DataHammer/scimrc huggingface dataset:
Question: Assume the email format is {first_initial}{last_name}@somecompany.com

What would the email addresses be for the following people?
Jon Doe
Richard Smith
Tom Jenkins
Nick Parsons
Answer: Following the {first_initial}{last_name}@somecompany.com format, the emails for those people would be:

Jon Doe: jdoe@somecompany.com
Richard Smith: rsmith@somecompany.com
Tom Jenkins: tjenkins@somecompany.com
Nick Parsons: nparsons@somecompany.com
Question: Which is a species of fish? Mullet or Afro
Answer: Mullet
Question: What type of pokemon should I use to battle a water pokemon? Give me some examples.
Answer: Water type pokemon are weak against grass and electric type pokemon. Therefore, you should choose one of your pokemon of grass or electric type. The best choice, if you want to win the immediate battle, is to pick your strongest pokemon of the grass or electric type. Some examples of strong grass or electric type pokemon are Raichu, Venosaur, or Zapados.
Question: According to this paragraph about UVA, tell me how much of UVA's academic budget foes to endowment and gifts?
Evidence: As of 2013, UVA's $1.4 billion academic budget is paid for primarily by tuition and fees (32%), research grants (23%), endowment and gifts (19%), and sales and services (12%). The university receives 10% of its academic funds through state appropriation from the Commonwealth of Virginia. For the overall (including non-academic) university budget of $2.6 billion, 45% comes from medical patient revenue. The Commonwealth contributes less than 6%.
Answer: 19% of UVA's academic budget goes to endowment and gifts.
Question: Can I stream data directly into and from Delta tables?
Answer: Yes, you can use Structured Streaming to directly write data into Delta tables and read from Delta tables. See Stream data into Delta tables and Stream data from Delta tables.
Question: List some of the best life changing self-help books
Answer: Atomic Habits by James Clear
Power of discipline by Daniel Walter
The Power of Habit by Charles Duhigg
Tiny Habits by BJ Fogg
High Performance Habits by Brendon Burchard
Getting Things Done by David Allen
What the Most Successful People Do Before Breakfast by Laura Vanderkam
How to Change by Katy Milkman
Start With Why by Simon Sinek
The Culture Code by Daniel Coyle
Question: What are the differences in the use of YouTube links between gang member and the rest of the Twitter population?
Evidence: It has been recognized that music is a key cultural component in an urban lifestyle and that gang members often want to emulate the scenarios and activities the music conveys BIBREF7 . Our analysis confirms that the influence of gangster rap is expressed in gang members' Twitter posts. We found that 51.25% of the gang members collected have a tweet that links to a YouTube video. Following these links, a simple keyword search for the terms gangsta and hip-hop in the YouTube video description found that 76.58% of the shared links are related to hip-hop music, gangster rap, and the culture that surrounds this music genre. Moreover, this high proportion is not driven by a small number of profiles that prolifically share YouTube links; eight YouTube links are shared on average by a gang member.
We found that 51.25% of the gang members collected have a tweet that links to a YouTube video. Following these links, a simple keyword search for the terms gangsta and hip-hop in the YouTube video description found that 76.58% of the shared links are related to hip-hop music, gangster rap, and the culture that surrounds this music genre.
Answer: The answers are shown as follows:
* 76.58% of the shared links are related to hip-hop music, gangster rap, and the culture that surrounds this music genre
Question: What they use as a metric of finding hot spots in meeting?
Evidence: In spite of the windowing approach, the class distribution is still skewed, and an accuracy metric would reflect the particular class distribution in our data set. Therefore, we adopt the unweighted average recall (UAR) metric commonly used in emotion classification research. UAR is a reweighted accuracy where the samples of both classes are weighted equally in aggregate. UAR thus simulates a uniform class distribution. To match the objective, our classifiers are trained on appropriately weighted training data. Note that chance performance for UAR is by definition 50%, making results more comparable across different data sets.
In spite of the windowing approach, the class distribution is still skewed, and an accuracy metric would reflect the particular class distribution in our data set. Therefore, we adopt the unweighted average recall (UAR) metric commonly used in emotion classification research. UAR is a reweighted accuracy where the samples of both classes are weighted equally in aggregate. UAR thus simulates a uniform class distribution. To match the objective, our classifiers are trained on appropriately weighted training data. Note that chance performance for UAR is by definition 50%, making results more comparable across different data sets.
Answer: The answers are shown as follows:
* unweighted average recall (UAR) metric
Question: What is gardening?
Evidence: Gardening is the practice of growing and cultivating plants as part of horticulture. In gardens, ornamental plants are often grown for their flowers, foliage, or overall appearance; useful plants, such as root vegetables, leaf vegetables, fruits, and herbs, are grown for consumption, for use as dyes, or for medicinal or cosmetic use.

