DataHammer/scimrc huggingface dataset:
Question: How does the scoring model work?
Evidence: To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
Answer: The answers are shown as follows:
* First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word
Question: What models do the authors investigate?
Evidence: Keyphrases are single or multi-word lexical units that best summarise a document BIBREF0. As such, they are of great importance for indexing, categorising and browsing digital libraries BIBREF1. Yet, very few documents have keyphrases assigned, thus raising the need for automatic keyphrase generation systems. This task falls under the task of automatic keyphrase extraction which can also be the subtask of finding keyphrases that only appear in the input document. Generating keyphrases can be seen as a particular instantiation of text summarization, where the goal is not to produce a well-formed piece of text, but a coherent set of phrases that convey the most salient information. Those phrases may or may not appear in the document, the latter requiring some form of abstraction to be generated. State-of-the-art systems for this task rely on recurrent neural networks BIBREF2, BIBREF3, BIBREF4, and hence require large amounts of annotated training data to achieve good performance. As gold annotated data is expensive and difficult to obtain BIBREF5, previous works focused on readily available scientific abstracts and used author-assigned keyphrases as a proxy for expert annotations. However, this poses two major issues: 1) neural models for keyphrase generation do not generalize well across domains, thus limiting their use in practice; 2) author-assigned keyphrases exhibit strong consistency issues that negatively impacts the model's performance. There is therefore a great need for annotated data from different sources, that is both sufficiently large to support the training of neural-based models and that comprises gold-standard labels provided by experts. In this study, we address this need by providing KPTimes, a dataset made of 279 923 news articles that comes with editor-assigned keyphrases.
Online news are particularly relevant to keyphrase generation since they are a natural fit for faceted navigation BIBREF6 or topic detection and tracking BIBREF7. Also, and not less importantly, they are available in large quantities and are sometimes accompanied by metadata containing human-assigned keyphrases initially intended for search engines. Here, we divert these annotations from their primary purpose, and use them as gold-standard labels to automatically build our dataset. More precisely, we collect data by crawling selected news websites and use heuristics to draw texts paired with gold keyphrases. We then explore the resulting dataset to better understand how editors tag documents, and how these expert annotations differ from author-assigned keyphrases found in scholarly documents. Finally, we analyse the performance of state-of-the-art keyphrase generation models and investigate their transferability to the news domain and the impact of domain shift.
Finally, we analyse the performance of state-of-the-art keyphrase generation models and investigate their transferability to the news domain and the impact of domain shift.
Answer: The performance of state-of-the-art keyphrase generation models.
Question: What dataset is the proposed system applied on?
Evidence: Deep learning systems have shown a lot of promise for extractive Question Answering (QA), with performance comparable to humans when large scale data is available. However, practitioners looking to build QA systems for specific applications may not have the resources to collect tens of thousands of questions on corpora of their choice. At the same time, state-of-the-art machine reading systems do not lend well to low-resource QA settings where the number of labeled question-answer pairs are limited (c.f. Table 2 ). Semi-supervised QA methods like BIBREF0 aim to improve this performance by leveraging unlabeled data which is easier to collect.
In this work, we present a semi-supervised QA system which requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents. Our proposed system consists of three stages. First, we construct cloze-style questions (predicting missing spans of text) from the unlabeled corpus; next, we use the generated clozes to pre-train a powerful neural network model for extractive QA BIBREF1 , BIBREF2 ; and finally, we fine-tune the model on the small set of provided QA pairs.
Our cloze construction process builds on a typical writing phenomenon and document structure: an introduction precedes and summarizes the main body of the article. Many large corpora follow such a structure, including Wikipedia, academic papers, and news articles. We hypothesize that we can benefit from the un-annotated corpora to better answer various questions – at least ones that are lexically similar to the content in base documents and directly require factual information.
We apply the proposed system on three datasets from different domains – SQuAD BIBREF3 , TriviaQA-Web BIBREF4 and the BioASQ challenge BIBREF5 . We observe significant improvements in a low-resource setting across all three datasets. For SQuAD and TriviaQA, we attain an F1 score of more than 50% by merely using 1% of the training data. Our system outperforms the approaches for semi-supervised QA presented in BIBREF0 , and a baseline which uses the same unlabeled data but with a language modeling objective for pretraining. In the BioASQ challenge, we outperform the best performing system from previous year's challenge, improving over a baseline which does transfer learning from the SQuAD dataset. Our analysis reveals that questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes.
We apply the proposed system on three datasets from different domains — SQuAD (Rajpurkar et al., 2016), TriviaQA-Web (Joshi et al., 2017) and the BioASQ challenge (Tsatsaronis et al., 2015).
Answer: They apply the proposed system on three datasets from different domains — SQuAD (Rajpurkar et al., 2016), TriviaQA-Web (Joshi et al., 2017) and the BioASQ challenge (Tsatsaronis et al., 2015).
Question: Classify each of the following as either Mexican food or Vietnamese food: pho, tacos, spring rolls, bun bo hue, burritos, frijoles, banh mi, nachos
Answer: Vietnamese food: pho, spring rolls, bun bo hue, banh mi
Mexican food: tacos, burritos, frijoles, nachos
Question: Who is the founder of 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. 

