DataHammer/scimrc huggingface dataset:
Question: What is geospatial data?
Answer: Geographic data and information, also known as geospatial data, is defined in the ISO/TC 211 series of standards as data and information having an implicit or explicit association with a location relative to Earth (a geographic location or geographic position). It is also called geospatial data and information,[citation needed] georeferenced data and information,[citation needed] as well as geodata and geoinformation.[citation needed]

Approximately 90% of government sourced data has a location component. Location information (known by the many names mentioned here) is stored in a geographic information system (GIS).

There are also many different types of geodata, including vector files, raster files, geographic databases, web files, and multi-temporal data.

Spatial data or spatial information is broader class of data whose geometry is relevant but it is not necessarily georeferenced, such as in computer-aided design (CAD), see geometric modeling.
Question: Based on this paragraph, who directed "Before Morning?"
Evidence: Before Morning is a 1933 American pre-Code crime drama directed by Arthur Hoerl, and starring Leo Carrillo, Lora Baxter, and Taylor Holmes. The film was adapted for the screen by Arthur Hoerl, from the 1933 Broadway play of the same name by Edward and Edna Riley.
Answer: Arthur Hoerl
Question: Given this paragraph about the history of triathlon, what is the earliest known triathlon event.
Evidence: The evolution of triathlon as a distinct sport is difficult to trace with precision. Many, including triathlon historian and author Scott Tinley, consider events in early twentieth century France to be the beginnings of triathlon, with many three element multisport events of differing composition appearing, all called by different names. The earliest record for an event was from 1901 in Joinville-le-Pont, Val-de-Marne: it called itself "Les Trois Sports" (The Three Sports). It was advertised as an event for "the sportsmen of the time" and consisted of a run, a bicycle and a canoe segment. By 19 June 1921, the event in Joinville-le-Pont had become more like a standard triathlon, with the canoe segment being replaced with a swim. According to the newspaper L’Auto, the race consisted of a 3 km (1.9 mi) run, a 12 km (7.5 mi) bike ride and the crossing of the river Marne, all staged consecutively and without a break. Throughout the 1920s other bike, run, and swim events appeared in different cities, such as the “Course des Trois Sports” in Marseille and "La Course des Débrouillards" in Poissy. These multisport events would continue to slowly spread and grow in popularity: by 1934 "Les Trois Sports" was being hosted in the city of La Rochelle, though it consisted of three distinct events, swimming a 200 m (660 ft) channel crossing, a 10 km (6 mi) bike competition around the harbour of La Rochelle and the parc Laleu, and a 1.2 km (0.75 mi) run in the stadium André-Barbeau. Throughout this growth with new events appearing no unified rules ever existed and as a whole triathlon would remain a minority event on the world stage
Answer: Triathlon events have evolved of the years, and its origins are not fully known. Triathlon historians have evidenced multisports events dating back to 1901. In this era "Les Trois Sports" did not include the swim as it does in modern day triathlon, but instead included a canoe discipline. As the name suggest, triathlon appears to have first surfaced in the history books in france in the suburbs of paris known as Joinville-le-pont, Val-de-marne.
Question: How many datasets do they consider?
Evidence: We consider three datasets, two of which are a contribution of this work.
Dataset I consists of 480 concepts previously analyzed in BIBREF1, BIBREF4. 240 are positive examples, titles from the Wikipedia list of controversial issues, and 240 are negative examples chosen at random and exclusive of the positives. Over this dataset, we compare the methodology suggested here to those reported by BIBREF1, BIBREF4. As the latter report overall accuracy of their binary prediction, we convert our controversiality estimates to a binary classification by classifying the higher-scored half as controversial, and the lower half as non-controversial.
Dataset II is based on a more recent version of the Wikipedia list of controversial issues (May 2017). As positive examples we take, from this list, all concepts which appear more than 50 times in Wikipedia. This leaves 608 controversial Wikipedia concepts. For negative examples, we follow BIBREF1, BIBREF4 and select a like number of concepts at random. Here too, since each concept only has a binary label, we convert our estimation into a binary classification, and report accuracy.
