DataHammer/scimrc huggingface dataset:
Question: What's the best way to learn something?
Answer: Learning is a constant process. One way to efficiently start learning something would be to understand the fundamentals first and dive deeper into the subtopics as you get more familiar with the core concepts. Read as much as possible and create an outline on the topic. Leverage the five Ws technique of problem solving - Who, What, When, Where and Why.

There are numerous ways to get familiarized with concepts. One way to do it would be to jot down your notes, try to make connections and try to simplify the ideas if possible. Assume that you are trying to explain this to a five year old, how would you build the topic, make it interesting either by visualizations, metaphors etc.? 

Another beneficial technique that can be of good help here, would be to put the concept into application in some form or the other. This can be done by either practicing the concepts in real life if feasible, or by observing its presence and relevance. 

Finally, try and teach the concepts to others. This is an extremely effective way to gain fluency and expertise on a subject.
Question: What are the four major subregions in Central Otago?
Evidence: The Central Otago wine region is broken into multiple sub-regions, each with its own climate and characteristics.

Bannockburn is a small Geographical Indication on the southern banks of the Kawarau River near Cromwell and is a very warm area that was known by gold miners as "the Heart of the Desert". Grapes ripen early on sandy, silty loam soils. The elevation ranges from 220 to 370 metres (720 to 1,210 ft).
Bendigo lies east of the Clutha River / Mata-Au and Lake Dunstan, with grapes planted at both medium elevation (220 metres (720 ft)) and high elevation terraces (330 to 350 metres (1,080 to 1,150 ft)). This warm area has semi arid soils at variable depths, with free draining soils at the lower levels and shallower soils at higher elevations. The Bendigo sub-region includes the vineyards at Tarras and Maori Point.
Gibbston is a tight valley enclosed by mountainous terrain. About 250 hectares of vines are planted on sloping land on the southern bank of the Kawarau River. Gibbston is the coolest and highest of the sub-regions with vineyards between 320 and 420 metres (1,050 and 1,380 ft) altitude. It is sometimes referred to as the Queenstown subregion.
Wānaka, the smallest of the sub-regions, has vineyards planted between the banks of Lake Wānaka and the town of Luggate to the east. Ranging between 290 to 320 metres (950 to 1,050 ft) above sea level, the vineyards have a similar but slightly warmer climate than those at Gibbston.
The Alexandra Basin is surrounded by the Clutha River / Mata-Au and Manuherikia River and regularly records New Zealand's hottest summer temperatures. Schist outcrops dominate the arid landscape. A wide diurnal shift moderates the high temperatures.
The Cromwell Basin contains the highest concentration of vines, in an area bounded by the Kawarau River, Lake Dunstan and the Pisa mountain range. It is a warm district characterised by semi arid, high terraces and moraines and gently sloping fans.
Answer: The Central Otago wine region is broken into multiple sub-regions: Bannockburn, Bendigo, Bendigo and Wānaka
Question: which of these cities are in asia: cardiff, amsterdam, mumbai, bangalore, delhi?
Answer: The following cities are in Asia: Mumbai, Bangalore( new name Bengalooru), Delhi. Cardiff is in the U.K. and Amsterdam in Europe.
Question: Ram was asked to repeat after the speaker. Speaker: Red, Ram: Red. Speaker: Blue, Ram: Blue. Speaker: Green, Ram: Green. Speaker: What's your name? , Ram: Ram

What went wrong here?
Answer: Haha, that's a nice one. While Ram was asked to repeat after the speaker, he ended up taking the speaker's last phrase as a question and tried answering it, which isn't what he was instructed to do.
Ram should have repeated the last phrase, which would be as follows:
Speaker: What's your name? , Ram: What's your name?
Question: What are the causes of Autism?
Evidence: Many causes of autism have been proposed, but understanding of the theory of causation of autism, or otherwise known as autism spectrum disorder (ASD) is incomplete. Attempts have been made to incorporate the known genetic and environmental causes into a comprehensive causative framework.ASD is a complex developmental condition marked by persistent challenges to social interaction, speech and nonverbal communication, and restricted/repetitive behaviors and its phenotypes vary significantly.


