DataHammer/scimrc huggingface dataset:
Question: What makes a good woodworking bench?
Answer: Woodworking benches come down to personal preferences, such as height, but they are some fundamental properties that people agree on. Some that most agree on are flatness, sturdiness (so that the bench does not move around) and the presence of vises (to allow clamping pieces). Most workbenches in woodworking history gravitate around arranging these 3 core elements in different configuration. One French example is the Roubo, which is hefty, and has two vises for front and tail clamping.
Ultimately, it comes down to preference and workflow.
Question: What is the difference between acoustic and classical guitar?
Answer: The main difference between acoustic and classical guitar is strings and genres of music each guitar is suitable for. Classical guitar uses nylon strings and produces warm sound. It is used for Classical, Latin, Flamenco and Spanish styles of music. Acoustic guitar uses steel strings and produces bright sound with longer note. It is used for variety of genres such as Pop, Rock, Folk, Blues, etc.
Question: Write a brief paragraph of the benefits of attending Arizona State University
Answer: Arizona State University is located in Tempe, Arizona and offers over 350 degree options across 17 colleges.  ASU has a sprawling campus giving students many housing options.  The climate in Arizona is desert and receives over 300 days of sunshine.  Arizona State is PAC-12 School supporting both mens and woman's athletics.  Wheather it's athletics, academics or social clubs Arizona State has you covered.  With over 38,000 students on campus, ASU also offers online courses to over 90,000 students.  In 2021 US News and World reports ranked ASU 103rd among universities in the United States.
Question: Who is István Szalay?
Evidence: István Szalay (22 March 1944 – 1 September 2022) was a Hungarian mathematician and politician. A member of the Hungarian Socialist Party, he served in the National Assembly from 1998 to 2002. Prior to that, he was mayor of Szeged from 1994 to 1998.

Szalay died on 1 September 2022, at the age of 78.
Answer: István Szalay was a Hungarian mathematician and politician.
Question: Who are co-founders of Google?
Answer: Larry Page and ‎Sergey Brin
Question: Write a letter I can send to the company that installed my swimming pool. Explain to them that the pool has two leaks and that I'd like to make a warranty claim. Request a reply within the next 30 days.
Answer: Date: April 4, 2023
From: Firstname Lastname
To: California Pool Company

