DataHammer/scimrc huggingface dataset:
Question: In golf, what is the proper technique when hitting a bunker shot? Assume the bunker is green adjacent.
Answer: If the bunker is near the pin, use your sand-wedge.  Also, ensure you properly examined the green, to understand its shape and slope.  Combine this with an understanding of your own skill levels to make a decision on where you wish the ball to land.  For example,  the more skilled you are at these shots, the more precise a location you can identify.  Conversely, the less skilled you are, the less precise you can be in your chosen location, and consequently the more you need to consider a range of potential directions.  In those latter cases, it is best to aim towards the middle of the green rather than the exact placement of the pin.  Once you've made a decision on location, go up to your ball in the bunker.  (Pro-tip: don't forget to bring the rake with you).  Choose an open stance, where your legs are aiming roughly 45 degrees more to the left of the selected landing location.  If you're left-handed, the reverse is true, and your legs are aiming to the right of the intended direction/location.  Dig your shoes roughly an inch into the sand, to get a solid footing.  Next, bring your club face down to the ball, without actually touching the sand (it is not legal to make contact with the sand prior to contact with the ball).  Open the club face so that it aims towards the intended location.  Look one more time at the intended direction, then look down at the ball, and commence your backswing.  The plane of your backswing should be out, inline with the direction of your legs, and roughly three quarters the length of a full swing.  Firmly and confidently accelerate in your downswing, and aim to make contact with the sand roughly half an inch behind the ball.  The sand between the ball and the club head will blunt some of the force from the swing, thereby reducing ball travel distance, while still conveying spin.  A well hit shot can cause the ball to land on the green and immediately stop, possibly even moving back in the direction of the player.  The force in the downswing, combined with the amount of sand caught by the club head, are significant contributors to the amount of backspin on the ball.  Once you're done. rake up your marks as you exit the bunker, leaving no trace that you were ever there.
Question: What side of the road do pedestrians walk on?
Answer: In India, the cars and vehicles keep left and pedestrians are encouraged to walk on the right. This is so the pedestrians can see oncoming traffic.  In the USA, all traffic, vehicles and pedestrian, usually stick to the right.
Question: What does democratizing data mean?
Answer: Making data accessible to all people and educating people on how to work with data, no matter their technical background.
Question: How does the train work?
Answer: How Does An Electric Locomotive Work:
Electric locomotives use electricity from overhead wires to convert electrical energy into mechanical energy, making them lighter, faster, and quieter than steam or diesel engines.


How Does a Diesel Locomotive Work:
Diesel-electric locomotives provide cheaper upfront costs and improved reliability.

Train wheels are conical, allowing them to travel a longer distance while turning at the same speed as the inner wheel. The wheel on the left is positioned further out from the center of the axle than the right, which is beneficial for stability and comfort of the passengers. Railways have wide curves due to the "flange" of the wheel limiting lateral movement. Distance between tracks must be greater than wheel flanges to allow wheels to move laterally in curves, reducing efficiency and comfort. The "nominal gauge" of a railway is defined for the straight section and widened in curves to allow the train to tilt.
Question: Provide an example of using modern materials science to study archaeological artifacts
Evidence: Ursula Martius Franklin was a German-Canadian metallurgist, research physicist, author, and educator who taught at the University of Toronto for more than 40 years. Franklin was a pioneer in the field of archaeometry, which applies modern materials analysis to archaeology. She worked for example, on the dating of prehistoric bronze, copper and ceramic artifacts. One small example of her work in this field regards what was a standing question on the nature of shiny black Chinese mirrors found in high quantities in ancient tombs. Franklin's use of microscopic etching, electron microprobe and x-ray fluorescence analyses produced evidence that what was thought by some to be a corrosive effect was in fact present in these ancient mirrors (and weapons) at their inception, in dark iron oxides intentionally added near the objects' surfaces. Franklin additionally pulled from historic and literary accounts of black mirrors in Chinese literature to support these findings. Franklin's expertise was also instrumental to dating glass; she guided a study on the remains of blue glass beads in North America remaining from early trade relationships between American Indian tribes and Europe
Answer: Archaeometry employs modern technology (such as x-ray fluorescence) to study the origins and environments of historical artifacts. Ursula Franklin was well known in this field; she found evidence of global trade in the remains of glass beads and proved that black mirrors present in large quantities in ancient Chinese tombs were purposely crafted, and not the result of corrosion.
Question: For the real-world dataset, how have the authors trained the model.?
Evidence: Then, using the trajectory observations, we train the policies. We show the pseudo-code of the modified REINFORCE-based trajectory EQMRL in Algorithm 2.****We set the number of resamples as m1 = 50, the size of each resample m2 = 5τ , the number of periods of return data τ = 60, the number of resampled subsets m3 = 50, and the size of each subset m4 = n0.7 , where m is number of assets; that is, m = 25 in FF25, m = 48 in FF48 and m = 100 in FF100.
