DataHammer/scimrc huggingface dataset:
Question: As a golfer, how can I fix my slice?
Answer: A common swing fault in golfers is a slice which is caused when the face of the club isn't aligned properly to the swing path of the club.  What will typically happen is at the time of impact, the face is open in relation to the target (ie aimed to the right if you are a right handed golfer) and the swing path is closed in relation to the target (ie aimed to the left if you are a left handed golfer).  When there is a large difference between the path of the swing and the face of the club, the impact will generate a high degree of lateral spin on the ball causing the flight of the ball to curve away from the target.  Here are some ways to fix this swing fault:

1. Grip the club with a stronger grip.  This means the v formed between your thumb and pointer finger on back hand is pointed to your rear shoulder and your opposite hand it also gripping the club more from the back.  This will help you close the face of the club at impact.
2. Focus on keeping the face closed in the backswing.  One fault many amateurs have is the club face opens toward the sky on the backswing too far meaning that the face will need to turn more on the downswing to get to square at impact.  Creating less rotation on the backswing can help with this.  
3. Keeping hips down and back in the downswing.  One fault that can cause inconsistent contact as well as a slice is when the hips thrust forward during the downswing causing the golfer to lift up to counter being too close to the ball which can often cause an over the top, out-to-in swing path.  Try to keep the hips back through the swing and focus on rotating the body to keep forward or lateral movement to a minimum.  

A slice is a very common issue among golfers but knowing the causes and swing faults that lead to a slice can help you think about how you can improve your swing to fix it.
Question: Classify the cities based on whether they are located in Namibia or Yemen.
Sanaa, Ta‘izz, Windhoek, Rundu, Oshakati, Al Ḩudaydah
Answer: Namibia - Windhoek, Rundu, Oshakati
Yemen - Sanaa, Ta‘izz, Al Ḩudaydah
Question: In table 1, what does tested on 100 objects mean? I thought the number of objects are 20 and 2000?
Evidence: From Table we observe the superiority of SpaLoc in terms of accuracy and efficiency. SpaLoc achieves perfect accuracy on test set. And when handling large input domains (N train = 2000), SpaLoc works well because of the sampling techniques, while most baselines cause out-of-memory errors.
Sparsity. In Fig., we show the number of non-zero elements in SpaLoc's intermediate groundings versus the size of input domains on HasSister, Grandparent and Uncle. We fit a cubic polynomial equation f to the data points to illustrate the learned inference complexity of SpaLoc.
The SpaLoc we use in these three datasets has an arity of 3 so the maximum capacity of the model is Θ(N 3 ). However, the memory complexity of SpaLoc converges to the optimal algorithm complexity of these tasks , which is O(N 2 ). In comparison, the memory complexity of original dense NLMs will be fixed to Θ(N 3 ) when the model architecture is fixed. We evaluate SpaLoc on the real-world knowledge-graph inductive reasoning benchmarks proposed in GraIL, whose training and evaluation sets are disjoint sub-graphs extracted from WN18RR, FB15k-237, and NELL-995. For each knowledge graph, there are four versions of inductive datasets with increasing sizes.
(Ntrain = 2000), SpaLoc works well because of the sampling techniques, while most baselines cause out-of-memory errors.
Answer: In the original paper, we used two training and testing settings. Column 1 (train on N=20, test on N=100 ), Column 2 (train on N=200, test on N=100). We fixed the test graph size to 100 because our baselines MemNN and NLM can not be applied (in testing) to larger graphs. We have clarified this in the paper.
Question: Best film in Motion Picture  27th Screen Actors Guild(SAG) Awards was given to which film?
Answer: Chicago 7
Question: Who manually annotated the semantic roles for the set of learner texts?
Evidence: In this paper, we manually annotate the predicate–argument structures for the 600 L2-L1 pairs as the basis for the semantic analysis of learner Chinese. It is from the above corpus that we carefully select 600 pairs of L2-L1 parallel sentences. We would choose the most appropriate one among multiple versions of corrections and recorrect the L1s if necessary. Because word structure is very fundamental for various NLP tasks, our annotation also contains gold word segmentation for both L2 and L1 sentences. Note that there are no natural word boundaries in Chinese text. We first employ a state-of-the-art word segmentation system to produce initial segmentation results and then manually fix segmentation errors.
In this paper, we manually annotate the predicate–argument structures for the 600 L2-L1 pairs as the basis for the semantic analysis of learner Chinese.
Answer: Authors
Question: When was the Australian city Canberra founded?
