['ɕɛdlt͡sɛ] (listen)] (Yiddish: שעדליץ Shedlits) is a city in eastern Poland with 77,354 inhabitants (as of 2021). Situated in the Masovian Voivodeship (since 1999), previously the city was the capital of a separate Siedlce Voivodeship (1975–1998). The city is situated between two small rivers, the Muchawka and the Helenka, and lies along the European route E30, around 90 kilometres (56 mi) east of Warsaw. It is the fourth largest city of the Voivodeship, and the seat of the Roman Catholic Diocese of Siedlce. Siedlce is a local educational, cultural and business center.
Answer: Siedlce is 90 kilometers east of Warsaw and has a population of 77,354 people.
Question: What is Unanimous?
Evidence: Unanimous is a United Kingdom-based game show broadcast on Channel 4 from 27 October 2006 to 15 December 2006. It was based on an American game show titled Unan1mous.
Answer: Unanimous is a United Kingdom-based game show broadcast on Channel 4 from 27 October 2006 to 15 December 2006. It was based on an American game show titled Unan1mous.

Unanimous: The Fallout, was shown on E4 later at night after Unanimous. It was hosted by comedians Paddy McGuinness and Olivia Lee and featured regular guest Karl Daly, played by Tom Bennett.

The 'Host' of the show was Alex Humes. Although fairly unknown, Humes starred in the reality TV programme Space Cadets, also for Channel 4, where he played one of the Russian pilots. The voice-over for the programme was provided by Phil Gallagher.
Question: How does the active learning model work?
Evidence: Active learning methods can generally be described into two parts: a learning engine and a selection engine BIBREF28 . The learning engine is essentially a classifier, which is mainly used for training of classification problems. The selection engine is based on the sampling strategy, which chooses samples that need to be relabeled by annotators from unlabeled data. Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the accuracy of the classifier. In this paper, a CRF-based segmenter and a scoring model are employed as learning engine and selection engine, respectively.
Active learning methods can generally be described into two parts: a learning engine and a selection engine BIBREF28 . The learning engine is essentially a classifier, which is mainly used for training of classification problems. The selection engine is based on the sampling strategy, which chooses samples that need to be relabeled by annotators from unlabeled data. Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the accuracy of the classifier. In this paper, a CRF-based segmenter and a scoring model are employed as learning engine and selection engine, respectively.
Answer: Active learning methods has a learning engine (mainly used for training of classification problems) and the selection engine (which chooses samples that need to be relabeled by annotators from unlabeled data). Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the accuracy of the classifier. In this paper, CRF-based segmenter and a scoring model are employed as learning engine and selection engine, respectively.
Question: Which of these actions should we do and not to do in case of security incidents? deleting forensic artefacts, hiding the security incident, informing the security team.
Answer: In case of security incidents, first you need to inform the security team. It is important to not delete any forensic artefacts or files so the security team can check what has happened. Hiding the security incident can have serious consequences for the company and should not be done.
Question: Tell me about the plot of the book Cold Granite
Evidence: Cold Granite is the debut novel written by Stuart MacBride. It features Detective Sergeant Logan McRae (who is later nicknamed "Lazarus") as its central character, who works for Grampian Police in Aberdeen, Scotland. Logan McRae went on to feature in a series of books which became a bestseller series for MacBride.

Plot
It is Logan McRae's first week back after being on sick leave for a year; courtesy of Angus Robertson (The Mastrick Monster) who carved him up with a knife. Someone is kidnapping children, murdering them and mutilating them afterwards. The local paper screams about police incompetence and is gunning for McRae's boss, Detective Inspector David Insch. McRae discovers that someone is leaking the stories to a journalist, Colin Miller, who inadvertently disrupts Grampian Polices' plans to apprehend the killer by revealing their plan to wait out in a secluded location which the killer thinks is safe.

McRae discovers that Miller's source is none other than his ex-girlfriend, Isobel McAlister, the police pathologist, who is now living with Miller and tells him about her day to unwind.