Research indicates that genetic factors predominate. The heritability of autism, however, is complex, and it is typically unclear which genes are involved.In rare cases, autism is associated with agents that cause birth defects.Many other causes have been proposed.
Numerous epidemiological studies have shown no scientific evidence supporting the controversial but popular theory that autism is caused by vaccines.
Answer: There is no clear cause for Autism which has been scientifically proven. However, some research indicates that genetic factors are one of the predominant causes. In rare cases, autism is associated with agents that cause congenital disabilities, and many other causes have been proposed.
Numerous epidemiological studies have shown no scientific evidence supporting the controversial but popular theory that vaccines cause autism.
Question: In what ways do the authors find that the quality of translation shows substantial improvements?
Evidence: Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
Answer: Measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation
Question: Why do people believe in God
Answer: People believe in God for many reasons and it cannot be limited to one reason. Some believe in God because their ancestors believed in God. Some believe in God because they think there needs to be someone supreme to control the world. Some also believe in God because they saw an event happen and they dont think it can be a simple coincidence. So it comes down to each individual and why that individual believes or does not believe in God
Question: Name some characters from the 2003 version of Battlestar Galactica.
Answer: Felix Gaeta, Helena Cain, Saul Tigh, Karl Agathon, D'Anna Biers, Galen Tyrol, Anastasia Dualla, Sherman Cottle, Cally Tyrol
Question: Give me a bulleted list of different ways to make a lot of money.
Answer: The following are different ways you can make a lot of money:
1. Obtain a high paying job
2. Start a business
3. Receive an inheritance
4. Invest in real estate
5. Buy stocks
Question: What cocktail contributed to the rise of vodka in America?
Answer: That would be the Moscow Mule. It was a struggle to sell vodka in the early 1900's due to numerous reasons and wasn't a spirit one would sip. A smirnoff distributor for America decided to come up with the copper cup, ginger beer, and vodka which became a sensation in the 1950's.
Question: What datasets or tasks do they conduct experiments on?
Evidence: Transformer is a versatile and powerful modeling tool and widely is used in various natural language process tasks. In order to verify the effectiveness of our method (i.e., Multi-linear attention) replacing multi-head attention in Transformer, we carry out two NLP tasks named language modeling (LM) and neural machine translation (NMT). Complete code for running experiments will be released after the paper is accepted, while the key code which is about our method can be found in Supplementary Materials F.
Specially, we take Transformer, the open source state-of-the art language modeling architecture, and replace the standard multi-head attention layers with our Multi-linear attention. Then, we test different model configurations on the PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets and report the results in Table 1 and Table 2 .
The goal is to map an input sequence $s=(x_1,x_2,\ldots ,x_n)$ representing a phrase in one language, to an output sequence $y=(y_1,y_2,\ldots , y_m)$ representing the same phrase in a different language. In this task, we have trained the Transformer model BIBREF2 on WMT 2016 English-German dataset BIBREF36 . Sentences were tokenized using the SentencePiece . For our experiments, we have replaced each of the attention layers with Multi-linear attention. For evaluation we used beam search with a beam size of 5 and length penalty $\alpha $ = $0.6$ . In this section, we only compared the results with Transformer BIBREF2 . Our results are summarized in Table 3 . $*$ indicates that the result is our own implementation.
In order to verify the effectiveness of our method (i.e., Multi-linear attention) replacing multi-head attention in Transformer, we carry out two NLP tasks named language modeling (LM) and neural machine translation (NMT).
Then, we test different model configurations on the PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets and report the results in Table 1 and Table 2 .
In this task, we have trained the Transformer model BIBREF2 on WMT 2016 English-German dataset BIBREF36 .
Answer: The answers are shown as follows:
* Language Modeling (LM)
* PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets
* neural machine translation (NMT)
* WMT 2016 English-German dataset
Question: What is grep used for?
Evidence: grep is a command-line utility for searching plain-text data sets for lines that match a regular expression. Its name comes from the ed command g/re/p (globally search for a regular expression and print matching lines), which has the same effect. "grep" was originally developed for the Unix operating system, but later available for all Unix-like systems and some others such as OS-9.
Answer: grep is a popular Unix command that is used to search plain-text data sets for lines that match a pattern or regular expression. grep (global regular expression print) was originally developed for the Unix operating system, but later available for all Unix-like systems.

