DataHammer/scimrc huggingface dataset:
Question: Why invest in the stock market?
Answer: Long term investing in the stock market is a good idea.  Short term growth is risky and fluctuates a lot but long term growth can show returns as much at 10% - 15% return per year on average.  Tracking the S&P 500 is a great way to get started because its a well diversified option that tracks 500 of the largest companies listed on stock exchanges.  It has a historical return of nearly 10% and the S&P 500 was introduced in 1957.
Question: Classify each of these car manufacturers as either French, German, American, Japanese, or other: Mercedes-Benz, Toyota, DS, Ford, Tesla, Fiat, KIA
Answer: French: DS
German: Mercedes-Benz
American: Ford, Tesla
Japanese: Toyota
other: Fiat, KIA
Question: Given a reference text about Tom Brady, tell me when they were born and what they are famous for.
Evidence: Thomas Edward Patrick Brady Jr. (born August 3, 1977) is a former American football quarterback who played in the National Football League (NFL) for 23 seasons. He spent his first 20 seasons with the New England Patriots and was a central contributor to the franchise's dynasty from 2001 to 2019. In his final three seasons, he was a member of the Tampa Bay Buccaneers. Brady is widely regarded as the greatest quarterback of all time.
Answer: Tom Brady was born on August 3rd, 1977. He is famous for being the greatest quarterback of all time.
Question: What achievements did Cade Cunningham receive during his first year in the NBA based on the provided paragraph?
Evidence: Cunningham was drafted with the first overall pick of the 2021 NBA draft by the Detroit Pistons. On August 8, 2021, he made his debut in the 2021 NBA Summer League in a 76–72 win against the Oklahoma City Thunder in which he posted 12 points, six rebounds, and two assists in 26 minutes. Cunningham lost part of the training camp, all of the preseason, and five of Detroit's first six games due to an ankle injury. On October 30, he made his NBA debut, putting up two points, two assists, and seven rebounds in a 110–103 win over the Orlando Magic. After five games, Cunningham posted his first 40-plus field-goal percentage, and he had an 18-point, 10-rebound double-double in his third NBA game. On November 15, Cunningham became the youngest player in NBA history to tally at least 25 points, eight rebounds, and eight assists with five three-pointers in a game at 20 years, 51 days. He topped LeBron James (20 years, 100 days) and Trae Young (20 years, 163 days). On November 21, he notched a triple-double with 13 points, 12 rebounds, and 10 assists in a 121—116 loss to the Los Angeles Lakers to become the eighth-youngest player in NBA history to record a triple-double and the youngest in Pistons history.
Answer: On November 15, 2021, Cade Cunningham became the youngest player in NBA history to tally at least 25 points, eight rebounds, and eight assists with five three-pointers in a game at 20 years, 51 days. He topped LeBron James (20 years, 100 days) and Trae Young (20 years, 163 days). On November 21, 2021, he notched a triple-double with 13 points, 12 rebounds, and 10 assists in a 121—116 loss to the Los Angeles Lakers to become the eighth-youngest player in NBA history to record a triple-double and the youngest in Pistons history.
Question: How did they find the synonyms when replacing some of the words in contexts or questions with their synonyms in another language?
Evidence: We observe linguistic-agnostic representations in the last subsection. If tokens are represented in a language-agnostic way, the model may be able to handle code-switching data. Because there is no code-switching data for RC, we create artificial code-switching datasets by replacing some of the words in contexts or questions with their synonyms in another language. The synonyms are found by word-by-word translation with given dictionaries. We use the bilingual dictionaries collected and released in facebookresearch/MUSE GitHub repository. We substitute the words if and only if the words are in the bilingual dictionaries.
Table TABREF14 shows that on all the code-switching datasets, the EM/F1 score drops, indicating that the semantics of representations are not totally disentangled from language. However, the examples of the answers of the model (Table TABREF21) show that multi-BERT could find the correct answer spans although some keywords in the spans have been translated into another language.
Because there is no code-switching data for RC, we create artificial code-switching datasets by replacing some of the words in contexts or questions with their synonyms in another language. The synonyms are found by word-by-word translation with given dictionaries. We use the bilingual dictionaries collected and released in facebookresearch/MUSE GitHub repository.
Answer: By word-by-word translation with given dictionaries.
Question: How is multi-tasking performed?
Evidence: We propose a novel two-layered multi-task attention based neural network for sentiment analysis where emotion analysis is utilized to improve its efficiency. Figure FIGREF1 illustrates the overall architecture of the proposed multi-task system. The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks. The BiLSTM encodes the word representation of each word. This representation is shared between the subsystems of sentiment and emotion analysis. Each of the shared representations is then fed to the primary attention mechanism of both the subsystems. The primary attention mechanism finds the best representation for each word for each task. The secondary attention mechanism acts on top of the primary attention to extract the best sentence representation by focusing on the suitable context for each task. Finally, the representations of both the tasks are fed to two different feed-forward neural networks to produce two outputs - one for sentiment analysis and one for emotion analysis. Each component is explained in the subsequent subsections.
