DataHammer/scimrc huggingface dataset:
Question: Write a brief passage arguing for mandating Electric Vehicles (EVs) over gas combustion vehicles in the United States.
Answer: Electric Vehicles (EVs) provide several key benefits to the United States over Internal Combustion Engine (ICE) vehicles. Firstly, EVs are powered strictly by electricity, which can be produced by renewable methods such as solar or wind power. Secondly, EVs produce significantly less greenhouse gases than ICEs, which will help reduce our carbon footprint and help improve the effects of climate change. Thirdly, EVs are safer and require less maintenance than ICEs, as there is no engine and less moving parts, and there is not a flammable fuel in the vehicle.
Question: Classify each of the following as something used for drinking or eating: cup, bottle, plate, bowl, fork, spoon, can
Answer: Cup: Drinking
Bottle: Drinking
Plate: Eating
Bowl: Eating
Fork: Eating
Spoon: Eating
Can: Drinking
Question: What is the pattern in this sequence and what is the next number: 0, 1, 1, 2, 3, 5, 8, 13
Answer: In this sequence the pattern is that each number is the sum of the two previous numbers. Therefore the next number in this sequence is 21.
Question: why is context switching bad for computer performance?
Answer: context switching, which occurs when a process is paused by the cpu and another process starts running can affect performance of a computer systems for a couple of reasons. The act of switching the context is not in itself very time consuming. The real performance hit comes from the fact that different processes have different virtual memory address spaces so all the caches in the system need to be invalidated and flushed. Since there is such a huge difference in how fast a cpu can run compared to how fast instructions and data can be retrieved from main memory, these things are cached close to the cpu in onboard caches. Even a pointer to a mapping of virtual addresses to physical addresses are cached so that memory access is faster. When a new process gets loaded by the cpu with a different address space and mapping of virtual to physical addresses, all of the data in the caches is invalid and needs to be flushed. This leaves the cpu with empty caches that have to be rebuilt from scratch for the new process, meaning there will be a big slowdown in retrieving instructions and data since these will now be coming from main memory, which is much slower than the cpu's cache.
Question: Do they compare to other models appart from HAN?
Evidence: We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. They fall into two categories: topic classification (Yahoo) and fine-grained sentiment analysis (Amazon, Yelp). Dataset statistics are shown in Table TABREF29. Classes are perfectly balanced, for all datasets.
We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5.
Answer: No.
Question: Classify the plants based on whether they are aquatic or mangrove
Pistia stratiotes, Lysichiton americanus , Rhizophora mangle, Avicennia germinans
Answer: Aquatic - Pistia stratiotes, Lysichiton americanus 
Mangrove  - Rhizophora mangle, Avicennia germinans
Question: Please classify the following items as either “waterproof” or “water absorbent”: umbrella, paper towel, sponge, raincoat, dish rag, car windshield.
Answer: The waterproof items are the umbrella, the raincoat and the car windshield. The water absorbent items are the sponge, paper towel, and dish rag.
Question: Could you plan a canoe camping trip in Michigan? I want to canoe the river from start to end, and need specific camping locations for each night.
Answer: I would recommend a canoe camping trip on the Au Sable River in Michigan. The river is about 114 miles long beginning in Grayling and ending in Oscoda. The river features numerous campgrounds which you will be able to camp at each night. 