I purchased the home at 123 Main Street, Riverside, California in July 2022. The property has a pool that you installed in 2021. We have had multiple issues with the pool, including two leaks in the underground water lines supplying the automatic pool leveler. I understand the pool has a two-year warranty; I would like you to inspect the issues we have found before the warranty expires. I have enclosed photographs of the issues we have found so far. Please call me at 123-345-3883 to schedule an inspection at your earliest convenience, but no later than 30 days from your receipt of this letter. Thank you.
Question: What are brambles?
Answer: A bramble is any rough, tangled, prickly shrub, usually in the genus Rubus, which grows blackberries, raspberries, or dewberries. "Bramble" is also used to describe other prickly shrubs, such as roses (Rosa species). The fruits include blackberries, arctic brambleberries, or raspberries, depending on the species, and are used to make jellies, jams, and preserves.
Question: How many datasets are used in their experiments?
Evidence: The organizer provides a speech translation corpus extracting from the TED talk (ST-TED), which consists of raw English wave files, English transcriptions, and aligned German translations. The corpus contains 272 hours of English speech with 171k segments. We split 2k segments from the corpus as dev set and tst2010, tst2013, tst2014, tst2015 are used as test sets.
Speech recognition data: Aside from ST-TED, TED-LIUM2 corpus BIBREF13 is provided as speech recognition data, which contains 207 hours of English speech and 93k transcript sentences.
Text translation data: We use transcription and translation pairs in the ST-TED corpus and WIT3 as in-domain MT data, which contains 130k and 200k sentence pairs respectively. WMT2018 is used as out-of-domain training data which consists of 41M sentence pairs.
Data preprocessing: For speech data, the utterances are segmented into multiple frames with a 25 ms window size and a 10 ms step size. Then we extract 80-channel log-Mel filter bank and 3-dimensional pitch features using Kaldi BIBREF14, resulting in 83-dimensional input features. We normalize them by the mean and the standard deviation on the whole training set. The utterances with more than 3000 frames are discarded. The transcripts in ST-TED are in true-case with punctuation while in TED-LIUM2, transcripts are in lower-case and unpunctuated. Thus, we lowercase all the sentences and remove the punctuation to keep consistent. To increase the amount of training data, we perform speed perturbation on the raw signals with speed factors 0.9 and 1.1. For the text translation data, sentences longer than 80 words or shorter than 10 words are removed. Besides, we discard pairs whose length ratio between source and target sentence is smaller than 0.5 or larger than 2.0. Word tokenization is performed using the Moses scripts and both English and German words are in lower-case.
We use two different sets of vocabulary for our experiments. For the subword experiments, both English and German vocabularies are generated using sentencepiece BIBREF15 with a fixed size of 5k tokens. BIBREF9 inaguma2018speech show that increasing the vocabulary size is not helpful for ST task. For the character experiments, both English and German sentences are represented in the character level.
For evaluation, we segment each audio with the LIUM SpkDiarization tool BIBREF16 and then perform MWER segmentation with RWTH toolkit BIBREF17. We use lowercase BLEU as evaluation metric.
We conduct experiments on the Speech Translation TED (ST-TED) En-De corpus (Jan et al. 2018) and the augmented Librispeech En-Fr corpus (Kocabiyikoglu, Besacier, and Kraif 2018).
Answer: Two. ST-TED En-De corpus (Jan et al. 2018) and Librispeech En-Fr corpus.
Question: What are the principles of Judo's philosophy called and what do they imply?
Evidence: Judo (Japanese: 柔道, Hepburn: Jūdō, lit. 'gentle way') is an unarmed modern Japanese martial art, Olympic sport (since 1964), and the most prominent form of jacket wrestling competed internationally. Judo was created in 1882 by Kanō Jigorō (嘉納 治五郎) as an eclectic martial art, distinguishing itself from its predecessors (primarily Tenjin Shinyo-ryu jujutsu and Kitō-ryū jujutsu) due to an emphasis on "randori" (乱取り, lit. 'free sparring') instead of "kata" (pre-arranged forms) alongside its removal of striking and weapon training elements. Judo rose to prominence for its dominance over established jujutsu schools in tournaments hosted by the Tokyo Metropolitan Police Department (警視庁武術大会, Keishicho Bujutsu Taikai), resulting in its adoption as the department's primary martial art. A judo practitioner is called a "judoka" (柔道家, jūdōka, lit. 'judo performer'), and the judo uniform is called "judogi" (柔道着, jūdōgi, lit. 'judo attire').

The objective of competitive judo is to throw an opponent, immobilize them with a pin, or force an opponent to submit with a joint lock or a choke. While strikes and use of weapons are included in some pre-arranged forms (kata), they are not frequently trained and are illegal in judo competition or free practice. Judo's international governing body is the International Judo Federation, and competitors compete in the international IJF professional circuit.

Judo's philosophy revolves around two primary principles: "Seiryoku-Zenyo" (精力善用, lit. 'good use of energy') and "Jita-Kyoei" (自他共栄, lit. 'mutual welfare and benefit'). The philosophy and subsequent pedagogy developed for judo became the model for other modern Japanese martial arts that developed from koryū (古流, traditional schools). Judo also spawned a number of derivative martial arts around the world, such as Brazilian jiu-jitsu, Krav Maga, sambo, and ARB. Judo also influenced other combat styles such as close-quarters combat (CQC), mixed martial arts (MMA), shoot wrestling and submission wrestling.
Answer: There are two main principles. One is called "Seiryoku-Zenyo" which means good use of energy. The other one is called "Jita-Kyoei" and means mutual welfare and benefit.
Question: What is a generic question?
Answer: A generic question is usually a question which is very plain in nature or very common.  Most people know the answers to generic questions using common sense, and general knowledge.  It usually implies that less though and creativity was used to come up with a generic question.  An example of a generic question is "why is it so hot outside?" or "why us the sky blue?"
Question: How to do discovery in sales?
Answer: A discovery call is a crucial step in the sales process . Here are some tips on how to conduct an effective discovery call:

Do your research: Before the call, research the prospect's company and industry to better understand their business and what challenges they may be facing. Google for companies, linkedin, people, word of mouth.

