DataHammer/scimrc huggingface dataset:
Question: Where are the best places to purchase fabric for your sewing projects?
Answer: Depending on your project, there are a couple of stores that can be your go-to for purchasing supplies, particularly fabric:
1 - Joann has lots of options and frequently has sales and coupons that allow you to get your fabric at a discount. They offer most types of fabrics, but lack lots of options for some specialized ones, like cotton lawn and velvet. They also offer an abundance of other supplies that you might need to complete your project.
2 - Spoonflower is a platform for artists/designers to get their artwork/patterns printed on various types of fabrics. The options range from basic quilting cotton to Belgian linen. This store also frequently has sales to ensure you're getting your fabric at the best price. If you're looking for a specific pattern and unique type of fabric, this is your go-to.
3 - Fabric.com (owned by Amazon) is another great option with multiple types of fabrics at a range of price points. You also get fast and free shipping since the store operates under Amazon.
4 - Etsy is another marketplace for unique fabrics from individual vendors across the world. The price points are more variable, but you could find something unique depending on what your project is.
Overall, the store you shop at will depend on the nature of your project, type of fabric you're looking for, and budget. The good news is, you're bound to find what you need at one of these stores.
Question: How many tweets did they extract from Twitter API?
Evidence: A total of 2,50,000 tweets over a period of August 31st, 2015 to August 25th,2016 on Microsoft are extracted from twitter API BIBREF15 . Twitter4J is a java application which helps us to extract tweets from twitter. The tweets were collected using Twitter API and filtered using keywords like $ MSFT, # Microsoft, #Windows etc. Not only the opinion of public about the company's stock but also the opinions about products and services offered by the company would have a significant impact and are worth studying. Based on this principle, the keywords used for filtering are devised with extensive care and tweets are extracted in such a way that they represent the exact emotions of public about Microsoft over a period of time. The news on twitter about Microsoft and tweets regarding the product releases were also included. Stock opening and closing prices of Microsoft from August 31st, 2015 to August 25th, 2016 are obtained from Yahoo! Finance BIBREF16 .
A total of 2,50,000 tweets over a period of August 31st, 2015 to August 25th,2016 on Microsoft are extracted from twitter API [16].
Answer: 2,50,000 tweets.
Question: Which gameshow host is a vegetarian?
Answer: Bob Barker, the longest serving host of The Price is Right, is a vegetarian and forbade meat products from being displayed on the gameshow.
Question: What is RTGS?
Answer: Real-time gross settlement (RTGS) systems are specialist funds transfer systems where the transfer of money or securities takes place from one bank to any other bank on a "real-time" and on a "gross" basis. Settlement in "real time" means a payment transaction is not subjected to any waiting period, with transactions being settled as soon as they are processed. "Gross settlement" means the transaction is settled on a one-to-one basis, without bundling or netting with any other transaction. "Settlement" means that once processed, payments are final and irrevocable.

RTGS systems are usually operated by a country's central bank as it is seen as critical infrastructure for a country's economy. Economists believe that an efficient national payment system reduces the cost of exchanging goods and services, and is indispensable to the functioning of the interbank, money, and capital markets. A weak payment system may severely drag on the stability and developmental capacity of a national economy; its failures can result in inefficient use of financial resources, inequitable risk-sharing among agents, actual losses for participants, and loss of confidence in the financial system and in the very use of money.
Question: What should I consider before taking a job with a lot of travel?
Answer: Many people are attracted to jobs that have significant travel.  The allure of traveling to new and exciting places, the potential for airline and hotel program status benefits, meeting new people, etc.  All of these things may be exactly what you are looking for.  But my advice for anyone who is considering a position with significant travel and who has not experienced that type of work-life before is to think about all the things in their life that they are involved with that make them happy.  Think hard about whether you can take them on the road with you, reproduce them wherever your business travel takes you, or do without them.  Perhaps you have a standing poker night each month with your friends.  You may not be able to attend that with any regularity.  You may love the climbing gym or cycling club at home.  Can you replace that with hotel gyms?  Do you stick to a diet that will be hard to keep when you need to eat out a lot?  Do you have a relationship at home that will suffer if you are gone a lot?  The list can go on and on and is different for each person.  But it is critical to evaluate this aspect of how your life will change when you travel a lot of the time and make sure you are happy with the accommodations and changes you will need to make.  Another important consideration is whether you will be traveling to different time zones.  Often the idea of globetrotting to different parts of the world is very exciting, but you will have to deal with jetlag while maintaining your work performance.  You will end up dealing with canceled or delayed flights, traffic, and other types of travel disruptions.  Ultimately, a job with significant travel can involve a lot of excitement and fun.  But it is not right for everyone. It is not vacation travel.  And being honest with yourself about what changes you will need to make, what hassles you will need to deal with, and what you will have to give up will help you make the right decision.  And you will never truly know until you do it.
