DataHammer/scimrc huggingface dataset:
Question: Write about a peculiar dream you have had
Answer: I had the most intriguing dream last night. I always remember my dreams starting in the middle of the story. It feels very natural and there is logical prelude to the event I am going through, but I don’t remember per say. In this dream I was taking part in my final university exams. This is a recurring dream I have. I was right before the beginning of the exam and could not remember any of my classes of the year. On the way to the exam room I was asking all my friends to share their knowledge with me to catch up. I was  trying to locate my class book in my backpack in an attempt to read through it one last time as the class doors were getting closer and closer. My anxiety was raising. When the doors opened, instead of entering a classroom. , I stepped onto a meadow on a sunny afternoon. Very naturally my exam became a scavenger hunt to find pages of my book hidden in trees or floating on the lake. I was not surprised and quickly went about looking for a boat to retrieve the pages floating on the lake.  As I was on the boat I discovered I could float and even fly, which made it much easier to go after all the pages. Dreams are always interesting to remember. They often revolve around common topics or events, like taking exams or flying and any many other events. I have read that dreams have meaning, I will need to research the meaning of this sequence of events.
Question: Why do they incorporate both sentiment and emotion clues in their framework?
Evidence: Sarcasm detection is an important subtask of sentiment analysis BIBREF27 . Since sarcastic sentences are subjective, they carry sentiment and emotion-bearing information. Most of the studies in the literature BIBREF28 , BIBREF29 , BIBREF9 , BIBREF30 include sentiment features in sarcasm detection with the use of a state-of-the-art sentiment lexicon. Below, we explain how sentiment information is key to express sarcastic opinions and the approach we undertake to exploit such information for sarcasm detection.
In general, most sarcastic sentences contradict the fact. In the sentence “I love the pain present in the breakups" (Figure FIGREF4 ), for example, the word “love" contradicts “pain present in the breakups”, because in general no-one loves to be in pain. In this case, the fact (i.e., “pain in the breakups") and the contradictory statement to that fact (i.e., “I love") express sentiment explicitly. Sentiment shifts from positive to negative but, according to sentic patterns BIBREF31 , the literal sentiment remains positive. Sentic patterns, in fact, aim to detect the polarity expressed by the speaker; thus, whenever the construction “I love” is encountered, the sentence is positive no matter what comes after it (e.g., “I love the movie that you hate”). In this case, however, the sentence carries sarcasm and, hence, reflects the negative sentiment of the speaker.
In another example (Figure FIGREF4 ), the fact, i.e., “I left the theater during the interval", has implicit negative sentiment. The statement “I love the movie" contradicts the fact “I left the theater during the interval"; thus, the sentence is sarcastic. Also in this case the sentiment shifts from positive to negative and hints at the sarcastic nature of the opinion.
The above discussion has made clear that sentiment (and, in particular, sentiment shifts) can largely help to detect sarcasm. In order to include sentiment shifting into the proposed framework, we train a sentiment model for sentiment-specific feature extraction. Training with a CNN helps to combine the local features in the lower layers into global features in the higher layers. We do not make use of sentic patterns BIBREF31 in this paper but we do plan to explore that research direction as a part of our future work. In the literature, it is found that sarcasm is user-specific too, i.e., some users have a particular tendency to post more sarcastic tweets than others. This acts as a primary intuition for us to extract personality-based features for sarcasm detection.
As discussed in the literature (Riloff et al., 2013), sarcasm detection may depend on sentiment and other cognitive aspects. For this reason, we incorporate both sentiment and emotion clues in our framework.
Answer: Sarcasm detection may depend on sentiment and other cognitive aspects.
Question: In what way did Liptchinsky's team improve wav2letter?
Evidence: Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words. Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system.
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6 . We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers. Liptchinsky et al. BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout.
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks. Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10 . We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR. As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs.
It is possible to increase the capacity of the Jasper model by stacking these operations. Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters). We use residual connections to enable this level of depth. We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR).
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2.95% WER and SOTA results among end-to-end models on LibriSpeech test-other. We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S). Using only greedy decoding without a language model we achieve 3.86% WER on LibriSpeech test-clean.
This paper makes the following contributions:
Liptchinsky et al. [8] improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) [9], weight normalization [10] and dropout.