Day 1
Whitepine Campground

Day 2
Parmalee Campground

Day 3
Mio Campground

Day 4
Alcona Dam Campground

Day 5
Loud Dam Campground

Day 6
End at Lake Huron
Question: Are the models compared to some baseline models?
Evidence: Our baseline is a GRU network for each of the three tasks. We use the same network architecture across the 3 tasks. For each network, the network contains a layer unidirectional GRU, with 500 units and an output linear layer. The network is trained end-to-end. Our input embedding layer is initialized with a standard normal distribution, with $\mu =0$, and $\sigma =1$, i.e., $W \sim N(0,1)$. We use a maximum sequence length of 50 tokens, and choose an arbitrary vocabulary size of 100,000 types, where we use the 100,000 most frequent words in TRAIN. To avoid over-fitting, we use dropout BIBREF2 with a rate of 0.5 on the hidden layer. For the training, we use the Adam BIBREF3 optimizer with a fixed learning rate of $1e-3$. We employ batch training with a batch size of 32 for this model. We train the network for 15 epochs and save the model at the end of each epoch, choosing the model that performs highest accuracy on DEV as our best model. We present our best result on DEV in Table TABREF7. We report all our results using accuracy. Our best model obtains 42.48% for age, 37.50% for dialect, and 57.81% for gender. All models obtains best results with 2 epochs.
Our baseline is a GRU network for each of the three tasks.
Answer: Yes.
Question: How to be happy?
Answer: To be happy for 1 hour   ----  sleep
To be happy for 3 hours  ----  go to cinema
To be happy for 1 day    ----  go to picnic
To be happy for lifetime  ----  Love your work.
Question: Who was the first couple to win the Nobel prize?
Evidence: Marie Salomea Skłodowska–Curie (/ˈkjʊəri/ KURE-ee, French pronunciation: ​[maʁi kyʁi], Polish pronunciation: [ˈmarja skwɔˈdɔfska kʲiˈri]; born Maria Salomea Skłodowska, Polish: [ˈmarja salɔˈmɛa skwɔˈdɔfska]; 7 November 1867 – 4 July 1934) was a Polish and naturalized-French physicist and chemist who conducted pioneering research on radioactivity. She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields. Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes. She was, in 1906, the first woman to become a professor at the University of Paris.
Answer: Marie Curie and Pierre Curie are the first couple to win the Nobel prize.
Question: Imagine you are the last person on Earth. Write a diary entry describing your thoughts and feelings.
Answer: Today is the 365th day that I've spent on this earth alone. While one year has passed, my days continue to remain the same. Every night, I wake up at 10:00pm, long before the scorching sunrise, to search for food, water, and any other essential items that I can cart back to my lonely den. My search concludes by 5:00am, which gives me a few hours before the first light and guaranteed death. The heat has become so destructive during the day, that I often find buildings and cars spontaneously up in flames during my nightly travels. It's becoming more and more difficult to find viable food that hasn't yet been burnt to a crisp.

My nocturnal transition is the only reason that I am alive today. I use to look up at the stars in anger that the universe could be so cruel. As time has passed, I have learned to forgive the dwarf star off to the east, as he is just as alone as I am and for far longer. Sometimes I feel ashamed that I don't have a plan other than simply surviving one more day. As the days heat up, so do the nights. It's likely only a matter of months before earth becomes uninhabitable at all hours of the day. I know my existence is becoming increasingly more finite, and even if there are others out there, their future is just as damning.
Question: Who created the grading system for the I Liq Chuan martial art?
Evidence: Sam F.S. Chin (Chin Fan Siong, 曾帆祥, born August 1, 1954) is the Grandmaster and Gatekeeper of the martial art called I Liq Chuan, and also known as "the Martial Art of Awareness". Chin developed the grading system for this system.
Answer: Sam F. S. Chin developed the grading system for the I Liq Chuan martial art.
Question: Classify each of the following as either a bird, animal, reptile or insect: tiger, heron, eagle, alligator, snake, spider, ant, dog, cat, rhinoceros, kingfisher, chameleon, hornet, butterfly
Answer: mammal- tiger, rhinoceros, dog, cat
bird- heron, kingfisher, eagle
reptile- snake, alligator, chameleon
inset- spider, ant, hornet, butterfly
Question: What previously annotated databases are available?
Evidence: There is usually a trade-off between the number of training samples and the number of trainable parameters in a deep network model BIBREF16. In general, more data we have, better result are generated by supervised deep learning methods. Data augmentation helps to increase the number of training data, but a bigger dataset needs a better and most likely bigger network architecture in terms of generalization. Otherwise, the model might over-fitted or under-fitted on training data. Using our annotation software, we automatically extracted landmarks of 2000 images from the UOttawa database BIBREF14 had been annotated for image segmentation tasks. The database was randomly divided into three sets: 90$\%$ training, 5$\%$ validation, and 5$\%$ testing datasets. For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model. During the training process of TongueNet, we employed an online data augmentation, including rotation (-25 to 25 degrees), translation (-30 to 30 points in two directions), scaling (from 0.5 to 2 times), horizontal flipping, and combination of these transformations, and annotation point locations are also was transformed, correspondingly. From our extensive random search hyper-parameter tuning, learning rate, the number of iterations, mini-batch sizes, the number of epochs was determined as 0.0005, 1000, 30, 10, respectively. We deployed our experiments using Keras with TensorFlow as the backend on a Windows PC with Core i7, 4.2 GHz speed using one NVIDIA 1080 GPU unit, and 32 GB of RAM. Adam optimization with fixed momentum values of 0.9, was utilized for training.
For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model.
Answer: The answers are shown as follows:
* the UBC database BIBREF14
Question: What is the difference between it's and its?
Answer: "It's" is a contraction for "it is".  Whereas "its" is the possessive pronoun for it
Question: Segregate the below words as palindrome or not.
kayak, deified, rotator, repaper, deed, peep, wow, noon, taste, bait, not, built
Answer: Palindrome - kayak, deified, rotator, repaper, deed, peep, wow, noon
Not a palindrome - taste, bait, not, built
Question: Give me a list of things I should bring when taking a baby to the park.
Answer: Here are some things you should bring when taking a baby to the park:
- Diaper Bag
- Toys
- Stroller
- Extra Diapers and Clothes
- Carrying Pack
- First Aid Kit
- Water
- Snacks
- Milk
Question: Classify the below devices based on the brand which manufactures them.
iPhone, iPad, MacBook, Pixel watch, Galaxy phone
Answer: iPhone, iPad, MacBook - Apple
Pixel watch - Google
Galaxy phone - Samsung
Question: What steps does the standard unsupervised SRL setup consist of?
Evidence: As established in previous work BIBREF7 , BIBREF8 , we use a standard unsupervised SRL setup, consisting of the following steps:
The task we model, unsupervised semantic role induction, is the step 4 of this pipeline.
1. Syntactic Parsing****2. Predicate Identification****3. Argument Identification****4. Argument Classification
Answer: Syntactic Parsing, Predicate Identification, Argument Identification and Argument Classification.
Question: Define unemployment and how it is expressed in most countries.
Answer: Percentage of total workforce who are unemployed and are actively seeking a paid job.
Unemployment rate (# of unemployed / # in labor force) = %
Unemployment rate is one of the most closely watched statistics because a rising rate is seen as a sign of weakening economy that may call for cut in interest rate. A falling rate, similarly, indicates a growing economy which is usually accompanied by higher inflation rate and may call for increase in interest rates.
Question: What is comparative genomics?
Answer: The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.