Meanwhile, the local council worker who removes all the dead animals from the roads (affectionately called "Roadkill") is found to have a dead girl in his tip and another dead girl is discovered on the local rubbish dump.
Answer: Cold Granite is the debut novel written by Stuart MacBride. It features Detective Sergeant Logan McRae (who is later nicknamed "Lazarus") as its central character, who works for Grampian Police in Aberdeen, Scotland. Logan McRae went on to feature in a series of books which became a bestseller series for MacBride.

The Plot goes as it is Logan McRae's first week back after being on sick leave for a year; courtesy of Angus Robertson (The Mastrick Monster) who carved him up with a knife. Someone is kidnapping children, murdering them and mutilating them afterwards. The local paper screams about police incompetence and is gunning for McRae's boss, Detective Inspector David Insch. McRae discovers that someone is leaking the stories to a journalist, Colin Miller, who inadvertently disrupts Grampian Polices' plans to apprehend the killer by revealing their plan to wait out in a secluded location which the killer thinks is safe.

McRae discovers that Miller's source is none other than his ex-girlfriend, Isobel McAlister, the police pathologist, who is now living with Miller and tells him about her day to unwind.

Meanwhile, the local council worker who removes all the dead animals from the roads (affectionately called "Roadkill") is found to have a dead girl in his tip and another dead girl is discovered on the local rubbish dump.
Question: Identify which instrument is string or percussion: Makuta, Tricordia
Answer: Tricordia is string, Makuta is percussion.
Question: What is the difference between a kidnapping and a vacation?
Answer: A kidnapping is when someone is abducted against their will, whereas a vacation is when someone takes time away from their work or school, typically choosing to travel somewhere for pleasure.
Question: Which attention mechanisms do they compare?
Evidence: We evaluate three models of the image attention mechanism INLINEFORM0 of equation EQREF11 . They have in common the fact that at each time step INLINEFORM1 of the decoding phase, all approaches first take as input the annotation sequence INLINEFORM2 to derive a time-dependent context vector that contain relevant information in the image to help predict the current target word INLINEFORM3 . Even though these models differ in how the time-dependent context vector is derived, they share the same subsequent steps. For each mechanism, we propose two hand-picked illustrations showing where the attention is placed in an image.
Soft attention
Hard Stochastic attention
Local Attention
We evaluate three models of the image attention mechanism INLINEFORM0 of equation EQREF11 .
Soft attention
Hard Stochastic attention
Local Attention
Answer: The answers are shown as follows:
* Soft attention
* Hard Stochastic attention
* Local Attention
Question: What gear do you need to go on a backpacking trip?
Answer: To go on a backpacking trip, you'll need a few categories of items: shelter, cooking and clothing. Start with a trekking pole tent like the X-Mid Pro. Don't forget 2 trekking poles. You'll need a sleeping pad like a Klymit Static-V and a sleeping bag or quilt such as the Kelty Cosmic 0. In order to cook and boil water, you'll need a stove such as a Jetboil and the corresponding fuel to power it. As far as clothing goes, several layers of wool/synthetic fabric with a light shell will work great at keeping you warm and dry. All of this needs to go in a backpack that's of the right capacity and sized to your frame.
Question: From this paragraph, give me a bullet points of colors sapphires be?
Evidence: Sapphires are a precious gemstone, a variety of the mineral corundum, consisting of aluminum oxide (α-Al2O3) with trace amounts of elements such as iron, titanium, chromium, copper, or magnesium. It is typically blue, but natural "fancy" sapphires also occur in yellow, purple, orange, and green colors; "parti sapphires" show two or more colors. The only color corundum stone that the term sapphire is not used for is red, which is called a ruby.  Pink colored corundum may be either classified as ruby or sapphire depending on locale. Commonly, natural sapphires are cut and polished into gemstones and worn in jewelry. They also may be created synthetically in laboratories for industrial or decorative purposes in large crystal boules. Because of the remarkable hardness of sapphires – 9 on the Mohs scale (the third hardest mineral, after diamond at 10 and moissanite at 9.5) – sapphires are also used in some non-ornamental applications, such as infrared optical components, high-durability windows, wristwatch crystals and movement bearings, and very thin electronic wafers, which are used as the insulating substrates of special-purpose solid-state electronics such as integrated circuits and GaN-based blue LEDs.
Answer: Sapphire can be :
- blue
- yellow
- purple
- orange
