DataHammer/scimrc huggingface dataset:
Question: Extract the types of yield curves from the paragraph, summarize their definitions, and place them in a bullet list.
Evidence: There is no single yield curve describing the cost of money for everybody. The most important factor in determining a yield curve is the currency in which the securities are denominated. The economic position of the countries and companies using each currency is a primary factor in determining the yield curve. Different institutions borrow money at different rates, depending on their creditworthiness.

The yield curves corresponding to the bonds issued by governments in their own currency are called the government bond yield curve (government curve). Banks with high credit ratings (Aa/AA or above) borrow money from each other at the LIBOR rates. These yield curves are typically a little higher than government curves. They are the most important and widely used in the financial markets, and are known variously as the LIBOR curve or the swap curve. The construction of the swap curve is described below.

Besides the government curve and the LIBOR curve, there are corporate (company) curves. These are constructed from the yields of bonds issued by corporations. Since corporations have less creditworthiness than most governments and most large banks, these yields are typically higher. Corporate yield curves are often quoted in terms of a "credit spread" over the relevant swap curve. For instance the five-year yield curve point for Vodafone might be quoted as LIBOR +0.25%, where 0.25% (often written as 25 basis points or 25bps) is the credit spread.
Answer: 1. Government curve. This corresponds to the bonds issued by governments in their own currency.
2. LIBOR curve (or swap curve). This corresponds to the rates at which large banks borrow money from each other (also called LIBOR rate).
3. Corporate curve (or company curve). This is constructed from the yields of bonds issued by corporations.
Question: Why is this work different from text-only UNMT?
Evidence: Our idea is originally inspired by the text-only unsupervised MT (UMT) BIBREF8 , BIBREF9 , BIBREF0 , investigating whether it is possible to train a general MT system without any form of supervision. As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot “language" to bridge the two languages without paralleled corpus, making the problem “more well-defined" by reducing the problem to supervised learning. However, unlike the text translation involving word generation (usually a discrete distribution), the task to generate a dense image from a sentence description itself is a challenging problem BIBREF10 . High quality image generation usually depends on a complicated or large scale neural network architecture BIBREF11 , BIBREF12 , BIBREF13 . Thus, it is not recommended to utilize the image dataset as a pivot “language" BIBREF14 . Motivated by the cycle-consistency BIBREF15 , we tackle the unsupervised translation with a multi-modal framework which includes two sequence-to-sequence encoder-decoder models and one shared image feature extractor. We don't introduce the adversarial learning via a discriminator because of the non-differentiable $\arg \max $ operation during word generation. With five modules in our framework, there are multiple data streaming paths in the computation graph, inducing the auto-encoding loss and cycle-consistency loss, in order to achieve the unsupervised translation.
 As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot “language" to bridge the two languages without paralleled corpus, making the problem “more well-defined" by reducing the problem to supervised learning.
Answer: The answers are shown as follows:
* the image can play the role of a pivot “language" to bridge the two languages without paralleled corpus
Question: What's Botola Pro ?
Evidence: The Botola Pro (Arabic: البطولة الاحترافية, romanized: al-buṭūla l-iḥtirāfiyya), is a Moroccan professional league for men's association football clubs. At the top of the Moroccan football league system, it is the country's primary football competition. Contested by 16 clubs, it operates on a system of promotion and relegation with the Botola 2.

Seasons run from August to May, with teams playing 30 matches each (playing each team in the league twice, home and away) totaling 240 matches in the season. Most games are played in the afternoons of Saturdays and Sundays, the other games during weekday evenings. It is sponsored by Inwi and thus known as the Botola Pro Inwi. From 2015 to 2019, the league was called Botola Maroc Telecom for sponsorship reasons.
Answer: Botola Pro is a moroccan professional league. It's contested by 16 clubs, it operates on a system of relegation and promotion.
Most championships are Wydad of Casablanca with 22 titles
Question: How to lose weight?
Answer: The general answer is eat less and exercise more. This really is a psychology question, not a knowledge question.