Chin was trained since childhood by his father, Chin Lik Keong, founder of I Liq Chuan, and in 1978, he won the state heavy weight kickboxing championships of Selangor (Malaysia). In August 2009 Chin was officially named the Gate Keeper and Lineage Holder of the I Liq Chuan. He is also the founder and President of Chin Family I Liq Chuan Association that supports the martial art's growth.

Chin has taught workshops worldwide , written two books on his family's martial art, co-authored articles, and produced a series of DVDs. Chin was interviewed by Jess O'Brien for his 2007 book "Nei Jia Quan: Internal Martial Arts".

In August 2009, Chin was also recognized by the USA Wushu Kung-Fu Federation as a Hall of Fame Outstanding Master. In 2014, he was written about by Ashe Higgs in Kung Fu Tai Chi Magazine "Finding the Center: I Liq Chuan’s Three Essential Qualities for Offense and Defense as One". Chin was also the subject of the 2016 CCTV Documentary "Kung Fu Abroad: I Liq Chuan 中央电视台《功夫在外》纪录片".

On November 15, 2017 Sam Chin became an honorary professor at the University of Plovdiv, Bulgaria in recognition of his lifetime of achievement in spreading I Liq Chuan.
Answer: The founder of I Liq Chuan is Chin Lik Keong.
Question: What athlete created the 'beast quake' for the Seattle Seahawks?
Answer: Marshan Lynch
Question: Classify each of the following as flexible or inflexible: gymnast, yoga instructor, steel, rubber, leather, wood
Answer: Gymnast: Flexible
Yoga Instructor: Flexible
Steel: Inflexible
Rubber: Flexible
Leather: Flexible
Wood: Inflexible
Question: Give me a bulleted list of some things that could go wrong skateboarding down a steep hill
Answer: - You could hit a rock in the street and go flying off your board
- You could gain too much speed and be unable to slow down
- You could be unable to stop and run a stop sign in front of a cop car
- You could be hit by a car
- You could have your board fly into traffic and be broken by a car
Question: What professional baseball team is based out of Seattle, WA.
Answer: The Seatttle Mariners are part of the American League within Major League Baseball.
Question: Classify the overall sentiment of this piece of text as either positive or negative: The little girl really wanted to buy that beautiful dress but her mom did not have enough money.
Answer: Negative
Question: Extract the creator of Amdahl's law from the Wikipedia paragraph.
Evidence: In computer architecture, Amdahl's law (or Amdahl's argument) is a formula which gives the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved. It states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used".[page needed] It is named after computer scientist Gene Amdahl, and was presented at the American Federation of Information Processing Societies (AFIPS) Spring Joint Computer Conference in 1967.
Answer: The Wikipedia paragraph does not specify the creator of Amdahl's law. However, it does state that the law was named after the computer scientist Gene Amdahl. Given the context, it is likely that Gene Amdahl is the creator of Amdahl's law.
Question: What is the name of the family at the centre of the TV show Succession?
Evidence: Succession is an American satirical black comedy-drama television series created by Jesse Armstrong. It premiered on June 3, 2018, on HBO. The series centers on the Roy family, the owners of Waystar RoyCo, a global media and entertainment conglomerate, who are fighting for control of the company amid uncertainty about the health of the family's patriarch, Logan Roy (Brian Cox). The series has been renewed for a fourth and final season, which premiered on March 26, 2023.
Answer: Succession centres on the Roy family, the owners of Waystar RoyCo
Question: What affects tiredness?
Answer: There are many factors which affect tiredness, including, but not limited to, amount of quality sleep, neurological state, nutrition, and both physical and mental health issues. It is extremely important to seek professional medical advice for tiredness if it is severely impacting your lifestyle and health.
Question: Which is a species of fish? John Dory or Jack Sparrow
Answer: John Dory
Question: Which of the star signs are earth signs?
Answer: Virgo, Taurus and Capricorn are the three astrological earth signs in the Zodiac.
Question: Tell me about cockney rhyming slang
Answer: Cockney rhyming slang is most closely associated with East London in England. It dates back to the 19th century. 