Answer: We assume the stationary of the time series and train the policy using resampled observations, as we show it in Algorithm 2 of the updated manuscript. At the camera-ready, we add a more detailed description.
Question: What is the name of the season that begins in the fall and ends in the spring?
Answer: Winter
Question: Give me a list of five films made in the 1950s whose stars are still alive
Answer: Rio Bravo (1959) Angie Dickinson
Brothers Karamazov (1958) William Shatner
Revenge of the Creature (1955) Clint Eastwood
An American in Paris (1951) Leslie Caron
Vertigo (1958) Kim Novak
Question: Extract the names of creators of The Needle from the text provided. Separate them with a comma.
Evidence: The Needle is a fictional character, a mutant supervillain appearing in American comic books published by Marvel Comics. Created by Mark Gruenwald, Carmine Infantino, and Al Gordon, the character first appeared in Spider-Woman #9 (December 1978). Creator Mark Gruenwald brought the character back during his run on the West Coast Avengers as a member of the villain team Night Shift, which included other former Spider-Woman foes.
Answer: Mark Gruenwald, Carmine Infantino, Al Gordon
Question: How can I improve my table tennis skills?
Answer: To improve your table tennis skills, first you need to play more to build muscle memory. You can also watch videos online to learn specific techniques. And you can also consider taking some personal training courses. All of the above will help you improve your table tennis skills.
Question: What is a credit card?
Answer: A credit card is a payment card issued to users (cardholders) to enable the cardholder to pay a merchant for goods and services based on the cardholder's accrued debt (i.e., promise to the card issuer to pay them for the amounts plus the other agreed charges).The card issuer (usually a bank or credit union) creates a revolving account and grants a line of credit to the cardholder, from which the cardholder can borrow money for payment to a merchant or as a cash advance. There are two credit card groups: consumer credit cards and business credit cards. Most cards are plastic, but some are metal cards (stainless steel, gold, palladium, titanium), and a few gemstone-encrusted metal cards.
Question: I would like to get a camper for the family vacations.  What things do I need to be aware of when buying one?
Answer: Top considerations for a camper would be budget, size, weight, & payload required by the tow vehicle.  Payload is usually the most overlooked factor as many times trailers can go well over the payload shown by the manufacturer.  It is usually best to have a tow vehicle that has a buffer of payload over so that you can safely tow a trailer.  Most car/truck manufacturers share must higher towing capabilities than actual since options can bring down the amount of payload and tow capacity.
Question: Which characters belong to DC or Marvel Universe? Vision, Red Tornado
Answer: Vision is Marvel, Red Tornado is DC
Question: How to manage a high performance team?
Answer: Create a supportive environment
Encourage innovation
Acknowledge and learn from conflict
Assign specific tasks
Create incentives
Foster independence
Question: What ensemble methods are used for best model?
Evidence: We constructed the ensembled predictions by choosing the answer from the network that had the highest probability and choosing no answer if any of the networks predicted no answer.
We constructed the ensembled predictions by choosing the answer from the network that had the highest probability and choosing no answer if any of the networks predicted no answer.
Answer: The answers are shown as follows:
* choosing the answer from the network that had the highest probability and choosing no answer if any of the networks predicted no answer
Question: Who first recorded the weather?
Answer: It is believed that people in China made notes about wind, rain and snow onto pieces of tortoiseshell.  Some evidence dates back to 3000 years, making the Chinese the first to record the weather.
Question: Identify which animal species is alive or extinct: Meganuera, Sei Whale
Answer: Sei Whale is alive, Meganuera is extinct.
Question: Make a bullet list of 5 gifts you can give someone for Valentine's Day
Answer: - Chocolates
- Bouquet of Roses
- Stuffed animal
- Heart shaped balloon
- Personalized Card
Question: What misbehavior is identified?
Evidence: It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example:
Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. 
Answer: The answers are shown as follows:
* if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations
Question: Is it a gas, liquid, or solid
Answer: Stone, Cloud, oxygen, water, hydrogen, dirt, nitrogen, brick, concrete, lava
Question: Which real-world datasets did they use?
Evidence: In the first task, we use the SST-2 (Stanford Sentiment Treebank, version 2) dataset BIBREF25 to conduct sentiment analysis experiments. SST-2 is a sentence binary classification dataset with train/dev/test splits provided and two types of sentence labels, i.e., positive and negative.
The second task involves two essential problems in SLU, which are intent classification (IC) and slot labeling (SL). In IC, the model needs to detect the intention of a text input (i.e., utterance, conveys). For example, for an input of I want to book a flight to Seattle, the intention is to book a flight ticket, hence the intent class is bookFlight. In SL, the model needs to extract the semantic entities that are related to the intent. From the same example, Seattle is a slot value related to booking the flight, i.e., the destination. Here we experiment with the Snips dataset BIBREF26, which is widely used in SLU research. This dataset contains test spoken utterances (text) classified into one of 7 intents.
