DataHammer/scimrc huggingface dataset:
Question: Write an email to the condo HOA as why including a flat rate of water bill into monthly HOA fee is a bad idea
Answer: To whom it may concern:

I am a resident at Greenwood community and hereby writing this email proposing to separate flat-rate water bill from monthly HOA fee and make it pro-rated. Here are the reasons:
1. When people don't pay for water bill explicitly, they start to feel it comes for free. Hence, they have no motivation to use water wisely. It leads to waste of water and more subtly, increase of HOA fee in the long-run since it's all running money after all.
2. It's not fair to residents with smaller condos. Although not aways true, small square footage usually means less people and less water usage.
3. People can abuse this policy. I noticed some people run commercial laundry in their condos which make the water usage skyrocket. However, no matter how much they use, they pay a flat rate each month. 

All in all, I propose to change flat rate water bill to pro-rated.

Look forward to your response.

Best,
Resident John Doe
Question: What is the best way to learn basketball?
Answer: The first skill to learn in basketball is ball handling.  The ability to dribble with your right and left hands is key.  Most people are either right or left handed.  If a player is right handed, they should spend the majority of their time dribbling with their left hand (weak hand).  The next thing to focus is on shooting.  Start with the Mikan drill.  This drill helps with developing shooting with both your right and left hand.  This will help when you begin shooting layups.  Gradually, start taking shots a little farther away from the basket.  Only move backwards when you consistently shoot a high percentage  of made shots.  Finally, you want to focus with movement without the ball.  This includes proper cuts when you are an offensive player without possession of the ball.  You also want to practice proper positioning on defense.  Practice good defensive positioning when you guard the person with and without the ball.
Question: Does Grail accept Prolog inputs?
Evidence: In its general form, a type-logical grammar consists of following components:
In its general form, a type-logical grammar consists of following components:
Answer: No.
Question: Where do they collect the news articles from?
Evidence: The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators. More details about the data collection and the annotation, as well as statistics about the corpus can be found in BIBREF10, where an earlier version of the corpus is described, which includes 450 news articles. We further annotated 47 additional articles for the purpose of the shared task using the same protocol and the same annotators.
The training, the development, and the test partitions of the corpus used for the shared task consist of 350, 61, and 86 articles and of 16,965, 2,235, and 3,526 sentences, respectively. Figure FIGREF15 shows an annotated example, which contains several propaganda techniques. For example, the fragment babies on line 1 is an instance of both Name_Calling and Labeling. Note that the fragment not looking as though Trump killed his grandma on line 4 is an instance of Exaggeration_or_Minimisation and it overlaps with the fragment killed his grandma, which is an instance of Loaded_Language.
Table TABREF23 reports the total number of instances per technique and the percentage with respect to the total number of annotations, for the training and for the development sets.
The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators.
Answer: From 36 propagandist and 12 non-propagandist news outlets.
Question: In the series A Song of Ice and Fire, who is the founder of House Beesbury?
Answer: Ellyn Ever Sweet was the mother of House Beesbury and purported to be the daughter of Garth Greenhand. Her brother, Garth the Gardener, founded House Gardener
Question: Which of these games are traditionally considered to be board games, and which are card games? Monopoly, Trivial Pursuit, Poker, Snap, Chess, Backgammon, Bridge and Scrabble.
Answer: Monopoly, Trivial Pursuit, Chess, Backgammon and Scrabble are all board games. Poker, Snap and Bridge are types of card games.
Question: How many states are part of New England?
Answer: New England is composed of six states.
Question: What is the ratio used in training data for training and validation?
Evidence: Here we compare and contrast our system to the baseline system. A diagram of our system is shown in Figure FIGREF4 .
The idea behind this modification is to provide the encoder with access to all morpho-syntactic cues present in the sentence. In contrast to the baseline, which only encodes the immediately adjacent context of a target word, we encode the entire context. All context word forms, lemmas, and MSD tags (in Track 1) are embedded in their respective high-dimensional spaces as before, and their embeddings are concatenated. However, we now reduce the entire past context to a fixed-size vector by encoding it with a forward LSTM, and we similarly represent the future context by encoding it with a backwards LSTM.