The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks. The BiLSTM encodes the word representation of each word. This representation is shared between the subsystems of sentiment and emotion analysis. Each of the shared representations is then fed to the primary attention mechanism of both the subsystems. The primary attention mechanism finds the best representation for each word for each task. The secondary attention mechanism acts on top of the primary attention to extract the best sentence representation by focusing on the suitable context for each task. Finally, the representations of both the tasks are fed to two different feed-forward neural networks to produce two outputs - one for sentiment analysis and one for emotion analysis. Each component is explained in the subsequent subsections.
Answer: The answers are shown as follows:
* The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks.
* Each of the shared representations is then fed to the primary attention mechanism
Question: What are dogs?
Evidence: The dog has been selectively bred over millennia for various behaviors, sensory capabilities, and physical attributes. Dog breeds vary widely in shape, size, and color. They perform many roles for humans, such as hunting, herding, pulling loads, protection, assisting police and the military, companionship, therapy, and aiding disabled people. Over the millennia, dogs became uniquely adapted to human behavior, and the human–canine bond has been a topic of frequent study. This influence on human society has given them the sobriquet of "man's best friend".
Answer: The dog (Canis familiaris or Canis lupus familiaris) is a domesticated descendant of the wolf. Also called the domestic dog, it is derived from the extinct Pleistocene wolf, and the modern wolf is the dog's nearest living relative. Dogs were the first species to be domesticated by hunter-gatherers over 15,000 years ago before the development of agriculture. Due to their long association with humans, dogs have expanded to a large number of domestic individuals and gained the ability to thrive on a starch-rich diet that would be inadequate for other canids.
Question: Why are NLG tasks for the Laptop and TV domains much harder than others？
Evidence: We assessed the proposed models on four different NLG domains: finding a restaurant, finding a hotel, buying a laptop, and buying a television. The Restaurant and Hotel were collected in BIBREF4 , while the Laptop and TV datasets have been released by BIBREF22 with a much larger input space but only one training example for each DA so that the system must learn partial realization of concepts and be able to recombine and apply them to unseen DAs. This makes the NLG tasks for the Laptop and TV domains become much harder. The dataset statistics are shown in Table 1 .
. The Restaurant and Hotel were collected in (Wen et al., 2015b), while the Laptop and TV datasets have been released by (Wen et al., 2016a) with a much larger input space but only one training example for each DA so that the system must learn partial realization of concepts and be able to recombine and apply them to unseen DAs.
Answer: It has a much larger input space but only one training example for each DA so that the system must learn partial realization of concepts and be able to recombine and apply them to unseen DAs.
Question: Why is Tatoeba (TT) chosen as an additional test set?
Evidence: Tatoeba (TT) is a community built language learning corpus having sentences aligned across multiple languages with the corresponding speech partially available. Its sentences are on average shorter than those in CoVoST (see also Table TABREF2) given the original purpose of language learning. Sentences in TT are licensed under CC BY 2.0 FR and part of the speeches are available under various CC licenses.
We construct an evaluation set from TT (for French, German, Dutch, Russian and Spanish) as a complement to CoVoST development and test sets. We collect (speech, transcript, English translation) triplets for the 5 languages and do not include those whose speech has a broken URL or is not CC licensed. We further filter these samples by sentence lengths (minimum 4 words including punctuations) to reduce the portion of short sentences. This makes the resulting evaluation set closer to real-world scenarios and more challenging.
We run the same quality checks for TT as for CoVoST but we do not find poor quality translations according to our criteria. Finally, we report the overlap between CoVo transcripts and TT sentences in Table TABREF5. We found a minimal overlap, which makes the TT evaluation set a suitable additional test set when training on CoVoST.
Tatoeba (TT) is a community built language learning corpus having sentences aligned across multiple languages with the corresponding speech partially available. Its sentences are on average shorter than those in CoVbST (see also Table 1) given the original purpose of language learning. Sentences in TT are licensed under CC BY 2.0 FR and part of the audio is available under various CC licenses. We construct an evaluation set from TT (for French, German, Dutch, Russian and Spanish) as a complement to CoVbST development and test sets. We collect (speech, transcript, English translation) triplets for the 5 languages and do not include those whose speech has a broken URL or is not CC licensed. We further filter these samples by sen_x001f_tence lengths (minimum 4 words including punctuations) to reduce the portion of short sentences. This makes the resulting evaluation set closer to real-world scenarios and more challenging. We run the same quality checks for TT as for CoVoST but we do not find poor quality translations according to our criteria. Finally, we report the overlap between CoVo transcripts and TT sentences in Table 2. We found a minimal overlap, which makes the TT evaluation set a suitable additional test set when training on CoVoST
Answer: TT is also a corpus having multiple languages, and it has corresponding speech partially available. After certain treatment of TT, it becomes quite convenient for evaluation as the overlap between CoVoST and TT is quite small.
