DataHammer/scimrc huggingface dataset:
Question: Why do cats purr?
Evidence: In domestic cats, many signals that occur when interacting with humans seem to originate from when the animal was dependent on the mother. Cats have been observed to purr for most of their lifespan, starting from when they were young and suckling from their mother. Purring may have developed as an evolutionary advantage as a signaling mechanism of reassurance between mother cats and nursing kittens. Post-nursing cats often purr as a sign of contentment: when being petted, becoming relaxed, or eating. Some purring may be a signal to another animal that the purring cat does not pose a threat.
Answer: Cats purr for a variety of reasons. Purring can be a sign of contentment or comfort, for example when being pet, while relaxing or eating. It is thought that purring may have developed as an evolutionary trait signaling reassurance between the mother cat and its nursing kittens.
Question: Do RVs have bathrooms?
Answer: Whether or not RVs have bathrooms, and the type of bathroom, depends on the RV. Most larger RVs have a “dry bath”. “Dry” means that there is a stand-alone shower. Some showers in dry baths are very small, and some are the size of residential showers. Many smaller RVs have “wet baths”, meaning there is no separate shower. There is a shower nozzle in the bathroom space, and when taking a shower everything in the space gets wet (including the toilet, shelves, walls, etc). Wet baths are usually carefully designed so that everything in the bathroom can get wet without damage. Some RVs do not have bathrooms, but have toilets that are hidden in a cabinet and slide out. This is more common in DIY conversions.
Question: From the passage provided, extract the diameter of eukaryotic ribosomes.
Evidence: Prokaryotic ribosomes are around 20 nm (200 Å) in diameter and are composed of 65% rRNA and 35% ribosomal proteins. Eukaryotic ribosomes are between 25 and 30 nm (250–300 Å) in diameter with an rRNA-to-protein ratio that is close to 1. Crystallographic work has shown that there are no ribosomal proteins close to the reaction site for polypeptide synthesis. This suggests that the protein components of ribosomes do not directly participate in peptide bond formation catalysis, but rather that these proteins act as a scaffold that may enhance the ability of rRNA to synthesize protein (See: Ribozyme).
Answer: Eukaryotic ribosomes are between 25 and 30 nm (250–300 Å) in diameter.
Question: Find the four eye shapes on Maitum anthropomorphic pottery and separate them with a comma
Evidence: Since the eyes are believed to be expressive, the eyes portrayed in Maitum jars are given high regard in describing the dead. The eyes are classified to four major shapes: (1) almond, (2) ovaloid, (3) round and (4) rectangular.
Answer: almond, ovaloid, round, rectangular
Question: What is a medallion architecture in data engineering?
Answer: A medallion architecture is a data engineering concept.  The idea is that system source data or system of record data is landed in a bronze layer.  In the bronze layer data is not changed, and it should match the source system exactly even if there are data quality issues.  Next in the silver layer the raw daw from bronze is transformed and joined together with data quality applied and landed in silver.  Next in the gold layer are produced datasets which are silver datasets joined together and enriched with other data like machine learning model output.  These gold datasets are then what are used and served up by the business in dashboards and reporting.
Question: Give me a list of the world's most popular leaders at the moment.
Answer: As per the "Morning Consult," here are the top five most popular leaders, arranged by percentage of popularity.

1. Narendra Modi (India)
2. Andrés Manuel López Obrador (Mexico)
3. Anthony Albanese (Australia)
4. Alain Berset (Switzerland)
5. Luiz Inácio Lula da Silva (Brazil)
Question: Tell me whether these pokemon are electric or grass type: Pikachu, Bulbasaur, Zapdos, Voltorb, Oddish, Bellsprout
Answer: Pikachu: Electric
Bulbasaur: Grass
Zapdos: Electric
Voltorb: Electric
Oddish: Grass
Bellsprout: Grass
Question: what is one good example on a hackathon project for a middle school student for programming?
Answer: After months of lockdowns and social distancing, schools finally reopened for in-person classes, and parents are thrilled to be able to attend their children's concerts and performances once again. Many parents had missed these events and were eager to show their support for their children's artistic pursuits. Parents are excited to attend her in-person concert, but many of them quickly realized that they didn't know much about classical music, which made it difficult for them to fully appreciate the performance.

As a kid who loves music, I always feel so proud and happy when my parents attend my concerts and performances. However, I often wonder if they are truly able to enjoy the music as much as I do, especially since they may not know much about the background and context of the pieces that are being played.

I believe that knowing the background of the music can make a huge difference in how much someone enjoys it. When you understand the story and history behind a piece of music, it becomes more than just a series of notes and melodies; it becomes a powerful expression of emotion and meaning. Also knowing the background of the music can help us appreciate and connect with the music on a deeper level, allowing us to use it as a powerful tool for stress relief and relaxation. By actively listening and understanding the expressive qualities of the music, we can harness its potential to enhance our mood and promote emotional wellbeing.