We introduce an auxiliary objective that is meant to increase the morpho-syntactic awareness of the encoder and to regularise the learning process—the task is to predict the MSD tag of the target form. MSD tag predictions are conditioned on the context encoding, as described in UID15 . Tags are generated with an LSTM one component at a time, e.g. the tag PRO;NOM;SG;1 is predicted as a sequence of four components, INLINEFORM0 PRO, NOM, SG, 1 INLINEFORM1 .
For every training instance, we backpropagate the sum of the main loss and the auxiliary loss without any weighting.
As MSD tags are only available in Track 1, this augmentation only applies to this track.
The parameters of the entire MSD (auxiliary-task) decoder are shared across languages.
Since a grouping of the languages based on language family would have left several languages in single-member groups (e.g. Russian is the sole representative of the Slavic family), we experiment with random groupings of two to three languages. Multilingual training is performed by randomly alternating between languages for every new minibatch. We do not pass any information to the auxiliary decoder as to the source language of the signal it is receiving, as we assume abstract morpho-syntactic features are shared across languages.
After 20 epochs of multilingual training, we perform 5 epochs of monolingual finetuning for each language. For this phase, we reduce the learning rate to a tenth of the original learning rate, i.e. 0.0001, to ensure that the models are indeed being finetuned rather than retrained.
We keep all hyperparameters the same as in the baseline. Training data is split 90:10 for training and validation. We train our models for 50 epochs, adding early stopping with a tolerance of five epochs of no improvement in the validation loss. We do not subsample from the training data.
We train models for 50 different random combinations of two to three languages in Track 1, and 50 monolingual models for each language in Track 2. Instead of picking the single model that performs best on the development set and thus risking to select a model that highly overfits that data, we use an ensemble of the five best models, and make the final prediction for a given target form with a majority vote over the five predictions.
Training data is split 90:10 for training and validation.
Answer: 90:10.
Question: What state-of-the-art models are compared with the authors' model?
Evidence: We apply the optimal hyperparameter settings and compare our model against the following state-of-the-art models:
NBOW Neural Bag-of-Words that simply sums up embedding vectors of all words.
PV Paragraph Vectors followed by logistic regression BIBREF21 .
MT-RNN Multi-Task learning with Recurrent Neural Networks by a shared-layer architecture BIBREF11 .
MT-CNN Multi-Task learning with Convolutional Neural Networks BIBREF8 where lookup tables are partially shared.
MT-DNN Multi-Task learning with Deep Neural Networks BIBREF9 that utilizes bag-of-word representations and a hidden shared layer.
GRNN Gated Recursive Neural Network for sentence modeling BIBREF1 .
As Table TABREF48 shows, our model obtains competitive or better performances on all tasks except for the QC dataset, as it contains poor correlations with other tasks. MT-RNN slightly outperforms our model on SST, as sentences from this dataset are much shorter than those from IMDB and MDSD, and another possible reason may be that our model are more complex and requires larger data for training. Our model proposes the designs of various interactions including coupling, local and global fusion, which can be further implemented by other state-of-the-art models and produce better performances.
We apply the optimal hyperparameter settings and compare our model against the following state-of-the-art models: • NBOW Neural Bag-of-Words that simply sums up embedding vectors of all words. • PV Paragraph Vectors followed by logistic regression [Le and Mikolov, 2014]. • MT-RNN Multi-Task learning with Recurrent Neural Networks by a shared-layer architecture [Liu et al., 2016c].• MT-CNN Multi-Task learning with Convolutional Neural Networks [Collobert and Weston, 2008] where lookup tables are partially shared. • MT-DNN Multi-Task learning with Deep Neural Networks [Liu et al., 2015b] that utilizes bag-of-word representations and a hidden shared layer. • GRNN Gated Recursive Neural Network for sentence modeling [Chen et al., 2015].
Answer: • NBOW Neural Bag-of-Words that simply sums up embedding vectors of all words. • PV Paragraph Vectors followed by logistic regression. • MT-RNN Multi-Task learning with Recurrent Neural Networks by a shared-layer architecture.• MT-CNN Multi-Task learning with Convolutional Neural Networks where lookup tables are partially shared. • MT-DNN Multi-Task learning with Deep Neural Networks that utilizes bag-of-word representations and a hidden shared layer. • GRNN Gated Recursive Neural Network for sentence modeling.
Question: What are the different types of loan or mortgage products for home buyers in the U.S.?
Answer: It is crucial to understand all your financing options when considering buying a new home. Some of the most common loan types for residential home buyers are listed below:

1. Conventional Loans: The most common home loan type that is not insured or guaranteed by the government. While it typically requires 20% down payment, you can put as little as 5% down.
2. VA Loans: This is offered by the U.S. Department of Veterans Affairs for people who have served in the military as well as their spouses. It requires no down payment and is an amazing option for anyone who is eligible.
3. FHA Loan: A mortgage insured by the Federal Housing Administration. First time home buyers find these loans very attractive due to the small down payment requirements of just 3.5% and the flexible credit requirements. Additionally there are children products to this loan vehicle including the FHA 203k loan which can help those purchasing distressed properties by funding the renovations, within certain predetermined guidelines.
4. Adjustable-Rate Mortgage (ARM): With this product, your interest rate varies over time depending on market and economic conditions. These seem attractive initially as they typically start with lower rates than fixed-rate mortgages but can increase over time.
5. Fixed-Rate Mortgages: This interest rate is locked in at the start of the loan and starts the same throughout the entire term.
7. USDA Loan: Guaranteed by the U.S. Department of Agriculture and eligible to suburban and rural homebuyers. This loan product also doesn't require down payment and has lower mortgage insurance requirements than an FHA loan

While these are the most common loan types, other options tend to appear from time to time. It is important to do your research and pick the product that best suits your individual needs and long-term financial goals. To help better understand these options many aspiring buyers reach out to local loan providers, real estate agents, and brokers.
Question: What do the authors use for the reduction of overfitting?
Evidence: MLP contains three layers - an input layer, an output layer and some hidden layers. Input layer receives the signal, the output layer gives a decision or prediction about the input and the computation of the MLP is conducted in the hidden layers. In our system, we use 100 layers. For weight optimization, we use Limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization algorithm.
SVM gives an optimal hyper-plane and it maximizes the margin between classes. We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped. For decision function shape, we use the original one-vs-one (ovo) decision function.
NBC is based on Bayes' Theorem which gives probability of an event occurrence based on some conditions related to that event. We use Multinomial Naive Bayes Classifier with smoothing parameter equals to 0.1. A zero probability cancels the effects of all the other probabilities.
Stochastic gradient descent optimizes an objective function with suitable smoothness properties BIBREF27. It selects few examples randomly instead of whole data for each iteration. We use 'L2' regularization for reduction of overfitting.
Gradient Boosting Classifier produces a prediction model consisting of weak prediction models. Gradient boosting uses decision trees. We use 100 boosting stages in this work.
K-NN is a supervised classification and regression algorithm. It uses the neighbours of the given sample to identify its class. K determines the number of neighbours needed to be considered. We set the value of K equals to 13 in this work.
RF is an ensemble learning technique. It constructs large number of decision trees during training and then predicts the majority class. We use 500 decision trees in the forest and "entropy" function to measure the quality of a split.
We use ’L2’ regularization for reduction of overfitting.
Answer: The authors use ’L2’ regularization for reduction of overfitting.
Question: What is a use case?
Answer: A use case is considered a business opportunity.  Use cases are usually identified as opportunities to deliver business value by the business.  Solution architects helped to define the use case and the requirements needed to deliver the use case into development and production.  Use case identification is an excellent way to sell platform products and services that help to deliver use case development for business and customers faster.
Question: Classify each of the following as "Can Fly" or "Can't Fly": Kite, bird, dog, plane, car, scooter
Answer: Kite: Can Fly
Bird: Can Fly
Dog: Can't Fly
Plane: Can Fly
Car: Can't Fly
Scooter: Can't Fly
Question: Where is the Black Strait Lake
Evidence: The Garabogazköl (also spelt Kara-Bogaz-Gol; "Black Strait Lake"), or Garabogazköl Aylagy ("Black Strait Lake Bay"), is a shallow, water-filled, highly-saline depression in the northwestern corner of Turkmenistan. It forms a lagoon of the Caspian Sea and has a variable surface area, about 18,000 km2 (6,900 sq mi). The Caspian Sea proper sits immediately to the west, mostly divorced by a narrow, rocky ridge having a very narrow opening through which the Caspian waters flow, overwhelmingly, into it. There is likely to be a subterranean highly saline flow, only when there is less evaporation in winter, as in the case of the Turkish Straits year-round. The lagoon's volume fluctuates seasonally, accentuated by its salt evaporation ponds and natural counterpart surrounding, seasonally dry salt pans.

