DataHammer/scimrc huggingface dataset:
Question: Classify each of the following as a character from The Matrix or from Divergent: Four, Neo, Trinity, Caleb, Jeanine, Persephone, Tris, Cypher, Marlene
Answer: The Matrix: Neo, Trinity, Persephone, Cypher
Divergent: Four, Caleb, Jeanine, Tris, Marlene
Question: What dataset does the method base on?
Evidence: All experiments are implemented on the hardware with Intel(R) Xeon(R) CPU E5-2682 v4 @ 2.50GHz and NVIDIA Tesla P100.
We evaluate our method on the SKE dataset used in this competition, which is the largest schema-based Chinese information extraction dataset in the industry, containing more than 430,000 SPO triples in over 210,000 real-world Chinese sentences, bounded by a pre-specified schema with 50 types of predicates. All sentences in SKE Dataset are extracted from Baidu Baike and Baidu News Feeds. The dataset is divided into a training set (170k sentences), a development set (20k sentences) and a testing set (20k sentences). The training set and the development set are to be used for training and are available for free download. The test set is divided into two parts, the test set 1 is available for self-verification, the test set 2 is released one week before the end of the competition and used for the final evaluation.
The max sequence length is set to 128, the number of fully connected layer of relation classification branch is set to 2, and that of global relation branch is set to 1. During training, we use Adam with the learning rate of 2e-5, dropout probability of 0.1. This model converges in 3 epoch.
All uppercase letters are converted to lowercase letters. We use max sequence length 128 so sentences longer than 128 are split by punctuation. According to FAQ, entities in book title mark should be completely extracted. Because the annotation criteria in trainset are diverse, we revise the incomplete entities. To keep consistence, book title marks around the entities are removed.
Our postprocessing mechanism is mainly based on the FAQ evaluation rules. After model prediction, we remove triplets whose entity-relation types are against the given schemas. For entities contained in book title mark, we complement them if they are incomplete. Date type entities are also complemented to the finest grain. These are implemented by regular expression matching.
Note that entity related preprocessing and postprocessing are also performed on the development set to keep consistency with the test set, thus the change of development metric is reliable.
We evaluate our method on the SKE dataset used in this competition, which is the largest schema-based Chinese information extraction dataset in the industry, containing more than 430,000 SPO triples in over 210,000 real-world Chinese sentences, bounded by a pre-specified schema with 50 types of predicates.
Answer: The method is based on SKE, which is a large schema-based Chinese information extraction dataset.
Question: What is an earphone?
Answer: Earphones are a little piece of electronic equipment that fits over or inside your ears. It can be used to listen to music, the radio, or your phone in private.
Question: What are simulated datasets collected?
Evidence: The generation process of the simulated data sets is designed to mimic the real world. Users open some existing documents in a file system, make some changes (e.g. addition, deletion or replacement), and save them as separate documents. These documents become revisions of the original documents. We started from an initial corpus that did not have revisions, and kept adding new documents and revising existing documents. Similar to a file system, at any moment new documents could be added and/or some of the current documents could be revised. The revision operations we used were deletion, addition and replacement of words, sentences, paragraphs, section names and document titles. The addition of words, ..., section names, and new documents were pulled from the Wikipedia abstracts. This corpus generation process had five time periods INLINEFORM0 . Figure FIGREF42 illustrates this simulation. We set a Poisson distribution with rate INLINEFORM1 (the number of documents in the initial corpus) to control the number of new documents added in each time period, and a Poisson distribution with rate INLINEFORM2 to control the number of documents revised in each time period.
We generated six data sets using different random seeds, and each data set contained six corpora (Corpus 0 - 5). Table TABREF48 summarizes the first data set. In each data set, we name the initial corpus Corpus 0, and define INLINEFORM0 as the timestamp when we started this simulation process. We set INLINEFORM1 , INLINEFORM2 . Corpus INLINEFORM3 corresponds to documents generated before timestamp INLINEFORM4 . We extracted document revisions from Corpus INLINEFORM5 and compared the revisions generated in (Corpus INLINEFORM6 - Corpus INLINEFORM7 ) with the ground truths in Table TABREF48 . Hence, we ran four experiments on this data set in total. In every experiment, INLINEFORM8 is calibrated based on Corpus INLINEFORM9 . For instance, the training set of the first experiment was Corpus 1. We trained INLINEFORM10 from Corpus 1. We extracted all revisions in Corpus 2, and compared revisions generated in the test set (Corpus 2 - Corpus 1) with the ground truth: 258 revised documents. The word2vec model shared in the four experiments was trained on Corpus 5.
We started from an initial corpus that did not have revisions, and kept adding new documents and revising existing documents.
The revision operations we used were deletion, addition and replacement of words, sentences, paragraphs, section names and document titles. The addition of words, ..., section names, and new documents were pulled from the Wikipedia abstracts.
We generated six data sets using different random seeds, and each data set contained six corpora (Corpus 0 - 5).
Answer: There are 6 simulated datasets collected which is initialised with a corpus of size 550 and simulated by generating new documents from Wikipedia extracts and replacing existing documents
Question: What are the best ramen places to eat in San Mateo, CA?
Answer: The top Ramen places in San Mateo are 1. Taishoken, 2 Kajiken, 3. Shegetsu 4. Himawari
Question: From the passage below, please extract the names of the general partners from a16z.
Evidence: Between 2006 and 2010, Marc Andreessen and Ben Horowitz actively invested in technology companies. Separately, and together, they invested $4 million in 45 start-ups including Twitter. During this time, the two became known as "super angel" investors.

