DataHammer/scimrc huggingface dataset:
Question: Did the BOW model provide the best results in terms of accuracy and F1 score?
Evidence: Table TABREF5 reflects that the BOW model provided the best results, both in terms of accuracy and F1-score. The best result achieved by a sentiment model was the Stanford Sentiment ($0.63$ F1-score), with others within the same range and linguistic-based features collectively giving the best performance.
Table 1 reflects that the BOW model provided the best results, both in terms of accuracy and F1-score. The best result achieved by a sentiment model was the Stanford Sentiment (0.63 F1-score), with others within the same range and linguistic-based features collectively giving the best performance.
Answer: Yes, it did.
Question: What are the most common meals?
Answer: The most common meals are breakfast, lunch and dinner.  Breakfast is eat when a person wakes up, lunch is eaten in the afternoon after 4 - 5 hours of being awake, and dinner is eaten in the evening before bed time.  Each of the meals is important to give the body energy to be able to function during the day.  Skipping meals is a form of fasting, and this is very common in certain cultures and certain religions too.  Eating in between meals is not a meal but a form of snacking.
Question: I need to write a deck summarising the benefits of delta in the lakehouse, can you give me some good slide suggestions?
Answer: Sure, here you go -

1 - Challenges of data warehousing and analytics
2 - Why Delta, what problem does it solve
3 - What is Delta - key values points
4 - References and customer stories
5 - Enablement and Training
6 - Next Steps and QA
Question: What is the difference between a red delicious and a Granny Smith apple?
Answer: A red delicious apple is red and sweet whereas a Granny Smith apple is green and tart.
Question: To which town in the UK did the Ford Motor Company relocate to in 1931?
Evidence: In 1931 the Ford Motor Company relocated from Trafford Park in Manchester, to a larger new plant in Dagenham, which was already the location of supplier Briggs Motorway Bodies. A 500-acre (200 ha) riverside site was developed to become Europe's largest car plant, a vast vertically integrated site with its own blast furnaces and power station, importing iron ore and exporting finished vehicles. By the 1950s Ford had taken over Briggs at Dagenham and its other sites at Doncaster, Southampton, Croydon and Romford. At its peak the Dagenham plant had 4,000,000 square feet (370,000 m2) of floor space and employed over 40,000 people, although this number gradually fell during the final three decades of the 20th century as production methods advanced and Ford invested in other European factories as well. Some of Britain's best selling cars, including the Fiesta, Escort, Cortina and Sierra, were produced at the plant over the next 71 years.
Answer: Dagenham
Question: Identify which animal species is alive or extinct: Polacanthus, Olive Ridley Turtle
Answer: Olive Ridley Turtle is alive, Polacanthus is extinct.
Question: Classify each of the following activities as "active" or "lazy": Running, jogging, biking, watching TV, drinking beer, laying down, lifting weights
Answer: Running: Active
Jogging: Active
Biking: Active
Watching TV: Lazy
Drinking Beer: Lazy
Laying Down: Lazy
Lifting Weights: Active
Question: How much is the gap between using the proposed objective and using only cross-entropy objective?
Evidence: FLOAT SELECTED: Table 2: Ablation study on the development set of SQuAD.
The contributions of each part of our model are shown in Table 2 . We note that the deep residual coattention yielded the highest contribution to model performance, followed by the mixed objective. The sparse mixture of experts layer in the decoder added minor improvements to the model performance.
FLOAT SELECTED: Table 2: Ablation study on the development set of SQuAD.
The contributions of each part of our model are shown in Table 2 .
Answer: The mixed objective improves EM by 2.5% and F1 by 2.2%
Question: Which is a species of fish? Sturgeon or Surgeon
Answer: Sturgeon
Question: What is an ATM?
Answer: An automated teller machine (ATM) is an electronic telecommunications device that enables customers of financial institutions to perform financial transactions, such as cash withdrawals, deposits, funds transfers, balance inquiries or account information inquiries, at any time and without the need for direct interaction with bank staff.

ATMs are known by a variety of names, including automatic teller machine (ATM) in the United States (sometimes redundantly as "ATM machine"). In Canada, the term automated banking machine (ABM) is also used, although ATM is also very commonly used in Canada, with many Canadian organizations using ATM over ABM. In British English, the terms cashpoint, cash machine and hole in the wall are most widely used. Other terms include any time money, cashline, tyme machine, cash dispenser, cash corner, bankomat, or bancomat. ATMs that are not operated by a financial institution are known as "white-label" ATMs.