The city of Garabogaz (formerly Bekdaş) lies on the ridge, about 50 km (31 mi) north of the channel between the main Caspian basin and the Garabogazköl lagoon. It has a population of about 10,000 people.
Answer: The Garabogazkoel (also spelt Kara-Bogaz-Gol; "Black Strait Lake"), or Garabogazkoel Aylagy ("Black Strait Lake Bay"), is a shallow, water-filled, highly-saline depression in the northwestern corner of Turkmenistan. 

It forms a lagoon of the Caspian Sea and has a variable surface area, about 18,000 km2 (6,900 sq mi).
Question: Share some details about paris city from the given text.
Evidence: Paris (English: /ˈpærɪs/; French pronunciation: [paʁi] ( listen)) is the capital and most populous city of France, with an official estimated population of 2,102,650 residents as of 1 January 2023 in an area of more than 105 km² (41 sq mi),  making it the fourth-most populated city in the European Union as well as the 30th most densely populated city in the world in 2022. Since the 17th century, Paris has been one of the world's major centres of finance, diplomacy, commerce, fashion, gastronomy, and science. For its leading role in the arts and sciences, as well as its early and extensive system of street lighting, in the 19th century, it became known as "the City of Light". Like London, prior to the Second World War, it was also sometimes called the capital of the world.
Answer: 1.	Paris is the capital of France and the most populous city, with an official estimated population of 2,102,650 residents as of 1 January 2023 in an area of more than 105 km² (41 sq mi).
2.	In 2022, it was the 30th most populous city in the world and the fourth-most populous city in the European Union.
3.	Since the 17th century, Paris has been one of the principal hubs for science, trade, fashion, diplomacy, and gastronomy.
4.	It earned the moniker "the City of Light" in the 19th century due to its prominence in the arts and sciences and its early and comprehensive system of street lighting.
5.	It was sometimes referred to as the capital of the world, just like London had been before the Second World War.
Question: How many dwarf planets are there in our solar system. Describe each of them
Answer: There is only 1 dwarf planet in the inner solar system and it is Ceres. There are 4 other dwarf planets, but they are present outside of the Solar system. Ceres is the largest object in the asteroid belt between the Mars and the Jupiter. NASA's spacecraft Dawn visited Ceres in 2015.  Day time surface temperatures in Ceres span from -136 degrees Fahrenheit to -28 degrees  Fahrenheit
Question: here is a blurb about byte pair encoding for encryption, what benefits does this concept have besides compression?
Evidence: Byte pair encoding (BPE) or digram coding is a simple and robust form of data compression in which the most common pair of contiguous bytes of data in a sequence are replaced with a byte that does not occur within the sequence. A lookup table of the replacements is required to rebuild the original data. The algorithm was first described publicly by Philip Gage in a February 1994 article "A New Algorithm for Data Compression" in the C Users Journal.