- green
- pink
Question: How can I tell the neighbors kids to quiet down without being perceived as a grouchy old man?
Answer: There isn't a way that you can not come off as a grouchy old man by telling kids to quiet down.
Question: What kind of classifier do they present?
Evidence: Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines. If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required.
LID is further also an important step in harvesting scarce language resources. Harvested data can be used to bootstrap more accurate LID models and in doing so continually improve the quality of the harvested data. Availability of data is still one of the big roadblocks for applying data driven approaches like supervised machine learning in developing countries.
Having 11 official languages of South Africa has lead to initiatives (discussed in the next section) that have had positive effect on the availability of language resources for research. However, many of the South African languages are still under resourced from the point of view of building data driven models for machine comprehension and process automation.
Table TABREF2 shows the percentages of first language speakers for each of the official languages of South Africa. These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso). The Nguni languages are similar to each other and harder to distinguish. The same is true of the Sotho languages.
This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long. The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks.
Section SECREF2 reviews existing works on the topic and summarises the remaining research problems. Section SECREF3 of the paper discusses the proposed algorithm and Section SECREF4 presents comparative results.
This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long.
Answer: A hierarchical naive Bayesian and lexicon based classifier for language identification (LID) of short pieces of text of 15-20 characters long.
Question: Which corpora do they use?
Evidence: In the experiments, we consider four data sets, two of them newly created and the remaining two already public: CNN, TIME, 20 Newsgroups, and Reuters-21578. The code and the two new data sets are available at github.com/baiyangwang/emgan. For the pre-processing of all the documents, we transformed all characters to lower case, stemmed the documents, and ran the word2vec model on each corpora to obtain word embeddings with a size of 300. In all subsequent models, we only consider the most frequent INLINEFORM0 words across all corpora in a data set.
In the experiments, we consider four data sets, two of them newly created and the remaining two already public: CNN, TIME, 20 Newsgroups, and Reuters-21578. The code and the two new data sets are available at github.com/baiyangwang/emgan. For the pre-processing of all the documents, we transformed all characters to lower case, stemmed the documents, and ran the word2vec model on each corpora to obtain word embeddings with a size of 300. In all subsequent models, we only consider the most frequent INLINEFORM0 words across all corpora in a data set.
Answer: The answers are shown as follows:
* CNN, TIME, 20 Newsgroups, and Reuters-21578
Question: How to perform the training in this paper?
Evidence: To recognize entailment between two questions INLINEFORM0 (premise) and INLINEFORM1 (hypothesis), we adapted the neural network proposed by Bowman et al. BIBREF13 . Our DL model, presented in Figure FIGREF20 , consists of three 600d ReLU layers, with a bottom layer taking the concatenated sentence representations as input and a top layer feeding a softmax classifier. The sentence embedding model sums the Recurrent neural network (RNN) embeddings of its words. The word embeddings are first initialized with pretrained GloVe vectors. This adaptation provided the best performance in previous experiments with RQE data.
GloVe is an unsupervised learning algorithm to generate vector representations for words BIBREF42 . Training is performed on aggregated word co-occurrence statistics from a large corpus, and the resulting representations show interesting linear substructures of the word vector space. We use the pretrained common crawl version with 840B tokens and 300d vectors, which are not updated during training.
Training is performed on aggregated word co-occurrence statistics from a large corpus, and the resulting representations show interesting linear substructures of the word vector space.
Answer: Training is performed on aggregated word co-occurrence statistics from a large corpus, and the resulting representations show interesting linear substructures of the word vector space.
Question: Give me a list of food items that I should prepare for American Thanksgiving.
Answer: Turkey, cornbread stuffing, mashed potatoes, cranberry sauce, roasted sweet potatoes, wild rice, pumpkin pie, and chocolate pecan pie.
Question: Identify which car manufacturer is German or American: BMW, Cadilliac
Answer: Cadillac is American, BMW is German