Using an ATM, customers can access their bank deposit or credit accounts in order to make a variety of financial transactions, most notably cash withdrawals and balance checking, as well as transferring credit to and from mobile phones. ATMs can also be used to withdraw cash in a foreign country. If the currency being withdrawn from the ATM is different from that in which the bank account is denominated, the money will be converted at the financial institution's exchange rate. Customers are typically identified by inserting a plastic ATM card (or some other acceptable payment card) into the ATM, with authentication being by the customer entering a personal identification number (PIN), which must match the PIN stored in the chip on the card (if the card is so equipped), or in the issuing financial institution's database.
Question: Who are your 3 favorite Star Wars characters and why?
Answer: There are so many great Star Wars characters, but if I have to narrow it down to 3, I'd have to say Chewy, Rey, and Grogu. Chewy is a classic Star Wars character who has always been on the 'good' side (the Rebellion). His communication mode is so adorable, he is extremely loyal, and he wears his emotions on his (hairy) sleeve.  While Princess Leia was an inspriational character, I think Rey is the most impressive of the two strong female protagonists. Rey is mentally and physically strong, passionate about her beliefs, and I hope she will continue to show up in Star Wars movies. Grogu is from the The Mandalorian series, on Disney Plus. Lots of people call him Baby Yoda because he looks like a tiny version of the famous and wise Yoda character. Grogu appeals to women because he makes baby-like sounds and has huge inquisitive eyes.  His curiosity gets him into trouble, but he doesn't know any better yet. He has impressive super powers for such a tiny creature, and I hope that we will get to watch Grogu grow up as the series continues.
Question: Who founded the city of Price, Quebec?
Evidence: It was the arrival of the Price Brothers and Company (of William Evan Price) that the village of Price was founded at the end of the 19th century. It was a suburb of Saint-Octave-de-Métis, which constituted a community of workers, who were employed at the saw mill.
Answer: The Price Brothers and Company
Question: What is the old name for The Go Between Bridge?
Evidence: The Go Between Bridge, formerly known as the Hale Street Link, is a toll bridge for vehicles, pedestrians and cyclists over the Brisbane River in inner-city Brisbane, Queensland, Australia. The bridge connects Merivale and Cordelia Streets in West End to Hale Street and the Inner City Bypass at Milton. It is Brisbane's first inner-city traffic bridge in 40 years and carries around 12,000 vehicles per day. The bridge opened to traffic on 5 July 2010 and is now operated by Transurban Queensland.
Answer: Hale Street Link
Question: Which type does the architecture focus on?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet.
Answer: Type-III
Question: Who was given EY Entrepreneur of the year award 2020 ?
Answer: Kiran Mazumdar Shaw
Question: Why are model-based attacks generally seen as grey box attacks?
Evidence: Most existing adversarial attack methods for text inputs are derived from those for image inputs. These methods can be categorised into three types including gradient-based attacks, optimisation-based attacks and model-based attacks.
Gradient-based attacks are mainly white-box attacks that rely on calculating the gradients of the target classifier with respect to the input representation. This class of attacking methods BIBREF6, BIBREF7, BIBREF6 are mainly derived from the fast gradient sign method (FGSM) BIBREF1, and it has been shown to be effective in attacking CV classifiers. However, these gradient-based methods could not be applied to text directly because perturbed word embeddings do not necessarily map to valid words. Other methods such as DeepFool BIBREF8 that rely on perturbing the word embedding space face similar roadblocks. BIBREF5 propose to use nearest neighbour search to find the closest word to the perturbed embedding.
Both optimisation-based and model-based attacks treat adversarial attack as an optimisation problem where the constraints are to maximise the loss of target classifiers and to minimise the difference between original and adversarial examples. Between these two, the former uses optimisation algorithms directly; while the latter trains a seperate model to generate adversarial examples and therefore involves a training process. Some of the most effective attacks for images are achieved by optimisation-based methods, such as the L-BFGS attack BIBREF1 and the C&W attack BIBREF9 in white-box attacks and the ZOO method BIBREF10 in black-box attacks. For texts, the white-box attack HotFlip BIBREF3 and black-box attack DeepWordBug BIBREF11 and TextBugger BIBREF12 are proposed in this category.
In a similar vein, a few model-based attacks have been proposed for images, e.g. BIBREF13 design a generative adversarial network (GAN) to generate the image perturbation from a noise map. The attacking method and target classifier typically form a single large network and the attacking method is trained using the loss from the target classifier. For this reason, it is not very straightforward to use these model-based techniques for text because there is a discontinuity in the network (since words in the adversarial examples are discrete) and so it is not fully differentiable.