In the first task, we use the SST-2 (Stanford Sentiment Treebank, version 2) dataset BIBREF25 to conduct sentiment analysis experiments. SST-2 is a sentence binary classification dataset with train/dev/test splits provided and two types of sentence labels, i.e., positive and negative.
Here we experiment with the Snips dataset BIBREF26, which is widely used in SLU research. This dataset contains test spoken utterances (text) classified into one of 7 intents.
Answer: The answers are shown as follows:
* SST-2 (Stanford Sentiment Treebank, version 2)
* Snips
Question: Given a reference text about the Mauritius fody, tell me what they look like.
Evidence: The Mauritius fody (Foudia rubra) is a rare species of bird in the weaver family. It is endemic to the island of Mauritius. It is classified by BirdLife International as being endangered. It is also on the United States' Endangered Species List with an endangered status.

This bird is 14 centimeters long. Breeding males are olive brown with a red head, breast and rump patch and black lores. While females, non-breeding males and juveniles are olive brown with white wing bars and a brown bill.

The bird lives in several types of forest, including degraded areas, as well as plantations. Stands of Japanese cedar (Cryptomeria japonica) have replaced native vegetation and now provide protection against predators. It feeds on insects like grasshoppers, beetle larvae, caterpillars, and also spiders. Berries are eaten regularly by some individuals. It feeds on nectar regularly, using its specialised brush-tipped tongue.

The bird is a weaver, the male and female cooperating to weave each nest, from material like grass, moss and small twigs.
Answer: Male and female Mauritius fody birds have different appearances. Breeding males are olive brown with red heads, breast and rump patches, and black lores. Females and non-breeding males are olive brown with white wing bars and brown bills.
Question: Given this paragraph about the Grammy Awards, who has the most Grammys of all time?
Evidence: The record for the most Grammy Awards won in a lifetime is held by Beyoncé, an American singer, songwriter, and dancer, who has won 32. It was previously held by Georg Solti, a Hungarian-British conductor, who won 31.
Answer: The current record holder for most Grammy Awards won in a lifetime is Beyoncé.
Question: Who directed the most episodes of season seven of Game of Thrones?
Answer: Jeremy Podeswa, Mark Mylod, and Matt Shakman each directed two episodes of the seventh season of Game of Thrones.
Question: Give me an enumerated list of Four Noble Truths mentioned in Buddhism
Answer: 1. Suffering: Buddhism teaches that struggle and suffering is part of life. Everyone is suffering in one way or another. It is to promote pragmatism and as an anti-dote of negativity. Once one accept it, then it gets easier for move forward in life and confidently march forward in the face of difficulties.
2. Cause of suffering: Desire, ignorance and attachments are often described as causes of suffering explained in previous point. For instance, Buddhism further explained that various vices, such as hatred, jealousy, temper and greed, are the by product of our ignorance.
3. End of suffering: Simply put, this Truth is inviting one person to be aware about the above causes of suffering and hence walk on the path, i.e. lead the life, with clarity and unshakable spirit where primary goal is to reach the state of Nirvana.
4. Path that leads to the end of suffering: To achieve the state of Nirvana, there are various practices defined to lead the life where despite suffering presents in our day to day life, one always keep the calm and composed attitude towards life.
Question: Which two models are compared together to further analyze the difference in performance between BoW representations and embeddings?
Evidence: We present our results for the binary classification tasks in Tables TABREF23 – TABREF24 in terms of average precision, average recall and macro average F1 score. The results of the regression tasks are reported in Tables TABREF25 and TABREF29 in terms of the mean absolute error between the predicted and actual scores, as well as the Spearman INLINEFORM0 correlation between the rankings induced by both sets of scores. It can be clearly seen from the results that our proposed method (EGEL-All) can effectively integrate Flickr tags with the available structured information. It outperforms the baselines for all the considered tasks. Furthermore, note that the PPMI-based weighting in EGEL-Tags consistently outperforms GloVe and that both the addition of negative examples and term selection lead to further improvements. The use of term selection leads to particularly substantial improvements for the regression problems.
While our experimental results confirm the usefulness of embeddings for predicting environmental features, this is only consistently the case for the variants that use both the tags and the structured datasets. In particular, comparing BOW-Tags with EGEL-Tags, we sometimes see that the former achieves the best results. While this might seem surprising, it is in accordance with the findings in BIBREF54 , BIBREF38 , among others, where it was also found that bag-of-words representations can sometimes lead to surprisingly effective baselines. Interestingly, we note that in all cases where EGEL-KL(Tags+NS) performs worse than BOW-Tags, we also find that BOW-KL(Tags) performs worse than BOW-Tags. This suggests that for these tasks there is a very large variation in the kind of tags that can inform the prediction model, possibly including e.g. user-specific tags. Some of the information captured by such highly specific but rare tags is likely to be lost in the embedding.