To lose weight, one needs to create a build a mental model that they can follow. For example, one pound of fat has 3500 calories, one needs to reduce the diet by that much to lose a pound of weight.

Another important factor is to get strong support from family and friends. The encourage from close people makes the process easier and more natural.

Controlling weight is one of the psychological challenge that many people face at some point. Create a plan to minimize the psychological challenge and follow the plan mentally.
Question: Where should I go for vacation?
Answer: Where you should go for vacation depends on your interests and budget. For example, if you want to immerse yourself in nature, you could visit a local national park and stay in a nearby town. You could spend your time hiking, picnicking, and sightseeing in the national park in a cost effective way. If you prefer nightlife, visiting a city may be for you. In the United States, some options are Los Angeles and New York City. Finally, if you prefer spending your time in museums, find a vacation spot with a rich history, and enjoy immersing yourself in the local culture. For example, India, Peru and Paris are three destinations with and endless supply of local history and culture.
Question: Give me a summary of the early history of snowboarding
Evidence: Modern snowboarding was pioneered by Tom Sims and Jake Burton Carpenter, who both contributed significant innovations and started influential companies. In February 1968, Poppen organized the first snurfing competition at a Michigan ski resort that attracted enthusiasts from all over the country. One of those early pioneers was Tom Sims, a devotee of skateboarding (a sport born in the 1950s when kids attached roller skate wheels to small boards that they steered by shifting their weight). In the 1960s, as an eighth grader in Haddonfield, New Jersey, Sims crafted a snowboard in his school shop class by gluing carpet to the top of a piece of wood and attaching aluminum sheeting to the bottom. He produced commercial snowboards in the mid-70s. Others experimented with board-on-snow configurations at this time, including Welsh skateboard enthusiasts Jon Roberts and Pete Matthews developed their own snowboards to use at their local dry ski slope.
Answer: In the early days snowboarding was founded by Tom Sims and Jake Burton. Jake Burton went on to found the popular snowboarding brand, Burton Snowboards. The first instance of snowboarding was in February 1968. In the 60's an eighth grader from Haddonfield, New Jersey. The first snowboard was made from a single piece of wood and an aluminum sheet attached to the bottom. The first commerial snowboards were produced in the mid-70's.
Question: List the members of the Ivy in reverse alphabetical order.
Answer: Yale University, University of Pennsylvania, Princeton University, Harvard University, Dartmouth College, Cornell University, Columbia University and Brown University.
Question: What do economists study?
Answer: Economists study the decision-making behavior of individuals, firms, organizations and governments. Most people can choose what to consume, where to work, whether to have children, or whether and how much to save for the future. Companies can decide what goods and what quantities to produce, but also how much to spend on research and development or which workers to hire. A government can decide what share of the national budget to spend on health care or education for its citizens. Through tariffs, taxes and subsidies, It can also influence under which conditions and with which countries trade is conducted. Similarly, economic policies guide the regulation of carbon emissions. These choices are often limited due to scarce resources (e.g., money, time, information). 