Set a clear agenda: At the beginning of the call, set a clear agenda for what you hope to accomplish during the call.

Ensure two-way conversation: Ask open-ended questions to encourage a dialogue and allow the prospect to share their thoughts and concerns.

Have a positive sign-off: At the end of the call, summarize the key points discussed and clarify any next steps or action items.

By conducting an effective discovery call, you can gather valuable information about the prospect and their needs, and begin to build a relationship of trust and understanding. This can lead to a more successful sales process and help you close more deals.
Question: Name some recurring villains on the TV show "The X-Files."
Answer: Victor Eugene Tooms, Donald Pfaster, Alex Krycek, The Cigarette Smoking Man, members of The Syndicate
Question: What layers does the model consist of?
Evidence: The overall architecture of DFN is depicted by Figure 3 . The input is a question $Q$ in length $l_q$ , a passage $P$ in length $l_p$ , and a list of $r$ answer candidates $\mathcal {A}=\lbrace A_1,...,A_r\rbrace $ in length $l_{a}^1,...,l_a^r$ . The model produces scores $c_1, c_2, ..., c_r$ for each answer candidate $A_1, A_2, ..., A_r$ respectively. The final prediction module selects the answer with the highest score.
The architecture consists of a standard Lexicon Encoding Layer and a Context Encoding Layer, on top of which are a Dynamic Fusion Layer and a Memory Generation Layer. The Dynamic Fusion Layer applies different attention strategies to different question types, and the Memory Generation Layer encodes question-related information in the passage for answer prediction. Multi-step reasoning is conducted over the output from the Dynamic Fusion and Memory Generation layers, in the Answer Scoring Module. The final output of the model is an answer choice $C\in \lbrace 1,2,...,r\rbrace $ from the Answer Prediction Module.
In the following subsections, we will describe the details of each component in DFN (bold letters represent trainable parameters).
The architecture consists of a standard Lexicon Encoding Layer and a Context Encoding Layer, on top of which are a Dynamic Fusion Layer and a Memory Generation Layer.
Answer: A standard Lexicon Encoding Layer and a Context Encoding Layer, on top of which are a Dynamic Fusion Layer and a Memory Generation Layer.
Question: Is Sicher a software company
Evidence: Sicher (German language word meaning "safe", "secure" or "certain") is a freeware instant messaging application for iOS, Android, and Windows Phone. Sicher allows users to exchange end-to-end encrypted text messages, media files and documents in both private and group chats. Sicher is developed by SHAPE GmbH, German company which pioneered mobile messaging with IM+ multi-messenger app it has been offering since 2002.
Answer: Sicher (German language word meaning "safe", "secure" or "certain") is a freeware instant messaging application for iOS, Android, and Windows Phone. Sicher allows users to exchange end-to-end encrypted text messages, media files and documents in both private and group chats. Sicher is developed by SHAPE GmbH, German company which pioneered mobile messaging with IM+ multi-messenger app it has been offering since 2002.
Question: Which of these are rappers? Eminem, Michael Jackson, Rihanna, 50 Cent
Answer: Eminem, 50 Cent
Question: What is Part of Speech Tagging?
Evidence: In the research field of Natural Language Processing (NLP) there are several tasks that contribute to understanding natural text. These tasks can manipulate natural language, such as tokenization process, and consequently can be used in other implementations, in order to extract syntactic or semantic information. One such task for syntactic components is Part of Speech Tagging (POS Tagging). Part of Speech Tagging in corpus linguistics is a process where a word is assigned with a label of the grammatical term, given the context it appears in. In many languages, POS Tagging models achieve an accuracy of 96 to 97 percent BIBREF0.
Part of Speech Tagging for highly inflective languages, such as Greek is quite a difficult task. In the Greek Language, words can have different morphological forms, depending on the part of speech (verbs have up to ten different forms). For that purpose, there is a need for a tagset that can support morphological features for improvement of Greek POS Tagging BIBREF1.
