DataHammer/scimrc huggingface dataset:
Question: What tasks do they determine the optimal combinations of word2vec hyper-parameters for?
Evidence: There have been many implementations of the word2vec model in either of the two architectures it provides: continuous skipgram and CBoW (BIBREF0). Similar distributed models of word or subword embeddings (or vector representations) find usage in sota, deep neural networks like BERT and its successors (BIBREF1, BIBREF2, BIBREF3). These deep networks generate contextual representations of words after been trained for extended periods on large corpora, unsupervised, using the attention mechanisms (BIBREF4).
It has been observed that various hyper-parameter combinations have been used in different research involving word2vec with the possibility of many of them being sub-optimal (BIBREF5, BIBREF6, BIBREF7). Therefore, the authors seek to address the research question: what is the optimal combination of word2vec hyper-parameters for intrinsic and extrinsic NLP purposes? There are astronomically high numbers of combinations of hyper-parameters possible for neural networks, even with just a few layers. Hence, the scope of our extensive work over three corpora is on dimension size, training epochs, window size and vocabulary size for the training algorithms (hierarchical softmax and negative sampling) of both skipgram and CBoW. The corpora used for word embeddings are English Wiki News Abstract by BIBREF8 of about 15MB, English Wiki Simple (SW) Articles by BIBREF9 of about 711MB and the Billion Word (BW) of 3.9GB by BIBREF10. The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples. The IMDb dataset used has a total of 25,000 sentences with half being positive sentiments and the other half being negative sentiments. The GMB dataset has 17 labels, with 9 main labels and 2 context tags. It is however unbalanced due to the high percentage of tokens with the label 'O'. This skew in the GMB dataset is typical with NER datasets.
The objective of this work is to determine the optimal combinations of word2vec hyper-parameters for intrinsic evaluation (semantic and syntactic analogies) and extrinsic evaluation tasks (BIBREF13, BIBREF14), like SA and NER. It is not our objective in this work to record sota results. Some of the main contributions of this research are the empirical establishment of optimal combinations of word2vec hyper-parameters for NLP tasks, discovering the behaviour of quality of vectors viz-a-viz increasing dimensions and the confirmation of embeddings being task-specific for the downstream. The rest of this paper is organised as follows: the literature review that briefly surveys distributed representation of words, particularly word2vec; the methodology employed in this research work; the results obtained and the conclusion.
The objective of this work is to determine the optimal combinations of word2vec hyper-parameters for intrinsic evaluation (semantic and syntactic analogies) and extrinsic evaluation tasks, like SA and NER.
Answer: Intrinsic evaluation and extrinsic evaluation tasks.
Question: List some characters from South Park.
Answer: Eric Cartman, Kenny McCormick, Butters Stotch, Stan Marsh, Kyle Broflovski, Tweek Tweak, Mr. Garrison, Mr. Hat, Craig Tucker
Question: Why do you only compare the robustness of their model with that of MLPs, rather than with other unsupervised learning approaches?
Evidence: It is possible that the observed robustness of SoftHebb can be reproduced by other unsupervised learning rules. To test this possibility, we compared SoftHebb with PCA and k-means. We used 100 neurons, principal components, or centroids respectively. In PCA and k-means we then treated the learned coefficients as weight vectors of neurons and applied an activation function to then train a supervised classifier on top. First we attempted softmax as in SoftHebb. However the unperturbed test accuracy achieved at convergence was much lower. For example, on MNIST, k-means only reached an accuracy of 53.64% and PCA 28.55%, whereas SoftHebb reached 91.06. Therefore, we performed the experiment again, but with ReLU activation for k-means and PCA, reaching 90.61% and 82.74% respectively. Then we tested for robustness, revealing that SoftHebb’s learned features are in fact more robust than those of other unsupervised algorithms (Fig. 6). For completeness, we also include the hard WTA network, which is essentially a special case of SoftHebb with very high base in the softmax (Section 2.7), and is therefore learning equally robust features.
Answer: We have included an additional Appendix E with new experiments on adversarial robustness. Section E.2 compares the adversarial robustness of SoftHebb with that of k-means and PCA. SoftHebb is more robust than both alternatives.
Question: Does the cleaning algorithm work well?
Evidence: Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
 INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
 INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
Sometimes this cleaning algorithm works well: in the German examples in Table 3, the raw German symbols /X/ and /?/ are both converted to /x/.****However, the cleaning algorithm can also have negative effects on the data quality.
Answer: Not always.
Question: If Newton's first law says an object in motion will stay in motion unless acted on by an outside force, why does my spinning top stop spinning?
Answer: Your spinning top stops spinning because it is being acted on by outside forces. Specifically, your spinning top is losing energy due to friction with the ground and air. That energy is being lost to sound and heat as your spinning top pushes against the air in the room and rubs against the floor.
Question: Given these paragraphs about battery storage power stations, what was the levelized cost of electricity from battery storage as of 2020?
Evidence: As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy.

