DataHammer/scimrc huggingface dataset:
Question: Who are the Green Bay Packers and who owns them?
Evidence: The Green Bay Packers are a professional American football team based in Green Bay, Wisconsin. The Packers compete in the National Football League (NFL) as a member club of the National Football Conference (NFC) North division. It is the third-oldest franchise in the NFL, dating back to 1919, and is the only non-profit, community-owned major league professional sports team based in the United States. Home games have been played at Lambeau Field since 1957. They have the most wins of any NFL franchise.
Answer: The Green Bay Packers are a professional American football team based in Green Bay, Wisconsin. The Packers compete in the National Football League (NFL) as a member club of the National Football Conference (NFC) North division. The 'Packers' are the third-oldest franchise in the NFL, dating back to 1919, and is the only non-profit, community-owned major league professional sports team based in the United States.
Question: Who amongst these are tennis players: Virat Kohli, Roger Federer, Rafael Nadal, Novak Djokovic, Serena Williams, PV Sindhu, Virender Sehwag, Maria Sharapova, Steffi Graf, Venus Williams.
Answer: Roger Federer, Rafael Nadal, Novak Djokovic, Serena Williams, Maria Sharapova, Steffi Graf, Venus Williams are tennis players.
Question: Given the passage about churches built in the village of Zijldijk in the Netherlands, when was the only running church built?
Evidence: Zijldijk has three churches. The Mennomite church was built in 1772 in a non conspicuous barn-like building. It is still in use as a church. The Dutch Reformed church has been reconstructed as a village house. The Reformed Church was constructed in 1886, and nowadays serves as a care facility.
Answer: The Mennomite church was built in 1772 and is still in use as a church.
Question: What content did the HispaBlogs corpus collect?
Evidence: In this section, we describe the corpus and the alternative representations that we employ in this work.
We have created the HispaBlogs dataset by collecting posts from Spanish blogs from five different countries: Argentina, Chile, Mexico, Peru and Spain. For each country, there are 450 and 200 blogs respectively for training and test, ensuring that each author appears only in one set.
Answer: It collected posts from Spanish blogs from five different countries: Argentina, Chile, Mexico, Peru and Spain.
Question: How many articles did they have?
Evidence: We select a large-scale Chinese dataset BIBREF0 with millions of real comments and a human-annotated test set to evaluate our model. The dataset is collected from Tencent News, which is one of the most popular Chinese websites for news and opinion articles. The dataset consists of 198,112 news articles. Each piece of news contains a title, the content of the article, and a list of the users' comments. Following the previous work BIBREF0 , we tokenize all text with the popular python package Jieba, and filter out short articles with less than 30 words in content and those with less than 20 comments. The dataset is split into training/validation/test sets, and they contain 191,502/5,000/1,610 pieces of news, respectively. The whole dataset has a vocabulary size of 1,858,452. The average lengths of the article titles and content are 15 and 554 Chinese words. The average comment length is 17 words.
The dataset consists of 198,112 news articles.
Answer: The answers are shown as follows:
* 198,112
Question: Table 3-4: Going by the table alone, are there a range of thresholds δ where HODA performs poorly?
Evidence: Figure 9: The histogram of Pearson distance between Hn and 10000 benign users’ hardness degree histogram and Hn and 10000 adversaries’ hardness degree histogram for various attacks. STL10 users are benign users for CIFAR10 target classifier and are adversaries for CIFAR100 target classifier.
Answer: To address the concern of the respected reviewer, Figure 9 in Appendix I shows the Pearson distance histogram of 10000 adversaries and 10000 benign users for all attacks. Figure 9 indicates the histogram of Pearson distance between Hn and benign users' hardness degree histogram (samples of negative class in confusion matrix) and also Hn and adversaries' hardness degree histogram (samples of positive class in confusion matrix) for nums=100. Since HODA can completely separate benign users from adversaries, the AUC score of HODA for all four attacks is 1.
Question: What does the new dataset of political debates contain?
Evidence: In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new dataset of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
Thus, we create a new dataset of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task.
Answer: It contains statements that have been fact-checked by nine reputable sources.
Question: Who was the prime minister of Spain when Spain joined NATO?
Answer: Spain joined NATO by referendum in 1986. The prime minister of Spain at the time was Felipe Gonzalez.
Question: How do you cook sous vide?
Evidence: Sealing the food in sturdy plastic bags retains juices and aroma that otherwise would be lost in the process. Placing the packaged food in a water bath, with the temperature set at the desired final cooking temperature of the food, prevents overcooking, because the food cannot get hotter than the bath it is in, as in bain-marie.