On July 6, 2009, Andreessen and Horowitz launched their venture capital fund with an initial capitalization of $300 million. In November 2010, at a time when the field of venture capitalism was contracting, the company raised another $650 million for a second venture fund. In less than two years, the firm was managing a total of $1.2 billion under the two funds.

In May 2011, Andreessen ranked number 10 on the 2011 Forbes Midas List of Tech's Top Investors while he and Horowitz ranked number 6 on Vanity Fair's 2011 New Establishment List and number 1 on CNET's 2011 most influential investors list.

As of March 27, 2014, the firm managed $4 billion in assets after the closing of its fourth fund at $1.5 billion.

In addition to Andreessen and Horowitz, the firm's general partners include John O'Farrell, Scott Weiss, Jeff Jordan, Peter Levine, Chris Dixon, Vijay Pande, Martin Casado and Sriram Krishnan. In March 2019, it was reported that Andreessen Horowitz was opening an office in San Francisco.

In January 2022, Andreessen Horowitz raised $9 billion for its venture capital, growth-stage and biotech-focused vehicles.

As of April 2022, the firm manages $28.2 billion in assets.
Answer: In addition to the founders, Marc Andreessen and Ben Horowitz, a16z general partners include John O'Farrell, Scott Weiss, Jeff Jordan, Peter Levine, Chris Dixon, Vijay Pande, Martin Casado and Sriram Krishnan.
Question: What is the volume of a square pyramid?
Answer: 1/3 * base^2 * height
Question: What evaluation is conducted?
Evidence: We conduct our experiments using the evaluation methodology of SemEval 2010 Task 14: Word Sense Induction & Disambiguation BIBREF5 . In the gold standard, each word is provided with a set of instances, i.e., the sentences containing the word. Each instance is manually annotated with the single sense identifier according to a pre-defined sense inventory. Each participating system estimates the sense labels for these ambiguous words, which can be viewed as a clustering of instances, according to sense labels. The system's clustering is compared to the gold-standard clustering for evaluation.
We conduct our experiments using the evaluation methodology of SemEval 2010 Task 14: Word Sense Induction & Disambiguation BIBREF5 . 
Answer: The answers are shown as follows:
* Word Sense Induction & Disambiguation
Question: Which of the following can fly: bird, cow, cat, dog, car, helicopter, drone, bike, pot, butterfly, feather?
Answer: The objects that can fly are: bird, helicopter, drone, butterfly.
The objects that cannot fly are: cow, cat, dog, car, bike, pot, feather.