Question: give me a list of counties in Virginia by population
Answer: Fairfax County, Prince William County, Virginia Beach county, Loudoun County, Chesterfield County, Henrico County, Chesapeake County, Norfolk County, Arlington County, Richmond County
Question: What will the team focus on in their future work?
Evidence: We have presented a method for quantifying the similarity and relatedness between two terms that integrates pair–wise similarity scores into second–order vectors. The goal of this approach is two–fold. First, we restrict the context used by the vector measure to words that exist in the biomedical domain, and second, we apply larger weights to those word pairs that are more similar to each other. Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments. We evaluated our method on datasets that have been manually annotated for relatedness and similarity and found evidence to support this hypothesis. In particular we discovered that guiding the creation of a second–order context vector by selecting term pairs from biomedical text based on their semantic similarity led to improved levels of correlation with human judgment.
We also explored using a threshold cutoff to include only those term pairs that obtained a sufficiently large level of similarity. We found that eliminating less similar pairs improved the overall results (to a point). In the future, we plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure. We also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness. We are particularly interested in approaches that learn word, phrase, and sentence embeddings from structured corpora such as literature BIBREF23 and dictionary entries BIBREF24 . Such embeddings could be integrated into a second–order vector or be used on their own.
Finally, we compared our proposed method to other distributional approaches, focusing on those that used word embeddings. Our results showed that integrating semantic similarity measures into second–order co–occurrence vectors obtains the same or higher correlation with human judgments as do various different word embedding approaches. However, a direct comparison was not possible due to variations in the subsets of the UMNSRS evaluation dataset used. In the future, we would not only like to conduct a direct comparison but also explore integrating semantic similarity into various kinds of word embeddings by training on pair–wise values of semantic similarity as well as co–occurrence statistics.
In the future, we plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure. We also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness.
Answer: In the future, they plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure. They also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness.
Question: Given this paragraph about Mark Arminski, why was he famous and what did he study last while still at school?
Evidence: Mark Arminski is an American rock concert poster artist born in 1950 in Detroit, Michigan. He began studying art at the Oakland Community College and pursued printmaking in stone lithography at the Kalamazoo Institute of Arts. Rounding out his formal education was his stay at the Dynamic Graphics Education Foundation in Peoria, Illinois, where he studied computer generated art.
Answer: Mark Arminski is an American rock concert poster artist.  He finished his formal education studying computer generated art.
Question: Was Jens Stub Swedish?
Evidence: Jens Stub (3 March 1764 – 24 October 1819) was a Norwegian priest and politician. He was vicar on the island of Veøya and served as a representative at the Norwegian Constitutional Assembly at Eidsvoll in 1814.
Answer: No, he was Norwegian.
Question: What domain does the dataset fall into?
Evidence: We perform our experiments using ActivityNet Captions dataset BIBREF2 that is considered as the standard benchmark for dense video captioning task. The dataset contains approximately 20k videos from YouTube and split into 50/25/25 % parts for training, validation, and testing, respectively. Each video, on average, contains 3.65 temporally localized captions, around 13.65 words each, and two minutes long. In addition, each video in the validation set is annotated twice by different annotators. We report all results using the validation set (no ground truth is provided for the test set).
We perform our experiments using ActivityNet Captions dataset BIBREF2 that is considered as the standard benchmark for dense video captioning task. The dataset contains approximately 20k videos from YouTube and split into 50/25/25 % parts for training, validation, and testing, respectively. 
Answer: YouTube videos
Question: What are the baselines for the SLC task and FLC task?
Evidence: The baseline system for the SLC task is a very simple logistic regression classifier with default parameters, where we represent the input instances with a single feature: the length of the sentence. The performance of this baseline on the SLC task is shown in Tables TABREF33 and TABREF34.
The baseline for the FLC task generates spans and selects one of the 18 techniques randomly. The inefficacy of such a simple random baseline is illustrated in Tables TABREF36 and TABREF41.
The baseline system for the SLC task is a very simple logistic regression classifier with default parameters, where we represent the input instances with a single feature: the length of the sentence. The performance of this baseline on the SLC task is shown in Tables TABREF33 and TABREF34.
The baseline for the FLC task generates spans and selects one of the 18 techniques randomly. The inefficacy of such a simple random baseline is illustrated in Tables TABREF36 and TABREF41.
The baseline system for the SLC task is a very simple logistic regression classifier with default parameters, where we represent the input instances with a single feature: the length of the sentence.****The baseline for the FLC task generates spans and selects one of the 18 techniques randomly.