As a result of precise temperature control of the bath and the fact that the bath temperature is the same as the target cooking temperature, very precise control of cooking can be achieved. Additionally, temperature, and thus cooking, can be very even throughout the food in sous vide cooking, even with irregularly shaped and very thick items, given enough time.
Answer: By contrast, in conventional high-heat cooking, such as oven roasting or grilling, the food is exposed to heat levels that are much higher than the desired internal cooking temperature, and it must be removed from the high heat prior to reaching the desired cooking temperature. If the food is removed from the heat too late, it becomes overcooked, and if it is removed too early, it is undercooked.

The use of temperatures much lower than those used for conventional cooking is an essential feature of sous vide. When cooking meat, tough collagen in connective tissue can be denatured into gelatin, without heating the meat's proteins high enough that they denature to a degree that the texture toughens and moisture is exuded from the meat.

In contrast, soft vegetables are often considered overcooked. Low-temperature cooking allows plant matter to remain more succulent, as the cell walls do not burst. Cooking vegetables at temperatures below the boiling point of water allows them to be thoroughly cooked (and pasteurized, if necessary) while maintaining a firm or somewhat crisp texture. While the cell walls will generally not burst, the depolymerization of the pectic polysaccharides that connect the vegetable cells together and the gelatinisation of starch in the vegetable can be achieved without overcooking.

From a culinary viewpoint, the exclusion of air is secondary, but this has practical importance. It allows cooked food to be stored, still sealed and refrigerated, for considerable times, which is especially useful for the catering industry, and it excludes oxygen from food that requires long cooking and is susceptible to oxidation, e.g., fat on meat, which may become rancid with prolonged exposure to air. It also improves the transfer of heat between the water bath and food, without the thermal insulating properties of any trapped air in the bag.

Apart from ensuring uniform cooking, sous vide cooking facilitates development of desired organoleptic flavors and limits off flavors due to oxidation
Question: The following are a mix of english and hindi words. separate them out logically:
your
mujhe
lamp
pyaar
tamanna
tomorrow
perhaps
kyunki
how
your
yaar
shade
Answer: English:
tomorrow
how
your
perhaps
lamp
shade