However, someone may claim that a feather can fly as well because it's light enough and a mild wind blow could make it fly.
Question: Why is a refrigerator door more difficult to open than usual soon after you have closed it?
Answer: This can be explained by the difference in pressure between the outside and inside of the refrigerator. In order to understand the phenomenon, let's first break down the relationship between pressure and temperature. Warmer air has a higher pressure than cooler air, it has to do with the energy of air molecules but that is a subject for another time. When you open the refrigerator door, warmer air from the surroundings, which would be at room temperature, rushes inside the refrigerator. This is because the air flows from the higher-pressure region (the room), into the lower-pressure region (inside the refrigerator). When you shut the door, the warm air that had moved inside is now actively being cooled down by the refrigerator. Remember what we said about cooler air? The pressure inside the refrigerator starts to drop. This means that warmer air at higher pressure outside the refrigerator is trying to get in and is pushing against the door, the next time you try to open the door, you need to apply an extra force to counteract that pressure that is acting on the door. So that begs the question, why is this more noticeable a short time after you shut the door versus coming back let's say, an hour later? Glad you asked! It is because the seal that keeps the warm air outside and cool air inside the refrigerator is not a perfect seal, it is not designed to be for good reason. Over time, since the seal allows the movement of air, the pressure equalizes. In the absence of that pressure differential, there is no force acting on the outside of the door forcing it shut. Fun fact, commercial refrigerators have seals designed to allow for a lot more movement of air compared to home refrigerators. Want to guess why? It is because content inside commercial refrigerators needs to be accessed a lot more frequently. If their seals were similar to home refrigerators, people would experience the extra force each time they want to get something! It is fascinating how much thought goes into designing everyday things we take for granted!
Question: When did Uber IPO?
Answer: Uber went public on May 9, 2019
Question: Classify each of the following as either an SSRI or MAOI depression drug: paroxetine, isocarboxazid, sertraline, vortioxetine, phenelzine, tranylcypromine, moclobemide, fluoxetine
Answer: SSRI: paroxetine, sertraline, vortioxetine, fluoxetine
MAOI: isocarboxazid, phenelzine, tranylcypromine, moclobemide
Question: Is Scala the best programming language?
Answer: That depends on your opinion, but most users of Scala enjoy it's use and think it is a good language.
Question: Give me a list of things I can do with a bunch of extra money.
Answer: Here's what you can do with extra money:

Invest in the stock market. For example, the average annualized return of the S&P 500 from 1928 through 2021 is 11.82%.

Invest in yourself by purchasing classes. You can also explore new hobbies for your enjoyment. Some examples are skiing, painting, or playing guitar. 

Travel around the world to gain new experiences and experience new cultures.