An example of using the grep command to search for a keyword in a file: grep keyword file.txt

You can pipe the output of other commands into grep. For example, grep can be used to search a directory for a file: ls ~/Desktop | grep keyword
Question: Extract and list the Barcelona players in the passage that came from the club's youth academy
Evidence: Barcelona is one of the most widely supported teams in the world, and the club has one of the largest social media following in the world among sports teams. Barcelona players have won a record twelve Ballon d'Or awards, with recipients including Johan Cruyff, as well as a record seven FIFA World Player of the Year awards, with winners including Romário, Ronaldo, Rivaldo and Ronaldinho. In 2010, three players who came through the club's youth academy (Lionel Messi, Andrés Iniesta and Xavi) were chosen as the three best players in the world in the FIFA Ballon d'Or awards, an unprecedented feat for players from the same football academy. Additionally, players representing the club have won a record eight European Golden Shoe awards.
Answer: Lionel Messi, Andrés Iniesta and Xavi
Question: Which team won the 2022 Fifa World Cup?
Answer: Argentina won the 2022 Fifa World Cup after defeating France, the former champion. Argentina beat France in penalty kicks with 4 scored versus 2 after a 3-3 tie. This 2022 victory was Argentina's third victory, making it the fourth country with most championships after Brazil with 5 and Germany and Italy with 4 each.
Question: How to evict a tenant in California
Answer: 1. Give the tenant the initial notice which clearly state the issue and what is required of them to resolve it. There must be a valid reason for eviction such as failure to pay rent, lease violation, unlawful activities, property damages.
2. Post the notice with a deadline for the tenant to respond
3. Once the deadline has passed, you can file with the court by filling out the Summons and Complaint for unlawful detainer forms
4. Once your tenant is notified, they will have five days to respond. If they do not respond, you can ask for default judgement which allow you to order the sheriff to remove the tenant from the property and the court will order the tenant to pay for court and attorney fees. If the tenant files a response or you do not receive default judgement, you will go to trial.
5. The judge will make a decision after hearing both sides. The judge will either ask the tenant to vacate or allow them to remain in the property.
Question: How can I be more productive?
Answer: The best way to be productive is to starting "doing" rather than "thinking"
* Set small goals
* Reward yourself for completing those small goals, small rewards like taking a break, eating your favourite snack, spending time with your kid.
* Do not multi-task because we really can not.
* Create a list of things to do and strike it off one by one. Do not get overwhelmed by the list. 
* If you can delegate something do that, but make sure to follow up.
* Actually there is a course by David Allen on LinkedIn Learning or you can also read his book "Getting Things Done" it tells you about a 5 step process. Capture , Clarify, Organize, Review, Engage. 
* And anything that can be done in 2 minutes, just finish it off. 

Starting with one small thing, will make you productive. It is like a dominos, you change one keystone habit and it changes all the others eventually.
Question: What are data warehouses design patterns
Evidence: Bottom-up design
In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes. These data marts can then be integrated to create a comprehensive data warehouse. The data warehouse bus architecture is primarily an implementation of "the bus", a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.

Top-down design
The top-down approach is designed using a normalized enterprise data model. "Atomic" data, that is, data at the greatest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse.

Hybrid design
Data warehouses (DW) often resemble the hub and spokes architecture. Legacy systems feeding the warehouse often include customer relationship management and enterprise resource planning, generating large amounts of data. To consolidate these various data models, and facilitate the extract transform load process, data warehouses often make use of an operational data store, the information from which is parsed into the actual DW. To reduce data redundancy, larger systems often store the data in a normalized way. Data marts for specific reports can then be built on top of the data warehouse.

A hybrid DW database is kept on third normal form to eliminate data redundancy. A normal relational database, however, is not efficient for business intelligence reports where dimensional modeling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The DW provides a single source of information from which the data marts can read, providing a wide range of business information. The hybrid architecture allows a DW to be replaced with a master data management repository where operational (not static) information could reside.