Answer: By increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU), weight normalization and dropout.
Question: In the series A Song of Ice and Fire, who is the founder of House Grafton?
Answer: Gerold Grafton
Question: What is the origin of the name of the herb Rosemary?
Evidence: alvia rosmarinus (/ˈsælviə ˌrɒsməˈraɪnəs/), commonly known as rosemary, is a shrub with fragrant, evergreen, needle-like leaves and white, pink, purple, or blue flowers, native to the Mediterranean region. Until 2017, it was known by the scientific name Rosmarinus officinalis (/ˌrɒsməˈraɪnəs əˌfɪsɪˈneɪlɪs/), now a synonym.

It is a member of the sage family Lamiaceae, which includes many other medicinal and culinary herbs. The name ""rosemary"" derives from Latin ros marinus (lit. 'dew of the sea'). Rosemary has a fibrous root system
Answer: The name "rosemary" derives from Latin ros marinus.
Question: Who was Bhagat Singh?
Answer: Bhagat Singh was a freedom fighter born in undivided India on 27th September 1907 and was hanged to death by the British Empire at a young age of 23 years on 23rd March 1931.
Question: Tell me which of these would be an instrument played in a Rock band: Drums, lawnmower, guitar, bicycle, bass, chair, keyboards
Answer: Drums, guitar, bass, keyboards
Question: Q: If I vape tobacco or nicotine am I at risk for complications from COVID-19?
Answer: A: E-cigarette use can expose the lungs to toxic chemicals, but whether those exposures increase the risk of COVID-19 or the severity of COVID-19 outcomes is not known. However, many e-cigarette users are current or former smokers, and cigarette smoking increases the risk of respiratory infections, including pneumonia.
Question: How to compare neural language models that compute sentence representations from unlabelled, naturally-occurring data?
Evidence: To constrain the analysis, we compare neural language models that compute sentence representations from unlabelled, naturally-ocurring data, as with the predominant methods for word representations. Likewise, we do not focus on `bottom up' models where phrase or sentence representations are built from fixed mathematical operations on word vectors (although we do consider a canonical case - see CBOW below); these were already compared by milajevs2014evaluating. Most space is devoted to our novel approaches, and we refer the reader to the original papers for more details of existing models.
To constrain the analysis, we compare neural language models that compute sentence representations from unlabelled, naturally-ocurring data, as with the predominant methods for word representations.
Answer: The same with the predominant methods for word representations.
Question: What are the northern lights?
Answer: The northern lights are a common term referring to the aurora borealis. The aurora borealis is a natural phenomenon seen in the northern hemisphere near the arctic circle. Auroras are caused by disturbances in the magnetosphere caused by solar winds which result in emissions of light of varying colours and complexities.
Question: Given a reference text about NATO, when did Finland become a member?
Evidence: The Republic of Finland has been a member of the North Atlantic Treaty Organization (NATO) since 4 April 2023. Finland had formal relations with NATO since 1994, when it joined the Partnership for Peace program, and has been a member of the European Union (EU), which largely overlaps with NATO in membership, since 1995.
Answer: April 4, 2023
Question: How many English dialogues each subset includes?
Evidence: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. The former comes from the scripts of the Friends TV sitcom. The other is made up of Facebook messenger chats. Each subset includes $1,000$ English dialogues, and each dialogue can be further divided into a few consecutive utterances. All the utterances are annotated by five annotators on a crowd-sourcing platform (Amazon Mechanical Turk), and the labeling work is only based on the textual content. Annotator votes for one of the seven emotions, namely Ekman’s six basic emotions BIBREF1, plus the neutral. If none of the emotion gets more than three votes, the utterance will be marked as “non-neutral”.
For the datasets, there are properties worth additional mentioning. Although Friends and EmotionPush share the same data format, they are quite different in nature. Friends is a speech-based dataset which is annotated dialogues from the TV sitcom. It means most of the utterances are generated by the a few main characters. The personality of a character often affects the way of speaking, and therefore “who is the speaker" might provide extra clues for emotion prediction. In contrast, EmotionPush does not have this trait due to the anonymous mechanism. In addition, features such as typo, hyperlink, and emoji that only appear in chat-based data will need some domain-specific techniques to process.