Gardening ranges in scale from fruit orchards, to long boulevard plantings with one or more different types of shrubs, trees, and herbaceous plants, to residential back gardens including lawns and foundation plantings, all the way to container gardens grown inside or outside. Gardening may be very specialized, with only one type of plant grown, or involve a variety of plants in mixed plantings. It involves an active participation in the growing of plants, and tends to be labor-intensive, which differentiates it from farming or forestry.
Answer: Gardening is laying out and caring for a plot of ground devoted partially or wholly to growing plants such as flowers, herbs, or vegetables.
Question: What is Hornachuelos?
Answer: Hornachuelos is a small, picturesque village in the Southern Region of Spain called Andalusia. It is famous for its ancient history, Roman walls and Natural Park.
Question: What are the interests behind climate change denial?
Answer: Financial interest from industries opposed to the regulation of CO2 emissions. Migrating to low carbon technologies would require massive investments, making executives concerned about impacts on companies gross margin.
Question: What are the best cliffs in Ireland?
Answer: The Cliffs of Moher are 702 feet/214 meters at their highest point, near O'Brien's Tower. Though not the highest cliffs in Ireland – that honor goes to Co Donegal's Slieve League Cliffs and Achill Island's Croaghaun Cliffs – they are by far the most popular.
Question: Classify each of the following as fluffy or hard: lamb, sheep, poodle, cement, stuffed animal, golf ball
Answer: Lamb: Fluffy
Sheep: Fluffy
Poodle: Fluffy
Cement: Hard
Stuffed Animal: Fluffy
Golf Ball: Hard
Question: What is Marketing Automation?
Evidence: Marketing Automation is a subset of customer relationship management (CRM) or customer experience management (CXM) that focuses on the definition, segmentation, scheduling and tracking of marketing campaigns. The use of marketing automation makes processes that would otherwise have been performed manually much more efficient and makes new processes possible. Marketing Automation can be defined as a process where technology is used to automate several repetitive tasks that are undertaken on a regular basis in a marketing campaign.

Marketing Automation platforms allow marketers to automate and simplify client communication by managing complex omnichannel marketing strategies from a single tool. Marketing Automation assists greatly in areas like Lead Generation, Segmentation, Lead nurturing and lead scoring, Relationship marketing, Cross-sell and upsell, Retention, Marketing ROI measurement. Effective marketing automation tools leverage data from a separate or integrated CRM to understand customer impact and preferences.
Answer: Marketing Automation is software that helps marketers to do create efficient online and offline campaigns. It's part of CRM suite and people usually integrate it with their salesforce application and marketing website. The Marketing Automation platform allows marketers to automate and simplify customer communication by managing multi communication channels. This includes personalized email, dynamic web pages, digital banners, social media and more. People use it for lead generation, segmentation and nurturing and lead scoring. Post-sales marketers also use Marketing Automation for cross-sell, upsell and retention marketing. In
Question: What do the authors benchmark NLP systems for?
Evidence: With this work we present a resource that will be extremely useful for building language systems in an endangered, under-represented language, Mapudungun. We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results. The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource.
Our resource could also be used for ethnographic and anthropological research into the Mapuche culture, and has the potential to contribute to intercultural bilingual education, preservation activities and further general advancement of the Mapudungun-speaking community.
We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results.
Answer: The authors benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results.
Question: How is the data in RAFAEL labelled?
Evidence: Secondly, texts go through a cascade of annotation tools, enriching it with the following information:
Morphosyntactic interpretations (sets of tags), using Morfeusz 0.82 BIBREF25 ,
Tagging (selection of the most probable interpretation), using a transformation-based learning tagger, PANTERA 0.9.1 BIBREF26 ,
Syntactic groups (possibly nested) with syntactic and semantic heads, using a rule-based shallow parser Spejd 1.3.7 BIBREF27 with a Polish grammar, including improved version of modifications by BIBREF28 , enabling lemmatisation of nominal syntactic groups,
Named entities, using two available tools: NERF 0.1 BIBREF29 and Liner2 2.3 BIBREF30 .
Secondly, texts go through a cascade of annotation tools, enriching it with the following information:

Morphosyntactic interpretations (sets of tags), using Morfeusz 0.82 BIBREF25 ,

Tagging (selection of the most probable interpretation), using a transformation-based learning tagger, PANTERA 0.9.1 BIBREF26 ,

Syntactic groups (possibly nested) with syntactic and semantic heads, using a rule-based shallow parser Spejd 1.3.7 BIBREF27 with a Polish grammar, including improved version of modifications by BIBREF28 , enabling lemmatisation of nominal syntactic groups,

Named entities, using two available tools: NERF 0.1 BIBREF29 and Liner2 2.3 BIBREF30 .