Economists study the drivers and impacts of choices. They help us understand the major social problems of our time - including climate change, inequality, and poverty. Effective solutions to these problems require the work of economists.
Question: Is a manned mission to Mars worth the cost ?
Answer: Since the beginning of the Space race in 1957, we have had an exponential growth in innovative technology that benefited the space program and had commercial usage. They range from  medical imaging techniques, healthcare monitoring  equipment, artificial limbs, water filtration systems, solar panels and baby formula. The challenges of traveling to Mars and maintaining a sustainable living environment will also foster rapid innovation in sustainable technologies that can help to solve our current challenges on Earth. This could be renewable fuels, rapidly deployable habitats, Carbon sequestration and advances in AI.
Question: The experiments only report PSNR. It would be better to report SSIM and FID to see if the pruning can hurt the high frequency perceptual details or not? This is more important for the pretrained GAN model.
Evidence: Figure 12: The learning curve plots when using different subnetworks towards the DIP task. In the figure, S denotes the sparsity of the model. We compare both PSNR and SSIM values. For fair comparisons, we trained these subnetworks on the denoising task on the Baby image with 3000 iterations, then trained in isolation (the iteration number is recommended by (Chen et al., 2020c) to capture the ”early-stopping” phenomenon of DIP), and summarized their performances.
Answer: We reported the SSIM results of LIP and other baselines in Figure 12, we found that LIP is better at capturing perceptually aligned details than SNIP and random pruning subnetworks.
Question: What are the steps to put an elephant into a fridge?
Answer: Wake up. You can't afford a fridge.
Question: Who was India's first Deputy Prime Minister and Home Minister
Answer: Sardar Vallabhai Patel
Question: Identify which instrument is string or woodwind: Tremolo bass, Rackett
Answer: Rackett is woodwind, Tremolo bass is string.
Question: Are datasets publicly available?
Evidence: Despite the focus on sharing datasets and source codes on popular software development platforms such as GitHub (github.com) or Zenodo (zenodo.org), it is still a challenge to use data or code from other groups. The use of FAIR (Findable, Accessible, Interoperable and Reusable) (meta)data principles can guide the management of scientific data BIBREF125. Automated workflows that are easy to use and do not require programming knowledge encourage the flow of information from one discipline to the other. Platform-free solutions such as Docker (docker.com) in which an image of the source code is saved and can be opened without requiring further installation could accelerate the reproduction process. A recent initiative to provide a unified-framework for predictive models in genomics can quickly be adopted by the medicinal chemistry community BIBREF126.
Despite the focus on sharing datasets and source codes on popular software development platforms such as GitHub (github.com) or Zenodo (zenodo.org), it is still a challenge to use data or code from other groups.
Answer: Yes.
Question: Name a few famous movies from 2013
Answer: A few examples of famous films are "Her", "The Wolf of Wall Street", and "12 Years a Slave" which won the Oscar for Best Picture
Question: Give me a bulleted list of the seven most recent presidents of Brazil
Answer: - Luiz Inacio Lula da Silva
- Jair Bolsonaro
- Michel Temer
- Dilma Rousseff
- Fernando Henrique Cardoso
- Itamar Franco
- Fernando Collor de Mello
Question: What platform is the corpus available on?
Evidence: In this paper we proposed two models for learning concept embeddings based on the skip-gram model. We also proposed an efficient and effective mechanism for BOC densification which outperformed the prior proposed densification schemes on dataless classification. Unlike these prior densification mechanisms, our method scales linearly with the # of the BOC dimensions. In addition, we demonstrated through the results how this efficient mechanism allows generating high quality dense BOC vectors from few concepts alleviating the need of obtaining hundreds of concepts when generating the concept vector.
A functional service based on the presented corpus is currently being integrated, as a NER service, in the QURA- TOR platform (Rehm et al., 2020)
Answer: It's available on QURA- TOR platform (Rehm et al., 2020)
Question: Tell me about the life of Adolphe Cohn
Evidence: Adolphe Cohn (May 29, 1851– 1930) was a Franco-American educator, born in Paris. He was graduated "bachelier ès lettres" from the University of Paris in 1868, and studied law, historical criticism, and philology at various institutions of higher learning in Paris, receiving the degrees of LL.B. in 1873. A pupil of the École des Chartes, his thesis was called Vues sur l’histoire de l’organisation judiciaire en France du IXe au XIIIe siècle considérée au point de vue des juridictions extraordinaires and he got the diploma of "archiviste paléographe" in 1874. At the commencement of the Franco-Prussian War in 1870, he enlisted and served in the French army throughout the struggle.