Question: What are some good slogan ideas for my new boat rental company?
Answer: Here are some potential slogans for your boat rental company:

- Have a boat but only when you want a boat
- There's nothing worse than being a boat owner. There's nothing better than being a boat renter.
- Just leave it where you found it
- We'll do the hard stuff. You just have fun out there.
- You're the king of the lake today
Question: Should I charge my phone if the battery is low?
Answer: Yes, typically it would be a good idea to charge your battery in that case. If your battery runs of charge, you may miss an important text message or phone call!
Question: Where was Alexander The Great from?
Evidence: Alexander III of Macedon (Ancient Greek: Ἀλέξανδρος, romanized: Alexandros; 20/21 July 356 BC – 10/11 June 323 BC), commonly known as Alexander the Great, was a king of the ancient Greek kingdom of Macedon. He succeeded his father Philip II to the throne in 336 BC at the age of 20, and spent most of his ruling years conducting a lengthy military campaign throughout Western Asia and Egypt. By the age of 30, he had created one of the largest empires in history, stretching from Greece to northwestern India. He was undefeated in battle and is
Answer: Alexender the Great was king of the ancient kingdom of Macedonia.
Question: What does the context-query attention layer generate?
Evidence: The used architecture of the QA model is briefly summarized below. Here we choose QANet BIBREF2 as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF BIBREF17 when reaching the same performance on SQuAD.
The network architecture is illustrated in Figure FIGREF2 . The left blocks and the right blocks form two QANets, each of which takes a document and a question as the input and outputs an answer. In QANet, firstly, an embedding encoder obtains word and character embeddings for each word in INLINEFORM0 or INLINEFORM1 and then models the temporal interactions between words and refines word vectors to contextualized word representations. All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention. The intuition here is that convolution components can model local interactions and self-attention components focus on modeling global interactions. The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words. After that, a model encoder layer containing seven encoder blocks captures the interactions among the context words conditioned on the question. Finally, the output layer predicts a start position and an end position in the document to extract the answer span from the document.
The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words.
Answer: The question-document similarity matrix.
Question: What kind of datasets does the author use to train the models?
Evidence: 
Alzheimer’s disease (AD) is a neurodegenerative disease affecting over 40 million people worldwide with high costs of acute and long-term care BIBREF0 . Recruitment of participants with cognitive impairment has historically been a bottleneck in clinical trials BIBREF1 , making AD datasets relatively small. Additionally, though cognitive assessments test domains of cognition through multiple tasks, most available datasets of pathological speech are restricted to participants performing a single task. Picture description using an image to elicit narrative discourse samples is one such task that has proved to be successful in detecting AD BIBREF2 . However, it is important to develop ML models of high performance that would produce results generalizable across different tasks.
Several studies have used natural language processing and machine learning to distinguish between healthy and cognitively impaired speech of participants describing a picture. Fraser et al. BIBREF3 used linguistic and acoustic features to classify healthy and pathological speech transcripts with an accuracy of INLINEFORM0 . Similarly, Karlekar et al. BIBREF4 classified utterances of speakers as AD or healthy (HC) with an accuracy of INLINEFORM1 using an enlarged, utterance-level view of transcripts of picture descriptions. In line with previous research, we use linguistic and acoustic features of speech as input to our ML model. Furthermore, we extend the model to using data from several different tasks.
Noorian et al. BIBREF5 demonstrated that using within-task data of healthy participants describing a picture improved AD detection performance by up to 13%. In this paper, we evaluate if model performance improves with the addition of data from healthy participants, with varying ages, performing either the same or different tasks. We find that models trained on datasets of picture description tasks augmented with conversational speech of healthy speakers learn decision boundaries that are more generalizable across activities with lower out-of-task errors. We observe a 9% increase in AD detection performance when normative data from different tasks are utilized. We also analyze if each task provides domain-specific inductive bias for other tasks to obtain a model setting capable of detecting AD from any sample of speech using high-precision model-agnostic explanations proposed by Ribeiro et al. BIBREF6 and computation of various error metrics related to classification.

We find that models trained on datasets of picture description tasks augmented with conversational speech of healthy speakers learn decision boundaries that are more generalizable across activities with lower out-of-task errors.