Dataset III is extracted from 3561 concepts whose Wikipedia pages are under edit protection, assuming that many of them are likely to be controversial. They were then crowd-annotated, with 10 or more annotators per concept. The annotation instructions were: “Given a topic and its description on Wikipedia, mark if this is a topic that people are likely to argue about.”. Average pairwise kappa agreement on this task was 0.532. Annotations were normalized to controversiality scores on an integer scale of 0 - 10. We used this dataset for testing the models trained on Dataset I.
In all datasets, to obtain the sentence-level context of the concepts (positive and negative), we randomly select two equal-sized sets of Wikipedia sentences, that explicitly reference these concepts – i.e., that contain a hyperlink to the article titled by the concept. Importantly, in each sentence we mask the words that reference the concept – i.e., the surface form of the hyperlink leading to the concept – by a fixed, singular token, thus focusing solely on the context within which the concepts are mentioned.
We consider three datasets, two of which are a contribution of this work.
Answer: Three datasets
Question: What models are evaluated with QAGS?
Evidence: For QA quality, we answer this question by training QA models of varying quality by fine-tuning different versions of BERT on SQuAD. We present results in Table . The QA models perform similarly despite substantially different performances on the SQuAD development set. Surprisingly, using the best QA model (bert-large-wwm) does not lead to the best correlations with human judgments. On CNN/DM, bert-large-wwm slightly underperforms bert-base and bert-large. On XSUM, bert-base slightly outperforms the other two BERT variants. These results indicate that QAGS is fairly robust to the quality of the underlying QA model, though we note that BERT is a strong QA baseline, and using weaker QA models might lead to larger performance dropoffs.
Surprisingly, using the best QA model (bert-large-wwm) does not lead to the best correlations with human judgments. On CNN/DM, bert-large-wwm slightly underperforms bert-base and bert-large.
Answer: The answers are shown as follows:
* bert-large-wwm
* bert-base
* bert-large
Question: What are the words of House Swyft?
Answer: "Awake! Awake!"
Question: Why pre-trained language models are indeed very useful in clustering sentences?
Evidence: As we showed that pre-trained language models are indeed very useful in clustering sentence representations by domains in an unsupervised manner, we now seek to harness this property for a down-stream task – domain data selection for machine translation. Domain data selection is the task of selecting examples from a large corpus which are as close as possible to the domain of interest, given a smaller set of in-domain examples. The selected examples can be used to either (1) train a domain-specific model from scratch BIBREF5, (2) fine-tune a pre-trained general-domain model BIBREF26, BIBREF7, or (3) prioritize data for annotation as in an Active-Learning framework, if only monolingual data is available BIBREF27. To demonstrate the need for domain data selection and set the stage for our data selection experiments, we perform preliminary experiments with NMT in a multi-domain scenario.
As we showed that pre-trained language models are indeed very useful in clustering sentence representations by domains in an unsupervised manner, we now seek to harness this property for a downstream task – domain data selection for machine translation.
Answer: For it domains in an unsupervised manner.
Question: Which is a species of fish? Pickerel or Pickle
Answer: Pickerel
Question: Given this paragraph, tell me the storyline of Sabapathy.
Evidence: Sabapathy is a 1941 Indian Tamil-language comedy film directed by A. V. Meiyappan and A. T. Krishnaswamy, and produced by Meiyappan. An adaptation of Pammal Sambandha Mudaliar's farce play of the same name, the film stars T. R. Ramachandran, Kali N. Rathnam, C. T. Rajakantham and K. Sarangapani. It focuses on the antics of two dim-witted men named Sabapathy: a wealthy man and his servant. The film was released on 14 December 1941 and became a commercial success.
Answer: Sabapathy is a comedy film about the antics of a wealthy man and his servant.