Answer: The Australian National Capital of Canberra was announced on the 12th of March 1913 by the Governor-General's wife, Lady Denman. The construction of the city and development was delayed by the two world wars and the great depression, however has steadily grown and now holds more than 400,000 people.
Question: Is Anthology 3 the White Album
Evidence: Anthology 3 is a compilation album by the Beatles, released on 29 October 1996 by Apple Records as part of The Beatles Anthology series. The album includes rarities and alternative tracks from the final three years of the band's career, ranging from the initial sessions for The Beatles (better known as "the White Album") to the last sessions for Let It Be and Abbey Road in 1969 and early 1970. It is the last in a trilogy of albums with Anthology 1 and Anthology 2, all of which tie in with the televised special The Beatles Anthology.

The album was certified 3× Platinum by the RIAA and was the group's third double album in a row to reach number one on the US charts, equalling a record set by Donna Summer in the 1970s.

The Anthology albums were remastered and made available digitally on the iTunes Store on 14 June 2011, individually and as part of the Anthology Box Set.
Answer: Anthology 3 is a compilation album by the Beatles, released on 29 October 1996 by Apple Records as part of The Beatles Anthology series. The album includes rarities and alternative tracks from the final three years of the band's career, ranging from the initial sessions for The Beatles (better known as "the White Album") to the last sessions for Let It Be and Abbey Road in 1969 and early 1970. It is the last in a trilogy of albums with Anthology 1 and Anthology 2, all of which tie in with the televised special The Beatles Anthology.
Question: Are the authors the first to experiment with deep learning architectures for the hate speech detection task?
Evidence: With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
Answer: Yes.
Question: What activities does a 4 year old like?
Answer: With the caveat that each kid is different, at 4 years old kids like fun & stimulation. They like physical activities like playing in the park on the swings, slides, jungle gyms to splashing in the water to simply running around. They like reading books to learn words and sentences, recognizing pictures of objects and animals, and reading visual stories. They like arts and crafts, playing with colors and shapes. They enjoy getting things right, so give them encouragement and positive reinforcement. They like singing and dancing, they have favorite songs and music. Some kids enjoy dressing up and making stories with their toys. They like building forts with boxes and blankets. So there's a whole range of activities to pick from. If you like spending time with kids this age it can be a lot of fun for you too !
Question: Classify each of the following as either a root vegetable or a fruit: cassava, ube, strawberry, carrot, potato, banana, blueberry, yam, lime
Answer: Root vegetable: cassava, ube, carrot, potato, yam
Fruit: strawberry, banana, blueberry, lime
Question: What do the particular samples consist of?
Evidence: We empirically validate EGL on speech recognition tasks. In our experiments, the RNN takes in spectrograms of utterances, passing them through two 2D-convolutional layers, followed by seven bi-directional recurrent layers and a fully-connected layer with softmax activation. All recurrent layers are batch normalized. At each timestep, the softmax activations give a probability distribution over the characters. CTC loss BIBREF8 is then computed from the timestep-wise probabilities.
A base model, INLINEFORM0 , is trained on 190 hours ( INLINEFORM1 100K instances) of transcribed speech data. Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset. We query labels for the selected subset and incorporate them into training. Learning rates are tuned on a small validation set of 2048 instances. The trained model is then tested on a 156-hour ( INLINEFORM3 100K instances) test set and we report CTC loss, Character Error Rate (CER) and Word Error Rate (WER).
The confidence score methods BIBREF1 , BIBREF2 can be easily extended to our setup. Specifically, from the probabilities over the characters, we can compute an entropy per timestep and then average them. This method is denoted as entropy. We could also take the most likely prediction and calculate its CTC loss, normalized by number of timesteps. This method is denoted as pCTC (predicted CTC) in the following sections.
We implement EGL by marginalizing over the most likely 100 labels, and compare it with: 1) a random selection baseline, 2) entropy, and 3) pCTC. Using the same base model, each method queries a variable percentage of the unlabeled dataset. The queries are then included into training set, and the model continues training until convergence. Fig. FIGREF9 reports the metrics (Exact values are reported in Table TABREF12 in the Appendix) on the test set as the query percentage varies. All the active learning methods outperform the random baseline. Moreover, EGL shows a steeper, more rapid reduction in error than all other approaches. Specifically, when querying 20% of the unlabeled dataset, EGL has 11.58% lower CER and 11.09% lower WER relative to random. The performance of EGL at querying 20% is on par with random at 40%, suggesting that using EGL can lead to an approximate 50% decrease in data labeling.