It works like this:

A word or phrase is replaced by another word or phrase that rhymes with it. For example instead of saying the word "face" you would say "boat race". The rhyming phrase is often longer than the original word and so the rhyme gets abbreviated. In our example "boat race" becomes "boat". 

So a sentence might become "you should have seen the look on their boat" instead of "you should have seen the look on their face". 

Another example might be "I'm off up the apple and pears to go to me Uncle Ned" - "I'm going upstairs to go to bed".
Question: Classify the animals based on their food habits into Herbivores or Carnivores.
Jackal, Hyena, Zebra, Giraffe
Answer: Herbivores  - Zebra, Giraffe
Carnivores. - Jackal, Hyena
Question: What are the primary causes of inflation?
Answer: In economics, inflation is the growth rate of the price of goods and services for a particular selection of items. For example, items that a typical household consumes are captured in the Consumer Price Index, whereas items that domestic producers buy are included in the Producer Price Index. The drivers behind changes in the general price levels can be attributed to two main reasons. First, changes in the real demand and supply of goods and services will drive prices up or down. Higher prices will encourage suppliers to increase supply and discourage consumer demand reestablishing market equilibrium. Second, changes in the money supply –i.e. the amount of currency in circulation, bank deposits, and other liquid monetary aggregates– can lead to inflation. When a country's central bank decides to increase the money supply through quantitative easing monetary policies, the amount of currency available to buy and sell the same amount of goods and services increases. If the rate at which money is exchanged in an economy remains unchanged (i.e. velocity of money is constant) then a higher money supply will lead to higher prices because there is more money to transact the same amount of goods and services in the economy.
Question: What layers does their model consist of?
Evidence: In our evaluation of the proposed scheme, each classifier is implemented as a deep learning model having four layers, as illustrated in Figure FIGREF16 , and is described as follows:
The Input (a.k.a Embedding) Layer. The input layer's size is defined by the number of inputs for that classifier. This number equals the size to the word vector plus the number of additional features. The word vector dimension was set to 30 so that to be able to encode every word in the vocabulary used.
The hidden layer. The sigmoid activation was selected for the the hidden LSTM layer. Based on preliminary experiments the dimensionality of the output space for this layer was set to 200. This layer is fully connected to both the Input and the subsequent layer.
The dense layer. The output of the LSTM was run through an additional layer to improve the learning and obtain more stable output. The ReLU activation function was used. Its size was selected equal to the size of the input layer.
The output layer. This layer has 3 neurons to provide output in the form of probabilities for each of the three classes Neutral, Racism, and Sexism. The softmax activation function was used for this layer.
In total we experimented with 11 different setups of the proposed scheme, each with a different ensemble of classifiers, see Table TABREF17 .
In our evaluation of the proposed scheme, each classifier is implemented as a deep learning model having four layers, as illustrated in Figure 2, and is described as follows: • The Input (a.k.a Embedding) Layer. The input layer’s size is defined by the number of inputs for that classifier. This number equals the size to the word vector plus the number of additional features. The word vector dimension was set to 30 so that to be able to encode every word in the vocabulary used. • The hidden layer. The sigmoid activation was selected for the the hidden LSTM layer. Based on preliminary experiments the dimensionality of the output space for this layer was set to 200. This layer is fully connected to both the Input and the subsequent layer. • The dense layer. The output of the LSTM was run through an additional layer to improve the learning and obtain more stable output. The ReLU activation function was used. Its size was selected equal to the size of the input layer. • The output layer. This layer has 3 neurons to provide output in the form of probabilities for each of the three classes Neutral, Racism, and Sexism. The softmax activation function was used for this layer.
Answer: The input layer, the hidden layer, the dense layer, and the output layer.
Question: What does the software company Teamwork do and where is it located
Answer: Teamwork is an Irish, privately owned, web-based software company headquartered in Cork, Ireland. Teamwork creates task management and team collaboration software. Founded in 2007, as of 2016 the company stated that its software was in use by over 370,000 organisations worldwide (including Disney, Spotify and HP), and that it had over 2.4m users.
Question: In what order did they sort single-trial FRPs in the paper?
Evidence: The aim of the technical validation of the data is to guarantee good recording quality and to replicate findings of previous studies investigating co-registration of EEG and eye movement data during natural reading tasks (e.g. dimigen2011coregistration). We also compare the results to ZuCo 1.0 BIBREF1, which allows a more direct comparison due to the analogous recording procedure.
Moreover, these previous studies were able to show an effect of fixation duration on the resulting FRPs. To show this dependency we followed two approaches. First, for each reading task, all single-trial FRPs were ordered by fixation duration and a vertical sliding time-window was used to smooth the data (Dimigen et al., 2011).
Answer: Fixation duration.
Question: How would it feel to be the only person left alive in the US?
Answer: It’s done. I am the only one left, at least in the US. When it first happened, I was convinced that there must be other pockets of people in the US. I have spent the last five years traveling throughout the country, to large cities, small towns and rural areas and I have not seen or heard any people or any evidence that anyone else survived.

