DataHammer/scimrc huggingface dataset:
Question: Write a creative ending scene for a thriller movie
Answer: After 18 months of finding the treasure, the people of Corinth gathered to praise Big B and his friends for their achievement. Amidst the celebrations, the friends saw a shady person new to the town. The person approached Big B and his friends, asking for a moment to speak. He presented them with a captain's log from 1867 for the friends to look into. The shady man was old and hence asked the friends for their assistance in locating what the captain's log pointed to. And when asked who the captain was, he said the diary belonged to Marshall Teach alias BLACKBEARD - one of the greatest and most vicious pirates to live.
Question: What are the main ingredients for bread?
Answer: Flour, water, yeast and salt.
Question: What is locus (in genomics)?
Answer: In genetics and genomics, a locus is a specific, fixed position on a chromosome where a particular gene or genetic marker is located. Each chromosome carries many genes, with each gene occupying a different position or locus; in humans, the total number of protein-coding genes in a complete haploid set of 23 chromosomes is estimated at 19,000–20,000.

Genes may possess multiple variants known as alleles, and an allele may also be said to reside at a particular locus. Diploid and polyploid cells whose chromosomes have the same allele at a given locus are called homozygous with respect to that locus, while those that have different alleles at a given locus are called heterozygous. The ordered list of loci known for a particular genome is called a gene map. Gene mapping is the process of determining the specific locus or loci responsible for producing a particular phenotype or biological trait. Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of historic linkage disequilibrium to link phenotypes (observable characteristics) to genotypes (the genetic constitution of organisms), uncovering genetic associations.
Question: Many important hominid fossils have been found in Tanzania, such as 6-million-year-old Pliocene hominid fossils. The genus Australopithecus ranged across Africa between 4 and 2 million years ago, and the oldest remains of the genus Homo are found near Lake Olduvai. Following the rise of Homo erectus 1.8 million years ago, humanity spread all over the Old World, and later in the New World and Australia under the species Homo sapiens. H. sapiens also overtook Africa and absorbed the older species of humanity. Later in the Stone and Bronze Age, prehistoric migrations into Tanzania included Southern Cushitic speakers who moved south from present-day Ethiopia; Eastern Cushitic people who moved into Tanzania from north of Lake Turkana about 2,000 and 4,000 years ago; and the Southern Nilotes, including the Datoog, who originated from the present-day South Sudan–Ethiopia border region between 2,900 and 2,400 years ago.: page 18  These movements took place at about the same time as the settlement of the Mashariki Bantu from West Africa in the Lake Victoria and Lake Tanganyika areas. They subsequently migrated across the rest of Tanzania between 2,300 and 1,700 years ago.
Evidence: In what country have many of the most important hominid fossils been found?
Answer: Many hominid fossils have been found inTanzania.
Question: Classify these countries as being in the northern hemisphere or the southern hemisphere: Brazil, India, Mexico, Australia, New Zealand, South Africa, Canada, United Kingdom
Answer: The countries in the northern hemisphere are: India, Mexico, Canada, United Kingdom
The countries in the southern hemisphere are: Brazil, Australia, New Zealand, South Africa
Question: what is the source of the new dataset?
Evidence: We collected all available comments in the stories from Reddit from August 2015. Reddit is popular website that allows registered users (without identity verification) to participate in forums specific a post or topic. These forums are of they hierarchical type, those that allow nested conversation, where the children of a comment are its direct response. To increase recall and make the annotation process feasible we created an inverted index with Lucene and queried for comments containing the word troll with an edit distance of 1, to include close variations of this word. We do so inspired by the method by BIBREF2 to created a bullying dataset, and because we hypothesize that such comments will be related or involved in a trolling event. As we observed in the dataset, people use the word troll in many different ways, sometimes it is to point out that some used is indeed trolling him or her or is accusing someone else of being a troll. Other times, people use the term, to express their frustration or dislike about a particular user, but there is no trolling event. Other times, people simple discuss about trolling and trolls, without actually participating or observing one directly. Nonetheless, we found that this search produced a dataset in which 44.3 % of the comments directly involved a trolling event. Moreover, as we exposed our trolling definition, it is possible for commentators in a conversation to believe that they are witnessing a trolling event and respond accordingly even where there is none. Therefore, even in the comments that do not involve trolling, we are interested in learning what triggers users interpretation of trolling where it is not present and what kind of response strategies are used. We define as a suspected trolling event in our dataset a comment in which at least one of its children contains the word troll.
We collected all available comments in the stories from Reddit from August 2015. 
Answer: The answers are shown as follows:
* Reddit
Question: What baseline and classification systems are used in experiments?
Evidence: The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features.
The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1. Intriguingly, the content-ignorant models ( INLINEFORM6 ) always perform significantly better than the models which only have access to the EAUs' content ( INLINEFORM7 , INLINEFORM8 ). In the most general task formulation ( INLINEFORM9 ), we observe that INLINEFORM10 even significantly outperforms the model which has maximum access (seeing both EAU spans and surrounding contexts: INLINEFORM11 ).
The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features.
The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1.
Answer: The answers are shown as follows:
* BIBREF13
* majority baseline
Question: Who developed the Black Swan theory?
Evidence: The black swan theory or theory of black swan events is a metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized after the fact with the benefit of hindsight. The term is based on an ancient saying that presumed black swans did not exist – a saying that became reinterpreted to teach a different lesson after they were discovered in Australia.