Another main task for extracting semantic information is Named Entity Recognition (NER). Named Entity Recognition is a process where a word or a set of words reference to a world object. Most Natural Language Processing models classify named entities that describe people, locations, organizations, following the ENAMEX type or can be more complex by detecting numerical types, like percentages (NUMEX) or dates (TIMEX) BIBREF2.
The greek Part of Speech Tagging and Named Entity Recognition models presented in this paper were developed using the spaCy library BIBREF3. SpaCy is an open source, Natural Language Processing library that supports a variety of tasks, including POS Tagging, Named Entity Recognition, Dependency Parsing, etc. SpaCy uses sophisticated neural network-based models for the implementation of Natural Language Processing components that achieve state-of-the-art results in many of these tasks.
In the following chapters the process for implementing Part of Speech Tagging and Named Entity Recognition for the Greek Language is explained. A dataset with extended POS Tags was found and matched to a set of morphological rules, according to a treebank. The dataset was then processed, fed to the spaCy model and used for training. Similarly, for Named Entity Recognition, datasets from different sources were compared to a custom set of rules for named entities. Finally, different experiments were conducted for evaluating the accuracy of the models.
Part of Speech Tagging in corpus linguistics is a process where a word is assigned with a label of the grammatical term, given the context it appears in.
Answer: A process where a word or a set of words reference to a world object is assigned with a label of the grammatical term, given the context it appears in.
Question: What are the top 3 GDP countries in 2022?
Answer: They are the United States ($20.89 trillion), China ($14.72 trillion), and Japan ($5.06 trillion).
Question: Classify the names based on whether they start with ’N’ or ‘M’
Nancy, Mandi, Michelle, Mark, Naomi, Nick
Answer: Names starting with ’M’ - Mandi, Michelle, Mark
Names starting with ’N’ - Nancy, Naomi, Nick
Question: List the ten episode titles from the second season of "Game of Thrones" in broadcast order.
Answer: The North Remembers, The Night Lands, What is Dead May Never Die, Garden of Bones, The Ghost of Harrenhal, The Old Gods and the New, A Man Without Honor, The Prince of Winterfell, Blackwater, Valar Morghulis
Question: What kind of guitar pick did Stevie Ray Vaughan prefer?
Evidence: Vaughan bought many Stratocasters and gave some away as gifts. A sunburst Diplomat Strat-style guitar was purchased by Vaughan and given to his girlfriend Janna Lapidus to learn to play on. Vaughan used a custom set of uncommonly heavy strings, gauges .013, .015, .019, .028, .038, .058, and tuned a half-step below standard tuning. With these heavy string sizes, it was not uncommon for him to separate his fingernail because of his quick movement along the strings. The owner of an Austin club recalled Vaughan coming into the office between sets to borrow super glue, which he used to keep a fingernail split from widening while he continued to play. The super glue was suggested by Rene Martinez, who was Stevie's guitar technician. Martinez eventually convinced Stevie to change to slightly lighter strings. He preferred a guitar neck with an asymmetrical profile (thicker at the top), which was more comfortable for his thumb-over style of playing. Heavy use of the vibrato bar necessitated frequent replacements; Vaughan often had his roadie, Byron Barr, obtain custom stainless steel bars made by Barr's father. As for his usage of plectrums, Vaughan preferred Fender medium gauge guitar picks, using one of the rounder shoulders of the pick rather than the pointed tip to pluck and strum the strings.
Answer: SRV is known to have used Fender's medium gauge picks, but with an interesting quirk - he preferred the rounder edge over the pointy tip, which most players use.
Question: From a 10-year old child's perspective, write a short summary of a visit to the Rijksmuseum for the Vermeer exhibition.
Answer: I went to the Rijksmuseum with my mom and dad to see the Vermeer exhibition. Vermeer was a painter who lived a long time ago and he painted really cool pictures. We saw a lot of paintings of people and things from a long time ago. Some of them were really big and some were really small. My favorite painting was one of a girl with a pearl earring. She looked so pretty and I wanted to take her home with me! We also got to see some other paintings by different artists and they were all really pretty too. It was a really fun day and I learned a lot about art!
