DataHammer/scimrc huggingface dataset:
Question: What is the ratio of  the test set, training set, and validation set?
Evidence: The articles from the newspaper were fed in spaCy library into the proper format for training. Different parameters were tested, in order to get the optimal result. The dataset was shuffled, using the same seed for all the experiments and was split into a train set (70%), a test set (20%) and a validation set (10%). Information was passed through the training algorithm in batches with an increasing batch size from 4 to 32 and a step of 1.001. Additionally, a dropout rate was configured in every batch, initialized to 0.6 which dropped during the training process to 0.4. Most of the experiments were trained using 30 epochs.
The main area of study for the experiments focuses on three important components. At first, we investigate the difference in results between part of speech taggers that classify morphological features and taggers that detect only the part of speech. Moreover, we explore the significance of pretrained vectors used from a model and their effect on the extraction of better results. Most importantly, the usage of subwords of tokens from a tagger as embeddings is issued. For the experiments, precision, recall and f1 score are used as evaluation metrics.
The dataset was shuffled, using the same seed for all the experiments and was split into a train set (70%), a test set (20%) and a validation set (10%).
Answer: 7:2:1.
Question: Where was Eric Church born?
Evidence: Church was born on May 3, 1977, in Granite Falls, North Carolina (Dudley Shoals area), to Ken and Rita Church. Church worked with his father at Clayton Marcus, a furniture upholstery company where his father was president. At 13, he bought a guitar and began writing songs of his own. By his senior year of high school, he had found a gig at a local bar, which occupied most of his time. He played many Jimmy Buffett cover songs and a few of his own original songs in some dive bars. Some of these places were so rough that he got into a few altercations from the stage. For a few years, the band played often in bars and restaurants throughout North Carolina.[citation needed] The band "Mountain Boys" consisted of his college roommate, brother, and a fellow guitarist.

Before moving to Nashville, Eric graduated from South Caldwell High School and then Appalachian State University with a degree in marketing. Upon graduation, Church became engaged to a Spanish teacher in the town of Lenoir, North Carolina. The future bride's father attempted to persuade Church into a corporate career, which he rejected as an aspiring musician (and later referenced in his song "What I Almost Was" from his debut album). She broke the engagement and he headed to Nashville with his father's financial backing. His father also provided the opportunity to make contacts, and more importantly, time to focus on developing his songwriting ability.[citation needed]
Answer: Church was born on May 3, 1977, in Granite Falls, North Carolina (Dudley Shoals area), to Ken and Rita Church
Question: What is Amazon Web Services?
Answer: AWS or Amazon Web Services is the cloud platform that is offered by Amazon.  They have services like compute, virtual machines, databases, storage, serverless sql and serverless functions, artificial intelligence, machine learning, and data science, data lakes, and internet of things.  A platform like AWS has all the tools for seamless integration to build an entire analytics platform for your business.  AWS also serves the most business who have moved to cloud compared to their competitors Microsoft Azure and Google Cloud Platform.
Question: What genus are beavers a part of?
Evidence: Beavers (genus Castor) are large, semiaquatic rodents of the Northern Hemisphere. There are two existing species: the North American beaver (Castor canadensis) and the Eurasian beaver (C. fiber). Beavers are the second-largest living rodents, after capybaras, weighing up to 30 kilograms (66 lb). Beavers have stout bodies with large heads, long chisel-like incisors, brown or gray fur, hand-like front feet, webbed back feet, and tails that are flat and scaly. The two species differ in skull and tail shape and fur color. Beavers can be found in a number of freshwater habitats, such as rivers, streams, lakes and ponds. They are herbivorous, consuming tree bark, aquatic plants, grasses and sedges.
Answer: Castor genus
Question: Wha is the Dynamite!! 2010
Evidence: Dynamite!! 2010 was a mixed martial arts and kickboxing event in the annual New Year's Eve event promoted by Fighting and Entertainment Group that took place on December 31, 2010 at the Saitama Super Arena in Saitama, Japan. The event included bouts that encompass the DREAM, K-1 and K-1 World MAX banners. Just as the previous Dynamite!! 2009, with the official commentator Nozomi Sasaki, the event aired on TBS in Japan and HDNet in North America.

