DataHammer/scimrc huggingface dataset:
Question: Which tasks does CamemBERT not improve on?
Evidence: Experiments ::: Results ::: Natural Language Inference: XNLI
On the XNLI benchmark, CamemBERT obtains improved performance over multilingual language models on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) while using less than half the parameters (110M vs. 250M). However, its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa. It should be noted that CamemBERT uses far fewer parameters than RoBERTa (110M vs. 355M parameters).
Experiments ::: Results ::: Natural Language Inference: XNLI
On the XNLI benchmark, CamemBERT obtains improved performance over multilingual language models on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) while using less than half the parameters (110M vs. 250M). However, its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa. It should be noted that CamemBERT uses far fewer parameters than RoBERTa (110M vs. 355M parameters).
Answer: The answers are shown as follows:
* its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa
Question: Who was given Stockholm Water Prize 2022 ?
Answer: Prof.Wilfried Brutsaert
Question: Summarize in bullet points some methods used to produce verdigris.
Evidence: A variety of recipes have been described for obtaining this blue-green patina on copper, brass, or bronze. It was originally made by hanging copper plates over hot vinegar in a sealed pot until a green crust formed on the copper. Another method, used in the Middle Ages, was to attach copper strips to a wooden block with acetic acid, then bury the sealed block in dung. A few weeks later, the block was to be dug up, and the verdigris scraped off. In eighteenth-century Montpellier, France, it was manufactured in household cellars, "where copper plates were stacked in clay pots filled with distilled wine." The verdigris was scraped off weekly by the women of the household. Copper(II) acetate is prepared by treatment of copper(II) hydroxide with acetic acid.
Answer: - Hang copper plates over hot vinegar
- Bury copper strips with wooden blocks and acetic acid in dung and dig up weeks later
- Stack copper plates in clay pots of wine
- Combine copper(II) hydroxide and acetic acid
Question: Tell me whether these are artists or football players: Andy Warhol, Joe Montana, Jackson Pollock, Tom Brady, Mark Rothko.
Answer: Andy Warhol was an American artist and leading figure in the pop art movement.
Joe Montana is an American football quarterback that played in the National Football League (NFL).
Jackson Pollock was an American painter and a major figure in the abstract expressionist movement.
Tom Brady is an American football quarterback that played in the National Football League (NFL).
Mark Rothko was a Latvian-American abstract painter.
Question: What is following the file format Delta Lake follows to write records? CSV, JSON, PARQUET, AVRO, ORC
Answer: Delta lake writes data in Parquet format. A transaction layer is on top of it to enforce ACID property, schema evaluation, time travel and few other benefits.
Question: What is seek speed in HDFS and why is it no longer going to be important?
Answer: Seek speed is the speed i.e. rpm (revolutions per minute) a magnetic drive or an optical reader rotates to read the data from those HDDs Hard Drive Disks; This speed is dependent on the motor's rotation speed which directly impacts reading and writing of data into these devices. The speed of the motor is limited as it is powered by 12V capacity and also should not drain the battery if they are in the laptops. However, more and more newer age laptops and devices are now using SSDs Solid State Drives which are not only faster because the speed is not dependent on the revolving motor and also does not drain your laptops battery charge. This allowed the laptops to be less bulkier/heavier and reduce the overall battery size to make it a little lighter.
Question: What is oxidation reduction potential?
Answer: Oxidation reduction potential or ORP is measurement for water.  A millivolt meter can measure ORP and it is a measure of the potential of water to carry or hold antioxidants.  It is not a guarantee of antioxidants in water.  One form of antioxidants in water is free floating dissolved hydrogen.  A good analogy of antioxidants in water is the following: ORP is like a big swimming pool of potential but at the moment its currently empty and dissolved hydrogen is like ball of finite size.  The larger the ORP 'swimming pool' the more dissolved hydrogen 'balls' can fit in that pool.  An ORP with a large negative value is huge swimming pool, and an OPR with a high positive value is no swimming pool.  ORP can also measure whether water is acidic or alkaline.  ORP that is negative tends more toward alkaline water, and ORP which is positive tends more towards neutral water and acidic water.
Question: How much is the dropout rate for fake news detection?
Evidence: Pre-processing - Processing useless and inappropriate information in text: (1) removing nonalphabetic characters; (2) removing website links of text content; (3) converting all words to lower case and tokenize texts.
Parameters - hyper-parameters configurations of our model: for each task, we strictly turn all the hyper-parameters on the validation dataset, and we achieve the best performance via a small grid search. The sizes of word embeddings and position embeddings are set to 200 and 100. In transformer encoder, attention heads and blocks are set to 6 and 2 respectively, and the dropout of multi-head attention is set to 0.7. Moreover, the minibatch size is 64; the initial learning rate is set to 0.001, the dropout rate to 0.3, and $\lambda $ to 0.6 for fake news detection.