To further analyze the difference in performance between BoW representations and embeddings, Figure TABREF29 compares the performance of the GloVe model with the bag-of-words model for predicting place scenicness, as a function of the number of tag occurrences at the considered locations. What is clearly noticeable in Figure TABREF29 is that GloVe performs better than the bag-of-words model for large corpora and worse for smaller corpora. This issue has been alleviated in our embedding method by the addition of negative examples.
To further analyze the difference in performance between BoW representations and embeddings, Figure 1 compares the performance of the GloVe model with the bag-of-words model for predicting place scenicness, as a function of the number of tag occurrences at the considered locations.
Answer: The GloVe model and the bag-of-words model.
Question: How many toes does a cat have?
Answer: A cat typically has five toes: four on its paw pad and one smaller one on the side of its arm called the declaw. Some cats are polydactyl, which means they could have more than the average number of toes.
Question: What is Spot rate in finance?
Answer: This is the exchange rate for foreign−exchange transactions which are being done straightaway.
Question: List all the names of people in this paragraph.  Separate the names with commas.
Evidence: When Guettel took up music composition in his mid-teens, he was encouraged by his family. His mother said that she offered him advice for around a year, "After that, he was so far beyond anything I could ever have dreamed of, I just backed off." Richard Rodgers, who died when Guettel was 15, overheard an early composition, said he liked it and asked him to play it louder. Guettel has qualified the compliment, noting that "He was literally on his deathbed on the other side of the living-room wall." In his high school and collegiate years and into his early twenties, Guettel worked as a rock and jazz musician, singing and playing bass, before realizing "that writing for character and telling stories through music was something that I really loved to do, and that allowed me to express love."
Answer: Guettel, Richard Rodgers
Question: What is the size of the datasets employed?
Evidence: are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. We trained two two-layer recurrent neural language models with long short-term memory architecture BIBREF11 on a relatively small corpus. The first model, referred as `LSTM (PTB)' in the following sections, was trained on the sentences from Penn Treebank BIBREF12. The second model, referred as `LSTM (FTB)', was trained on the sentences from French Treebank BIBREF13. We set the size of input word embedding and LSTM hidden layer of both models as 256.
We also compare LSTM language models trained on large corpora. We incorporate two pretrained English language models: one trained on the Billion Word benchmark (referred as `LSTM (1B)') from BIBREF14, and the other trained on English Wikipedia (referred as `LSTM (enWiki)') from BIBREF3. For French, we trained a large LSTM language model (referred as `LSTM (frWaC)') on a random subset (about 4 million sentences, 138 million word tokens) of the frWaC dataset BIBREF15. We set the size of the input embeddings and hidden layers to 400 for the LSTM (frWaC) model since it is trained on a large dataset.
The first model, referred as `LSTM (PTB)' in the following sections, was trained on the sentences from Penn Treebank BIBREF12. The second model, referred as `LSTM (FTB)', was trained on the sentences from French Treebank BIBREF13.
We incorporate two pretrained English language models: one trained on the Billion Word benchmark (referred as `LSTM (1B)') from BIBREF14, and the other trained on English Wikipedia (referred as `LSTM (enWiki)') from BIBREF3. For French, we trained a large LSTM language model (referred as `LSTM (frWaC)') on a random subset (about 4 million sentences, 138 million word tokens) of the frWaC dataset BIBREF15.
Answer: The answers are shown as follows:
* (about 4 million sentences, 138 million word tokens)
* one trained on the Billion Word benchmark
Question: What is the Boone and Crockett Club?
Evidence: Yellowstone was part of a federally governed territory. With no state government that could assume stewardship of the land, the federal government took on direct responsibility for the park, the official first national park of the United States. The combined effort and interest of conservationists, politicians and the Northern Pacific Railroad ensured the passage of enabling legislation by the United States Congress to create Yellowstone National Park. Theodore Roosevelt and his group of conservationists, the Boone and Crockett Club, were active campaigners and were highly influential in convincing fellow Republicans and big business to back the bill. Yellowstone National Park soon played a pivotal role in the conservation of these national treasures, as it was suffering at the hands of poachers and others who stood at the ready to pillage what they could from the area. Theodore Roosevelt and his newly formed Boone and Crockett Club successfully took the lead in protecting Yellowstone National Park from this plight, resulting in laws designed to conserve the natural resources in Yellowstone and other parks under the Government's purview.
Answer: The Boone and Crockett Club is a group of conservationists who campaigned with Theodore Roosevelt to pass a bill in the US Congress that established Yellowstone National Park.