A variant of the technique has shown to be useful in several natural language processing (NLP) domains, for applications such as tokenisation, as seen in Google's SentencePiece and OpenAI's GPT-3. Here, the goal is not data compression, but tokenisation of text in a given language to produce a variable sequence of terms from a fixed-size vocabulary of tokens. Typically, most words will be encoded as a single token, while rare words will be encoded as a sequence of a few tokens, where these tokens represent meaningful word parts. This translation of text into tokens can be found by variants of byte pair encoding, such as subword units.

Byte pair encoding lends itself to NLP tasks due to its simplicity and speed; BPE is suitably effective for the tokenisation of terms, does not require large computational overheads, and remains consistent, making it reliable.
Answer: An alternate use for byte pair encoding besides compression would be tokenization.  Tokenization can be used in Natural Language Processing to encode tokens of words for simplicity and speed in NLP tasks.
Question: Is Python or Java a better programming language?
Answer: Python and Java are two of the most popular and robust programming languages.  Java is the fifth most commonly used programming language according to Stack Overflow. Java is generally faster and more efficient than Python because it is a compiled language. While it's one of the coding languages considered to be easy to learn because many of the processes of this high-level language run automatically. However, Python still takes the cake in terms of simplicity. 

 As an interpreted language, Python has simpler, more concise syntax than Java. It can perform the same function as Java in fewer lines of code. Python also offers extensive libraries, especially around data analysis and machine learning. Because it's dynamically typed, it allows for even fast development compared to Java. 

Other key differences include how each handles memory management and the ability to run multiple threads at the same time. 

For example, lets dive into this threading difference. Java fully supports multithreading. When you use Java, you can run more than one thread at a time. When running multiple threads, they share a common memory area to increase efficiency and performance. Now if you search the web for threading in Python you might be a bit confused. You will find articles claiming that Python can't support multithreading but then you will also find tutorials and packages for threading and parallel processing. Python has a Global Interpreter Lock that only lets one thread execute at a time, so if you're working on a multi-threaded CPU-bound program, it'll likely be even slower. However, it's threading package allows you to speed up I/O bound programs. Using multiprocessing programs instead of multithreaded programs can be an effective workaround.

The answer to which is better is largely dependent on the user and the use case. Before learning one or starting a new project with either, think about what your goals are and decide which one fits those more closely.
Question: how to get out of maze
Answer: Getting out of a maze can be a challenging task, but with a few tips and strategies, it can be accomplished. Here are some steps to follow to help you get out of a maze:

Stay Calm and Focused: Getting lost in a maze can be frustrating, but it's important to stay calm and focused. Take deep breaths, relax, and clear your mind. This will help you to think more clearly and make better decisions.

Plan Your Route: Take a few minutes to look at the maze from above, if possible, and try to identify the route to the exit. Identify landmarks or walls that you can use as reference points. Look for any dead ends, and try to avoid them.

Follow One Wall: If you can't identify the exit route, try to follow one wall of the maze. Keep your hand on the wall and follow it all the way around the maze. This will help you to stay on track and avoid getting lost in circles.

Mark Your Path: If you have something to mark your path, such as chalk, breadcrumbs, or a pen, use it to mark your route as you go. This way, you can avoid taking the same path multiple times and can backtrack if necessary.

Look for Clues: Keep an eye out for clues that may help you find your way out, such as arrows or signs. Many mazes also have hidden clues or puzzles that can help you find the exit, so keep your eyes open.

Ask for Help: If you're still struggling to find your way out, don't be afraid to ask for help. Many mazes have staff members who can guide you out or provide you with clues.

With these tips in mind, you should be able to find your way out of a maze. Remember to stay calm, plan your route, and look for clues, and you'll be out in no time!
Question: Give me some strategies for charity fundraising
Answer: Ask for donations or clothes, toys and other bric-a-brac and have a car boot sale. Organise fun nights in - or out - such as karaoke, talent contests and a fancy dress day. Run a raffle - ask friends and family to donate prizes. Organise a charity car wash at work, or at home.