Answer: The datasets of picture description tasks.
Question: Classify these tasks by how easy they would be for a 10 year old to do (either easy or hard): drive a car, tie their shoes, throw a baseball, go to college, ride a bike, spell the word "water"
Answer: drive a car: hard, tie their shoes: easy, throw a baseball: easy, go to college: hard, ride a bike: easy, spell the word "water": easy
Question: Was the degree of offensiveness taken as how generally offensive the text was, or how personally offensive it was to the annotator?
Evidence: The surveys consisted of 20 tweets presented in a random order. For each tweet, each participant was asked three questions. Depending on the survey, participants were asked (1) to answer (yes/no) if they considered the tweet hate speech, either based on the definition or based on their personal opinion. Afterwards they were asked (2) to answer (yes/no) if the tweet should be banned from Twitter. Participants were finally asked (3) to answer how offensive they thought the tweet was on a 6-point Likert scale from 1 (Not offensive at all) to 6 (Very offensive). If they answered 4 or higher, the participants had the option to state which particular words they found offensive.
to answer how offensive they thought the tweet was on a 6-point Likert scale from 1 (Not offensive at all) to 6 (Very offensive). If they answered 4 or higher, the participants had the option to state which particular words they found offensive.
Answer: Personal thought of the annotator.
Question: List the prior presidents of Boston College
Answer: Monan, Leahy, Fulton, Gasson, Lyons, Devlin
Question: Write a story about a toddler who woke up in the middle of the night after a nightmare about a monster under their bed.
Answer: The toddler woke up after hearing a shuffling noise and feeling their bed rattle. The bedroom was eerily dark and quiet all of a sudden. He turned on the light and saw a quick glimpse of what looked like hands retreating under the bed. "DAD!", he yelled out. As his dad ran into the room, he started crying - muttering, in between sniffles, about a monster under the bed. Dad bravely poked under the bed, squealed with excitement, and pulled out a toy fork from under the bed. Smiling, he showed the toddler his new toy - that he'd forgotten about. After a few cuddles and another bedtime story, he was fast asleep again. And the monster under the bed was back to planning its next move.
Question: What is a bear in finance?
Answer: A bear is someone who expects share prices to fall in the future and sells shares now so that they can buy them back later at a lower price.
Question: What is the standard loft of a 9-iron in golf?
Answer: The standard loft in today's 9-irons ranges from 45 to 48 degrees.
Question: What does Tianwen means?
Answer: Tianwen means quest for heavenly truth
Question: Name the most famous detective books in the history
Answer: - Murder on the Orient Express by Agatha Christie (1934) 
- The Hound of the Baskervilles by Sir Arthur Conan Doyle (1902)
- The Maltese Falcon by Dashiell Hammett (1930)
- The Big Sleep by Raymond Chandler (1939)
- The Name of the Rose by Umberto Eco (1980)
- In the Woods by Tana French (2007)
Question: What caused the millenium tower to sink in san francisco?
Answer: The critics blame the city for allowing Millennium partners to anchor the building into packed sand rather than bedrock. The problem with Millennium tower arose because the 60 to 90-foot-long friction piles supporting it were driven into sandy soil rather than the bedrock, 200 feet down.
Question: Summarise the ways an employer can find workers using the given text as a reference
Evidence: The main ways for employers to find workers and for people to find employers are via jobs listings in newspapers (via classified advertising) and online, also called job boards. Employers and job seekers also often find each other via professional recruitment consultants which receive a commission from the employer to find, screen and select suitable candidates. However, a study has shown that such consultants may not be reliable when they fail to use established principles in selecting employees. A more traditional approach is with a "Help Wanted" sign in the establishment (usually hung on a window or door or placed on a store counter). Evaluating different employees can be quite laborious but setting up different techniques to analyze their skills to measure their talents within the field can be best through assessments. Employer and potential employee commonly take the additional step of getting to know each other through the process of a job interview.
Answer: 1. Job listing in newspapers through classified advertising.
2. Posting Job listings online (Job Boards) .
3. Through Recruitment consultants who takes a commission from the employer to find, screen and select suitable candidates. 
4. By displaying a 'Help Wanted' sign in the establishment  near a window or door or on the reception.
Question: Where should you visit in Belize if you are looking for a nice beach?
Answer: You should visit Secret Beach on Ambergris Caye if you want to experience calm waters without sargassum (sea grass) on the beach. In Secret Beach you will find the typical postcard beaches with nice sand, calm waters and additionally you get to watch the sunset since this side of the island faces west. On the east side of Ambergris Caye, you will experience more wind, more waves and sargassum. However, this side of the island has better access to the reef for snorkeling and fishing.