DataHammer/scimrc huggingface dataset:
Question: According to this passage, what percent of the lithography machine market do Ultratech, Canon and Nikon, MKS Instruments, Lam Research and Cadence Design Systems have in aggregate?
Evidence: ASML produces the photolithography machines used in the production of computer chips. In these machines, patterns are optically imaged onto a silicon wafer that is covered with a film of light-sensitive material (photoresist). This procedure is repeated dozens of times on a single wafer. The photoresist is then further processed to create the actual electronic circuits on the silicon. The optical imaging that ASML's machines deal with is used in the fabrication of nearly all integrated circuits and, as of 2011, ASML had 67 percent of the worldwide sales of lithography machines.

ASML's competition consisted of Ultratech, Canon and Nikon, MKS Instruments, Lam Research and Cadence Design Systems.[citation needed]
Answer: If ASML has 67 percent of the worldwide sales of lithography machines and if Ultratech, Canon and Nikon, MKS Instruments, Lam Research and Cadence Design Systems represent their sole competition, than these companies must share the remaining 33 percent of the market.
Question: Are there experiments justifying the reasoning given in the last paragraph of 3.1?
Evidence: This section demonstrates the importance of learning rate annealing for learning a robust energy landscape. We repeat the midrun and longrun learning experiments for CIFAR-10 except that we never anneal the learning rate. We then sample with the models for 1500 steps for the model trained with the midrun method and 100K steps for the model trained with the longrun method. The results in Figure 7 show that learning rate annealing is essential for stabilizing both midrun and longrun trajectories. The importance of annealing can be understood as follows. If the EBM is being updated with a very low learning rate, then samples from recent EBM snapshots can function as samples from the current EBM. In the case of midrun trajectory, annealing allows the model to robustify trajectories that are approximately as long as the lifetime of a persistent sample between rejuvenation. In the case of longrun learning, annealing allows the burnin samples to approximately reach the model steady-state before they are included in the update bank. This allows the persistent samples in the update bank to function as approximate steady-state samples from the current EBM, leading to proper modeling of probability mass.
Answer: Yes, we included this experiment in the supplementary material and will include in a future appendix. Annealing is needed to stabilize the midrun trajectories.
Question: How does a tack work in windsurfing?
Answer: There are two basic turn maneuvers in windsurfing, the tack and the jibe. The tack turns upwind and takes the nose of the board through the wind. To start tacking, steer the board upwind by moving the sail to the back of the board. When the nose of the boards points right into the wind, step around the front of the sail to the other side. Then, move the rig forward to continue turning the board downwind.
Question: Which of the following are currently considered gender neutral names: Jaime, River, Mary, Charlie, Jessie, Rene, John, Maggie, Liam
Answer: Jaime, River, Charlie, Rene
Question: What is the difference between NumPy and pandas?
Answer: Pandas is mostly used for data analysis tasks in Python. NumPy is mostly used for working with numerical values as it makes it easy to apply mathematical functions.
Question: Who is Ibrahima Tamba?
Evidence: Ibrahima Tamba (born 1 January 1967) is a Senegalese sprinter. He competed in the 200 meters at the 1988 Summer Olympics and the 1992 Summer Olympics
Answer: Ibrahima Tamba is an olympic sprinter from Senegal who participated in the 88 and 92 summer olympics
Question: Classify these vegetables based on their color.
mushroom, spinach, cauliflower, broccoli
Answer: White - cauliflower, mushroom
Green - broccoli, spinach
Question: What dataset do they build and test a multimodal model on?
Evidence: We build and test our MMT models on the Multi30K dataset BIBREF21 . Each image in Multi30K contains one English (EN) description taken from Flickr30K BIBREF22 and human translations into German (DE), French (FR) and Czech BIBREF23 , BIBREF24 , BIBREF25 . The dataset contains 29,000 instances for training, 1,014 for development, and 1,000 for test. We only experiment with German and French, which are languages for which we have in-house expertise for the type of analysis we present. In addition to the official Multi30K test set (test 2016), we also use the test set from the latest WMT evaluation competition, test 2018 BIBREF25 .
We build and test our MMT models on the Multi30K dataset (Elliott et al., 2016).
Answer: The Multi30K dataset.
Question: Does the author presuppose any prior knowledge about language similarities?
Evidence: Neural language models BIBREF0 , BIBREF1 , BIBREF2 have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning. They have also become a common benchmarking application in machine learning research on recurrent neural networks (RNN), because producing an accurate probabilistic model of human language is a very challenging task which requires all levels of linguistic analysis, from pragmatics to phonology, to be taken into account.
A typical language model is trained on text in a single language, and if one needs to model multiple languages the standard solution is to train a separate model for each language. This presupposes large quantities of monolingual data in each of the languages that needs to be covered and each model with its parameters is completely independent of any of the other models.
We propose instead to use a single model with real-valued vectors to indicate the language used, and to train this model with a large number of languages. We thus get a language model whose predictive distribution INLINEFORM0 is a continuous function of the language vector INLINEFORM1 , a property that is trivially extended to other neural NLP models. In this paper, we explore the “language space” containing these vectors, and in particular explore what happens when we move beyond the points representing the languages of the training corpus.
The motivation of combining languages into one single model is at least two-fold: First of all, languages are related and share many features and properties, a fact that is ignored when using independent models. The second motivation is data sparseness, an issue that heavily influences the reliability of data-driven models. Resources are scarce for most languages in the world (and also for most domains in otherwise well-supported languages), which makes it hard to train reasonable parameters. By combining data from many languages, we hope to mitigate this issue.
In contrast to related work, we focus on massively multilingual data sets to cover for the first time a substantial amount of the linguistic diversity in the world in a project related to data-driven language modeling. We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding. From that and the actual training examples, the system learns dense vector representations for each language included in our data set along with the character-level RNN parameters of the language model itself.
We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data.
Answer: No.
Question: Why do people try to reach the top of Mount Everest?
Answer: Reaching the summit of Mount Everest presents a challenge few can actually achieve.  The summit is the highest point on Earth and is located within the "death zone" at an altitude of 29,000 feet, where no human life can survive sustained periods of time.  Most lack the technical climbing skills necessary to reach the summit, of those who attempt it, 4% die on the mountain which is littered with corpses as the bodies cannot be easily removed.  Expeditions are also cost prohibitive, in total they can cost over $100,000 and that does not guarantee summiting nor surviving.  Because of all this, most put summiting Everest near the top of most difficult feats a person can accomplish, only about 6,000 people have done it as of 2023.
Question: What bad information is removed in pre-processing?
Evidence: Pre-processing - Processing useless and inappropriate information in text: (1) removing nonalphabetic characters; (2) removing website links of text content; (3) converting all words to lower case and tokenize texts.
Parameters - hyper-parameters configurations of our model: for each task, we strictly turn all the hyper-parameters on the validation dataset, and we achieve the best performance via a small grid search. The sizes of word embeddings and position embeddings are set to 200 and 100. In transformer encoder, attention heads and blocks are set to 6 and 2 respectively, and the dropout of multi-head attention is set to 0.7. Moreover, the minibatch size is 64; the initial learning rate is set to 0.001, the dropout rate to 0.3, and $\lambda $ to 0.6 for fake news detection.
Pre-processing - Processing useless and inappropriate information in text: (1) removing nonalphabetic characters; (2) removing website links of text content; (3) converting all words to lower case and tokenize texts.
Answer: Nonalphabetic characters and website links.
Question: Which planets are part of the inner solar system and which are part of the outer solar system?
Answer: Mercury - inner
Venus - inner
Earth - inner
Mars - inner
Jupiter - outer
Saturn - outer
Uranus - outer
Neptune - outer
Question: in hsa-1099-sa form, what does "Gross distribution" mean?
Answer: In the context of the HSA-1099-SA form, "Gross distribution" refers to the total amount of money that was withdrawn from your Health Savings Account (HSA) during the tax year in question.

This amount includes any funds used to pay for qualified medical expenses, as well as any non-qualified distributions or withdrawals. Non-qualified distributions are subject to income tax and may also be subject to a penalty unless you meet certain exceptions.

The Gross distribution amount is reported in Box 1 of the HSA-1099-SA form. You'll need this information when you file your taxes, as you'll need to report any taxable distributions on your tax return. It's important to keep accurate records of your HSA distributions and expenses throughout the year to ensure you're properly reporting and avoiding any penalties.
Question: What are some good coffee shops in the Cambridge, Massachusetts area?
Answer: Cambridge has a number of good coffee shops: Broadsheet coffee, which is a popular coffee roaster in the Boston area, is in Cambridge; Curio coffee in East Cambridge has both coffee and waffles; and there are multiple Tatte locations in Cambridge, which is also known for its pastries and French-Israeli food options for breakfast and lunch.
Question: Whether the typology manipulation on the training set has a significant influence?
Evidence: There are various types of typology in languages. For example, in English the typology order is subject-verb-object (SVO) order, but in Japanese and Korean the order is subject-object-verb (SOV). We construct a typology-manipulated dataset to examine if the typology order of the training data influences the transfer learning results. If the model only learns the semantic mapping between different languages, changing English typology order from SVO to SOV should improve the transfer ability from English to Japanese. The method used to generate datasets is the same as BIBREF21.
The source code is from a GitHub repository named Shaul1321/rnn_typology, which labels given sentences to CoNLL format with StanfordCoreNLP and then re-arranges them greedily.
Table TABREF23 shows that when we change the English typology order to SOV or OSV order, the performance on Korean is improved and worsen on English and Chinese, but very slightly. The results show that the typology manipulation on the training set has little influence. It is possible that multi-BERT normalizes the typology order of different languages to some extent.
Table 6 shows that when we change the English typology order to SOV or OSV order, the performance on Korean is improved and worsen on English and Chinese, but very slightly. The results show that the typology manipulation on the training set has little influence. It is possible that multiBERT normalizes the typology order of different languages to some extent.
Answer: No.
Question: Make me a list of types of pies I can make
Answer: You could make a pumpkin, pecan, berry, apple, peach or chocolate pie
Question: Which temple was the first female holocaust survivor ordained as a rabbi?
Evidence: Helga Newmark, née Helga Hoflich, (1932–2012) was the first female Holocaust survivor ordained as a rabbi. 

She was born in Germany, and was sent to the concentration camps of Westerbork, Bergen-Belsen, and Terezin (known in German as Theresienstadt) in Czechoslovakia.  She was freed at the age of twelve, and immigrated to America at the age of sixteen. When she had her first child, a daughter, she began to wonder how she would answer her daughter's questions about God. After considering several religions, she joined a [Conservative ] synagogue, Temple Emanuel in [Ridgefield Park, New Jersey] There she learned so much from the rabbi and his wife that she eventually became principal of the synagogue.

She was accepted to the Reform movement's Hebrew Union College - Jewish Institute of Religion on her second attempt, and was ordained in 2000 after eight years of study. She served as a rabbi at Barnert Temple in Franklin Lakes, New Jersey, for two years.
Answer: Temple Emanuel in Ridgefield Park, NJ
Question: List the Pixar movies that have won Academy Awards.
Evidence: Pixar has produced 26 feature films, starting with Toy Story (1995), which is the first fully computer-animated feature film; its most recent film was Lightyear (2022). The studio has also produced many short films. As of July 2019, its feature films have earned approximately $14 billion at the worldwide box office, with an average worldwide gross of $680 million per film. Toy Story 3 (2010), Finding Dory (2016), Incredibles 2 (2018), and Toy Story 4 (2019) are all among the 50 highest-grossing films of all time. Incredibles 2 is the studio's highest grossing film as well as the fourth-highest-grossing animated film of all time, with a gross of $1.2 billion; the other three also grossed over $1 billion. Moreover, 15 of Pixar's films are in the 50 highest-grossing animated films of all time.
Pixar has earned 23 Academy Awards, 10 Golden Globe Awards, and 11 Grammy Awards, along with numerous other awards and acknowledgments. Its films are frequently nominated for the Academy Award for Best Animated Feature, since its inauguration in 2001, with eleven winners being Finding Nemo (2003), The Incredibles (2004), Ratatouille (2007), WALL-E (2008), Up (2009), Toy Story 3 (2010), Brave (2012), Inside Out (2015), Coco (2017), Toy Story 4 (2019), and Soul (2020). The six nominated films that did not win are Monsters, Inc. (2001), Cars (2006), Incredibles 2 (2018), Onward (2020), Luca (2021) and Turning Red (2022). While Cars 2 (2011), Monsters University (2013), The Good Dinosaur (2015), Finding Dory (2016), Cars 3 (2017) and the aforementioned Lightyear were not nominated. In addition, Toy Story 3 and Up were nominated for the Academy Award for Best Picture.
On February 10, 2009, Pixar executives John Lasseter, Brad Bird, Pete Docter, Andrew Stanton, and Lee Unkrich were presented with the Golden Lion award for Lifetime Achievement by the Venice Film Festival. The physical award was ceremoniously handed to Lucasfilm's founder, George Lucas.
Answer: Finding Nemo (2003), The Incredibles (2004), Ratatouille (2007), WALL-E (2008), Up (2009), Toy Story 3 (2010), Brave (2012), Inside Out (2015), Coco (2017), Toy Story 4 (2019), Soul (2020)
Question: How many players participate in an ultimate frisbee game?
Answer: A standard ultimate frisbee game consists of two teams with seven players on each side. Other less popular variations exist, such as beach ultimate frisbee which typically has five players on each team.
Question: What does moha mean
Evidence: Moha (Chinese: 膜蛤; pinyin: Mó Há, pronounced [muǒ.xǎ]), literally "admiring toad" or "toad worship", is an internet meme spoofing Jiang Zemin, former General Secretary of the Chinese Communist Party and paramount leader. It originated among the netizens in mainland China and has become a subculture on the Chinese internet. According to another explanation, it comes from China's social media Baidu Tieba. In the culture, Jiang is nicknamed há, or "toad", because of his supposed resemblance to a toad. Netizens who móhá (worship the toad) call themselves "toad fans", "toad lovers" or "toad worshippers" (simplified Chinese: 蛤丝; traditional Chinese: 蛤絲), or "mogicians" (膜法师; 膜法師) which is a wordplay on mófǎshī (魔法师; 魔法師, magician) in Mandarin.[note 1]

Another nickname for Jiang is "elder" or "senior" (长者; 長者; Zhǎngzhě), because he once called himself an "elder" or "senior" when he was berating Hong Kong journalist Sharon Cheung who questioned him. A video clip recording this event spread on the internet and led to the rise of the culture, which later greatly rose in popularity around 2014, when Hong Kong was experiencing a period of political instability. Initially, netizens extracted Jiang's quotes from the video and imitated his wording and tone, for parody and insult. However, as the culture developed, some imitations have taken to carrying affection toward him. The quotes for imitation have also evolved to include what he said during his leadership, and in his personal life.
Answer: It means toad worship, an internet meme spoofing Jiang Zemin
Question: Write a short story about a man who discovers a partially built treehouse.
Answer: A man purchased a piece of property and began to explore the land.  After some time had passed, the man discovered a partially built treehouse not too far from the ground that would be safe for his children to play in.  The man returned home to his workshop to gather wood and tools to complete the treehouse construction.  However, upon arrival at his workshop, he learned that he would need more wood to finish the treehouse.  Disappointed, he began to put his tools away when he heard a truck passing by with lumber for sale.  After purchasing the material, he returned to the treehouse with his tools and material and began to work.  Upon completion of the treehouse, he called for his children to surprise them and when they saw the treehouse they shouted for joy and immediately began to play in it and this made the man very happy.
Question: How can I run Spark on a cluster?
Answer: You can use either the standalone deploy mode, which only needs Java to be installed on each node, or the Mesos and YARN cluster managers. If you'd like to run on Amazon EC2, AMPLab provides EC2 scripts to automatically launch a cluster.

Note that you can also run Spark locally (possibly on multiple cores) without any special setup by just passing local[N] as the master URL, where N is the number of parallel threads you want.
Question: Where is Lukaya River?
Evidence: The Lukaya is a river in the Democratic Republic of the Congo. Its source is located in the Crystal Mountains (Montagnes de Cristal), from which it runs eastward through Bas-Congo, then runs into the banks of the Ndjili River. The rail line from Matadi to Kinshasa runs along the river valley for a time, passing to the south and then to the east of Kinshasa. At one point the river was the namesake of a district in the Congo Free State.

Just to the south of Kinshasa, a small cascade on the river, the Petites Chutes de la Lukaya, is a gathering place for several tourist activities including the lake formed by the river valley, beaches and waterfalls, and the Lola Ya Bonobo Sanctuary of Kinshasa. This is located in the Mont Ngafula neighborhood, which the river runs through.
Answer: The Lukaya is a river in the Democratic Republic of the Congo, originating in the Crystal Mountains and running eastward through Bas-Congo and into the Ndjili River. The Petites Chutes de la Lukaya is a gathering place for tourist activities.
Question: Tell me about the football career of Adao
Evidence: Carlos Manuel Pereira Pinto (born 3 April 1960), commonly known as Adão, is a Portuguese former footballer who played as a central midfielder.

Club career
Born in Chaves, Vila Real District, Adão started his career with hometown club G.D. Chaves, moving to the Primeira Liga in 1980 with Varzim SC. He appeared in only eight games in his first season, which ended in relegation, but helped with 27 and three goals to an immediate promotion.

In summer 1985, Adão joined Vitória de Guimarães. In his second year he played 30 matches as starter and scored twice – including once in 1–0 away win over S.C. Braga in the Minho derby – as his team finished in third position.

Adão signed with C.F. Os Belenenses for 1988–89. On 12 October 1988, in that campaign's UEFA Cup, he scored the game's only goal at home against Bayer 04 Leverkusen, as the Lisbon side ousted the title holders 2–0 on aggregate. Later in the season he also appeared in the final of the Portuguese Cup, won after beating S.L. Benfica.

Aged 30, Adão moved to F.C. Penafiel, spending his last three years (of five overall) in the second division. Over the course of 11 seasons, he amassed top-tier totals of 279 games and 16 goals.
Answer: Carlos Manuel Pereira Pinto (born 3 April 1960), commonly known as Adao, is a Portuguese former footballer who played as a central midfielder.

Born in Chaves, Vila Real District, Adao started his career with hometown club G.D. Chaves, moving to the Primeira Liga in 1980 with Varzim SC. He appeared in only eight games in his first season, which ended in relegation, but helped with 27 and three goals to an immediate promotion.

In summer 1985, Adao joined Vitoria de Guimaraes. In his second year he played 30 matches as starter and scored twice – including once in 1–0 away win over S.C. Braga in the Minho derby – as his team finished in third position.

Adao signed with C.F. Os Belenenses for 1988–89. On 12 October 1988, in that campaign's UEFA Cup, he scored the game's only goal at home against Bayer 04 Leverkusen, as the Lisbon side ousted the title holders 2–0 on aggregate. Later in the season he also appeared in the final of the Portuguese Cup, won after beating S.L. Benfica.

Aged 30, Adao moved to F.C. Penafiel, spending his last three years (of five overall) in the second division. Over the course of 11 seasons, he amassed top-tier totals of 279 games and 16 goals.