As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.
Answer: US$150 per MWh.
Question: Extract from the text what are the Proteins functions in the organism, and separate them with a comma,
Evidence: Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific 3D structure that determines its activity.
Answer: catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, transporting molecules from one location to another
Question: What is the best wideangle lens for sony apec camera
Answer: Sigma DC DN 16MM 1.4 . The picture quality is so sharp and the focus is fast.
Question: What are the 7 Wonders of the Solar System:
Answer: These are 7 wonders that can be found in the Solar System:

- Enceladus, a moon of Saturn
- The Great Red Spot of Jupiter, a massive and persistent anticyclonic storm in the planet's southern hemisphere
- The asteroid belt, a region of innumerable small solid bodies located between the orbits of Mars and Jupiter
- The surface of the Sun
- The oceans of Earth
- The Rings of Saturn
- Olympus Mons, an enormous shield volcano on Mars and the tallest planetary mountain in the Solar System
Question: What are some important things that have happened on the Day April 6?
Answer: - In 1652 the settlement that later became Cape Town was established
- In 1945, the Battle of Slater's Knoll of the Second World War happened
- In 2009, a 6.3M earthquake hit a town in Italy
Question: What state of the art models were used in experiments?
Evidence: We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6. We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6.
We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6.
Answer: The answers are shown as follows:
* SEQ2SEQ
* CVAE
* Transformer
* HRED
* DialogWAE
Question: Can you summarize the Korematsu case?
Evidence: Korematsu v. United States, 323 U.S. 214 (1944), was a landmark decision by the Supreme Court of the United States to uphold the exclusion of Japanese Americans from the West Coast Military Area during World War II. The decision has been widely criticized, with some scholars describing it as "an odious and discredited artifact of popular bigotry", and as "a stain on American jurisprudence". The case is often cited as one of the worst Supreme Court decisions of all time. Chief Justice John Roberts repudiated the Korematsu decision in his majority opinion in the 2018 case of Trump v. Hawaii.

In the aftermath of Imperial Japan's attack on Pearl Harbor, President Franklin D. Roosevelt had issued Executive Order 9066 on February 19, 1942, authorizing the U.S. War Department to create military areas from which any or all Americans might be excluded. Subsequently, the Western Defense Command, a U.S. Army military command charged with coordinating the defense of the West Coast of the United States, ordered "all persons of Japanese ancestry, including aliens and non-aliens" to relocate to internment camps. However, a 23-year-old Japanese-American man, Fred Korematsu, refused to leave the exclusion zone and instead challenged the order on the grounds that it violated the Fifth Amendment.