Question: Why you need to be honest?
Answer: Because you should not cheat to anyone and that will give you a good live
Question: Can you provide a list of foods that are both high in protein and low in fat?
Answer: Certainly! Here is a list of foods, ranked by their protein-fat ratio from the USDA, which can be a great source of protein while helping you maintain a fit and healthy body:

1. Shrimp: 80
2. Egg white: 55
3. Nonfat Milk: 34
4. Tuna: 21.5
5. Rabbit meat: 9.4
6. Chicken breast: 8.6

However, it is important to keep in mind that healthy fats are also essential in a balanced diet. Incorporating sources of healthy fats like fatty fish, nuts, and seeds is important for overall health and well-being.
Question: Given this paragraph about bicycle manufacturer Colnago, what Italian automobile company did Colnago work with to develop new carbon fiber technology?
Evidence: Since the 1980s, while Colnago continued to produce high-end steel bikes, they began to produce cycle frames using material other than steel, including titanium, aluminum, carbon fiber and mixed materials. One unique frame from this period, the Bititan, has a dual titanium down tube. Crimped and oversize tubes were used on the Tecnos–one of the lightest production steel bikes produced. Similarly crimped oversized aluminum tubes were used on the Dream frame. In 1981 Colnago prototyped the CX Pista–a full monocoque carbon fiber cycle with disc wheels, which was shown at the Milan bike show. Subsequently, Colnago worked with Ferrari to develop new carbon fiber technology, and Ernesto also credits their engineers for challenging him regarding fork design, which led to Colnago's innovative Precisa straight-bladed steel fork (1987). Colnago also experimented with multi-material frames, including the CT-1 and CT-2 constructed with titanium main tubes, carbon fiber forks and rear stays, and a similarly constructed (although short-lived) Master frame constructed with steel main tubes, carbon forks and stays.
Answer: Colnago worked with Ferrari to develop new carbon fiber technology.
Question: When was the Goan civil code established?
Evidence: The Goa Civil Code, also called the Goa Family Law, is the set of civil laws that governs the residents of the Indian state of Goa. The Goan civil code was introduced after Portuguese Goa and Damaon were elevated from being mere Portuguese colonies to the status of a Província Ultramarina (Overseas possession) in 1869 AD. The Goan civil code is a Indianised variant of Portuguese legal system that draws largely from Code Napoleon, a common legal system in a number of Continental European nations, Indian law mostly derives from English common law that was formulated and applied in British India, and remains pegged to developments in the "Charter of the British Commonwealth". With a number of amendments, post the Partition of India, Indian laws as a whole, have religion-specific civil codes that separately govern adherents of different religions; (like the Muslim and Hindu personal laws,) and also has caste reservations. Goa and Damaon are an exception to that rule, in that a single code governs all the native Goans and the native Damanese of Damaon, Diu& Silvassa, irrespective of affiliation to religion, ethnicity& social strata. The English translation of the civil code| is available on the Government of Goa's e-Gazette| dated 19/10/2018.
Answer: It was established in 1869 AD after the Portuguese Goa and Damaon were no longer considered colonies.
Question: Please extract the stops of the South Western Mail in order in a list delimited by underscores and exclude return service.
Evidence: The South Western Mail was introduced as a twice weekly service in 1910. Upon the opening of the line to Dirranbandi, the train departed Brisbane at 20:45, arrived at Warwick 04:00, Goondiwindi 10:00 and Dirranbandi 16:30. The return service departed 11.30, arrived Goondiwindi 17.35, Warwick 23.35 and Brisbane 07.10 the following morning.
Answer: Brisbane_Warwick_Goondiwindi_Dirranbandi
Question: What is the percentage of offensive tweets in the authors' tagged tweets?
Evidence: Disclaimer: Due to the nature of the paper, some examples contain highly offensive language and hate speech. They don't reflect the views of the authors in any way, and the point of the paper is to help fight such speech. Much recent interest has focused on the detection of offensive language and hate speech in online social media. Such language is often associated with undesirable online behaviors such as trolling, cyberbullying, online extremism, political polarization, and propaganda. Thus, offensive language detection is instrumental for a variety of application such as: quantifying polarization BIBREF0, BIBREF1, trolls and propaganda account detection BIBREF2, detecting the likelihood of hate crimes BIBREF3; and predicting conflict BIBREF4. In this paper, we describe our methodology for building a large dataset of Arabic offensive tweets. Given that roughly 1-2% of all Arabic tweets are offensive BIBREF5, targeted annotation is essential for efficiently building a large dataset. Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language. Though we suspect that there are common features that span different languages and cultures, some characteristics of Arabic offensive language is language and culture specific. Thus, we conduct a thorough analysis of how Arabic users use offensive language. Next, we use the dataset to train strong Arabic offensive language classifiers using state-of-the-art representations and classification techniques. Specifically, we experiment with static and contextualized embeddings for representation along with a variety of classifiers such as a deep neural network classifier and Support Vector Machine (SVM).