The data vault modeling components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both third normal form and star schema. The data vault model is not a true third normal form, and breaks some of its rules, but it is a top-down architecture with a bottom up design. The data vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which, when built, still requires the use of a data mart or star schema-based release area for business purposes.
Answer: * Bottom-Up approach: Supporting f
Question: Is it able to train machine translation models with the corpus?
Evidence: MT models take transcripts (without punctuation) as inputs and outputs translations (with punctuation). For simplicity, we do not change the text preprocessing methods for MT to correct this mismatch. Moreover, this mismatch also exists in cascading ST systems, where MT model inputs are the outputs of an ASR model. Table TABREF20 shows the BLEU scores of MT models. We notice that the results are consistent with what we see from ASR models. For example thanks to abundant training data, French has a decent BLEU score of 29.8/25.4. German doesn't perform well, because of less richness of content (transcripts). The other languages are low resource in CoVoST and it is difficult to train decent models without additional data or pre-training techniques.
MT models take transcripts (without punctuation) as inputs and outputs translations (with punctuation). For simplicity, we do not change the text preprocessing methods for MT to correct this mismatch. Moreover, this mismatch also exists in cascading ST systems, where MT model inputs are the outputs of an ASR model. Table 4 shows the BLEU scores of MT models. We notice that the results are consistent with what we see from ASR models. For example thanks to abundant training data, French has a decent BLEU score of 29.8/25.4. German doesn't perform well, because of less richness of content (transcripts). The other languages are relatively low resource in CoVoST and it is difficult to train decent models without additional data or pre-training techniques.
Answer: It would be quite difficult to as other languages are relatively low resource in CoVoST except French.
Question: Extract what previous provinces Kardzhali a part of in the past
Evidence: The territory of Kardzhali province was acquired by Bulgaria during the First Balkan War in 1912. In 1913 the region was organized as the district (окръг, okrăg in Bulgarian) of Mestanli. This district was part of Stara Zagora province from 1934 until 1949, then it was transferred to the newly formed Haskovo district. In 1959 Kardzhali became the center of a new district with similar borders to the current province. Between 1987 and 1999, the region was part of Haskovo Province, after which it was restored, now as a province and with slightly changed borders.
Answer: Kardzhali was once a part of Stara Zagora province and Haskovo Province until it was restored in 1999
Question: Battles between England and Scotland
Evidence: Given the paragraph about battles between England and Scotland, which battles occurred between both countries
Answer: The Kingdom of England and the Kingdom of Scotland fought dozens of battles with each other. They fought typically over land, and the Anglo-Scottish border frequently changed as a result. Prior to the establishment of the two kingdoms, in the 10th and 9th centuries, their predecessors, the Northumbrians, Picts and Dal Riatans, also fought a number of battles. Major conflicts between the two parties include the Wars of Scottish Independence (1296–1357), and the Rough Wooing (1544–1551), as well as numerous smaller campaigns and individual confrontations. In 1603, England and Scotland were joined in a "personal union" when King James VI of Scotland succeeded to the throne of England as King James I. War between the two states largely ceased, although the Wars of the Three Kingdoms in the 17th century, and the Jacobite risings of the 18th century, are sometimes characterised as Anglo-Scottish conflicts.
Question: Identify which instrument is string or woodwind: Tenoroon, Tembor
Answer: Tembor is string, Tenoroon is woodwind.
Question: Give me bulleted list of the five most common shots in lawn tennis
Answer: The bulleted list of the five most common shots in lawn tennis are
1. Forehand
2. Backhand
3. Smash
4. Volley
5. Drop shot
Question: Which episode does Jon Snow concede to Daenerys Targaryen's authority?
Answer: Jon Snow finally refers to Daenerys as "My Queen" in the last episode of the seventh season of Game of Thrones.
Question: What codemixed language pairs are evaluated?
Evidence: We use the Universal Dependencies' Hindi-English codemixed data set BIBREF9 to test the model's ability to label code-mixed data. This dataset is based on code-switching tweets of Hindi and English multilingual speakers. We use the Devanagari script provided by the data set as input tokens.
We use the Universal Dependencies' Hindi-English codemixed data set BIBREF9 to test the model's ability to label code-mixed data. This dataset is based on code-switching tweets of Hindi and English multilingual speakers. We use the Devanagari script provided by the data set as input tokens.
Answer: The answers are shown as follows:
* Hindi-English