Question: What movie won best picture at the Oscars in 2023
Answer: The movie Everything Everywhere All at Once won the Oscar for 2022 in the year 2023. In addition to winning best picture it was also the overall winner of the night, winning multiple awards including Best Directing, Best Actress in a Leading Role, Best Actor in a Supporting Role, Best Actress in a Supporting Role, etc. The movie was produced by A24 which is well known for producing excellent content
Question: Is the approach robust to user variation？
Evidence: We study textual autocomplete---the task of predicting a full sentence from a partial sentence---as a human-machine communication game. Specifically, we consider three competing goals for effective communication: use as few tokens as possible (efficiency), transmit sentences faithfully (accuracy), and be learnable to humans (interpretability). We propose an unsupervised approach which tackles all three desiderata by constraining the communication scheme to keywords extracted from a source sentence for interpretability and optimizing the efficiency-accuracy tradeoff. Our experiments show that this approach results in an autocomplete system that is 52% more accurate at a given efficiency level compared to baselines, is robust to user variations, and saves time by nearly 50% compared to typing full sentences.
Our experiments show that this approach results in an autocomplete system that is 52% more accurate at a given efficiency level compared to baselines, is robust to user variations, and saves time by nearly 50% compared to typing full sentences.
Answer: Yes.
Question: Who won the MLB World Series in 2020?
Answer: Los Angeles Dodgers
Question: Is there a relation between wavelength and frequency?
Answer: Wavelength is inversely proportional to frequency. Wavelength is the distance between crests of waves whereas Frequency is number of waves occur in given time. Higher the frequency , lower the wavelength and vice-versa
Question: What year was the movie Big Trouble in Little China released?
Evidence: Big Trouble in Little China (also known as John Carpenter's Big Trouble in Little China) is a 1986 American fantasy action-comedy film directed by John Carpenter, and starring Kurt Russell, Kim Cattrall, Dennis Dun and James Hong. The film tells the story of truck driver Jack Burton (Russell), who helps his friend Wang Chi (Dun) rescue Wang's green-eyed fiancée from bandits in San Francisco's Chinatown. They go into the mysterious underworld beneath Chinatown, where they face an ancient sorcerer named David Lo Pan (Hong), who requires a woman with green eyes to marry him in order to be released from a centuries-old curse.
Answer: 1986
Question: In what year was the evidence of an exoplanet first confirmed?
Evidence: An exoplanet or extrasolar planet is a planet outside the Solar System. The first possible evidence of an exoplanet was noted in 1917 but was not recognized as such. The first confirmation of detection occurred in 1992. A different planet, initially detected in 1988, was confirmed in 2003. As of 1 April 2023, there are 5,346 confirmed exoplanets in 3,943 planetary systems, with 855 systems having more than one planet. The James Webb Space Telescope (JWST) is expected to discover more exoplanets, and also much more about exoplanets, including composition, environmental conditions and potential for life.
Answer: The detection of an exoplanet was first confirmed in 1992
Question: Can AI systems fall in love?
Answer: AI, unlike humans, has no emotions or feelings. It does, however, have the unique ability to perceive and analyze human sentiments from vast amounts of data on which it has been trained. It can even outperform humans when it comes to mimicking and reproducing these emotions in a more efficient and effective manner. While AI does not experience emotions in the same way that humans do, it is still capable of understanding and replicating them in a remarkable and unprecedented way.
Question: What are some fun things to do with my 6 year old son
Answer: There are a number of interesting things you can do with your 6 year old son such as playing sports, card games, going to the park or visiting a museum
Question: What's the appeal of a vacation rental on the Big Island of Hawaii?
Answer: The Big Island of Hawaii is a bit quieter than the island of Oahu, where Waikiki Beach is. While Waikiki is a beautiful beach and place, the Big Island of Hawaii has so much to offer that is unique only to this island. First, you can visit black sand beaches. This is due to all of the volcanic activity on the island. There are still active volcanoes on the island. You can also take tours around them should you wish to get closer. You still get all the beautiful beaches Hawaii offers, but with features, you can't find on the other islands. Also, if you want a slightly less touristy destination, the Big Island is for you!