Invest in the real estate market.
Question: How did they do compared to other teams?
Evidence: JESSI is trained using only the datasets given on the shared task, without using any additional external data. Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%.
Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%.
Answer: The answers are shown as follows:
* second on Subtask A with an F1 score of 77.78% among 33 other team submissions
* performs well on Subtask B with an F1 score of 79.59%
Question: What coloring of cat is usually almost always female?
Answer: Tortoiseshell cats are frequently female.
Question: Tell me what is a good manager and a bad manager: Sympathy, open, democratic, Micro management, always show trust, always show doubt, intimidating
Answer: A good manager: Sympathy, Open, Democratic, always shows trust. 
A bad manager: Micro Management, always shows doubt, intimidating.
Question: What are some of Whitney Houston's top hits?
Answer: Whitney Houston had many top hits including but not limited to: 
- I'm Your Baby Tonight
- How Will I Know
- I Have Nothing
- I'm Every Woman
- I Will Always Love You
- I Wanna Dance With Somebody
Question: What are the 4 upper-level categories defined by the authors?
Evidence: Here, we improve the baseline model with novel and specifically crafted features that rely on the medical domain and that capture details on the specific content of an article. As shown in Figure 3 , medical model features, the bio-medical entities, have been extracted from the free text only, exploiting advanced NLP techniques and using domain dictionaries.
In details, we newly define and extract from the dataset the following novel features:
The idea of considering infoboxes is not novel: for example, in BIBREF7 the authors noticed that the presence of an infobox is a characteristic featured by good articles. However, in the specific case of the Medicine Portal, the presence of an infobox does not seem strictly related to the quality class the article belongs to (according to the manual labelling). Indeed, it is recurrent that articles, spanning all classes, have an infobox, containing a schematic synthesis of the article. In particular, pages with descriptions of diseases usually have an infobox with the medical standard code of the disease (i.e., IDC-9 and IDC-10), as in Figure 2 .
As done for the baseline, also the first two features of the medical model have been extracted with ad hoc Python scripts, extracting HTML structures and excerpts of the textual content within the MediaWiki tags. For their extraction of the bio-medical entities, we consider the textual part of the article only, obtained after removing the MediaWiki tags, and we apply a NLP analysis, which is presented in Section "Bio-medical entities" .
We have defined 4 upper level categories of our interest: – A anatomy: an article is about anatomy; – B biography: an article is a biography of someone or tell the history of something; – D disorder: it is about a disorder; – F first aid: it reports information for first aid or emergency contacts; – O other: none of the above.
Answer: – A anatomy: an article is about anatomy; – B biography: an article is a biography of someone or tell the history of something; – D disorder: it is about a disorder; – F first aid: it reports information for first aid or emergency contacts; – O other: none of the above.
Question: Why isn't Puerto Rico a US state?
Answer: Because there isn't a significant ROI
Question: What are the key issues around whether the gold standard data produced in such an annotation is reliable? 
Evidence: Our paper is also in conversation with various meta-research and standardization efforts in linguistics, crowdsourcing, and other related disciplines. Linguistics and Natural Language Processing have long struggled with issues around standardization and reliability of linguistic tagging. Linguistics researchers have long developed best practices for corpus annotation BIBREF27, including recent work about using crowdworkers BIBREF28. Annotated corpus projects often release guidelines and reflections about their process. For example, the Linguistic Data Consortium's guidelines for annotation of English-language entities (version 6.6) is 72 single-spaced pages BIBREF29. A universal problem of standardization is that there are often too many standards and not enough enforcement. As BIBREF30 notes, 33-81% of linguistics/NLP papers in various venues do not even mention the name of the language being studied (usually English). A meta-research study found only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics BIBREF31.
Another related area are meta-research and methods papers focused on identifying or preventing low-effort responses from crowdworkers — sometimes called “spam” or “random” responses, or alternatively ”fraudsters” or ”cheaters.” Rates of “self-agreement” are often used, determining if the same person labels the same item differently at a later stage. One paper BIBREF32 examined 17 crowdsourced datasets for sentiment analysis and found none had self-agreement rates (Krippendorf's alpha) above 0.8, with some lower than 0.5. Another paper recommends the self-agreement strategy in conjunction with asking crowdworkers to give a short explanation of their response, even if the response is never actually examined. BIBREF33. One highly-cited paper BIBREF34 proposes a strategy in which crowdworkers are given some items with known labels (a gold/ground truth), and those who answer incorrectly are successively given more items with known labels, with a Bayesian approach to identifying those who are answering randomly.
A meta-research study found only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics BIBREF31.
Another related area are meta-research and methods papers focused on identifying or preventing low-effort responses from crowdworkers — sometimes called “spam” or “random” responses, or alternatively ”fraudsters” or ”cheaters.” Rates of “self-agreement” are often used, determining if the same person labels the same item differently at a later stage.
Answer: The answers are shown as follows:
*  only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics
* low-effort responses from crowdworkers
Question: What is an unsecured loan?
Answer: Unsecured loans are monetary loans that are not secured against the borrower's assets.
Question: How to become a millionaire?
Answer: If you want to become a millionaire you first need to make up a consistent, long-term financial plan. Ask yourself questions like what does it mean rich for you? Any particular net worth you would like to hit? Once you have the big-picture established, break it down into smaller short-term goals that are easier to achieve. Start investing earlier and identify high-margin businesses to focus on. Constantly increase your income by balancing your investments and savings.
Question: Ms. Murmu becomes 2nd woman to hold the post after
Answer: Ms.Pratibha Patil
Question: What is a sandworm?
Answer: A sandworm is a creature described by Frank Herbert in his science-fiction book Dune. Sandworms are huge and aggressive and live in the sand seas of planet Arrakis.
Question: Is there any benefit to pain?
Evidence: Pain is a distressing feeling often caused by intense or damaging stimuli. The International Association for the Study of Pain defines pain as "an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage."