Hindi:
mujhe
kyunki
yaar
tamanna
pyaar
Question: Who is the most popular president ever in the USA?
Answer: The most popular president ever in the USA is Franklin D. Roosevelt. He was president from 1933 to 1945 and is remembered for his New Deal programs that helped to improve the economy and the lives of Americans during the Great Depression.
Question: How much energy is released in a deuterium-tritium fusion reaction?
Answer: A Deuterium-Tritium (DT) fusion reaction results in 17.6 MeV of energy, released in the form of a Helium nucleus and a free Neutron.
Question: Classify the following elements as metal or noble gas: Argon, Iron, Copper, Krypton, Neon, Silver, Thorium, Radon, Helium, Xenon, Erbium, Tin, Protactinium
Answer: Argon - Noble Gas
Iron - Metal
Copper - Metal
Krypton - Noble Gas
Neon - Noble Gas
Silver - Metal
Thorium - Metal
Radon - Noble Gas
Helium - Noble Gas
Xenon - Noble Gas
Erbium - Metal
Tin - Metal
Protactinium - Metal
Question: What dataset/corpus is this evaluated over?
Evidence: Stanford Sentiment Treebank Stanford Sentiment Treebank (SST) BIBREF14 . This concerns predicting movie review sentiment. Two datasets are derived from this corpus: (1) SST-1, containing five classes: very negative, negative, neutral, positive, and very positive. (2) SST-2, which has only two classes: negative and positive. For both, we remove phrases of length less than 4 from the training set.
Subj BIBREF15 . The aim here is to classify sentences as either subjective or objective. This comprises 5000 instances of each.
TREC BIBREF16 . A question classification dataset containing six classes: abbreviation, entity, description, human, location and numeric. There are 5500 training and 500 test instances.
Irony BIBREF17 . This dataset contains 16,006 sentences from reddit labeled as ironic (or not). The dataset is imbalanced (relatively few sentences are ironic). Thus before training, we under-sampled negative instances to make classes sizes equal. Note that for this dataset we report the Area Under Curve (AUC), rather than accuracy, because it is imbalanced.
Stanford Sentiment Treebank Stanford Sentiment Treebank (SST) BIBREF14 . This concerns predicting movie review sentiment. Two datasets are derived from this corpus: (1) SST-1, containing five classes: very negative, negative, neutral, positive, and very positive. (2) SST-2, which has only two classes: negative and positive. For both, we remove phrases of length less than 4 from the training set.
Subj BIBREF15 . The aim here is to classify sentences as either subjective or objective. This comprises 5000 instances of each.
TREC BIBREF16 . A question classification dataset containing six classes: abbreviation, entity, description, human, location and numeric. There are 5500 training and 500 test instances.
Irony BIBREF17 . This dataset contains 16,006 sentences from reddit labeled as ironic (or not). The dataset is imbalanced (relatively few sentences are ironic). Thus before training, we under-sampled negative instances to make classes sizes equal. Note that for this dataset we report the Area Under Curve (AUC), rather than accuracy, because it is imbalanced.
Answer: The answers are shown as follows:
*  SST-1
* SST-2
* Subj 
* TREC 
* Irony
Question: What was the Battle of Midway?
Evidence: The Battle of Midway was a major naval battle in the Pacific Theater of World War II that took place from 4–7 June 1942, six months after Japan's attack on Pearl Harbor and one month after the Battle of the Coral Sea. The U.S. Navy under Admirals Chester W. Nimitz, Frank J. Fletcher, and Raymond A. Spruance defeated an attacking fleet of the Imperial Japanese Navy under Admirals Isoroku Yamamoto, Chūichi Nagumo, and Nobutake Kondō north of Midway Atoll, inflicting devastating damage on the Japanese fleet. Military historian John Keegan called it "the most stunning and decisive blow in the history of naval warfare", while naval historian Craig Symonds called it "one of the most consequential naval engagements in world history, ranking alongside Salamis, Trafalgar, and Tsushima Strait, as both tactically decisive and strategically influential"
Answer: The Battle of Midway was the turning point of the Pacific Naval Campaign of WW2 and occurred June 4-7, 1942.  The US Navy, under Chester Nimitz defeated the Imperial Japanese Navy and sunk 4 of their aircraft carriers.  This was a significant setback for the Japanese and marked the first major defeat of the Imperial Japanese fleet in WW2.
Question: Extract universities Moret studied at and his year of graduation for each, and put them in a bulleted list.
Evidence: Moret was born in 1953 in Vevey Switzerland, and did his undergraduate studies at the École Polytechnique Fédérale de Lausanne (EPFL), graduating in 1975. He went on to graduate studies at the University of Tennessee, earning a Ph.D. in 1980. He then joined the faculty of the University of New Mexico, where he remained until 2006, when he moved to EPFL. He retired from EPFL in December 2016.

In 1996, Moret founded the ACM Journal of Experimental Algorithmics, and he remained editor in chief of the journal until 2003. In 2001, Moret founded the Workshop in Algorithms for Bioinformatics (WABI) and remains on the Steering Committee for the conference.