The contributions of this paper are as follows:
We built the largest Arabic offensive language dataset to date that includes special tags for vulgar language and hate speech. We describe the methodology for building it along with annotation guidelines.
We performed thorough analysis of the dataset and described the peculiarities of Arabic offensive language.
We experimented with Support Vector Machine classifiers on character and word ngrams classification techniques to provide strong results on Arabic offensive language classification.
Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets.
Answer: The offensive tweets account for roughly 19%.
Question: I have a question regarding git. I first have a master branch, based on which i created a working branch called feature; we normally send pull request to merge the changes in feature branch to master branch. But then the team has renamed master branch to main, how should i deal with my feature branch, in order to sync with the new main branch and later pull request to merge the changes in feature to main branch instead of master branch.
Answer: Here is some suggestions to do this:
1. First create a local “feature” branch from the remote “feature” branch
		git branch -u origin/feature feature
2. Merge the local “main” branch to the local feature branch
		git checkout feature
		git merge main
3. Resolve any conflicts, sync feature with main
4. Commit and push to remote feature
5. Create a pull request to merge from remote feature branch to remote main branch
Question: Please describe a fantasy animal
Answer: An unicorn, a white horse like animal with a horn in the forehead
Question: Is it football or soccer that they play in the Netherlands?
Answer: In the Netherlands they play football.
Question: Tell me which of the following have been known to attack humans: mouse, goldfish,  bears, mugs, cactus, moss, nudibranches, corals, hippos, wolves.
Answer: bears, hippos, wolves
Question: Who was the first person to do spacewalk?
Answer: Alexei Leonov, a soviet cosmonaut was the first person to do spacewalk
Question: What is the difference between Kuchipudi and Kathak dance forms of India?
Answer: Though both are Indian classical dance forms, the difference lies in their place of origin the associated music and mudras ( body movement).
Kuchipudi originated in South Indian region of Andhra, is performed on Carnatic music, dance movement can have bending the knee.
Kathak originated in North India regions of UP, Bihar, is performed on Gharana music, dance movement is fast involves lot of rotations but the knee is not bend
Question: What are some ways in which I can use a pen for other than writing?
Answer: Here are some ways in which you could use a pen for anything other than writing: 

- you could use it to punch holes into bubble wrap 
- you could use it as a weapon 
- you could use a hollow pen as a straw 
- you could use it as a cover for pointed objects 
- you could use it for practicing pen spinning 
- you could use the nib as a tooth pick 
- you could use the nib to open the sim card tray on your phone
Question: What do the authors first investigate in the paper?
Evidence: Neural machine translation has attracted much attention in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3. Given a sentence $x=(x_1, \dots ,x_{T_x})$ from the source language, the straight-forward way for translation is to generate the target words $y=(y_1, \dots , y_{T_y})$ one by one from left to right. This is also known as the AutoRegressive Translation (ART) models, in which the joint probability is decomposed into a chain of conditional probabilities:
While the ART models have achieved great success in terms of translation quality, the time consumption during inference is still far away from satisfactory. During training, the predictions at different positions can be estimated in parallel since the ground truth pair $(x,y)$ is exposed to the model. However, during inference, the model has to generate tokens sequentially as $y_{<t}$ must be inferred on the fly. Such autoregressive behavior becomes the bottleneck of the computational time BIBREF4.
In order to speed up the inference process, a line of works begin to develop non-autoregressive translation models. These models break the autoregressive dependency by decomposing the joint probability with
The lost of autoregressive dependency largely hurt the consistency of the output sentences, increase the difficulty in the learning process and thus lead to a low quality translation. Previous works mainly focus on adding different components into the NART model to improve the expressiveness of the network structure to overcome the loss of autoregressive dependency BIBREF5, BIBREF6, BIBREF7. However, the computational overhead of new components will hurt the inference speed, contradicting with the goal of the NART models: to parallelize and speed up neural machine translation models.
To tackle this, we proposed a novel hint-based method for NART model training. We first investigate the causes of the poor performance of the NART model. Comparing with the ART model, we find that: (1) the positions where the NART model outputs incoherent tokens will have very high hidden states similarity; (2) the attention distributions of the NART model are more ambiguous than those of ART model. Therefore, we design two kinds of hints from the hidden states and attention distributions of the ART model to help the training of the NART model. The experimental results show that our model achieves significant improvement over the NART baseline models and is even comparable to a strong ART baseline in BIBREF4.