Model-based attacks are generally seen as greybox attacks as it requires access to target classifier during training phase, but once it’s trained it can generate adversarial examples independently.
Answer: Because it requires access to target classifier during training phase.
Question: What is Intel Xeon good for?
Answer: Intel's Xeon line of processors is intended for business workstations and servers. For servers and workstations running data-intensive applications, particularly those that are mission-critical, Xeon CPUs are the best option.
Question: Given a reference text about Derby Museum and Art Gallery, tell me when the it was established and what the gallery contains.
Evidence: Derby Museum and Art Gallery is a museum and art gallery in Derby, England. It was established in 1879, along with Derby Central Library, in a new building designed by Richard Knill Freeman and given to Derby by Michael Thomas Bass. The collection includes a gallery displaying many paintings by Joseph Wright of Derby; there is also a large display of Royal Crown Derby and other porcelain from Derby and the surrounding area. Further displays include archaeology, natural history, geology, military collections and world cultures. The Art Gallery was opened in 1882.
Answer: The Derby Museum and Art Gallery was established in 1879 and includes a gallery with many paintings by Joseph Wright of Derby and other displays with archaeology, natural history, geology, military collections, and world cultures.
Question: In the case of Experts, which version had better F-score performance?
Evidence: In this section we will compare the results of the different segmentation systems through automatic evaluations. First of all, the human segmentation, from the subcorpus $D$ composed of common documents. The results are presented in the table tab:humains. The first row shows the performance of the $I$ segments, taking the experts as a reference, while the second presents the process in the opposite direction.
We have found that segmentation by experts and naive produces two subcorpus $E$ and $N$ with very similar characteristics. This surprised us, as we expected a more important difference between them. In any case, we deduced that, at least in this corpus, it is not necessary to be an expert in linguistics to discursively segment the documents. As far as system evaluations are concerned, we use the 78 $E$ documents as reference. Table TABREF26 shows the results.
In the case of the Experts, the grammatical verb-nominal version (V-N) had better F-score performance. The verbal version (V) obtained a better accuracy $P$ than the verb-nominal (V-N). In the case of the Naive, the performance F-score, $P$ and $R$ is very similar from the Experts.
In the case of the Experts, the grammatical verb-nominal version (V-N) had better F-score performance. The verbal version (V) obtained a better accuracy P than the verb-nominal (V-N). In the case of the Naive, the performance F-score, P and R is very similar from the Experts.
Answer: Verb-nominal version.
Question: What are the two training sets in this paper?
Evidence: The resource is comprised of 142 hours of spoken Mapudungun that was recorded during the AVENUE project BIBREF6 in 2001 to 2005. The data was recorded under a partnership between the AVENUE project, funded by the US National Science Foundation at Carnegie Mellon University, the Chilean Ministry of Education (Mineduc), and the Instituto de Estudios Indígenas at Universidad de La Frontera, originally spanning 170 hours of audio. We have recently cleaned the data and are releasing it publicly for the first time (although it has been shared with individual researchers in the past) along with NLP baselines.
The recordings were transcribed and translated into Spanish at the Instituto de Estudios Indígenas at Universidad de La Frontera. The corpus covers three dialects of Mapudungun: about 110 hours of Nguluche, 20 hours of Lafkenche and 10 hours of Pewenche. The three dialects are quite similar, with some minor semantic and phonetic differences. The fourth traditionally distinguished dialect, Huilliche, has several grammatical differences from the other three and is classified by Ethnologue as a separate language, iso 639-3: huh, and as nearly extinct.
The recordings are restricted to a single domain: primary, preventive, and treatment health care, including both Western and Mapuche traditional medicine. The recording sessions were conducted as interactive conversations so as to be natural in Mapuche culture, and they were open-ended, following an ethnographic approach. The interviewer was trained in these methods along with the use of the digital recording systems that were available at the time. We also followed human subject protocol. Each person signed a consent form to release the recordings for research purposes and the data have been accordingly anonymized. Because Machi (traditional Mapuche healers) were interviewed, we asked the transcribers to delete any culturally proprietary knowledge that a Machi may have revealed during the conversation. Similarly, we deleted any names or any information that may identify the participants.
The corpus is culturally relevant because it was created by Mapuche people, using traditional ways of relating to each other in conversations. They discussed personal experiences with primary health care in the traditional Mapuche system and the Chilean health care system, talking about illnesses and the way they were cured. The participants ranged from 16 years old to 100 years old, almost in equal numbers of men and women, and they were all native speakers of Mapudungun.