In 2018, Moret was elected as a Fellow of the International Society for Computational Biology, for his outstanding contributions to the fields of computational biology and bioinformatics.
Answer: * École Polytechnique Fédérale de Lausanne (EPFL) (graduated 1975)
* University of Tennessee (graduated 1980)
Question: what were the baselines?
Evidence: We compare our system to a variety of baselines. (1) Rank by the number of times a citation is mentioned in the document. (2) Rank by the number of times the citation is cited in the literature (citation impact). (3) Rank using Google Scholar Related Articles. (4) Rank by the TF*IDF weighted cosine similarity. (5) Rank using a learning-to-rank model trained on text similarity rankings. The first two baseline systems are models where the values are ordered from highest to lowest to generate the ranking. The idea behind them is that the number of times a citation is mentioned in an article, or the citation impact may already be good indicators of their closeness. The text similarity model is trained using the same features and methods used by the annotation model, but trained using text similarity rankings instead of the author's judgments.
We compare our system to a variety of baselines. (1) Rank by the number of times a citation is mentioned in the document. (2) Rank by the number of times the citation is cited in the literature (citation impact). (3) Rank using Google Scholar Related Articles. (4) Rank by the TF*IDF weighted cosine similarity. (5) Rank using a learning-to-rank model trained on text similarity rankings.
Answer: The answers are shown as follows:
* (1) Rank by the number of times a citation is mentioned in the document.
* (2) Rank by the number of times the citation is cited in the literature (citation impact).
* (3) Rank using Google Scholar Related Articles.
* (4) Rank by the TF*IDF weighted cosine similarity.
* (5) Rank using a learning-to-rank model trained on text similarity rankings.
Question: What is Solidarity Tax?
Answer: The IMF has proposed a Solidarity Tax on the Companies those prospered in Covid crises should pay additional tax to show solidarity with those who were hit hardest by pandemic
Question: From the passage provided, extract the year when iPhones first came with satellite communications.
Evidence: Apple directly sub-contracts hardware production to external OEM companies, maintaining a high degree of control over the end product. The iPhone contains most of the hardware parts of a typical modern smartphone. Some hardware elements, such as 3D Touch and the Taptic Engine, are unique to the iPhone. The main hardware of the iPhone is the touchscreen, with current models offering screens of 4.7 inches and larger. All iPhones include a rear-facing camera; the front-facing camera dates back to the iPhone 4. The iPhone 7 Plus introduced multiple lenses to the rear-facing camera. A range of sensors are also included on the device, such as a proximity sensor, ambient light sensor, accelerometer, gyroscopic sensor, magnetometer, facial recognition sensor or fingerprint sensor (depending on the model) and barometer. In 2022, Apple added satellite communications to the iPhone, with the release of the iPhone 14 and iPhone 14 Pro.
Answer: iPhones first came with satellite communications in 2022 with the release of the iPhone 14 and iPhone 14 Pro.
Question: What is a genome-wide association study (GWAS)?
Answer: In genomics, a genome-wide association study (GWA study, or GWAS), is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.

When applied to human data, GWA studies compare the DNA of participants having varying phenotypes for a particular trait or disease. These participants may be people with a disease (cases) and similar people without the disease (controls), or they may be people with different phenotypes for a particular trait, for example blood pressure. This approach is known as phenotype-first, in which the participants are classified first by their clinical manifestation(s), as opposed to genotype-first. Each person gives a sample of DNA, from which millions of genetic variants are read using SNP arrays. If there is significant statistical evidence that one type of the variant (one allele) is more frequent in people with the disease, the variant is said to be associated with the disease. The associated SNPs are then considered to mark a region of the human genome that may influence the risk of disease.

GWA studies investigate the entire genome, in contrast to methods that specifically test a small number of pre-specified genetic regions. Hence, GWAS is a non-candidate-driven approach, in contrast to gene-specific candidate-driven studies. GWA studies identify SNPs and other variants in DNA associated with a disease, but they cannot on their own specify which genes are causal.