Incidentally, the objective of the challenge is to predict the emotion for each utterance. Just, according to EmotionX 2019 specification, there are only four emotions be selected as our label candidates, which are Joy, Sadness, Anger, and Neutral. These emotions will be considered during performance evaluation. The technical detail will also be introduced and discussed in following Section SECREF13 and Section SECREF26.
Each subset includes $1,000$ English dialogues, and each dialogue can be further divided into a few consecutive utterances.
Answer: 1000.
Question: Who won the 2008 Democratic primary that Martin Connor competed in?
Evidence: Martin Edward Connor (born March 3, 1945) is a former member of the New York State Senate from Brooklyn, New York. He was first elected to the State Senate in a special election in 1978. He is a Democrat. The 25th Senate District that he represented covers lower Manhattan and an area of Brooklyn down the East River from part of Greenpoint to Carroll Gardens, and eastward to part of Downtown Brooklyn. He lost the 2008 Democratic primary to challenger Dan Squadron.
Answer: Dan Squadron won the 2008 Democratic primary over Martin Connor.
Question: Why is the Uniqlo round mini shoulder bag so popular right now?
Answer: Uniqlo, a popular Japanese apparel company, first released the round mini shoulder bag in 2022. The bag went viral on TikTok with young millennial and Gen Z consumer for its versatility, wide range of color options, affordable price point (equivalent to $20 USD), and ability to fit all essentials while still being compact. The bag is also coated with a water-repellent agent and considered very durable for outdoor activities such as hiking and Coachella.
Question: What are the aesthetic emotions formalized?
Evidence: To emotionally move readers is considered a prime goal of literature since Latin antiquity BIBREF1, BIBREF2, BIBREF3. Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives that have the capacity to move readers are evaluated as good and powerful texts for this very reason. Similarly, feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking). Emotions that exhibit this dual capacity have been defined as “aesthetic emotions” BIBREF2. Contrary to the negativity bias of classical emotion catalogues, emotion terms used for aesthetic evaluation purposes include far more positive than negative emotions. At the same time, many overall positive aesthetic emotions encompass negative or mixed emotional ingredients BIBREF2, e.g., feelings of suspense include both hopeful and fearful anticipations.
Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives that have the capacity to move readers are evaluated as good and powerful texts for this very reason. Similarly, feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking). Emotions that exhibit this dual capacity have been defined as “aesthetic emotions” BIBREF2.
Answer: The answers are shown as follows:
* feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking)
* Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”
Question: How do formula one cars generate downforce?
Evidence: Formula One cars are the fastest regulated road-course racing cars in the world, owing to very high cornering speeds achieved through the generation of large amounts of aerodynamic downforce. Much of this downforce is generated by front and rear wings, which have the side effect of causing severe turbulence behind each car. The turbulence reduces the downforce generated by the cars following directly behind, making it hard to overtake. Major changes made to the cars for the 2022 season has seen greater use of ground effect aerodynamics and modified wings to reduce the turbulence behind the cars, with the goal of making overtaking easier. The cars are dependent on electronics, aerodynamics, suspension and tyres. Traction control, launch control, and automatic shifting, plus other electronic driving aids, were first banned in 1994. They were briefly reintroduced in 2001, and have more recently been banned since 2004 and 2008, respectively.
Answer: Formula one cars generate downforce mainly using front and rear wings.
Question: Where is Bogota located?
Answer: Bogota is located in the country of Colombia. Bogota is the capital and the largest city of the country. Bogota was founded on August 6th, 1538 by Gonzalo Jimenez de Quesada and as of 2022 it has a population of about 8 million people. Within Colombia, the city is located in the department of Cundinamarca.
Question: How long has the Masters golf tournament been held?
Answer: The first Masters golf tournament was played in 1934.
Question: When did Google purchase YouTube
Evidence: In October 2006, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube also approved creators to participate in Google's AdSense program, which seeks to generate more revenue for both parties. YouTube reported revenue of $29.2 billion in 2022. In 2021, YouTube's annual advertising revenue increased to $28.8 billion, an increase in revenue of 9 billion from the previous year.
Answer: Google purchased YouTube in October 2006 for $1.65 billion
Question: Do they authors account for differences in usage of Twitter amongst MPs into their model?