After five years the loneliness is crushing.  Every day I hope to find another human being – to talk to, to touch, perhaps even beginning to rebuild community together. I vacillate between rage and despair that we were unable to save our planet. We had so much global technology and innovation and yet as climate change grew continuously worse nations were unable to collaborate to find solutions. As more and more locations became uninhabitable due to rising heat and humidity, increasingly frequent and intense natural hazards, and failed crops, we simply let people die. Although I still cannot imagine what I could have done differently, I wish I had done more.
Question: Identify which car manufacturer is Italian or American: Ferrari, GMC
Answer: GMC is American, Ferrari is Italian
Question: When was Kyoto the capital of Japan?
Evidence: Kyoto is one of the oldest municipalities in Japan, having been chosen in 794 as the new seat of Japan's imperial court by Emperor Kanmu. The original city, named Heian-kyō, was arranged in accordance with traditional Chinese feng shui following the model of the ancient Chinese capitals of Chang'an and Luoyang. The emperors of Japan ruled from Kyoto in the following eleven centuries until 1869. It was the scene of several key events of the Muromachi period, Sengoku period, and the Boshin War, such as the Ōnin War, the Honnō-ji Incident, the Kinmon incident and the Battle of Toba–Fushimi. The capital was relocated from Kyoto to Tokyo after the Meiji Restoration.
Answer: The emperors of Japan ruled from Kyoto from 794 as the new seat of Japan's imperial court for eleven centuries until 1869.
Question: Do you have any dietary restrictions? If so, what are they.
Answer: Yes. I'm both kosher AND vegetarian. That mean simply getting kosher food that contains meat or fish won't be something I can eat. Likewise, a vegetarian entree from a regular restaurant also won't work as there are all sorts of problems that might ensure. These include: cross-contamination w/ utensils used to prepare meat or fish, inclusion of ingredients that appear vegetarian but aren't (e.g., cheese made with animal rennet or enzymes, pastries containing gelatin made from bones, natural colorants or flavorings derived from insects or animals, egg-substitute made from plasmolyzed fish blood, etc.). My own personal standard doesn't require certification by a kosher agency; but in general, I'll only eat at places that only serve vegan or vegetarian items (and from the latter, there may be items I can't eat).