Question: Give me a one line summary about smelting
Evidence: Smelting is a process of applying heat to an ore, to extract a base metal. It is a form of extractive metallurgy. It is used to extract many metals from their ores, including silver, iron, copper, and other base metals. Smelting uses heat and a chemical- reducing agent to decompose the ore, driving off other elements as gases or slag and leaving the metal base behind. The reducing agent is commonly a fossil fuel source of carbon, such as coke—or, in earlier times, charcoal.The oxygen in the ore binds to carbon at high temperatures as the chemical potential energy of the bonds in carbon dioxide (CO2) is lower than the bonds in the ore.
Answer: Smelting is the process of extracting metals from their ores by applying heat and a chemical-reducing agent to drive off non-wanted materials as gases and leaving the base (wanted) material behind.
Question: Which Game of Thrones episode does Arya go blind?
Answer: Arya goes temporarily blind in the last episode of season 5, entitled "Mother's Mercy"
Question: How many criteria are set for the selection of the most informative relation?
Evidence: We utilize an off-the-shelf toolbox of OpenIE to the derive structured answer-relevant relations from sentences as to the point contexts. Relations extracted by OpenIE can be represented either in a triple format or in an n-ary format with several secondary arguments, and we employ the latter to keep the extractions as informative as possible and avoid extracting too many similar relations in different granularities from one sentence. We join all arguments in the extracted n-ary relation into a sequence as our to the point context. Figure FIGREF5 shows n-ary relations extracted from OpenIE. As we can see, OpenIE extracts multiple relations for complex sentences. Here we select the most informative relation according to three criteria in the order of descending importance: (1) having the maximal number of overlapped tokens between the answer and the relation; (2) being assigned the highest confidence score by OpenIE; (3) containing maximum non-stop words. As shown in Figure FIGREF5, our criteria can select answer-relevant relations (waved in Figure FIGREF5), which is especially useful for sentences with extraneous information. In rare cases, OpenIE cannot extract any relation, we treat the sentence itself as the to the point context.
Table TABREF8 shows some statistics to verify the intuition that the extracted relations can serve as more to the point context. We find that the tokens in relations are 61% more likely to be used in the target question than the tokens in sentences, and thus they are more to the point. On the other hand, on average the sentences contain one more question token than the relations (1.86 v.s. 2.87). Therefore, it is still necessary to take the original sentence into account to generate a more accurate question.
Here we select the most informative relation according to three criteria in the order of descending importance:
Answer: Three.
Question: Why would people prefer cats over dogs?
Answer: Cats are easy to care for, quiet, and relatively affordable. They even groom themselves. There are certainly benefits to owning a cat that you won't get from owning a dog.
Question: What is the underlying question answering algorithm?
Evidence: We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA. We reuse all hyperparameters from docqa with the exception of number of paragraphs sampled in training: 8 instead of 4. Using more negative examples was important when learning from both fine and coarse annotations. The model uses character embeddings with dimension 50, pre-trained Glove embeddings, and hidden units for bi-directional GRU encoders with size 100. Adadelta is used for optimization for all methods. We tune two hyperparameters separately for each condition based on the held-out set: (1) $\alpha \in \lbrace .01, .1, .5, 1, 5, 10, 100 \rbrace $ , the weight of the coarse loss, and (2) the number of steps for early stopping. The training time for all methods using both coarse and fine supervision is comparable. We use Adadelta for optimization for all methods.
We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA. 
Answer: The answers are shown as follows:
* The system extends BiDAF BIBREF4 with self-attention
Question: Which chapel on Rutger's campus was built 7 years after the New Jersey College for Women?
Evidence: Voorhees Chapel is one of two chapels on the campus of Rutgers, The State University of New Jersey in New Brunswick, New Jersey. Built in 1925 with a donation from Elizabeth Rodman Voorhees, wife of Rutgers trustee Ralph Voorhees, the chapel once served the community of Douglass College. Douglass, founded the New Jersey College for Women (founded in 1918), was the women's residential college at Rutgers.