Many of these studies are based on the detection of sequence homology to assign sequences to protein families.
Question: What are some key factors to consider in choosing a place to live?
Answer: 1) Cost of living
2) Climate/weather
3) Access to quality healthcare
4) Access to quality education
5) Employment opportunities
Question: What is the Interview in this paper？
Evidence: Large repositories of textual communications (e.g. forum and microblog posts) have gained recent popularity as proxies for dialog BIBREF0, BIBREF1, BIBREF2. However, conversations in these settings differ from natural dialog: turns may be sparsely scattered over a large temporal span, contain distinct syntax and vocabulary BIBREF3, and differ greatly in formality and focus BIBREF4. In this paper, we investigate how appropriate such data is for modeling natural dialog, and introduce Interview, a new high-quality large-scale open-domain conversational dataset grounded in interview settings with annotations for specific speaker roles.
We compare the performance of state-of-the-art language models fine-tuned on Interview and other popular conversational datasets, demonstrating that Interview contains more complex dialog and better models the characteristics of natural spoken conversations. Our dataset is an order of magnitude larger than existing high-quality natural dialog datasets and contains speaker role annotations for each turn, facilitating the development of conversational agents and assistive systems for settings involving specific speaker roles, such as doctor-patient interviews or hosted talk shows.
In particular, we explore the tasks of role modeling in media dialog and role change detection on Interview and find that leveraging role information can enable more nuanced, on-topic and natural dialog generation, as well as improve role change classification performance.
In summary, we present Interview, the first large-scale open-domain media dialog dataset. We explore two tasks for which it serves as a promising benchmark dataset: speaker role modeling and speaker change detection. We build simple yet strong models to show quantitatively that role labels from Interview improve performance on such tasks. Interview's scale, spoken origins, role diversity, and complex utterances make it a better source for grounded open-domain conversations.
In this paper, we investigate how appropriate such data is for modeling natural dialog, and introduce Interview, a new high-quality large-scale open-domain conversational dataset grounded in interview settings with annotations for specific speaker roles.
Answer: It is an new high-quality large-scale open-domain conversational dataset grounded in interview settings with annotations for specific speaker roles.
Question: Give me a brief synopsis of Larry David’s contribution to Seinfeld based on the text.
Evidence: In 1989, David teamed up with comedian Jerry Seinfeld to create a pilot for NBC called The Seinfeld Chronicles, which became the basis for Seinfeld, one of the most successful shows in history, reaching the top of TV Guide's list of the 50 greatest TV shows of all time. Entertainment Weekly ranked it the third-best TV show of all time. David made occasional uncredited appearances on the show, playing such roles as Frank Costanza's cape-wearing lawyer and the voice of George Steinbrenner. He was also the primary inspiration for the show's character George Costanza. David left Seinfeld on friendly terms after the seventh season but returned to write the series finale in 1998, two years later. He also continued to provide the voice for the Steinbrenner character.
David wrote 62 of the episodes of Seinfeld, including 1992's "The Contest", for which he won a Primetime Emmy Award and which TV Guide ranked as episode No. 1 on its list of "TV's Top 100 Episodes of All Time". The syndication of Seinfeld earned David an estimated $250 million in 1998 alone. This amount has been steadily decreasing each year, but payments will continue until the full $1.7 billion from the original syndication deal has been paid. In 2008, David made $55 million from Seinfeld syndication, DVD sales, and Curb Your Enthusiasm. He was nominated for an Emmy award 19 times for Seinfeld, winning twice — once for best comedy and once for writing.
Answer: Larry David was a co-writer for seven seasons on the hit TV show Seinfeld. In addition to writing 62 episodes, he played several characters on the show, including Frank Costanza’s lawyer and the voice of George Steinbrenner. The character of George Costanza was also based on Larry David. His work on the show earned him 19 Emmy award nominations and two wins, numerous accolades, and considerable syndication income.
Question: Identify which car manufacturer is Italian or American: Lamborghini, Chrysler
Answer: Chrysler is American, Lamborghini is Italian