Moreover, the minibatch size is 64; the initial learning rate is set to 0.001, the dropout rate to 0.3, and λ to 0.6 for fake news detection.
Answer: 0.3.
Question: Which is a disease? Asthma, Depression, Cancer, AIDS, Schizophrenia, Tuberculosis, PTSD
Answer: According to wikipedia, a disease is an abnormal condition affecting the body of an organism and a disorder is a functional abnormality or disturbance.
Based on the above list the following are diseases: Asthma, Cancer, AIDS, Tuberculosis.
Question: Which of the following are Pixar/Disney movies: How to Train Your Dragon: The Hidden World, The Secret Life of Pets 2, Turning Red, Toy Story, The Boss Baby: Family Business, Soul, Coco, Inside Out.
Answer: Turning Red, Toy Story, Soul, Coco, and Inside Out are Pixar/Disney movies.
Question: Are seed oils bad for you?
Answer: Seed oils such as vegetable, canola, soy, sunflower, etc. are high in omega 6 fatty acids. Research has shown that diets high in omega 6 fatty acids lead to systemic inflammation. Unlike seed oils, saturated fat is usually derived from animal products, however they’ve been demonized by various communities with political agendas. Unlike seed oils, saturated fats do not contain a high amount of omega 6 fatty acid.
Question: What evaluation metrics are used?
Evidence: The probability of activating inter-layer and inner-layer teacher forcing is set to 0.5, the probability of teacher forcing is attenuated every epoch, and the decaying ratio is 0.9. The models are trained for 20 training epochs without early stop; when curriculum learning is applied, only the first layer is trained during first five epochs, the second decoder layer starts to be trained at the sixth epoch, and so on. To evaluate the quality of the generated sequences regarding both precision and recall, the evaluation metrics include BLEU and ROUGE (1, 2, L) scores with multiple references BIBREF22 .
o evaluate the quality of the generated sequences regarding both precision and recall, the evaluation metrics include BLEU and ROUGE (1, 2, L) scores with multiple references BIBREF22 .
Answer: The answers are shown as follows:
* the evaluation metrics include BLEU and ROUGE (1, 2, L) scores
Question: How many oscars India won this year 2023?
Answer: Two, one for best documentary Short Film (The Elephant Whisperers) another for best Original Music (RRR)
Question: Extract the ways the asthma is classified and create a bulleted list.
Evidence: Asthma is thought to be caused by a combination of genetic and environmental factors. Environmental factors include exposure to air pollution and allergens. Other potential triggers include medications such as aspirin and beta blockers. Diagnosis is usually based on the pattern of symptoms, response to therapy over time, and spirometry lung function testing. Asthma is classified according to the frequency of symptoms, forced expiratory volume in one second (FEV1), and peak expiratory flow rate. It may also be classified as atopic or non-atopic, where atopy refers to a predisposition toward developing a type 1 hypersensitivity reaction.
https://en.wikipedia.org/wiki/Asthma
Answer: -Frequency of symptoms
-FEV1
-Peak expiratory flow rate
-Atopic
-Non-atopic
Question: What is CSS?
Answer: The Cascading Style Sheets (CSS) is the language to style an HTML document. CSS describes how HTML elements should be displayed on a web page.
Question: How better does the model behave than existing ones?
Evidence: We present a model for generating natural language descriptions from structured data. To address specific characteristics of the problem we propose neural components for fused bifocal attention and gated orthogonalization to address stay on and never look back behavior while decoding. Our final model outperforms an existing state of the art model on a large scale WikiBio dataset by 21%. We also introduce datasets for French and German and demonstrate that our model gives state of the art results on these datasets. Finally, we perform experiments with an out-of-domain model and show that if such a model is fine-tuned with small amounts of in domain data then it can give an improved performance on the target domain.
Given the multilingual nature of the new datasets, as future work, we would like to build models which can jointly learn to generate natural language descriptions from structured data in multiple languages. One idea is to replace the concepts in the input infobox by Wikidata concept ids which are language agnostic. A large amount of input vocabulary could thus be shared across languages thereby facilitating joint learning.
Our final model outperforms an existing state of the art model on a large scale WIKIBIO dataset by 21%.
Answer: Our final model outperforms  by 21%.
Question: What does a reverse osmosis filter do?