Cohn emigrated to New York City on May 13, 1875, and from 1876 to 1884 was the American correspondent of La République française, then edited by Léon Gambetta, whom he had known in France, and whose political views he had adopted. In March, 1882, Cohn was appointed tutor in French at Columbia College, and soon afterward made an instructor. By a popular vote of the French residents of New York he was chosen to deliver the funeral oration on Gambetta in 1883 at Tammany Hall, and in 1885 was called from Cambridge, Massachusetts, for a similar purpose, upon the death of Victor Hugo.
Answer: Adolphe Cohn (May 29, 1851 – 1930) was a Franco-American educator, born in Paris. He was graduated "bachelier es lettres" from the University of Paris in 1868, and studied law, historical criticism, and philology at various institutions of higher learning in Paris, receiving the degrees of LL.B. in 1873. 

A pupil of the École des Chartes, his thesis was called Vues sur l’histoire de l’organisation judiciaire en France du IXe au XIIIe siècle considérée au point de vue des juridictions extraordinaires and he got the diploma of "archiviste paléographe" in 1874. At the commencement of the Franco-Prussian War in 1870, he enlisted and served in the French army throughout the struggle.

Cohn emigrated to New York City on May 13, 1875, and from 1876 to 1884 was the American correspondent of La République française, then edited by Léon Gambetta, whom he had known in France, and whose political views he had adopted. In March, 1882, Cohn was appointed tutor in French at Columbia College, and soon afterward made an instructor. By a popular vote of the French residents of New York he was chosen to deliver the funeral oration on Gambetta in 1883 at Tammany Hall, and in 1885 was called from Cambridge, Massachusetts, for a similar purpose, upon the death of Victor Hugo.
Question: What are the different names of Passover and what are the meanings?
Answer: Passover has four names identified in the Jewish bible, each highlighting a different aspect of the holiday. 

Holiday of Freedom. This name relates to the Bible story of the freeing of the Hebrew slaves from the Ancient Egyptian rulers. 

Holiday of Pesach (Lamb). This name relates to the offering made by the Hebrews before being freed, as well as the yearly offering made in honor of the holiday. 

Holiday of Matzah. This name relates to the unleavened bread eaten by the Hebrews while being freed, as well as the yearly offering made in honor of the holiday. 

Holiday of Spring. This name relates to the season in which the holiday is celebrated.
Question: Based on this paragraph from Wikipedia, what's the origin of the word Kubernetes?
Evidence: Kubernetes (/ˌk(j)uːbərˈnɛtɪs, -ˈneɪtɪs, -ˈneɪtiːz, -ˈnɛtiːz/, commonly abbreviated K8s) is an open-source container orchestration system for automating software deployment, scaling, and management. Originally designed by Google, the project is now maintained by the Cloud Native Computing Foundation.

The name Kubernetes originates from Greek, meaning 'helmsman' or 'pilot'. Kubernetes is often abbreviated as K8s, counting the eight letters between the K and the s (a numeronym).

Kubernetes works with containerd and CRI-O. Its suitability for running and managing large cloud-native workloads has led to widespread adoption of it in the data center. There are multiple distributions of this platform – from ISVs as well as hosted-on cloud offerings from all the major public cloud vendors.
Answer: Kubernetes originates from the Greek word for Helmsman.
Question: How do they model the neural retrieval modules?
Evidence: Throughout all our experiments, we used BERT-Base BIBREF2 to provide the state-of-the-art contextualized modeling of the input text.
Semantic Retrieval: We treated the neural semantic retrieval at both the paragraph and sentence level as binary classification problems with models' parameters updated by minimizing binary cross entropy loss. To be specific, we fed the query and context into BERT as:
Throughout all our experiments, we used BERT-Base BIBREF2 to provide the state-of-the-art contextualized modeling of the input text.

Semantic Retrieval: We treated the neural semantic retrieval at both the paragraph and sentence level as binary classification problems with models' parameters updated by minimizing binary cross entropy loss.
Answer: The answers are shown as follows:
* BERT-Base BIBREF2 to provide the state-of-the-art contextualized modeling
Question: From the passage provided, extract when Bloomberg L.P. was founded
Evidence: Bloomberg L.P. is a privately held financial, software, data, and media company headquartered in Midtown Manhattan, New York City. It was co-founded by Michael Bloomberg in 1981, with Thomas Secunda, Duncan MacMillan, Charles Zegar, and a 12% ownership investment by Bank of America through their brokerage subsidiary Merrill Lynch.
Answer: Bloomberg L.P. was founded in 1981
Question: Give me five different names I could use for my female cat
Answer: Lulu, Fluffy, Lady, Princess, Belle
Question: Why MS-MARCO is different from SQuAD?
Evidence: We propose a multi-task learning framework for evidence extraction. Unlike the SQuAD dataset, which only has one passage given a question, there are several related passages for each question in the MS-MARCO dataset. In addition to annotating the answer, MS-MARCO also annotates which passage is correct. To this end, we propose improving text span prediction with passage ranking. Specifically, as shown in Figure 2 , in addition to predicting a text span, we apply another task to rank candidate passages with the passage-level representation.
Unlike the SQuAD dataset, which only has one passage given a question, there are several related passages for each question in the MS-MARCO dataset. In addition to annotating the answer, MS-MARCO also annotates which passage is correct. 
Answer: The answers are shown as follows:
* there are several related passages for each question in the MS-MARCO dataset.
* MS-MARCO also annotates which passage is correct
Question: If I wanted a play games with my computer, what components should I pay attention to?
Answer: To play games with your PC, you should get a good graphics processor, a screen with sufficient space and refresh rate, and audio acoustics to match your taste, either speakers or a good headset.  If you want to play team sports that have chat, you might want to consider a headset with a microphone.
Question: What dataset is used?
Evidence: We study human-human negotiations in the buyer-seller bargaining scenario, which has been a key research area in the literature BIBREF0. In this section, we first describe our problem setup and key terminologies by discussing the dataset used. Later, we formalize our problem definition.
Dataset: For our explorations, we use the Craigslist Bargaining dataset (CB) introduced by BIBREF4. Instead of focusing on the previously studied game environments BIBREF5, BIBREF6, the dataset considers a more realistic setup: negotiating the price of products listed on Craigslist. The dataset consists of 6682 dialogues between a buyer and a seller who converse in natural language to negotiate the price of a given product (sample in Table TABREF1). In total, 1402 product ad postings were scraped from Craigslist, belonging to six categories: phones, bikes, housing, furniture, car and electronics. Each ad posting contains details such as Product Title, Category Type and a Listing Price. Moreover, a secret target price is also pre-decided for the buyer. The final price after the agreement is called the Agreed Price, which we aim to predict.
Defining the problem: Say we are provided with a product scenario $S$, a tuple: (Category, Title, Listing Price, Target Price). Define the interactions between a buyer and seller using a sequence of $n$ events $E_n:<e_{1}, e_{2}, ..., e_{n}>$, where $e_{i}$ occurs before $e_{j}$ iff $i<j$. Event $e_{i}$ is also a tuple: (Initiator, Type, Data). Initiator is either the Buyer or Seller, Type can be one of (message, offer, accept, reject or quit) and Data consists of either the corresponding natural language dialogue, offer price or can be empty. Nearly $80\%$ of events in CB dataset are of type `message', each consisting a textual message as Data. An offer is usually made and accepted at the end of each negotiation. Since the offers directly contain the agreed price (which we want to predict), we only consider `message' events in our models. Given the scenario $S$ and first $n$ events $E_n$, our problem is then to learn the function $f_{n}$: $A = f_{n}(S, E_n)$ where $A$ refers to the final agreed price between the two negotiating parties.

We use the Craigslist Bargaining dataset (CB) introduced by He et al. (2018).
Answer: the Craigslist Bargaining dataset (CB)
Question: Who wrote the song House of Love
Evidence: "House of Love" is a song by Australian duo Vika and Linda. It was released as the second single from her debut studio album Vika and Linda (1994). It peaked at number 98 in Australia and 32 in New Zealand.
Answer: House of Love is a song by Australian duo Vika and Linda. It was released as the second single from her debut studio album Vika and Linda (1994). It peaked at number 98 in Australia and 32 in New Zealand.