Pain motivates us to withdraw from damaging situations, to protect a damaged body part while it heals, and to avoid similar experiences in the future. Most pain resolves once the noxious stimulus is removed and the body has healed, but it may persist despite removal of the stimulus and apparent healing of the body. Sometimes pain arises in the absence of any detectable stimulus, damage or disease.
Answer: Pain helps us withdraw from further severe damage to body
Question: Name some of the popular sitcoms from 1990s
Answer: Some of the popular sitcoms from 1990s are:
* Friends
* Sienfeld
* The Fresh Prince of Bel-Air
* Family Matters
* The Nanny
Question: For which language, the highest accuracy are obtained?
Evidence: Table TABREF13 shows the results obtained for the baseline and the CNNs trained for each language individually. Similar accuracies are obtained between the baseline and the CNN model for Spanish language, which also exhibit the highest accuracy among the three languages. Note that the highest accuracy for German language was obtained with the baseline model. Conversely, for Czech language the CNN produces the highest accuracy. Note also that for the three languages, the results are unbalanced towards one of the two classes according to the specificity and sensitivity values. The difference in the results obtained among the three languages can be explained considering the information provided in Table TABREF5. For the patients in the Spanish language, the average MDS-UPDRS-III score is higher compared with the German and Czech patients, i.e, there are patients with higher disease severity in the Spanish data compared to German and Czech patients.
Table 3 shows the results obtained for the baseline and the CNNs trained for each language individually. Similar accuracies are obtained between the baseline and the CNN model for Spanish language, which also exhibit the highest accuracy among the three languages. Note that the highest accuracy for German language was obtained with the baseline model.
Answer: Spanish language.
Question: What are the three commonly accepted periods of human pre-history?
Answer: The Stone Age, the Bronze Age, and the Iron Age
Question: Given this paragraph about a singer, what was Rihanna's first album?
Evidence: Robyn Rihanna Fenty NH (/riˈænə/ (listen) ree-AN-ə;[n 1] born February 20, 1988) is a Barbadian singer, actress, and businesswoman. Born in Saint Michael and raised in Bridgetown, Barbados, Rihanna auditioned for American record producer Evan Rogers who invited her to the United States to record demo tapes. After signing with Def Jam in 2005, she soon gained recognition with the release of her first two studio albums, Music of the Sun (2005) and A Girl Like Me (2006), both of which were influenced by Caribbean music and peaked within the top ten of the US Billboard 200 chart.
Answer: Music of the Sun (2005)
Question: What are some different types of salt?
Answer: There are numerous types of salt and their uses vary greatly.  Here are some common and popular types used for cooking:  kosher salt, table salt, finishing salt, sea salt, smoked salt, curing salt, Himalayan pink salt, and french grey salt.
Question: Give me a bulleted list of the 5 most recent UK Prime Ministers
Answer: 1. Rishi Sunak
2. Elizabeth Truss
3. Boris Johnson
4. Theresa May
5. David Cameron
Question: What is Github Copilot?
Evidence: GitHub Copilot is a cloud-based artificial intelligence tool developed by GitHub and OpenAI to assist users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. Currently available by subscription to individual developers, the tool was first announced by GitHub on 29 June 2021, and works best for users coding in Python, JavaScript, TypeScript, Ruby, and Go.
Answer: GitHub Copilot is a cloud-based artificial intelligence tool developed by GitHub and OpenAI