Answer: For SLC task, it is a very simple logistic regression classifier with default parameters, for the FLC task, it is one of the 18 techniques randomly.
Question: Classify each of the following a golf tournament or a tennis tournament: Masters, Dell Matchplay, PGA Championship, Wimbledon, Miami Open, French Open, Players Championship
Answer: Masters: Golf Tournament
Dell Matchplay: Golf Tournament
PGA Championship: Golf Tournament
Wimbledon: Tennis Tournament
Miami Open: Tennis Tournament
French Open: Tennis Tournament
Players Championship: Golf Tournament
Question: What was their result on Stance Sentiment Emotion Corpus?
Evidence: FLOAT SELECTED: TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.
We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.
We implement our model in Python using Tensorflow on a single GPU. We experiment with six different BiLSTM based architectures. The three architectures correspond to BiLSTM based systems without primary attention i.e. only with secondary attention for sentiment analysis (S1), emotion analysis (E1) and the multi-task system (M1) for joint sentiment and emotion analysis. The remaining three architectures correspond to the systems for sentiment analysis (S2), emotion analysis (E2) and multi-task system (M2), with both primary and secondary attention. The weight matrices were initialized randomly using numbers form a truncated normal distribution. The batch size was 64 and the dropout BIBREF34 was 0.6 with the Adam optimizer BIBREF35. The hidden state vectors of both the forward and backward LSTM were 300-dimensional, whereas the context vector was 150-dimensional. Relu BIBREF36 was used as the activation for the hidden layers, whereas in the output layer we used sigmoid as the activation function. Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis.
FLOAT SELECTED: TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.
We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.
F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. 
Answer: F1 score of 66.66%
Question: Classify the cities based on whether they are located in Yemen or Saint Kitts and Nevis.
Al Ḩudaydah, Ta‘izz, Basseterre, Sanaa
Answer: Yemen - Sanaa, Ta‘izz, Al Ḩudaydah
Saint Kitts and Nevis - Basseterre
Question: Who was the first person to have more than 100 million likes on Facebook?
Answer: Shakira is the first person to have more than 100 million likes on Facebook.
Question: Which city is the capital city of California?
Answer: Sacramento is the capital of California.
Question: When does Ramsay Bolton kill his stepmother and half-brother?
Answer: In the second episode of the sixth season of Game of Thrones ("Home"), Ramsay Bolton tricks his stepmother, Walda Frey, into going into the kennels with her newborn son (and Ramsay's half-brother), where the two are killed by Ramsay's dog.
Question: What is Victoria González's westling pedigree?
Evidence: Victoria González (born January 12, 1991), better known as Raquel González, is an American professional wrestler. She is currently signed to WWE, where she performs on the SmackDown brand under the ring name Raquel Rodriguez. She is a former NXT Women's Champion and two-time NXT Women's Tag Team Champion and WWE Women's Tag Team Champion.

González is a second generation professional wrestler, following her father Rick González. In 2021, she and Dakota Kai won the inaugural Women's Dusty Rhodes Tag Team Classic and became the inaugural NXT Women's Tag Team Champions, while González won the NXT Women's Championship.
Answer: Victoria Gonzalez's father, Rick González, was a professional wrestler.
Question: What mechanism do the authors introduce in the paper?
Evidence: In recent studies, deep learning models have achieved top performances in emotion detection and classification. Access to large amount of data has contributed to these high results. Numerous efforts have been dedicated to build emotion classification models, and successful results have been reported. In this work, we combine several popular emotional data sets in different genres, plus the one given for this task to train the emotion model we developed. We introduce a multigenre training mechanism, our intuition to combine different genres are a) to augment more training data, b) to generalize detection of emotion. We utilize Portable textual information such as subjectivity, sentiment, and presence of emotion words, because emotional sentences are subjective and affectual states like sentiment are strong indicator for presence of emotion.
The rest of this paper is structured as followings: section SECREF2 introduce our neural net model, in section SECREF3 we explain the experimental setup and data that is been used for training and development sets, section SECREF4 discuss the results and analyze the errors, section SECREF5 describe related works, section SECREF6 conclude our study and discuss future direction.
We introduce a multigenre training mechanism, our intuition to combine different genres are a) to augment more training data, b) to generalize detection of emotion.
Answer: A multigenre training mechanism.
Question: Name some of the bravolebrities from The Real Housewives of Salt Lake City
Answer: Heather Gay, Lisa Barlow, Meredith Marks and Whitney Rose
Question: Which domains did they explored?
Evidence: We use the publicly available dataset KVRET BIBREF5 in our experiments. This dataset is created by the Wizard-of-Oz method BIBREF10 on Amazon Mechanical Turk platform. This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12.