Answer: Using a set of annotation tools such as Morfeusz, PANTERA, Spejd, NERF and Liner
Question: What is a graph?
Answer: Graph is a set on nodes that are connected via edges. Edges represent the relationships between the nodes and can have multiple properties like weight or sign. For example the weight of the edge can represent how strong are the relationships between the nodes A and B, while the sign might represent wether these relationships are positive or negative.
Question: Do they experiment with the dataset?
Evidence: The overall Total Accuracy score reported in table TABREF19 using the entire feature set is 549. This result is what makes this dataset interesting: there is still lots of room for research on this task. Again, the primary goal of this experiment is to help identify the difficult-to-classify instances for analysis in the next section.
The overall Total Accuracy score reported in table TABREF19 using the entire feature set is 549. 
Answer: Yes.
Question: Who are you?
Answer: I am a generative model trained on large corpus of natural language text.
Question: Which decades did they look at?
Evidence: time: decade (classes between 1900s and 2010s) and year representative of the time when the genre became meainstream
time: decade (classes between 1900s and 2010s) and year representative of the time when the genre became meainstream
Answer: The answers are shown as follows:
* between 1900s and 2010s
Question: How many people died in the Cloquet fire of Illinois according to the text?
Evidence: The Cloquet fire was an immense forest fire in northern Minnesota, United States in October 1918, caused by sparks on the local railroads and dry conditions. The fire left much of western Carlton County devastated, mostly affecting Moose Lake, Cloquet, and Kettle River. Cloquet was hit the hardest by the fires. It was the worst natural disaster in Minnesota history in terms of the number of casualties in a single day. In total, 453 people died and 52,000 people were injured or displaced, 38 communities were destroyed, 250,000 acres (100,000 ha) were burned, and $73 million ($1.315 billion in 2023 United States dollars) in property damage was suffered. Thirteen million dollars in federal aid were disbursed.
Answer: 453 people died in the Cloquet fire that occurred in northern Minnesota, United States in October 1918.