Evidence: The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs. The resulting retweet network is an undirected, weighted network.
We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2 . The higher the ratio, the more each MEP (on average) retweets the MEPs from the same political group, hence, the higher the cohesion of the political group. The definition of the average retweeets ( INLINEFORM3 ) of a group INLINEFORM4 is: INLINEFORM5
The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs
We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2 
Answer: No.
Question: How can the simple act of lifting weights a few times provide many benefits?
Answer: Lifting weights causes tiny tears in your muscle fibers that expedite muscle-protein synthesis. This process uses amino acids to repair and reinforce the fibers, making them resistant to further damage. When you lift weights regularly, your muscles adapt and become bigger and stronger. Regularly lifting weights reduces fat, burns calories, increases bone density, and improves blood flow.
Question: Why is a meme compared to a gene?
Evidence: A meme (/miːm/ MEEM) is an idea, behavior, or style that spreads by means of imitation from person to person within a culture and often carries symbolic meaning representing a particular phenomenon or theme. A meme acts as a unit for carrying cultural ideas, symbols, or practices, that can be transmitted from one mind to another through writing, speech, gestures, rituals, or other imitable phenomena with a mimicked theme. Supporters of the concept regard memes as cultural analogues to genes in that they self-replicate, mutate, and respond to selective pressures. In popular language, a meme may refer to an Internet meme, typically an image, that is remixed, copied, and circulated in a shared cultural experience online.
Answer: A meme is compared to a gene because it can self-replicate, mutate, and respond to selective pressures similar to that of a gene.
Question: What’s the easiest coffee brewing method?
Answer: It depends. If you have already have a coffee machine, you can just add coffee and room temperature water, and brew it. This will probably not going to yield the best coffee you can drink, so maybe looking at a similar approach could give you better tasting coffee. In order to do that you can add intermediate steps: grind your own coffee for freshness, measure the amount of coffee vs water for better ratio, and use better coffee paper filters.
Question: What is absolute address in memory?
Answer: An absolute (machine) address specifying a physical location in memory
Question: Help me with spend a day in New Delhi. I like architecture, culture, and am a foodie
Answer: North India has a lot to offer for an aesthete, epicure, and a xenophile. Day 1 arrive in Delhi as many flights arrive early in the morning, settle into a hotel of your choice. Today travel around Delhi especially old Delhi and start your trip by visiting Red Fort which is an historical Mogul Fort commissioned by Mogul Emperor Shah Jahan in 1638. The fort is steeped in history and has a beautiful combination of Persian architecture with Indian influence. After that have lunch in Chandi Chowk where you will find incredible street food and especially Karim's which specializes in Mughlai food. Chandi Chowk is full of verve and energy and you will experience a slice of Indian culture. After the delicious lunch and walking through the narrow streets of Chandi Chowk, it is time to visit Qutb Minar a Unesco World Heritage Site which is considered the tallest minaret in the world at 240 ft constructed out of sandstone in 1192. Close by you can also visit Jamal Kalami mosque and relax at the nearby 200 acre park. In the evening you can travel to visit the Lotus Temple in Delhi which is a Baháʼí House of Worship and architected by Fariborz Sahba in 1976. For dinner, it is time to indulge yourself at Oberoi Delhi at Dhilli under the mentorship of Michelin-starred Chef Vineet Bhatia MBE and finish your night at the rooftop bar Cirrus 9 with a smooth cocktail.
Question: What is a Synchronous Concurrent Algorithm (SCA)?
Answer: A Synchronous Concurrent Algorithm is an algorithm consisting of a networks of modules and channels, computing and communicating in parallel, and synchronised by a global clock. There are many classes of algorithms that share these general properties such as clocked digital hardware, systolic arrays, neural networks, cellular automata and coupled map lattice dynamical systems.
Question: Is tomato a fruit or a vegetable ?
Answer: Tomatoes are both a fruit and a vegetable. It is considered as a fruit by botanists because it grows from a flower and contains seeds. It is considered as a vegetable by nutritionists because it does not contain as much fructose as other fruits like oranges or apples and consequently they are not served in many cultures as deserts but rather as a first dish and cooked in a second dish.