We use the publicly available dataset KVRET BIBREF5 in our experiments. 
This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments. 
Answer: The answers are shown as follows:
* calendar
* weather
* navigation
Question: How many Grammy Awards has Bad Bunny won?
Answer: Bad Bunny has won 2 Grammy Awards and has been nominated for 6
Question: What goes into a credit score?
Answer: In the US, a credit score is based off information given in a credit report. These are analyzed and grouped into five categories: payment history (35%), amounts owed (30%), length of credit history (15%), new credit (10%) and credit mix (10%). Both positive and negative information is gathered in each category.
Question: What color is an orange fruit?
Answer: A ripe orange fruit is typically orange unless it is a blood orange in which case it can have a slight red tinge. An unripe orange fruit is green. A rotten orange fruit is not orange or green or red, it is brown or black.
Question: What evaluation metrics were used in the experiment?
Evidence: Metrics. We use tolerance accuracy BIBREF16, which measures how far away the predicted span is from the gold standard span, as a metric. The rationale behind the metric is that, in practice, it suffices to recommend a rough span which contains the answer – a difference of a few seconds would not matter much to the user.
Metrics. We used accuracy and MRR (Mean Reciprocal Ranking) as metrics. The accuracy is
Metrics. To evaluate our pipeline approach we use overall accuracy after filtering and accuracy given that the segment is in the top 10 videos. While the first metric is similar to SECREF17, the second can indicate if initially searching on the video space can be used to improve our selection:
Metrics. We use tolerance accuracy BIBREF16, which measures how far away the predicted span is from the gold standard span, as a metric. The rationale behind the metric is that, in practice, it suffices to recommend a rough span which contains the answer – a difference of a few seconds would not matter much to the user.
Metrics. We used accuracy and MRR (Mean Reciprocal Ranking) as metrics. 
Metrics. To evaluate our pipeline approach we use overall accuracy after filtering and accuracy given that the segment is in the top 10 videos. 
Answer: For sentence-level prediction they used tolerance accuracy, for segment retrieval accuracy and MRR and for the pipeline approach they used overall accuracy
Question: Please provide a list of Michael Jordan's accomplishments in the 1987-1988 NBA basketball season.
Evidence: NBA achievements
Naismith Memorial Basketball Hall of Fame Class of 2009
6× NBA champion: 1991, 1992, 1993, 1996, 1997, 1998
5× NBA Most Valuable Player: 1987–88, 1990–91, 1991–92, 1995–96, 1997–98
6× NBA Finals Most Valuable Player: 1991, 1992, 1993, 1996, 1997, 1998
10× Scoring leader: 1986–87, 1987–88, 1988–89, 1989–90, 1990–91, 1991–92, 1992–93, 1995–96, 1996–97, 1997–98
NBA Defensive Player of the Year: 1987-88
NBA Rookie of the Year: 1984-85
14× NBA All-Star: 1985, 1986 (selected but injured), 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1996, 1997, 1998, 2002, 2003
3× NBA All-Star Game Most Valuable Player: 1988, 1996, 1998
2× NBA Slam Dunk Contest champion: 1987, 1988
Runner-up in 1985
3× Steals leader: 1987–88, 1989–90, 1992–93
2× Minutes leader: 1987–88, 1988–89
2× IBM Award winner: 1985, 1989
11× All-NBA selection:
First Team: 1987–93, 1996–98
Second Team: 1985
9× All-Defensive selection:
First Team: 1988–93, 1996–98
NBA All-Rookie selection:
First Team: 1985
7× The Sporting News Most Valuable Player: 1987–88, 1988–89, 1990–91, 1991–92, 1995–96, 1996–97, 1997–98
The Sporting News Rookie of the Year: 1985
Sports Illustrated Sportsman of the Year: 1991
Ranked #1 by SLAM Magazine's Top 50 Players of All-time
Ranked #1 by ESPN SportsCentury's Top North American Athletes of the 20th Century
Selected in 1996 as one of the "50 Greatest Players in NBA History"
Selected in 1996 as member of two of the "Top 10 Teams in NBA History"
1991–92 Chicago Bulls (67–15; .817)
1995–96 Chicago Bulls (72–10; .878)
25 NBA Player of the Week  16 NBA Player of the Month
Answer: In the 1987-1988 NBA Season, Michael Jordan had several achievements, including:
-NBA Most Valuable Player
-NBA Scoring Leader
-NBA Defensive Player of the Year
-NBA Slam Dunk Contest Champion
-NBA Steals Leader
-NBA Minutes Leader
-All-NBA First Team
-NBA All-Defensive Team
-The Sporting News Most Valuable Player