The theory was developed by Nassim Nicholas Taleb, starting in 2001, to explain:

1. The disproportionate role of high-profile, hard-to-predict, and rare events that are beyond the realm of normal expectations in history, science, finance, and technology.
2. The non-computability of the probability of consequential rare events using scientific methods (owing to the very nature of small probabilities).
3. The psychological biases that blind people, both individually and collectively, to uncertainty and the substantial role of rare events in historical affairs.

Taleb's "black swan theory" refers only to unexpected events of large magnitude and consequence and their dominant role in history. Such events, considered extreme outliers, collectively play vastly larger roles than regular occurrences.  More technically, in the scientific monograph "Silent Risk", Taleb mathematically defines the black swan problem as "stemming from the use of degenerate metaprobability".
Answer: The black Swan Theory was developed by Nassim Nicholas Taleb in 2001.
Question: What are hiccups and why do we get them?
Answer: "Hiccup comes from the Latin phrase "to catch one's breath while sobbing". Hiccups are involuntary contractions of the diaphragm that produces a "hic" sound. The time between these "hics" can be erratic but are usually pretty consistant.  Pregnant women have been known to say they can feel their unborn babies hiccuping in the womb.

If you wait long enough hiccups usually just go away on their own.
There are many "Folk cures" for hiccups.  These are very varied. There are no real medical remedies.  The more people you talk to the more varied these remedies are.  Everyone seems to have their own unique remedy.

Some of these include: head standing, drinking a glass of water upside-down, being frightened by someone, breathing into a bag, eating a large spoonful of peanut butter and placing sugar on or under the tongue and my favorite -Drinking through a straw with the ears plugged!
Question: What is the SKE dataset?
Evidence: All experiments are implemented on the hardware with Intel(R) Xeon(R) CPU E5-2682 v4 @ 2.50GHz and NVIDIA Tesla P100.
We evaluate our method on the SKE dataset used in this competition, which is the largest schema-based Chinese information extraction dataset in the industry, containing more than 430,000 SPO triples in over 210,000 real-world Chinese sentences, bounded by a pre-specified schema with 50 types of predicates. All sentences in SKE Dataset are extracted from Baidu Baike and Baidu News Feeds. The dataset is divided into a training set (170k sentences), a development set (20k sentences) and a testing set (20k sentences). The training set and the development set are to be used for training and are available for free download. The test set is divided into two parts, the test set 1 is available for self-verification, the test set 2 is released one week before the end of the competition and used for the final evaluation.
The max sequence length is set to 128, the number of fully connected layer of relation classification branch is set to 2, and that of global relation branch is set to 1. During training, we use Adam with the learning rate of 2e-5, dropout probability of 0.1. This model converges in 3 epoch.
All uppercase letters are converted to lowercase letters. We use max sequence length 128 so sentences longer than 128 are split by punctuation. According to FAQ, entities in book title mark should be completely extracted. Because the annotation criteria in trainset are diverse, we revise the incomplete entities. To keep consistence, book title marks around the entities are removed.
Our postprocessing mechanism is mainly based on the FAQ evaluation rules. After model prediction, we remove triplets whose entity-relation types are against the given schemas. For entities contained in book title mark, we complement them if they are incomplete. Date type entities are also complemented to the finest grain. These are implemented by regular expression matching.
Note that entity related preprocessing and postprocessing are also performed on the development set to keep consistency with the test set, thus the change of development metric is reliable.
We evaluate our method on the SKE dataset used in this competition, which is the largest schema-based Chinese information extraction dataset in the industry, containing more than 430,000 SPO triples in over 210,000 real-world Chinese sentences, bounded by a pre-specified schema with 50 types of predicates.
Answer: It is the largest schema-based Chinese information extraction dataset in the industry, containing more than 430,000 SPO triples in over 210,000 real-world Chinese sentences, bounded by a pre-specified schema with 50 types of predicates.
Question: Will the team focus on analyzing the reasons for the varying effect of multi-task learning for different languages in their future work?
Evidence: In this paper we described our system for the CoNLL–SIGMORPHON 2018 shared task on Universal Morphological Reinflection, Task 2, which achieved the best performance out of all systems submitted, an overall accuracy of 49.87. We showed in an ablation study that this is due to three core innovations, which extend a character-based encoder-decoder model: (1) a wide context window, encoding the entire available context; (2) multi-task learning with the auxiliary task of MSD prediction, which acts as a regulariser; (3) a multilingual approach, exploiting information across languages. In future work we aim to gain better understanding of the increase in variance of the results introduced by each of our modifications and the reasons for the varying effect of multi-task learning for different languages.
In future work we aim to gain better understanding of the increase in variance of the results introduced by each of our modifications and the reasons for the varying effect of multi-task learning for different languages.
Answer: Yes.
Question: Identify which car manufacturer is British or American: Jaguar, GMC
Answer: GMC is American, Jaguar is British
Question: Will, a 45-yr-old former collegiate swimmer turned lifelong triathlete, requests assistance with run training. His only significant medical history is a series of overuse injuries to his shoulders and Achilles tendon. In recent weeks, he notes his workouts are unusually difficult and reports feeling constriction in his chest with exertion – something he attributes to deficiencies in core strength. Upon further questioning, he explains that the chest constriction is improved with rest and that he often feels dizzy during recovery. What do you think is the underlying problem that could be causing Will’s symptoms during/after exercise?
Answer: Will probably has some form of  cardiovascular disease (CVD). As he was a collegiate athlete and now triathlete, he has undergone many years of intense exercise training, which could induce pathological cardiac remodeling, ultimately leading to myocardial fibrosis. This form of CVD can be common among trained athletes, particularly endurance athletes such as Will (swimming, triathlon).
Question: What is the function of the global memory storage designed by authors?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
We also design a global memory storage to share common features and collect interactions among all tasks.
Answer: It is used to share common features and collect interactions among all tasks.
Question: Are the samples from different tasks trained in parallel in the architecture?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel.
Answer: Yes, they are.
Question: What is the future trend of job industry
Answer: More of cloud Machine learning AI security dive into these industries to be on top notch
Question: Should I visit Chicago?
Answer: Chicago is one of the largest cities in the United States situated on Lake Michigan in the Midwest region of the country. It is a popular travel destination known for its architecture including the world famous Willis Tower (formerly the Sears Tower), the iconic John Hancock Center and the neo-Gothic Tribune Tower. It is also home to some world famous museums including the Art Institute of Chicago and several sports teams such as the Chicago Bulls, Chicago Bears and Chicago Cubs.
Question: What are all the different evolutions for Eevee in Pokemon GO?
Answer: Eevee has eight different evolutions including Espeon, Vaporeon, Flareon, Jolteon, Leafeon, Glaceon, Umbreon and Sylveon.
Question: Give me the answer to this riddle, explain the answer, and explain why the answer may be challenging to identify: what gets wetter as it dries?
Answer: The answer to this riddle is a towel. A towel gets wetter when it is used to dry something off. This answer may be challenging to determine because of the contradictory nature of the qualities the riddle is asking for - drying and getting wetter.
Question: Identify which instrument is string or percussion: Talempong, Guitar
Answer: Guitar is string, Talempong is percussion.
Question: Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}
Evidence: James Leslie Mitchell gained attention from his earliest attempts at fiction, notably from H. G. Wells, but it was his trilogy entitled A Scots Quair, and in particular its first book Sunset Song, with which he made his mark. A Scots Quair, with its combination of stream-of-consciousness, lyrical use of dialect, and social realism, is considered to be among the defining works of the 20th century Scottish Renaissance. It tells the story of Chris Guthrie, a young woman growing up in the north-east of Scotland in the early 20th century. All three parts of the trilogy have been turned into serials by BBC Scotland, written by Bill Craig, with Vivien Heilbron as Chris. Additionally, Sunset Song has been adapted into a film, released in 2015. Spartacus, a novel set in the famous slave revolt, is his best-known full-length work outside this trilogy.
Answer: • James Leslie Mitchell
• H. G. Wells
• Chris Guthrie
• Bill Craig
• Vivien Heilbron
Question: Who created pilates?
Answer: Pilates was developed by Joseph Pilates.