Question: Is streaming better than cable?
Answer: Streaming, which is a short-hand term for on-demand programming streamed over the internet, is available from many service providers including Netflix, Disney, Hulu, and Amazon. It is often considered better than cable because there is more original content and the content can be downloaded or consumed at any time, which is more flexible as compared to the standard cable packages offered. While streaming is considered more flexible, it can be expensive since most people consume streaming services from many providers, increasing the aggregate cost of subscriptions.
Question: How to people buy parts for classic cars that are no longer in production?
Answer: A challenge to owning a classic car is buying replacement parts when original components fail. There are a few options, some specialty supply houses create reproduction parts, but depending on the popularity of the car the availability is mixed, it can take some time for a custom part to be fabricated and it can be a costly option.  If the failed part is on hand, you can take the part to a local supply house, and compare the specs to modern parts in the parts catalog, and select the closest matching part. This is a good option for items like fuel filters and brake pads that have similar dimensions and functionality of the original part. In some cases, it may be easier to upgrade a component, such as removing a generator and replacing it with an alternator to upgrade the reliability of the classic car.
Question: How many sinking ships did Violet Jessop survive? Be precise.
Evidence: Violet Constance Jessop (2 October 1887 – 5 May 1971), often referred to as the "Queen of sinking ships" or "Miss Unsinkable," was an Argentine woman of Irish heritage who worked as an ocean liner stewardess, memoirist, and nurse in the early 20th century. Jessop is most well known for having survived the sinking of both the RMS Titanic in 1912 and her sister ship the HMHS Britannic in 1916, as well as having been onboard the eldest of the three sister ships, the RMS Olympic, when it collided with the British warship HMS Hawke in 1911.
Answer: 2.
Question: What does further analysis using black-box probes from Liu et al.(2019) confirm?
Evidence: Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain. We investigate the role of shallow syntax-aware representations for NLP tasks using two techniques. First, we enhance the ELMo architecture to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context. Our second method involves shallow syntactic features obtained automatically on downstream task data. Neither approach leads to a significant gain on any of the four downstream tasks we considered relative to ELMo-only baselines. Further analysis using black-box probes confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo's embeddings. We take these findings as evidence that ELMo-style pretraining discovers representations which make additional awareness of shallow syntax redundant.
Further analysis using black-box probes from Liu et al.(2019) confirms that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo’s embeddings.
Answer: Confirming that their shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo’s embeddings.
Question: Identify which instrument is string or percussion: Snare drum, Zither
Answer: Zither is string, Snare drum is percussion.
Question: What awards did Bill Murray receive for Lost in Translation?
Evidence: Murray's performance in Sofia Coppola's Lost in Translation (2003) earned him a Golden Globe and a British Academy Film Award and an Oscar nomination for Best Actor. He has frequently collaborated with directors Ivan Reitman, Harold Ramis, Wes Anderson, Sofia Coppola, Jim Jarmusch, John McNaughton and the Farrelly brothers. He received Golden Globe nominations for his roles in Ghostbusters, Rushmore (1998), Hyde Park on Hudson (2012), St. Vincent (2014), On the Rocks (2020) and the HBO miniseries Olive Kitteridge (2014), for which he later won his second Primetime Emmy Award.
Answer: Bill Murray earned a Golden Globe and a British Academy Film Award and an Oscar nomination for Best Actor for his role in Lost in Translation.
Question: Where does the suburb of Rozelle's name come from?
Evidence: The name Rozelle and Rozelle Bay (often shown as "Rozella Bay" on old maps), originated from the parrots found in abundance at Rose Hill (near Parramatta) the first suburb of Sydney, established as a prime farming area for the new colony. The parrots, also in abundance in the inner west Bay area of Sydney, were commonly called "Rose Hill parrots" or "Rose-hillers" then Rosella.
Answer: The name Rozelle originated from the parrots found in abundance at Rose Hill the first suburb of Sydney. The parrots, also in abundance in the inner west Bay area of Sydney, were commonly called "Rose Hill parrots" or "Rose-hillers" then Rosella