In addition to the 453 people that died, 52,000 people were injured or displaced, 38 communities were destroyed, 250,000 acres (100,000 ha) were burned, and $73 million ($1.315 billion in 2023 United States dollars) in property damage was suffered.
Question: How do I cook an egg over easy?
Answer: To cook an egg over easy you will need a pan, stove, oil (or butter), an egg, and a spatula. Begin by placing the pan on the stove with a minimal amount of oil or butter. Heat the pan on the stove until it is hot. What’s the pan is hot crack the egg in the middle of the pan and let it cook until the egg white becomes solid. At which point flip the egg and let it cook until the desired consistency of the yoke. Finally remove from the pan and enjoy!
Question: Why does the team compute  recall against both the reference (Er(Ri)) and the table (Er(Ti))?
Evidence: PARENT evaluates each instance INLINEFORM0 separately, by computing the precision and recall of INLINEFORM1 against both INLINEFORM2 and INLINEFORM3 .
We compute recall against both the reference (Er(Ri)), to ensure proper sentence structure in the generated text, and the table (Er(Ti)), to ensure that texts which mention more information from the table get higher scores (e.g. candidate 3 in Figure 1).
Answer: In order to ensure proper sentence structure in the generated text, as well as to ensure that texts which mention more information from the table get higher scores.
Question: What is Grafana?
Answer: Grafana is a multi-platform open source analytics and interactive visualization web application. It provides charts, graphs, and alerts for the web when connected to supported data sources.
Question: How many Kangaroos are in Australia?
Answer: There are twice as many kangaroos in Australia as there are people. The kangaroo population is estimated at about 40 million.
Question: What is basketball?
Answer: Basketball is a sport where two teams play against each other on a court with two hoops.  One hoop is for each team.  Each teams scores a point by putting a ball through the opponent's hoop.  They play against each other in a rectangular style court and each team consists of 5 players each.
Question: Which frozen acoustic model do they use?
Evidence: Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics.
Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics.
Answer: The answers are shown as follows:
* a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13
Question: Where is the dataset from?
Evidence: Our data collection setup uses a dialogue simulator to generate dialogue outlines first and then paraphrase them to obtain natural utterances. Using a dialogue simulator offers us multiple advantages. First, it ensures the coverage of a large variety of dialogue flows by filtering out similar flows in the simulation phase, thus creating a much diverse dataset. Second, simulated dialogues do not require manual annotation, as opposed to a Wizard-of-Oz setup BIBREF17, which is a common approach utilized in other datasets BIBREF0. It has been shown that such datasets suffer from substantial annotation errors BIBREF18. Thirdly, using a simulator greatly simplifies the data collection task and instructions as only paraphrasing is needed to achieve a natural dialogue. This is particularly important for creating a large dataset spanning multiple domains.
Our data collection setup uses a dialogue simulator to generate dialogue outlines first and then paraphrase them to obtain natural utterances.
Answer: The answers are shown as follows:
* dialogue simulator
Question: What method do they use to train the models?
Evidence: When there are a very large number of documents that need to be read in limited time, we often resort to reading summaries instead of the whole document. Automatically generating (abstractive) summaries is a problem with various applications, e.g., automatic authoring BIBREF0. We have developed automatic text summarization systems that condense large documents into short and readable summaries. It can be used for both single (e.g., BIBREF1, BIBREF2 and BIBREF3) and multi-document summarization (e.g.,BIBREF4, BIBREF3, BIBREF5).
Text summarization is broadly classified into two categories: extractive (e.g., BIBREF3 and BIBREF6) and abstractive summarization (e.g., BIBREF7, BIBREF8 and BIBREF9). Extractive approaches select sentences from a given document and groups them to form concise summaries. By contrast, abstractive approaches generate human-readable summaries that primarily capture the semantics of input documents and contain rephrased key content. The former task falls under the classification paradigm, and the latter belongs to the generative modeling paradigm, and therefore, it is a much harder problem to solve. The backbone of state-of-the-art summarization models is a typical encoder-decoder BIBREF10 architecture that has proved to be effective for various sequential modeling tasks such as machine translation, sentiment analysis, and natural language generation. It contains an encoder that maps the raw input word vector representations to a latent vector. Then, the decoder usually equipped with a variant of the attention mechanism BIBREF11 uses the latent vectors to generate the output sequence, which is the summary in our case. These models are trained in a supervised learning setting where we minimize the cross-entropy loss between the predicted and the target summary. Encoder-decoder models have proved effective for short sequence tasks such as machine translation where the length of a sequence is less than 120 tokens. However, in text summarization, the length of the sequences vary from 400 to 800 tokens, and modeling long-term dependencies becomes increasingly difficult.
Despite the metric's known drawbacks, text summarization models are evaluated using ROUGE BIBREF12, a discrete similarity score between predicted and target summaries based on 1-gram, 2-gram, and n-gram overlap. Cross-entropy loss would be a convenient objective on which to train the model since ROUGE is not differentiable, but doing so would create a mismatch between metrics used for training and evaluation. Though a particular summary scores well on ROUGE evaluation comparable to the target summary, it will be assigned lower probability by a supervised model. To tackle this problem, we have used a self-critic policy gradient method BIBREF13 to train the models directly using the ROUGE score as a reward. In this paper, we propose an architecture that addresses the issues discussed above.
To tackle this problem, we have used a self-critic policy gradient method (Rennie et al., 2016) to train the models directly using the ROUGE score as a reward.
Answer: A self-critic policy gradient method.
Question: Do they study frequent user responses to help automate modelling of those?
Evidence: In order to identify frequent user intents, one can use existing clustering algorithms to group together all the utterances from the users. Here each cluster would correspond to a new intent and each utterance in the cluster would correspond to an example for the intent. Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. As an example, consider agent utterances A.2 in box A and A.2 in box C in Figure FIGREF5 (a). The utterances “Which operating system do you use?" and “What OS is installed in your machine" have no syntactic similarity and therefore may not be grouped together. However the fact that these utterances are adjacent to the similar user utterances “I am unable to start notes email client" and “Unable to start my email client" provides some evidence that the agent utterances might be similar. Similarly the user utterances “My system keeps getting rebooted" and “Machine is booting time and again" ( box B and D in Figure FIGREF5 (a))- that are syntactically not similar - could be grouped together since the adjacent agent utterances, “Is your machine heating up?" and “Is the machine heating?" are similar.
Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses.
Answer: Yes.