We created two training sets, one appropriate for single-speaker speech synthesis experiments, and one appropriate for multiple-speaker speech recognition and machine translation experiments.
Answer: One appropriate for single-speaker speech synthesis experiments, and one appropriate for multiple-speaker speech recognition and machine translation experiments.
Question: What countries are these names most likely to be found among native-born citizens? John Smith, Elise Monet, Helmut Schneider, Jing Li, Raj Patel
Answer: England or United States, France, Germany, China, India
Question: What are the causes for Sensory processing disorder?
Evidence: The exact cause of SPD is not known.However, it is known that the midbrain and brainstem regions of the central nervous system are early centers in the processing pathway for multisensory integration; these brain regions are involved in processes including coordination, attention, arousal, and autonomic function. After sensory information passes through these centers, it is then routed to brain regions responsible for emotions, memory, and higher level cognitive functions. Damage in any part of the brain involved in multisensory processing can cause difficulties in adequately processing stimuli in a functional way.
Answer: The exact cause of Sensory processing disorder is not known. However, it is known that the midbrain and brainstem regions of the central nervous system are early centers in the processing pathway for multisensory integration; these brain regions are involved in processes including coordination, attention, arousal, and autonomic function. After sensory information passes through these centers, it is then routed to brain regions responsible for emotions, memory, and higher level cognitive functions. Damage in any part of the brain involved in multisensory processing can cause difficulties in adequately processing stimuli in a functional way.
Question: What is Akshardham?
Answer: Akshardham in New Delhi, India is recognized by the Guinness Book of World Records as the World's largest comprehensive Hindu Temple. The 100-acre Akshardham complex on the banks of Yamuna has become an architectural landmark of India. The monument is a fusion of pink stone and pure white marble, where pink stone symbolizes bhakti (devotion) in eternal bloom and white marble that of absolute purity and eternal peace. It measures 356 ft. long, 316 ft. wide and 141 ft. hight, covering an area of 86,342 sq.ft. The marvelous, ornately hand-carved stone temple has been built without structural steel. It took five years for the 11,000 artisans and volunteers to build this grand structure. It cost about 200 crore Indian rupees. It was built by the Bochasanwasi Akshar Purushottam Swaminarayan Sanstha (BAPS) organization.
Question: Jokic or Embiid for the MVP?
Answer: I would give the MVP award to Nikola Jokic.
Question: What is Debezium?
Answer: Debezium is a change data capture (CDC) tool for capturing CDC change logs from databases related to updates, deletes, and inserts to source tables.  Debezium can access these logs and that information can be used downstream to update CDC changes against copies of the source tables in a different platform system.  Debezium is also open source.  FiveTran is a competitor to Debezium but they charge for the CDC service they offer.
Question: Classify each item as "soft" or "hard": bowling ball, cinder block, tissue, memory foam, croissant, hammer
Answer: Bowling Ball: Hard
Cinder Block: Hard
Tissue: Soft
Memory Foam: Soft
Croissant: Soft
Hammer: Hard
Question: What are some different ways I can use many fresh lemons?
Answer: If you have multiple lemons and want to use them before they go bad, consider the following uses: 
1) Lemon cleaning spray. Mix 1 parts water, 1 parts white vinegar, and the juice of one lemon into a spray bottle. A great non-toxic cleaner for around the house!
2) Limoncello. Add 1 cup simple syrup to 4 cups vodka and pour over the peels from 12 lemons. Leave in a sealed glass container for 30 days. Serve over ice and enjoy. (Drink responsibly!) 
3) Lemon flavored olive oil. Slice 4 lemons and put in 4 cups of extra-virgin olive oil. Leave in a sealed container for 30 days. Enjoy in salads and soups!
4) Lemon bars. There are many ways to make these using all parts of a lemon. Check online for a recipe to make these deliciously tart bars. 
5) Lemon-aid. Mix 1 parts fresh squeezed lemon juice to 6 parts water. Sweeten to taste. Serve over ice 
6) Lemon Centerpiece. Brighten up the room by arranging lemons in a decorative bowl. Place on the table or counter to add a pop of color.
7) Lemon Detox water. Start your morning with 16oz of warm water, the juice of one lemon, and a pinch of cayenne.
Question: All planets in our solar system rotate on their axis either in a clockwise or anti-clockwise direction. Tell me which planets have clockwise rotation, and which have anti-clockwise rotations. The planets are: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune.
Answer: Mercury, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune have clockwise rotations. Venus has an anti-clockwise rotation. In summary, all planets except for Venus have a clockwise rotation.