We first investigate the causes of the poor performance of the NART model. 
Answer: The causes of the poor performance of the NART model.
Question: How to make a cup of spiced chai?
Answer: Among all ingredients, the most important element is cardamom. Of course, you cannot skimp on loose black tea leaves too!
Question: Identify which instrument is string or woodwind: Wheelharp, Clarinet
Answer: Wheelharp is string, Clarinet is woodwind.
Question: What are the differences between the phone-loop model and the AUD model?
Evidence: The AUD model described in BIBREF0 , BIBREF1 is a non-parametric Bayesian Hidden Markov Model (HMM). This model is topologically equivalent to a phone-loop model with two major differences:
In this work, we have used two variants of this original model. The first one (called HMM model in the remainder of this paper), following the analysis led in BIBREF8 , approximates the Dirichlet Process prior by a mere symmetric Dirichlet prior. This approximation, while retaining the sparsity constraint, avoids the complication of dealing with the variational treatment of the stick breaking process frequent in Bayesian non-parametric models. The second variant, which we shall denote Structured Variational AutoEncoder (SVAE) AUD, is based upon the work of BIBREF4 and embeds the HMM model into the Variational AutoEncoder framework BIBREF9 . A very similar version of the SVAE for AUD was developed independently and presented in BIBREF5 . The main noteworthy difference between BIBREF5 and our model is that we consider a fully Bayesian version of the HMM embedded in the VAE; and the posterior distribution and the VAE parameters are trained jointly using the Stochastic Variational Bayes BIBREF4 , BIBREF10 . For both variants, the prior over the HMM parameters were set to the conjugate of the likelihood density: Normal-Gamma prior for the mean and variance of the Gaussian components, symmetric Dirichlet prior over the HMM's state mixture's weights and symmetric Dirichlet prior over the acoustic units' weights. For the case of the uninformative prior, the prior was set to be vague prior with one pseudo-observation BIBREF11 .
This model is topologically equivalent to a phone-loop model with two major differences: • since it is trained in an unsupervised fashion the elements of the loop cannot directly be interpreted as the actual phones of the target language but rather as some acoustic units (defined as 3-states left-to-right subHMM) whose time scale approximately corresponds the phonetic time scale. • to cope with the unknown number of acoustic units needed to properly describe speech, the model assumes a theoretically infinite number of potential acoustic units.
Answer: This model is topologically equivalent to a phone-loop model with two major differences: • since it is trained in an unsupervised fashion the elements of the loop cannot directly be interpreted as the actual phones of the target language but rather as some acoustic units (defined as 3-states left-to-right subHMM) whose time scale approximately corresponds the phonetic time scale. • to cope with the unknown number of acoustic units needed to properly describe speech, the model assumes a theoretically infinite number of potential acoustic units.
Question: Given the paragraph summarizing the life of Arnold Boate, list the countries in which he lived.
Evidence: Arnold Boate, originally called de Boot (1606–1653) was a Dutch physician, writer and Hebraist who spent much of his life abroad, and lived for several years in Dublin. There he married Margaret Dongan, a judge's daughter, whom he portrayed lovingly in his book The Character of a Truly Virtuous and Pious Woman. He was the brother of Gerard Boate, author of The Natural History of Ireland, for which Arnold supplied much of the material. Both Gerard and Arnold were members of the Hartlib circle.
Answer: Arnold Boate was born in the Netherlands and spent much of his life abroad, most notably in Ireland.
Question: What metric is used to validate the model?
Evidence: Our systems are attentional encoder-decoder networks BIBREF0 . We base our implementation on the dl4mt-tutorial, which we enhanced with new features such as ensemble decoding and pervasive dropout.
We use minibatches of size 80, a maximum sentence length of 50, word embeddings of size 500, and hidden layers of size 1024. We clip the gradient norm to 1.0 BIBREF4 . We train the models with Adadelta BIBREF5 , reshuffling the training corpus between epochs. We validate the model every 10000 minibatches via Bleu on a validation set (newstest2013, newstest2014, or half of newsdev2016 for EN INLINEFORM0 RO). We perform early stopping for single models, and use the 4 last saved models (with models saved every 30000 minibatches) for the ensemble results. Note that ensemble scores are the result of a single training run. Due to resource limitations, we did not train ensemble components independently, which could result in more diverse models and better ensembles.
Decoding is performed with beam search with a beam size of 12. For some language pairs, we used the AmuNMT C++ decoder as a more efficient alternative to the theano implementation of the dl4mt tutorial.
We validate the model every 10 000 minibatches via BLEU on a validation set (newstest2013, newstest2014, or half of newsdev2016 for EN↔RO).
Answer: BLEU.