Question: Which is a species of fish? Sea bream or Sea shell
Answer: Sea bream
Question: Identify which car manufacturer is Chinese or American: Dongfeng, Chrysler
Answer: Chrysler is American, Dongfeng is Chinese
Question: Extract the names of the television series Dixie Violet Egerickx starred in and the year those series were released. List them using bullets in the format {Series} - {Year}
Evidence: Dixie Violet Egerickx (born 31 October 2005) is an English actress. Egerickx has appeared three times on the London stage, as Iphigenia in Robert Icke's adaptation of Oresteia, Rosalind in Alexi Kay Campbell's Sunset at the Villa Thalia at the National Theatre and as Jenny Caroline 'Qui Qui' Marx in Richard Bean and Clive Coleman's Young Marx at The Bridge Theatre, directed by Sir Nicholas Hytner. She appeared in the 2017 National Geographic series Genius and the 2018 television series Patrick Melrose making her feature film debut in the 2018 film The Little Stranger. Egerickx was named in Screen International's Screen Stars of Tomorrow list in 2019 at the age of 13. She starred as Edie in the 2020 drama film Summerland, and portrayed Mary Lennox in the 2020 film The Secret Garden. In 2019 she filmed HBO's Unaired Game of Thrones Prequel Pilot and most recently has completed filming the role of Jo Ransome in the Apple TV miniseries of The Essex Serpent, directed by Clio Barnard.
Answer: Genius - 2017
Patrick Melrose - 2018
The Essex Serpent - 2022
Question: Classify each of the following countries as either democratic or authoritarian: USA, Russia, China, Sweden, UK.
Answer: According to the Democracy Index, compiled by the Economist Intelligence Unit (EIU) USA, Sweden and UK are fully democratic countries  whereas Russia and China belong to authoritarian regimes.
Question: Which methods do the authors use to reach the conclusion that LSTMs and HMMs learn complementary information?
Evidence: Several promising approaches to interpreting RNNs have been developed recently. BIBREF3 have approached this by using gradient boosting trees to predict LSTM output probabilities and explain which features played a part in the prediction. They do not model the internal structure of the LSTM, but instead approximate the entire architecture as a black box. BIBREF4 showed that in LSTM language models, around 10% of the memory state dimensions can be interpreted with the naked eye by color-coding the text data with the state values; some of them track quotes, brackets and other clearly identifiable aspects of the text. Building on these results, we take a somewhat more systematic approach to looking for interpretable hidden state dimensions, by using decision trees to predict individual hidden state dimensions (Figure 2 ). We visualize the overall dynamics of the hidden states by coloring the training data with the k-means clusters on the state vectors (Figures 3 , 3 ).
We interpret the HMM and LSTM states in the hybrid algorithm with 10 LSTM state dimensions and 10 HMM states in Figures 3 and 3 , showing which features are identified by the HMM and LSTM components. In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters. The HMM and LSTM states pick up on spaces, indentation, and special characters in the data (such as comment symbols in Linux data). We see some examples where the HMM and LSTM complement each other, such as learning different things about spaces and comments on Linux data, or punctuation on the Shakespeare data. In Figure 2 , we see that some individual LSTM hidden state dimensions identify similar features, such as comment symbols in the Linux data.
Building on these results, we take a somewhat more systematic approach to looking for interpretable hidden state dimensions, by using decision trees to predict individual hidden state dimensions (Figure 2 ). We visualize the overall dynamics of the hidden states by coloring the training data with the k-means clusters on the state vectors (Figures 3 , 3 ).
In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters.
Answer: The answers are shown as follows:
* decision trees to predict individual hidden state dimensions
* apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters