DataHammer/scimrc huggingface dataset:
Question: Write a short story about a person who discovers a hidden room in their house. The story should include a plot twist and a clear resolution at the end.
Answer: It was going to be a long day.  With her brother-in-law out of town, Lisa had agreed to watch her niece Stella for the afternoon and the little girl was known to be a bit of a handful. Her plan had been to spend the afternoon at the park to let Stella run and climb, to wear her out a bit.  Unfortunately, this outing was foiled by the constant downpour of rain so Lisa resigned herself to a day inside with the tiny tyrant. Stella arrived early and excited to play.  Lisa decided to begin with a game of hide and seek, after all this could take up a good part of the morning without too much trouble.  Stella ran off to hide and Lisa sat quietly drinking coffee while calling out to the little girl, pretending to seek. Eventually she set off to find the little girl but soon she realized that something was wrong.  No matter where she looked, Stella was nowhere to be found. The house wasn't that large, making her disappearance even more perplexing.  Lisa had only recently moved into the home that she bought from her great aunt shortly before her death and she hasn't even moved out all of the existing furniture. Maybe Stella had gotten stuck somewhere behind the large wardrobe in the bedroom or she had climbed up into a cupboard in the laundry room.  Panic was starting to settle in as she moved the wardrobe further from the wall, where only a small child could have hidden before.  Suddenly, saw a small crack in the wall, and then realized it was a small door that she never knew was there.  The door was slightly ajar and she eventually moved the wardrobe enough to open the door and climb inside. What was this place?  The entrance opened up to reveal a small room that Lisa never knew existed!  It was full of treasure, gold bars and jewels.  Stella sat in the middle of the room quietly playing with the treasure, unaware of the immense value she was surrounded by.  Lisa had known her aunt was a secretive woman but this was an incredible find and would change her life forever.  Stella's visit was the good luck charm she needed to unlock this bounty and she would be forever grateful to the little girl!  Time to call her brother-in-law to thank him for letting Stella visit.
Question: What does the network compose of?
Evidence: In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps. Then, the two-staged CNN architecture extracts features through word-level and sentence-level. At the same time, attention mechanism helps CNN focus more on the important part of the passage based on the query information. Finally, the prediction layer find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.
This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer.
Answer: Compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer.
Question: Does the model use both spectrogram images and raw waveforms as features?
Evidence: FLOAT SELECTED: Table 4: Results of the two models and all its variations
FLOAT SELECTED: Table 4: Results of the two models and all its variations
Answer: No.
Question: Extract the text the name of the architect who designed the Josephine Shaw Lowell Memorial Fountain
Evidence: The Josephine Shaw Lowell Memorial Fountain is an outdoor fountain in Bryant Park, Manhattan, New York memorializing Josephine Shaw Lowell, a social worker active in the late 19th century. The fountain was designed by architect Charles A. Platt and dedicated in 1912.
Answer: Charles A. Platt
Question: What position is best to use in your Fantasy Football flex slot?
Answer: The majority of fantasy football players believe it is best to use a running-back in your flex spot because running-backs traditionally receive the ball (touches) more often than wider receivers or tight-ends.  More touches theoretically means more opportunities to score points.  However, with recent NFL rule changes, stronger and faster wide receivers, and fantasy football scoring systems that provide extra points for catching a pass (catches) wide receivers are scoring significantly more points than running backs on a weekly basis.  Therefore, it is advised to play a wider receiver in your fantasy football flex spot.
Question: Complete this saying: The early birds get the ___"
Answer: The saying is "The early bird gets the worm"
Question: How many football national championships has the University of Georgia won?
Answer: The University of Georgia has won the football national championship four times - in 1942, 1980, 2021, and 2022.
Question: In reference to given text, Note some points about Kabenau River
Evidence: Kabenau River (also Gabina or St. Augustin River) is a river in Madang Province, Papua New Guinea. It is located at about 5.466667°S 145.85°E. It was discovered in 1887 by geologist C. Schneider and explored in 1888 by Hugo Zöller. It flows westward and empties near Rimba to the Astrolabe Bay.
Answer: 1. The Kabenau River in Papua New Guinea's Madang Province is also known as the Gabina River or the St. Augustin River.
2. The coordinates are approximately 5.466667°S 145.85°E.
3. Geologist C. Schneider found it in 1887, and Hugo Zöller explored it in 1888.
4. It drains into Astrolabe Bay close to Rimba as it flows westward.
Question: best ways to search google
Answer: Use of Tabs
Use a hyphen to exclude words
use of colon when searching specific sites
Use asterisk when necessary
Find search sites similar to other
Question: What institutions make up the Ivy League?
Answer: The eight institutions are Brown University, Columbia University, Cornell University, Dartmouth College, Harvard University, the University of Pennsylvania, Princeton University, and Yale University.
Question: What does the framework generate?
Evidence: Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form-stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University (Guo et al., 2019).
In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems.
Answer: Major types of Chinese classical poems.
Question: Who is Travis Payne?
Evidence: Travis Payne (born July 5, 1971) is an American choreographer, director and producer. He was the choreographer for Michael Jackson's This Is It until Jackson's death.
Answer: Travis Payne (born July 5, 1971) is an American choreographer, director and producer. He was the choreographer for Michael Jackson's This Is It until Jackson's death. 
Payne also served as the associate producer for This Is It and along with the director, Kenny Ortega, was extensively and intimately involved in the making of the film. To date, This Is It worldwide gross revenue totaled $261.3 million during its theatrical run making it the highest grossing documentary or concert movie of all time.
Travis Payne is the youngest inductee into the Gallery of Greats and the recipient of numerous nominations and awards. He has received the MTV Video Music Award for Best Choreography four times for his work with En Vogue, Salt-N-Pepa as well as Michael Jackson and Janet Jackson on the music video/short-film "Scream" which was listed in the Guinness Book of World Records as the most expensive music video ever made. He also received three American Choreography Awards, including honors for his work on "Scream" and Michael Jackson's Ghosts. Payne was nominated for an Emmy Award for his choreography work with Michael Jackson on the "Dangerous" performance for the 1995 MTV Video Music Awards opening segment. He was again nominated for an Emmy Award in 2006 for his work on Disney's The Suite Life of Zack & Cody. He is also a two-time recipient of the Music Video Producers Association Award for Outstanding Achievement in Choreography for his work with the Brian Setzer Orchestra and Ally McBeal. In December 2009, Payne was featured with a write-up in the Michael Jackson Opus and recognized for his choreography and contribution for his work with the King of Pop.
Question: Name three landlocked countries in Africa
Answer: Three landlocked countries in Africa are Chad, Zambia, and Botswana.
Question: how do you smoke a brisket?
Answer: you first need to fire up your smoker, traditionally in texas this would be an offset smoker. You trim your brisket to a quarter inch of fat ontop and make this arrowdynamic. You then season generously with salt and pepper. when your smoker reached a temperature of 250  you insert the brisket. You should burn a clean fire with only hard wood such as oak, beech, post oak, apple, cherry or nut wood. the smoke out of the smoke stack should be blue almost invisible. you then smoke the brisket until it reaches an internal temp of 165 if the exterior is nice and dark, take the brisket wrap it with beef tallow in butchers paper add bac to the smoker and let smoke until reaching an internal temperature of 205. then preheat an oven to 150 and place the brisket in the oven overnight. serve for lunch the day after or after resting for at least 10-12 hours. enjoy
Question: Given a reference text about Marcus Morton, tell me what year he was born and why he let the Democratic Party.
Evidence: Marcus Morton (1784 – February 6, 1864) was an American lawyer, jurist, and politician from Taunton, Massachusetts. He served two terms as Governor of Massachusetts and several months as Acting Governor following the death in 1825 of William Eustis. He served for 15 years as an associate justice of the Massachusetts Supreme Judicial Court, all the while running unsuccessfully as a Democrat for governor. He finally won the 1839 election, acquiring exactly the number of votes required for a majority win over Edward Everett. After losing the 1840 and 1841 elections, he was elected in a narrow victory in 1842.

The Massachusetts Democratic Party was highly factionalized, which contributed to Morton's long string of defeats. His brief periods of ascendancy, however, resulted in no substantive Democratic-supported reforms, since the dominant Whigs reversed most of the changes enacted during his terms. An opponent of the extension of slavery, he split with longtime friend John C. Calhoun over that issue, and eventually left the party for the Free Soil movement. He was considered by Martin Van Buren as a potential vice presidential running mate in 1848.
Answer: Marcus Morton was born in 1784. He left the Democratic Party because he opposed the extension of slavery.
Question: What is the emblem of the United States Marine Corps?
Answer: Approved and adopted in 1868, the Eagle, Global and Anchor is the official emblem and insignia of the United States Marine Corps.
Question: What are the two locations the A's are considering for building their new baseball stadium?
Answer: The A's are considering building their stadium in Jack London Square in Oakland, CA and in Las Vegas.
Question: What is the full name of DUVN? Is this sth proposed in previous work? It is a bit unclear how this variant relates to your proposed method. Might want to explain it in a bit more detail.
Evidence: To evaluate the performances of DroQ, we compared DroQ with three baseline methods in MuJoCo benchmark environments
Here, layer normalization is not applied in Q Dr, φ1 (s ′ , a ′ ). We use this method to investigate the performance of the existing single dropout Q-function (non-ensemble) method.
Following, we set the hyperparameters as G = 20, N = 10, and M = 2 for all methods except DUVN (M = 1). More detailed hyperparameter settings are given in Appendix H.
The methods were compared on the basis of average return and estimation bias.
Average return: An average return over episodes. We regarded 1000 environment steps in Hopper and 3000 environment steps in the other environments as one epoch, respectively. After every epoch, we ran ten test episodes with the current policy and recorded the average return. Average/std. bias: Average and standard deviation of the normalized estimation error (bias) of Q-functions Q ϕi. The error represents how significantly the Q-value estimate differs from the true one. Formally, the error is defined as
where Q π (s, a) is the true Q-value under the current policy π and Q(s, a) is its estimate. In our experiment, Q π (s, a) was approximated by the discounted Monte Carlo return obtained with π in the test episodes. In addition, Q(s, a) was evaluated as
for SAC and REDQ and as for DroQ and DUVN, respectively. The comparison results (Figure) indicate that DroQ achieved almost the same level of performance as REDQ. Regarding the average return, REDQ and DroQ achieved almost the same sample efficiency overall. In Walker2d and Ant, their learning curves highly overlapped, and there was no significant difference between them. In Humanoid, REDQ was slightly better than DroQ. In Hopper, DroQ was better than REDQ. In all environments except Hopper, REDQ and DroQ improved their average return significantly earlier than SAC and DUVN. Regarding the bias, REDQ and DroQ consistently kept the value-estimation bias closer to zero than SAC and DUVN in all environments.
Double uncertainty value network (DUVN): Baseline method. This method is a DroQ variant that injects model uncertainty into a target in the same manner as the original DUVN (Moerland et al.,2017).
Answer: The full name of DUVN is Double Uncertain Value Network, which is proposed in [1].
The original DUVN [7] injects model uncertainty into the target using a single dropout Q-function as y = r + \gamma Q_{\text{Dr}, \bar{\phi}_1}(s', a').
Here,  Q_{\text{Dr}, \bar{\phi}_1}(s', a')  is a dropout Q-function without layer normalization.
Dr.Q variant (in which Dr.Q target is replaced with the above target + an entropy bonus) is the DUVN described in Section 4.1 in our paper.
We have revised the paper to make these points clearer.
[1] Thomas M Moerland, Joost Broekens, and Catholijn M Jonker. Efficient exploration with double uncertain value networks. arXiv preprint arXiv:1711.10789, 2017.
Question: What is the difference between hardwood and softwood?
Answer: These refer to two very large categories of wood species. Softwoods are generally fast-growing trees such as pine and fir commonly found throughout the world. They are most commonly used for construction and large volume projects as they are cheaper to produce and readily available, as the trees themselves do not take as long to grow. Hardwoods, on the other hand, are called "hard" because of much denser grains: compared to softwoods, hardwoods weigh more per volume due to this property. In other words, a board of walnut (a hardwood) weighs more than a board of the same size of pine (a softwood). They are more expensive because the trees take longer to grow and are typically more rare, but are used in fine wood-crafting arts like furniture making due to their robustness and beauty in the grain structure.
Question: Do they have any plans for future work?
Evidence: In this paper, we presented a new human-AI loop approach for keyword discovery and expectation estimation to better train event detection models. Our approach takes advantage of the disagreement between the crowd and the model to discover informative keywords and leverages the joint power of the crowd and the model in expectation inference. We evaluated our approach on real-world datasets and showed that it significantly outperforms the state of the art and that it is particularly useful for detecting events where relevant microposts are semantically complex, e.g., the death of a politician. As future work, we plan to parallelize the crowdsourcing tasks and optimize our pipeline in order to use our event detection approach in real-time.
As future work, we plan to parallelize the crowdsourcing tasks and optimize our pipeline in order to use our event detection approach in real-time.
Answer: They plan to parallelize the crowdsourcing tasks and optimize their pipeline in order to use their event detection approach in real-time.
Question: How is Robert Lugn's leadership characterized?
Evidence: Lugn was promoted to lieutenant in 1950 and attended the Royal Swedish Army Staff College in from 1955 to 1957 and conducted military studies in Germany, Belgium and The Netherlands in 1956. He became captain of the General Staff Corps in 1960 and was appointed section chief in the Organisation Department of the Army Staff. Lugn conducted military studies in Norway in 1961.

In 1965, Lugn served in the Swedish Battalion in Cyprus, part of the United Nations Peacekeeping Force in Cyprus (UNFICYP) and in 1966 he was promoted to major of the General Staff Corps. He was promoted to lieutenant colonel of the General Staff Corps in 1968 and served in Göta Life Guards (P 1) in 1970. In 1972, Lugn was promoted to colonel of the General Staff Corps and appointed section chief in the Army Staff. From 1975 to 1977, he served as acting regimental commander of Hälsinge Regiment (I 14) and Gävleborg Defence District (Gävleborgs försvarsområde, Fo 49) when he was promoted to senior colonel and was appointed Chief of Staff of the Western Military District. Lugn was promoted to major general and appointed Chief of the Army Staff and Chief of the General Staff Corps in 1979.