In a majority opinion joined by five other justices, Associate Justice Hugo Black held that the need to protect against espionage by Japan outweighed the rights of Americans of Japanese ancestry. Black wrote that "Korematsu was not excluded from the Military Area because of hostility to him or his race", but rather "because the properly constituted military authorities ... decided that the military urgency of the situation demanded that all citizens of Japanese ancestry be segregated from the West Coast" during the war against Japan. Dissenting justices Frank Murphy, Robert H. Jackson, and Owen J. Roberts all criticized the exclusion as racially discriminatory; Murphy wrote that the exclusion of Japanese "falls into the ugly abyss of racism" and resembled "the abhorrent and despicable treatment of minority groups by the dictatorial tyrannies which this nation is now pledged to destroy."
Answer: Korematsu v United States was a Supreme Court decision that upheld the removal of citizens of Japanese descent from the West Coast of the US during WWII. The decision has been widely criticized and is often cited as one of the worst Supreme Court decisions in US history. The case was repudiated by the court in 2018. 

The case was brought by Fred Korematsu, who refused to move and challenged the order on the grounds that it violated the Fifth Amendment. 

The majority held that the need to protect the US against espionage by Japan outweighed the individual rights of Japanese-American citizens during wartime. Three dissenting justices argued that the removal was racially discriminatory.
Question: Give me a bulleted list of 5 hobbies I should try.
Answer: Sure, I'd be happy to give you a bulleted list of 5 hobbies you should try. 

- Pickle ball
- Woodworking
- Painting
- Biking
- Crocheting

I hope you like the 5 suggestions above and try a new hobby!
Question: Tell me which of the following numbers are whole numbers and which are integers: -9, -8, -7, -6, -5, -4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Answer: Whole numbers include all positive numbers and 0: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Integers are numbers that include positive and negative numbers and 0: -9, -8, -7, -6, -5, -4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9

Whole numbers are a subset of Integers.
Question: Tell me about the most amazing trip an American could take.
Answer: Many Americans could experience amazing trips from just traveling within the United States. However, the most amazing trip might consist of traveling outside the United States and heading to a country they have never been to. For instance, a trip to Thailand in Asia or Spain in Europe might make for an amazing experience due to being able to experience new cultures, see completely new sites, and taste incredible local cuisines.
Question: Who was the founder of the Methodist Church?
Evidence: Samuel Wesley (24 February 1766 – 11 October 1837) was an English organist and composer in the late Georgian period. Wesley was a contemporary of Mozart (1756–1791) and was called by some "the English Mozart".