Background
FEG president Tanikawa had stated that the company planned on holding an event together with Sengoku Raiden Championship and this event may have a similar DREAM vs SRC format as Dynamite!! 2009. However, this did not happen as SRC held its own event, World Victory Road Presents: Soul of Fight.

Tanikawa has also stated that this Dynamite!! event will most likely be shortened (to around three hours) and the annual K-1 Koshien tournament will not take place at this event.

The title fight between Bibiano Fernandes and Hiroyuki Takaya was the last in the 63 kg. (139 lbs) weight class before it gets broken into the standard MMA bantamweight and featherweight divisions. DREAM plans on holding a Grand Prix in whichever of the two weight classes the champion does not decide to fight in. After winning the title, Hiroyuki Takaya stated that he would become the Featherweight (145 lbs) Champion.

The Aoki/Nagashima fight was first announced that there would be an alternate of four rounds, with one round under MMA rules and the next round, then they will change gloves and, fight under kickboxing rules. A coin toss will determine what rules they start under. However, it has later announced that the fight will feature one three-minute kickboxing round, followed by a standard five-minute MMA round. The fighters used open-fingered gloves throughout. If no winner were decided in the eight minutes of competition, the fight would have been declared an automatic draw.

A fight between Bob Sapp and Shinichi Suzukawa in a super-heavyweight “IGF rules” bout was set to feature a rule set similar to MMA, but with no closed fist strikes. However, the fight was canceled due to Sapp attempting to re-negotiate his contract backstage prior to the event and eventually electing to withdraw from the contest.
Answer: The Dynamite!! 2010 was a mixed martial arts and kickboxing event in the annual New Year's Eve event promoted by Fighting and Entertainment Group that took place on December 31, 2010 at the Saitama Super Arena in Saitama, Japan.

The event included bouts that encompass the DREAM, K-1 and K-1 World MAX banners. Just as the previous Dynamite!! 2009, with the official commentator Nozomi Sasaki, the event aired on TBS in Japan and HDNet in North America.
Question: What are the two reference standards to evaluate the semantic similarity and relatedness measures?
Evidence: We use two reference standards to evaluate the semantic similarity and relatedness measures . UMNSRS was annotated for both similarity and relatedness by medical residents. MiniMayoSRS was annotated for relatedness by medical doctors (MD) and medical coders (coder). In this section, we describe these data sets and describe a few of their differences.
MiniMayoSRS: The MayoSRS, developed by PakhomovPMMRC10, consists of 101 clinical term pairs whose relatedness was determined by nine medical coders and three physicians from the Mayo Clinic. The relatedness of each term pair was assessed based on a four point scale: (4.0) practically synonymous, (3.0) related, (2.0) marginally related and (1.0) unrelated. MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter–annotator agreement was achieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78. We evaluate our method on the mean of the physician scores, and the mean of the coders scores in this subset in the same manner as reported by PedersenPPC07.
UMNSRS: The University of Minnesota Semantic Relatedness Set (UMNSRS) was developed by PakhomovMALPM10, and consists of 725 clinical term pairs whose semantic similarity and relatedness was determined independently by four medical residents from the University of Minnesota Medical School. The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness. The Intraclass Correlation Coefficient (ICC) for the reference standard tagged for similarity was 0.47, and 0.50 for relatedness. Therefore, as suggested by Pakhomov and colleagues,we use a subset of the ratings consisting of 401 pairs for the similarity set and 430 pairs for the relatedness set which each have an ICC of 0.73.
We use two reference standards to evaluate the semantic similarity and relatedness measures1. UMNSRS was annotated for both similarity and relatedness by medical residents. MiniMayoSRS was annotated for relatedness by medical doctors (MD) and medical coders (coder).
Answer: UMNSRS and MiniMayoSRS.
Question: How do they gather human reviews?
Evidence: Our application is the escalation of Internet chats. To maintain quality of service, users are transferred to human representatives when their conversations with an intelligent virtual assistant (IVA) fail to progress. These transfers are known as escalations. We apply Han to such conversations in a sequential manner by feeding each user turn to Han as they occur, to determine if the conversation should escalate. If so, the user will be transferred to a live chat representative to continue the conversation. To help the human representative quickly determine the cause of the escalation, we generate a visualization of the user's turns using the attention weights to highlight the turns influential in the escalation decision. This helps the representative quickly scan the conversation history and determine the best course of action based on problematic turns.
Our application requires that the visualizations be generated in real-time at the point of escalation. The user must wait for the human representative to review the IVA chat history and resume the failed task. Therefore, we seek visualization methods that do not add significant latency to the escalation transfer. Using the attention weights for turn influence is fast as they were already computed at the time of classification. However, these weights will not generate useful visualizations for the representatives when their values are similar across all turns (see Han Weight in Table TABREF1 ). To overcome this problem, we develop a visualization method to be applied in the instances where the attention weights are uniform. Our method produces informative visuals for determining influential samples in a sequence by observing the changes in sample importance over the cumulative sequence (see Our Weight in Table TABREF1 ). Note that we present a technique that only serves to resolve situations when the existing attention weights are ambiguous; we are not developing a new attention mechanism, and, as our method is external, it does not require any changes to the existing model to apply.
We apply Han to such conversations in a sequential manner by feeding each user turn to Han as they occur, to determine if the conversation should escalate. If so, the user will be transferred to a live chat representative to continue the conversation.
The user must wait for the human representative to review the IVA chat history and resume the failed task.
Answer: The answers are shown as follows:
* human representative to review the IVA chat history and resume the failed task
Question: From the passage identify the domains in which Athena is considered God. Display the results in a comma separated format.
Evidence: Athena (Ἀθηνᾶ, Athēnâ)

Goddess of reason, wisdom, intelligence, skill, peace, warfare, battle strategy, and handicrafts. According to most traditions, she was born from Zeus's forehead, fully formed and armored, after Zeus swallowed her mother, Metis, whole. She is depicted as being crowned with a crested helm, armed with shield and spear, and wearing the aegis over a long dress. Poets describe her as "grey-eyed" or having especially bright, keen eyes. She is a special patron of heroes such as Odysseus. She is the patron of the city Athens (from which she takes her name) and is attributed to various inventions in arts and literature. Her symbol is the olive tree. She is commonly shown as being accompanied by her sacred animal, the owl. Her Roman counterpart is Minerva.
Answer: reason, wisdom, intelligence, skill, peace, warfare, battle strategy, handicrafts
Question: What are the names of the lines on Hong Kong's MTR network?
Answer: - Island Line
- Tsuen Wan Line
- Kwun Tong Line
- South Island Line
- Tseung Kwan O Line
- Tung Chung Line & Disneyland Resort Line
- East Rail Line
- Tuen Ma Line
- Airport Express
Question: Where is the data used by Balduzzi et al. from?
Evidence: There are several recent works that attempt to match profiles across different Internet services. Some of these works utilize private user data, while some, like ours, use publicly available data. An example of a work that uses private data is Balduzzi et al. BIBREF8 . They use data from the Friend Finder system (which includes some private data) provided by various social networks to link users across services. Though one can achieve a relatively high level of success by using private data to link user accounts, we are interested in using only publicly available data for this task. In fact, as mentioned earlier, we do not even consider publicly available information that could explicitly identify a user, such as names, birthdays and locations.
Several methods have been proposed for matching user profiles using public data BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 . These works differ from ours in two main aspects. First, in some of these works, the ground truth data is collected by assuming that all profiles that have the same screen name are from the same users BIBREF15 , BIBREF16 . This is not a valid assumption. In fact, it has been suggested that close to $20\%$ of accounts with the same screen name in Twitter and Facebook are not matching BIBREF17 . Second, almost all of these works use features extracted from the user profiles BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 . Our work, on the other hand, is blind to the profile information and only utilizes users' activity patterns (linguistic and temporal) to match their accounts across different social networks. Using profile information to match accounts is contrary to the best practices of stylometry since it assumes and relies on the honesty, consistency and willingness of the users to explicitly share identifiable information about themselves (such as location).
An example of a work that uses private data is Balduzzi et al. [2]. They use data from the Friend Finder system (which includes some private data) provided by various social networks to link users across services.
Answer: The Friend Finder system.
Question: Extract all of the dates mentioned in this paragraph and list them using bullets in the format {Date} - {Description}
Evidence: The Yayhoos formed as a "temporary band" in 1993 and consisted of "an all-star lineout of country punk veterans," including Eric "Rosco" Ambel on guitar/vocals, Dan Baird on guitar/vocals, Keith Christopher on bass, and Terry Anderson on drums/vocals. With three solo artists from singer-songwriter backgrounds—Anderson, Ambel, and Baird—the band features three lead vocalists as well as three songwriters.
Answer: • 1993 - The Yayhoos were formed during this year.
Question: How does interest rates and inflation impact bond prices?
Answer: Interest rates have an inverse effect on bond prices. Typically as the interest rate go up, the existing bonds that were sold previously at a certain maturity and coupon, now stands devalued as investors can get a newer bond for higher interest payments.  Similarly during inflation as Fed tries to control inflation by raising interest rates it has the inverse effect on bond prices. It is important to note that not all bonds react the same way to interest rates. For example, short term interest rate bonds may feel the impact almost immediately while long term bonds may have a gradual but likely to see more impactful price change. It is important to note that there are Treasury Inflation protected securities, a type of a bond that adjust principal value of the bond based on inflation.
Question: In what ways can having children improve your quality of life?
Answer: This is my own opinion, and completely anecdotal, but having children can help improve your quality of life in several ways. Children can provide parents a sense of purpose. Parents are responsible for the well-being of their children, and this obligation in-turn forces parents to become less self-centered, and focus on something other than themselves. The relationships parents develop with their children is also unique to any other bond. They represent a piece of you, and are completely dependent on your for everything. That dependence, when coupled with the love they have for you, can give your life new meaning and joy.
Question: How to evaluate LD2NL?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
To validate our framework, we evaluated LD2NL using experts 66 in Natural Language Processing (NLP) and SW as well as 20 non-experts who were lay users or non-users of SW.
Answer: The authors evaluated LD2NL using experts 66 in Natural Language Processing (NLP) and SW as well as 20 non-experts who were lay users or non-users of SW.
Question: Which languages are used in the multi-lingual caption model?
Evidence: We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also can induce the two types of features, linguistic and visual features, for different languages in joint spaces. Our proposed method consistently outperforms previous state-of-the-art vision-based bilingual word induction approaches on different languages. The contributions of this paper are as follows:
We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. 
Answer: The answers are shown as follows:
* German-English, French-English, and Japanese-English
Question: List the years in which Anne Rochelle Steely Ramirez participated in Sprint events based on the passage. Use bulleted format to represent the years and sort in ascending order.
Evidence: Anne Rochelle Steely Ramirez (born October 23, 1962), née Anne Rochelle Steely, is a former long-distance runner who competed internationally for the United States. She specialized in the 3,000 meters on the track and later competed in road running events.

She made her international debut in cross country and helped the United States the women's team title at the IAAF World Cross Country Championships in 1985. Her focus changed to the 3,000 meters, a distance in which she was a two-time US champion (outdoors in 1991 and indoors in 1992). She reached the final of that event at the 1991 World Championships in Athletics and the 1992 Barcelona Olympics.

Steely's performances deteriorated and she was diagnosed with hyperthyroidism. However, she made an athletic comeback in 1998 and she won two national road titles, as well as appearing at the Goodwill Games and IAAF World Half Marathon Championships that year. She made one more appearance at the World Half Marathon event in 2000, but retired to focus on education studies.
Answer: 1985
1991
1992
1998
2000