These particular samples consist of short utterances containing silence (with background noise) or filler words.
Answer: These particular samples consist of short utterances containing silence (with background noise) or filler words.
Question: How can I tell my sourdough starter is ready to use?
Answer: You can look for a few signs to see if your sourdough starter is ready to use. One common sign to look for is whether the starter has at least doubled in volume. Time can be a difficult indicator as how long it takes for a starter to double volume can vary based on temperature, hydration, and other factors. Another sign to look for is a lot of bubbles throughout the starter - this can be seen if your starter is in a glass container. Lastly, get to know your starter as you bake with it because each starter has its own unique characteristics. Learn what it will smell like when it matures - this can help you establish a baseline and identify when something is wrong with the starter as it smells "funky".
Question: Which of the following exercises are push exercises: bench press, bicep curl, skull crusher, tricep extension, pull up, dumbbell row.
Answer: Bench press, skull crusher, tricep extension.
Question: What is a sitcom?
Answer: A sitcom is a comedy that typically airs on a television network once per week for 30 minutes.
Question: Which model do they use to convert between masculine-inflected and feminine-inflected sentences?
Evidence: We presented a new approach for converting between masculine-inflected and feminine-inflected noun phrases in morphologically rich languages. To do this, we introduced a Markov random field with an optional neural parameterization that infers the manner in which a sentence must change to preserve morpho-syntactic agreement when altering the grammatical gender of particular nouns. To the best of our knowledge, this task has not been studied previously. As a result, there is no existing annotated corpus of paired sentences that can be used as “ground truth.” Despite this limitation, we evaluated our approach both intrinsically and extrinsically, achieving promising results. For example, we demonstrated that our approach reduces gender stereotyping in neural language models. Finally, we also identified avenues for future work, such as the inclusion of co-reference information.
We presented a new approach for converting between masculine-inflected and feminine-inflected noun phrases in morphologically rich languages. To do this, we introduced a Markov random field with an optional neural parameterization that infers the manner in which a sentence must change to preserve morpho-syntactic agreement when altering the grammatical gender of particular nouns.
Answer: The answers are shown as follows:
* Markov random field with an optional neural parameterization
Question: What are ten woodworking projects that I could complete in a weekend?
Answer: 1. A wine box
2. A coat rack
3. A cutting board
4. A simple desk
5. A bottle stopper
6. A bowl
7. A small shelf
8. A picture frame
9. A serving tray
10. A garden bench
Question: What is PySpark?
Answer: PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core.
Question: how to use as wax
Answer: Use it to stick objects
Use it to cover wounds
use it to clean stains
use it to smooth itchy shoes
use it as holder to stick things
Question: Given this paragraph about Outlander, who stars in Outlander?
Evidence: Outlander is a historical drama television series based on the Outlander novel series by Diana Gabaldon. Developed by Ronald D. Moore, the show premiered on August 9, 2014, on Starz. It stars Caitríona Balfe as Claire Randall, a former Second World War military nurse in Scotland who, in 1945, finds herself transported back in time to 1743. There she encounters, falls in love with and marries a dashing Highland warrior named Jamie Fraser (Sam Heughan), a tacksman of Gabaldon's fictionalized version of Clan Fraser of Lovat. Here, Claire becomes embroiled in the Jacobite rising.
Answer: Outlander stars Caitriona Balfe (as Claire Randall).  Her co-start is Sam Heughan (as Jamie Fraser).  It is a Starz TV Series portraying star crossed lovers who meet across time. Claire travels from 1945, back in time to 1743 and encounters: adventure, mystery, and political intrigue of the Jacobite uprising; all the time growing closer and closer to her true love Jamie Fraser. The 2014 (to present) show is based on the Outlander book series written by Diana Gabaldon, originally published in the 1990's.
Question: What is the best way to lose weight?
Answer: Cut carbs, calorie deficit.
Question: What is it like living in Chicago, Illinois?
Answer: Chicago is a large city located in the midwest. It is known for tourist attractions like Navy Pier and The Bean, as well as famous architecture. It's on Lake Michigan which makes the winters extremely cold but the summers pleasant. People call Chicago the "Windy City".
Question: What is a skiff?
Answer: Skiff is a type of boat.  A skiff is commonly used for flats fishing.  Fishing in shallow water requires a boat that has a very shallow draft.