Born in Bristol, he was the son of noted Methodist and hymnodist Charles Wesley, the grandson of Samuel Wesley (a poet of the late Stuart period) and the nephew of John Wesley, the founder of the Methodist Church.
Answer: John Wesley
Question: Why is pickleball popular?
Answer: Pickleball has become popular because the equipment (paddles and balls) is cheap and existing tennis courts are easily converted.  Pickleball is easily learned without lessons.  There are social benefits to pickleball due to small courts that tend to promote conversation.
Question: How do they perform data augmentation?
Evidence: We first inspect the frequencies of object and subject RCs in the training data, by parsing them with the state-of-the-art Berkeley neural parser BIBREF19. In total, while subject RCs occur 373,186 times, object RCs only occur 106,558 times. We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences. Among the test cases about object RCs, we compare accuracies on subject-verb agreement, to make a comparison with subject RCs. We also evaluate on “animate only” subset, which has a correspondence to the test cases for subject RC with only differences in word order and inflection (like (UNKREF45) and (UNKREF46); see footnote FOOTREF47). Of particular interest to us is the accuracy on these animate cases. Since the vocabularies are exactly the same, we hypothesize that the accuracy will reach the same level as that on subject RCs with our augmentation.
We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences. 
Answer: They randomly sample sentences from Wikipedia that contains an object RC and add them to training data
Question: Which product is part of the Security & Governance model in Lakehouse and which is used for Datawarehouse in Delta Lake? Unity Catalog, SQL endpoint, Serverless SQL, Storage credential.
Answer: Unity Catalog and Storage credential is part of the Security & Governance model in Lakehouse . SQL endpoint and Serverless SQL is used for Datawarehouse in Delta Lake.
Question: Who are the best basketball players of all time?
Answer: 1) Micheal Jordan
2) LeBron James
3) Bill Russell
4) Magic Johnson
5) Larry Bird
6) Kareem Abdul Jabbar
7) Stephen Curry
8) Kobe Bryant
9) Shaquille O'Neal
10) Tim Duncan
Question: List down some facts regarding the Republican Party of Arkansas from given text
Evidence: The Republican Party of Arkansas (RPA), headquartered at 1201 West 6th Street in downtown Little Rock, is the affiliate of the Republican Party in Arkansas. It is currently the dominant party in the state, controlling all four of Arkansas' U.S. House seats, both U.S. Senate seats, all statewide executive offices, including the governorship, and supermajorities in both houses of the state legislature.
Answer: 1. The Republican Party in Arkansas is affiliated with the Republican Party of Arkansas (RPA), which has its headquarters at 1201 West 6th Street in downtown Little Rock.
2. It currently holds the majority in the state.
3. Possessing supermajorities in both houses of the state legislature and control of all four U.S. House seats, both U.S. Senate seats, all statewide executive positions, and the governorship.
Question: Which of the following words are considered a palindrome, a word that can be spelled the same forwards and backward: deified, kayak, chicken, taco, wow, peep, noon, abba, lizard, bob, penguin, margarita, square
Answer: deified, kayak, wow, peep, noon, abba, bob
Question: Which one of these do not belong to the group:  Tomato, Carrot, Ginger, Potato, Turmeric
Answer: All except Tomato grow underground.
Question: What are drop bears?
Answer: Drop bears are carnivorous version of the Koala bear from Australian folklore. Stories of drop bears are typically used by locals to scare tourists.
Question: What is Collateral?
Answer: If there is a main security for a debt, such as a house securing a mortgage, any extra security supplied is called collateral.
Question: Which is better - skiing or snowboarding
Answer: I prefer skiing over snowboarding. For three reasons: safety, reliability, and expediency

Safety - snowboarding can be a very dangerous sport. I'd been skiing and snowboarding for years. One winter I had a snowboarding accident and slid into a tree. Snowboarding requires larger turns with a bigger radius. I wasn't able to turn in time to avoid the tree. 

Reliability - The thing about being on only a single piece of equipment is that your balance relies much more on the snow conditions. Under ideal conditions, its great to be able to snowboard and maintain control with the edge. Skiing on powder is also a dream. However, if the conditions are less than ideal, then snowboarding on ice becomes a dangerous, out of control situation. Skiing on ice is also no picnic, but at least you can stabilize and balance on your two skis. 

Expediency - Skiing with friends inevitably means that you are waiting for the slowest one before you can start down the mountain. I was that friend. Snowboarders have to sit down and strap in, while skiers can pretty much get right off the lift and start down the mountain immediately. This makes mixed groups of skiers and snowboarders the trickiest. So I would sometimes snowboard if I was traveling with snowboarders, and ski if I was traveling with skiers.
Question: Who was the first female to swim the English Channel?
Answer: In 1926, Gertrude Ederle was the first woman to swim the English Channel. She was only 19 years old and an Olympic medalist when she made the crossing.
Question: Give me three reasons why cactuses aren't real.
Answer: My first point is that rich people make cactuses to prevent the prevention of desertification, which is a weird statement in itself. This leads to more deserts, and therefore more oil, and more money for rich people.

Secondly, everyone I know with a cactus hasn't watered it in about eighteen centuries and somehow it is still quote unquote "alive." Shallow roots, really? You never water it ever, it is not real, admit it.

Third and finally, two thousand species of cacti is just too many. It's really because rich people aren't very good at creating the same cactuses twice, so we end up with all of their bespoke versions.

Thank you for coming to my TED Talk.
Question: What do you understand by Boosting in machine learning?
Answer: Boosting refers to any Ensemble method combining several weak learners into a strong learner.  The idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available, but by far the most popular are Gradient Boosting and AdaBoost.