Answer: A reverse osmosis filter is a fine grained small pore filter that is used in water filtration.  The RO filter membrane reduces 99.9% of 1000+ contaminants down to 0.0001 microns.  Some of the contaminants it removes are chlorine, lead, heavy metals, fluoride, volatile organic compounds (VOCs), chloramine, total dissolved solids (TDS), limescale, and arsenic.  A reverse osmosis membrane is a great way to make clean water at home if you are trying to get rid of bottled water consumption.
Question: Which day is the hardest for crossword puzzles?
Answer: There are many sources for crosswords, but the most popular might be the New York Times crossword. Its puzzle gets progressively harder from Monday to Saturday. Sunday is always a larger puzzle, but it is typically less difficult than Saturday, making Saturday the hardest day of the week.
Question: How does soft contextual data augmentation work?
Evidence: While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is relatively limited. SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary. It was applied in Russian$\rightarrow $English translation in our submitted systems.
SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary.
Answer: The answers are shown as follows:
* softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words
* replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary
Question: Which of the followings are Chinese martial arts? Tai chi, Shaolin, Wing chun, Boxing, Taekwondo, Karate
Answer: Tai chi, Shaolin, and Wing chun are Chinese martial arts. Notably Bruce Lee was a practitioner of Wing chun.

Boxing, Taekwondo, and Karate are not Chinese martial arts.
Question: Could you please help classify these cars as "compact" vs. "midsized"?
Toyota Camry, Nissan Altima, Honda Civic, Mazda3, Honda Accord, Toyota Corolla, Toyota Prius, Hyundai Sonata
Answer: Yes, here is the list:
- Compact Cars: Honda Civic, Mazda3, Toyota Corolla, and Toyota Prius
- Midsized Cars: Toyota Camry, Nissan Altima, Honda Accord, Hyundai Sonata
Question: Who was Calvin Parker MacDonald
Answer: Calvin Parker MacDonald (June 14, 1933 — August 17, 2017) was a Canadian professional ice hockey left winger who played for five National Hockey League teams between 1953 and 1969. He later coached the Minnesota North Stars and the Los Angeles Kings.
Question: From the passage provided, extract the names of the sisters of Jawaharlal Nehru. Separate them with a comma.
Evidence: Jawaharlal Nehru was born on 14 November 1889 in Allahabad in British India. His father, Motilal Nehru (1861–1931), a self-made wealthy barrister who belonged to the Kashmiri Pandit community, served twice as president of the Indian National Congress, in 1919 and 1928. His mother, Swarup Rani Thussu (1868–1938), who came from a well-known Kashmiri Brahmin family settled in Lahore, was Motilal's second wife, his first having died in childbirth. Jawaharlal was the eldest of three children. His elder sister, Vijaya Lakshmi, later became the first female president of the United Nations General Assembly. His youngest sister, Krishna Hutheesing, became a noted writer and authored several books on her brother.
Answer: Vijaya Lakshmi, Krishna Hutheesing
Question: What languages are considered?
Evidence: Our learning data, which was created in-house, has 20 keywords to be spotted about television models of a consumer electronics brand. It was collected by making 40 participants utter each keyword 3 times. Each participant recorded in normal ambient noise conditions. As a result, after collection of learning data we have 120 (3 x 40) instances of each of the 20 keywords. We split the learning data 80:20 into train and validation sets. Train/Validation split was done on speaker level, so as to make sure that all occurrences of a particular speaker is present only on either of two sets. For testing, we used 10 different 5 minutes long simulated conversational recordings of television salesmen and customers from a shopping mall in India. These recordings contain background noise (as is expected in a mall) and have different languages (Indians speak a mixture of English and Hindi). The CSKS algorithm trained on instances of keywords in learning data is supposed to detect keywords embedded in conversations of test set.
These recordings contain background noise (as is expected in a mall) and have different languages (Indians speak a mixture of English and Hindi).
Answer: The answers are shown as follows:
* English
* Hindi
Question: How strategy-based methods handle obstacles in NLG?
Evidence: In response to the above challenges, two lines of work are proposed by resorting to architecture-based and strategy-based solutions, respectively. Architecture-based methods either try to induce task-specific architecture during pre-training (task-specific methods), or aim at building a general pre-training architecture to fit all downstream tasks (task-agnostic methods). Strategy-based methods depart from the pre-training stage, seeking to take advantage of the pre-trained models during the process of target task learning. The approaches include fine-tuning schedules that elaborately design the control of learning rates for optimization, proxy tasks that leverage labeled data to help the pre-trained model better fit the target data distribution, and knowledge distillation approaches that ditch the paradigm of initialization with pre-trained parameters by adopting the pre-trained model as a teacher network.