Question: What are some Machine Learning Algorithms
Answer: Linear Regression
K Means
Decision Trees
Neural Networks
Support Vector Machines
Question: What is flying trapeze?
Evidence: Flying trapeze refers to a trapeze act where a performer, or "flyer," grabs the trapeze bar and jumps off a high platform, or pedestal board, so that gravity creates the swing. The swing's parts are the "force out" (sometimes called the "cast out") at the far end of the first swing, the beat back and the rise (also known as "seven") as the performer swings back above the pedestal board, and then the trick is thrown at the far end of the second swing. The performer often releases the bar and is caught by another performer, the "catcher," who hangs by their knees on another trapeze, or sometimes on a cradle, which can be either stationary or also swinging. People of any size are able to execute basic trapeze maneuvers. Flying trapeze is generally done over a net, or occasionally over water. However, some smaller rigs, usually created for teaching purposes, use mats instead of a net.
Answer: Flying trapeze is a performance act where a "flyer" uses a trapeze bar to swing from a high platform, using gravity to create a swing.  The performer then releases the bar and is caught by another performer who is hanging by their knees (the "catcher") on another trapeze bar.
Question: How many months have less than 31 days?
Answer: 5 months have less than 31 days. April, June, September and November have 30 days. February has 28 or 29 days, depending on the year.
Question: I have a vegan friend coming over for dinner, which of the items in my kitchen can I use to make vegan food: chicken, kale, lettuce, honey, eggplant, tomato, rice,  cheese, yoghurt, ice-cream and chocolate.
Answer: To prepare a vegan meal you will be able to use any items that do not contain animal products. From the list provided, you can use: lettuce, eggplant, tomato and rice.
Question: Where is the best place in my home to store valuable and sensitive documents?
Answer: You should store sensitive documents in a secure location, such as a locked drawer inside a room with a lock. Depending on the nature of the documents, you might also consider storing them in a locking safe. Some safes are also waterproof and fireproof to a certain degree, so in the event of a natural disaster to your home, you would still be able to retrieve them. Examples of sensitive documents to be stored include birth certificates, wills, and deeds.
Question: Identify which animal is domesticated or wild: Horse, Red deer
Answer: Red deer is wild, Horse is domesticated
Question: What caused the 2007-2008 financial crisis?
Evidence: The preconditioning for the financial crisis were complex and multi-causal. Almost two decades prior, the U.S. Congress had passed legislation encouraging financing for affordable housing. However, in 1999, parts of the Glass-Steagall legislation, which had been adopted in 1933, were repealed, permitting financial institutions to commingle their commercial (risk-averse) and proprietary trading (risk-taking) operations. Arguably the largest contributor to the conditions necessary for financial collapse was the rapid development in predatory financial products which targeted low-income, low-information homebuyers who largely belonged to racial minorities. This market development went unattended by regulators and thus caught the U.S. government by surprise.
Answer: Arguably the largest contributor to the conditions necessary for the financial collapse was the rapid development of predatory financial products which targeted low-income, low-information homebuyers who largely belonged to racial minorities.
Question: Write an email to my friend who lives in Minneapolis to help convince them to move to Austin Texas.
Answer: Austin Texas is a booming city in central Texas for many reasons that might appeal to you. There are many opportunities for career growth because of all the technology businesses that have chosen to move here. In the job market right now, it is helpful to be near these businesses in case you get laid off from your job. The weather is mild all throughout the winter, so you won't need to scrape snow and ice off of your car every day, although there have been a number of snow storms in the last couple years but they quickly melt and only last a day or two at a time. The summers get hot and can stay above 100 degrees Fahrenheit for weeks at a time. This may sound like a negative, but one of the best things about Austin is the access to water. Lady Bird Lake, sometimes called Town Lake, is actually a river that runs through the middle of the city. Paddle boarding, swimming in Barton Springs, and party boats on Lake Travis are all water related activities that are particularly fun for a young demographic. There is a vibrant music scene with many local bands and plenty of art related communities. The conscious community in Austin is growing as well through many different types of activities like ecstatic dance, authentic relating, and tantra. There is something for everyone in Austin. The city is a tourist destination with many bachelor and bachelorette parties choosing to visit. Austin also hosts SXSW and ACL, both of which draw many hundreds of thousands of visitors each year. The people are friendly, similar to Minneapolis. The tech scene feels a bit like the New York City hustle, while the West coast chill is present through the laid back, fun oriented lifestyle. You can find whatever you're looking for here, and it is worth spending time here to see how you can make the city your own.