The first successful GWAS published in 2002 studied myocardial infarction. This study design was then implemented in the landmark GWA 2005 study investigating patients with age-related macular degeneration, and found two SNPs with significantly altered allele frequency compared to healthy controls. As of 2017, over 3,000 human GWA studies have examined over 1,800 diseases and traits, and thousands of SNP associations have been found. Except in the case of rare genetic diseases, these associations are very weak, but while they may not explain much of the risk, they provide insight into genes and pathways that can be important.
Question: Which building is the most higher?
Answer: The tallest building in the world, as of my knowledge cutoff of September 2021, is the Burj Khalifa, located in Dubai, United Arab Emirates. The Burj Khalifa stands at a height of 828 meters (2,716 feet) and has 163 floors above ground. It was completed in 2010 and has held the title of the world's tallest building since its construction.

However, there are always new skyscrapers and buildings being constructed around the world, so it's possible that another building may surpass the height of the Burj Khalifa in the future.
Question: Identify which instrument is string or percussion: Txalaparta, Clavinet
Answer: Clavinet is string, Txalaparta is percussion.
Question: Write an essay describing why baseball should be considered America's pastime.
Answer: Baseball is the oldest professional sport in America. It began in the late 1800s. The sport dates back to to the civil war era, which is where it started to become known as America's pastime. It coincides with a plethora of historical and cultural aspects of life in America. While the game has a vast amount of historical significance, it also seems to reflect changes that occur in our society. For example, over the past several decades we have seen a huge influx in players from all over the globe, with greater representation from countries like Dominican Republic, Venezuela, Puerto Rico, Japan and many others. America has always been considered a land of opportunity and this is evident with baseball. The MLB is the most prestigious league when it comes to baseball and is dream destination for any player. If you are good enough, you are sometime given the opportunity to play here. Recently we have seen a huge increase in the international representation of players in the league. As times have changed and the world has become more global, we also see this transpire in baseball. Baseball is America's pastime because it is a preservation of historical values, but also adapts and reflects current cultural values, which always keeps it relevant.
Question: Extract the different storage configurations available to the iPhone 14 that are mentioned in the text. Put them in a comma separated list.
Evidence: iPhone 14 and 14 Plus are available in three internal storage configurations: 128, 256, and 512 GB. It has 6 GB of RAM, an increase over the iPhone 13 and 13 mini model's 4 GB of RAM. The iPhone 14 and 14 Plus have the same IP68 rating for dust and water resistance as their predecessors.
Answer: 128 GB, 256 GB, 512 GB
Question: How are multimodal representations combined?
Evidence: To investigate whether our BERT-based model can transfer knowledge beyond language, we consider image features as simple visual tokens that can be presented to the model analogously to textual token embeddings. In order to make the $o_j$ vectors (of dimension $2048+4=2052$) comparable to BERT embeddings (of dimension 768), we use a simple linear cross-modal projection layer $W$ of dimensions $2052\hspace{-1.00006pt}\times \hspace{-1.00006pt}768$. The $N$ object regions detected in an image, are thus represented as $X_{img} = (W.o_1,...,W.o_N)$. Once mapped into the BERT embedding space with $W$, the image is seen by the rest of the model as a sequence of units with no explicit indication if it is a text or an image embedding.
To investigate whether our BERT-based model can transfer knowledge beyond language, we consider image features as simple visual tokens that can be presented to the model analogously to textual token embeddings. In order to make the $o_j$ vectors (of dimension $2048+4=2052$) comparable to BERT embeddings (of dimension 768), we use a simple linear cross-modal projection layer $W$ of dimensions $2052\hspace{-1.00006pt}\times \hspace{-1.00006pt}768$. The $N$ object regions detected in an image, are thus represented as $X_{img} = (W.o_1,...,W.o_N)$. Once mapped into the BERT embedding space with $W$, the image is seen by the rest of the model as a sequence of units with no explicit indication if it is a text or an image embedding.
Answer: The image feature vectors are mapped into BERT embedding dimensions and treated like a text sequence afterwards.