Answer: The Voorhees Chapel was built in 1925, seven years after the New Jersey College for Women was founded by Elizabeth Rodman Voorhees.
Question: what baseline do they compare to?
Evidence: We compare Blse (Sections UID23 – UID30 ) to VecMap, Muse, and Barista (Section "Previous Work" ) as baselines, which have similar data requirements and to machine translation (MT) and monolingual (Mono) upper bounds which request more resources. For all models (Mono, MT, VecMap, Muse, Barista), we take the average of the word embeddings in the source-language training examples and train a linear SVM. We report this instead of using the same feed-forward network as in Blse as it is the stronger upper bound. We choose the parameter $c$ on the target language development set and evaluate on the target language test set.
We compare Blse (Sections UID23 – UID30 ) to VecMap, Muse, and Barista (Section "Previous Work" ) as baselines, which have similar data requirements and to machine translation (MT) and monolingual (Mono) upper bounds which request more resources.
Answer: The answers are shown as follows:
* VecMap
* Muse
* Barista
Question: In a section labeled "Isaac Asimov's Autobiography Volumes", from the given paragraph list the names of the volumes of Asimov's autobiography in chronological order with the following format {YEAR} - {TITLE} separated by newlines. Then, in a new section, list Janet Asimov's contributions in a section labeled "Asimov Biographies Janet Asimov Wrote or Contributed To" in the same format.
Evidence: Asimov published three volumes of autobiography. In Memory Yet Green (1979) and In Joy Still Felt (1980) cover his life up to 1978. The third volume, I. Asimov: A Memoir (1994), covered his whole life (rather than following on from where the second volume left off). The epilogue was written by his widow Janet Asimov after his death. The book won a Hugo Award in 1995. Janet Asimov edited It's Been a Good Life (2002), a condensed version of his three autobiographies. He also published three volumes of retrospectives of his writing, Opus 100 (1969), Opus 200 (1979), and Opus 300 (1984).
Answer: Isaac Asimov's Autobiography Volumes
1979 - In Memory Yet Green
1980 - In Joy Still Felt
1994 - I. Asimov: A Memoir

Asimov Biographies Janet Asimov Wrote or Contributed To
1994 - I. Asimov: A Memoir
2002 - It's Been a Good Life
Question: Who is the greatest Russian writer?
Answer: Alexander Sergeyevich Pushkin was a Russian author of the Romantic era who is considered by many to be the greatest Russian poet and the founder of modern Russian literature.
Question: Who wrote Mahabharata?
Answer: According to ancient Indian tradition, Vyasa is the author of Mahabharata who is also a major character in the epic. It is first recited the sage Vaisampayana to the King Janamejaya who was the great-grandson of the Pandavas.  The story is then recited again by a Ugrasrava Sauti to an assembly of sages in Naimisa forest.
Question: What rock icon was the founder of The Society for the Prevention of Cruelty to Long-haired Men?
Answer: David Bowie.
Question: Why is recycling important
Answer: The planet has a finite amount of resources and it is important to use these wisely to ensure availability for future generations. Recycling also reduces the amount of waste which will otherwise end up in landfills or be burnt for energy recovery.
Question: How does a human do arithmetic calculation as fast as a calculator?
Answer: Use a tech called Mental abacus. When a person reads the numbers, he or she will mentally visualize an abacus and do the calculation by moving the beads in the abacus. The calculation can be done in a great speed.
Question: What methods is RelNet compared to?
Evidence: We evaluate the model's performance on the bAbI tasks BIBREF18 , a collection of 20 question answering tasks which have become a benchmark for evaluating memory-augmented neural networks. We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17 . Performance is measured in terms of mean percentage error on the tasks.
The results are shown in Table 1 . The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks.
We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17 .
 The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks.
Answer: The answers are shown as follows:
* We compare the performance with the Recurrent Entity Networks model (EntNet) BIBREF17