As Chief of the Army Staff, he led the difficult balancing work between quality and quantity in the conscript army within the given financial framework. His written instructions to the staff were marked by a good pen. They expressed his enduring interest in quality in the army's war organization. Lugn's interaction with the staff members was characterized by consideration pairing with great humor. His laid-back leadership style created a good atmosphere that eased employees' sometimes heavy workload. Lugn then served as Chief of Home Guard from 1 October 1983.

As Chief of Home Guard, Lugn made radical changes to the Home Guard. He got the servicemen to accept that they belonged to an army unit and introduced military training to all servicemen. It was revolutionary for the Home Guard, which until then admittedly received weapons training but very little unit training. Now the servicemen were trained in groups and also in working platoons and the training included all the elements that a soldier needs to be able to have. He also made sure that the platoons got heavier weapons such as machine guns and recoilless rifles. Lugn also took hold of the higher education of the commander and introduced a higher education of circuit Home Guard commanders, which led to competence as battalion commander. Lugn served as Chief of Home Guard until his retirement on 1 March 1988.
Answer: Major General Per Erick Robert Lugn leadership carried a laid back leadership style that ultimately created a good atmosphere, specially easing the workload of the staff members.
Question: Why are cats so picky about what they eat?
Evidence: Cats have relatively few taste buds compared to humans (470 or so versus more than 9,000 on the human tongue). Domestic and wild cats share a taste receptor gene mutation that keeps their sweet taste buds from binding to sugary molecules, leaving them with no ability to taste sweetness. Their taste buds instead respond to acids, amino acids like protein, and bitter tastes. Cats also have a distinct temperature preference for their food, preferring food with a temperature around 38 °C (100 °F) which is similar to that of a fresh kill and routinely rejecting food presented cold or refrigerated (which would signal to the cat that the "prey" item is long dead and therefore possibly toxic or decomposing).
Answer: Cats' taste buds respond to acids, protein, and bitter tastes. The taste buds do not respond to sweetness. Additionally, cats prefer food with a temperature near 100 °F (38 °C). For wild cats, this temperature is associated with fresh food as opposed to food that may be decomposing.
Question: Classify the following celebrities as Indian or Non-Indian: tom holland, gigi hadid, sara ali khan, vijay sethupathi, priyanka chopra, tom cruise, brad pitt.
Answer: Inidan: sara ali khan, vijay sethupathi, priyanka chopra
Non-Indian: tom holland, gigi hadi, tom cruise, brad pitt
Question: Pancakes or waffles?
Answer: It isn't easy to decide between two delicious breakfast items. There are pros and cons for both pancakes and waffles. On one hand, pancakes are light, and fluffy and can easily be eaten in one mouthful. On the other hand, waffles are airy, chewy and can be crispy with the right amount of sugar. Pancake and waffle batter are similar. Pancakes require a pan; waffles require a waffle iron. Both pancakes and waffles can be eaten with syrup, which is delicious. The decision to eat a pancake or waffle in the morning can be decided by which way you feel that morning.
Question: What is Share capital in finance?
Answer: Share capital is the money invested directly in a company by its members (shareholders).
Question: How to extract biomedical entities from a text in this paper?
Evidence: In the literature, there are several methods available for extracting bio-medical entities from a text (i.e., from medical notes and/or articles). We refer to BIBREF13 for an overview of valuable existing techniques. In this work, we have adopted a dictionary-based approach, which exploits lexical features and domain knowledge extracted from the Unified Medical Languages System (UMLS) Metathesaurus BIBREF14 . The approach has been proposed for the Italian language in a past work BIBREF15 . Since the approach combines the usage of linguistic analysis and domain resources, we were able to conveniently adapt it for the English language, being both the linguistic pipeline and UMLS available for multiple languages (including English and Italian).
Dictionary-based approaches have been proved valid for the task of entities' extraction, see, for example, another well known, similar approach to the one adopted here, i.e., Metamap. It is worth noting like, even though dictionary-based approaches could be less precise than Named Entity Recognition BIBREF13 , in our context even an approximate solution is enough, since we are not annotating medical records. Instead, we are quantifying the mole of inherent information within a text.
In this work, we have adopted a dictionary-based approach, which exploits lexical features and domain knowledge extracted from the Unified Medical Languages System (UMLS) Metathe_x0002_saurus [7].
Answer: The authors adopted a dictionary-based approach, which exploits lexical features and domain knowledge extracted from the Unified Medical Languages System (UMLS) Metathesaurus.