Strategy-based methods depart from the pre-training stage, seeking to take advantage of the pre-trained models during the process of target task learning. The approaches include fine-tuning schedules that elaborately design the control of learning rates for optimization, proxy tasks that leverage labeled data to help the pre-trained model better fit the target data distribution, and knowledge distillation approaches that ditch the paradigm of initialization with pre-trained parameters by adopting the pre-trained model as a teacher network.
Answer: The answers are shown as follows:
* fine-tuning schedules that elaborately design the control of learning rates for optimization
* proxy tasks that leverage labeled data to help the pre-trained model better fit the target data distribution
* knowledge distillation approaches that ditch the paradigm of initialization with pre-trained parameters by adopting the pre-trained model as a teacher network
Question: Name five fire Pokemon.
Answer: Five fire pokemon are Rapidash, Magmar, Vulpix, Charmeleon, and Charmander.
Question: Which baseline methods are used?
Evidence: Table 1 shows the BLEU scores of our model on different sequence lengths while varying $K$ . This is a study of the trade-off between computational time and representational power. A large $K$ allows us to compute complex source representations, while a $K$ of 1 limits the source representation to a single vector. We can see that performance consistently increases with $K$ up to a point that depends on the data length, with longer sequences requiring more complex representations. The results with and without position encodings are almost identical on the toy data. Our technique learns to fit the data as well as the standard attention mechanism despite having less representational power. Both beat the non-attention baseline by a significant margin.
All models are implemented using TensorFlow based on the seq2seq implementation of BIBREF15 and trained on a single machine with a Nvidia K40m GPU. We use a 2-layer 256-unit, a bidirectional LSTM BIBREF16 encoder, a 2-layer 256-unit LSTM decoder, and 256-dimensional embeddings. For the attention baseline, we use the standard parametrized attention BIBREF2 . Dropout of 0.2 (0.8 keep probability) is applied to the input of each cell and we optimize using Adam BIBREF17 at a learning rate of 0.0001 and batch size 128. We train for at most 200,000 steps (see Figure 3 for sample learning curves). BLEU scores are calculated on tokenized data using the multi-bleu.perl script in Moses. We decode using beam search with a beam
Both beat the non-attention baseline by a significant margin.
For the attention baseline, we use the standard parametrized attention BIBREF2 .
Answer: standard parametrized attention and a non-attention baseline
Question: What is Go?
Answer: Go is a statically typed, compiled high-level programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. It is syntactically similar to C, but with memory safety, garbage collection, structural typing, and CSP-style concurrency. It is often referred to as Golang because of its former domain name, golang.org, but its proper name is Go.
Question: What are the 5 biggest cities in France in terms of inhabitants?
Answer: The five biggest cities in France are : 
1) Paris, which is also the capital, with more than 2200000 inhabitants.
2) Marseille with more than 865000 inhabitants.
3) Lyon with more than 520000 inhabitants.
4) Toulouse with more than 480000 inhabitants.
5) Nice with more than 340000 inhabitants.
Question: What is A State of Trance?
Answer: A State of Trance is a weekly internet radio show hosted by the Dutch DJ Armin van Buuren that plays 2 hours of the latest progressive and trance tracks.
Question: What is a bokoblin?
Answer: It is an enemy type in The Legend of Zelda, Breath of the Wild.  Some would refer to it (and other in-game enemies as "mobs").
Question: What is the architecture of the system?
Evidence: In order to train the translation model between text-to-code an open source Neural Machine Translation (NMT) - OpenNMT implementation is utilized BIBREF11. PyTorch is used as Neural Network coding framework. For training, three types of Recurrent Neural Network (RNN) layers are used – an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.
For training, three types of Recurrent Neural Network (RNN) layers are used – an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.
Answer: The answers are shown as follows:
* seq2seq translation
Question: List the types of seed funding available for startups?
Evidence: Types of Seed funding

Friends and family funding: This type of seed funding involves raising money from friends and family members.

Angel investing: As mentioned above, angel investors are wealthy individuals who provide seed funding in exchange for equity ownership.
Seed accelerators: These are programs that provide startups with seed funding, mentorship, and resources to help them grow their businesses.
Crowdfunding: This type of funding allows startups to raise money from a large number of people, typically through an online platform.
Incubators: These are organizations that provide startups with seed funding, office space, and resources to help them grow their businesses.
Government grants: Some government agencies provide seed funding for startups working on specific projects or in specific industries.
Corporate ventures: Some big companies set up venture arms to provide seed funding to startups in their industry or complementary field.
Micro-Venture Capital: A type of venture capital that provides seed funding to new startups and early-stage companies with a small amount of money.
Answer: Below is a list of seed funding options for startups
Friends and family funding
Funding from Angel investors
Seed accelerators
Crowdfunding from a large number of participants
Government grants
Corporate ventures
Micro-venture capital
Startup incubators