DataHammer/scimrc huggingface dataset:
Question: What settings are commonly used to evaluate the implicit discourse relation recognition performance on PDTB corpus?
Evidence: On the PDTB corpus, both binary classification and multi-way classification settings are commonly used to evaluate the implicit discourse relation recognition performance. We noticed that all the recent works report class-wise implicit relation prediction performance in the binary classification setting, while none of them report detailed performance in the multi-way classification setting. In the binary classification setting, separate “one-versus-all” binary classifiers were trained, and each classifier is to identify one class of discourse relations. Although separate classifiers are generally more flexible in combating with imbalanced distributions of discourse relation classes and obtain higher class-wise prediction performance, one pair of discourse units may be tagged with all four discourse relations without proper conflict resolution. Therefore, the multi-way classification setting is more appropriate and natural in evaluating a practical end-to-end discourse parser, and we mainly evaluate our proposed models using the four-way multi-class classification setting.
Since none of the recent previous work reported class-wise implicit relation classification performance in the multi-way classification setting, for better comparisons, we re-implemented the neural tensor network architecture (so-called SWIM in BIBREF18 ) which is essentially a Bi-LSTM model with tensors and report its detailed evaluation result in the multi-way classification setting. As another baseline, we report the performance of a Bi-LSTM model without tensors as well. Both baseline models take two relevant discourse units as the only input.
For additional comparisons, We also report the performance of our proposed models in the binary classification setting.
On the PDTB corpus, both binary classification and multi-way classification settings are commonly used to evaluate the implicit discourse relation recognition performance.
Answer: Binary classification and multi-way classification settings.
Question: What are the four parts of the architecture of MTNet?
Evidence: The architecture of our proposed MTNet is shown in Fig. FIGREF24 . It includes 4 parts: an input layer for receiving raw data; shared layers for general feature extraction through stacked layers and non-linear transformation; branched layers for specific feature extraction; and the output layer with one neuron. The output of the last shared layer is fed into different branches. These branches are trained jointly. In the last shared layer, the information flow is split into many branches BIBREF7 , which enables feature sharing and reuse. Finally, the output result is calculated in the output layer by averaging outputs from these branches BIBREF10 . The overall neural network with different branches is trained in parallel. The detailed configuration of MTNet is listed in Tabel TABREF21 .
The advantages of MTNet are as follows:
With multi-branched layers, different data under diverse levels can be fed into different branches, which enables MTNet extract multi-level features for later regression.
Multi-branched architecture in our MTNet can also act as an ensemble method BIBREF10 , which promotes the performance as well.
We use mean square error (MSE) with INLINEFORM0 regularization as the cost function. DISPLAYFORM0 
where INLINEFORM0 denotes the raw input of INLINEFORM1 -th data sample, INLINEFORM2 denotes the capacity of dataset, INLINEFORM3 denotes groundtruth score of INLINEFORM4 -th Zhihu Live. INLINEFORM5 denotes INLINEFORM6 regularization to prevent from overfitting.
The architecture of our proposed MTNet is shown in Fig. 3. It includes 4 parts: an input layer for receiving raw data; shared layers for general feature extraction through stacked layers and non-linear transformation; branched layers for specific feature extraction; and the output layer with one neuron.
Answer: An input layer for receiving raw data; shared layers for general feature extraction through stacked layers and non-linear transformation; branched layers for specific feature extraction; and the output layer with one neuron.
Question: What are three challenging tasks authors evaluated their sequentially aligned representations?
Evidence: We consider three tasks representing a broad selection of natural language understanding scenarios: paper acceptance prediction based on the PeerRead data set BIBREF2, Named Entity Recognition (NER) based on the Broad Twitter Corpus BIBREF3, and author stance prediction based on the RumEval-19 data set BIBREF6. These tasks were chosen so as to represent i) different textual domains, across ii) differing time scales, and iii) operating at varying levels of linguistic granularity. As we are dealing with dynamical learning, the vast majority of NLP data sets can unfortunately not be used since they do not include time stamps.
We consider three tasks representing a broad selection of natural language understanding scenarios: paper acceptance prediction based on the PeerRead data set BIBREF2, Named Entity Recognition (NER) based on the Broad Twitter Corpus BIBREF3, and author stance prediction based on the RumEval-19 data set BIBREF6. 
Answer: The answers are shown as follows:
* paper acceptance prediction
* Named Entity Recognition (NER)
* author stance prediction
Question: Given a reference text about the Annapurna Post, tell me when it launched its online news portal and when it became successful.
Evidence: Annapurna Post (Nepali: अन्नपुर्ण पोस्ट) is a daily broadsheet newspaper published in Nepal. It started printing in 2002 and launched its online news portal in 2013 by Annapurna Media Network, which also owns The Annapurna Express, AP1 TV and Radio Annapurna Nepal. By the 4 April 2014, its online news portal was listed among 10 most visited news portal in Nepal. It also launched its own mobile news app by 2018.

In the annual newspaper classification report 2073/2074 BS by Press Council Nepal, this newspaper was categorized in the A category, the second highest possible rank below A+.
Answer: Annapurna Post launched its online news portal in 2013. By April 2014, it became among the 10 most visited news portals in Nepal.
Question: Why is prioritizing diversity hiring in an organization important?
Evidence: Diversity is believed to bring substantial benefits such as better decision making and improved problem solving, greater creativity and innovation, which leads to enhanced product development, and more successful marketing to different types of customers
Answer: Diversity drives creativity, innovation, and personal growth.
Question: Extract the most famous songs of the slashtones in a comma separated list.
Evidence: The Slashtones (also known as "Harry Slash & The Slashtones") are an American musical group, formed in 1993 by New York native Harry Slash under the name "Harry & The Slashtones". Harry would later change the band's name many times, from "The Slashtones", to "Life?!","Not The Slashtones" and then to "Harry Slash & The Slashtones" during the era that Harry Slash produced music for Extreme Championship Wrestling. Their best known songs are the ECW theme ("This Is Extreme!"), Taz's theme ("Survive! (If I Let You)") and Sabu's theme ("Huka Blues"). After Extreme Championship Wrestling went bankrupt in 2001, Harry Slash continued to record and perform with the band after changing its name back to "The Slashtones". The name of the band is a parody of Béla Fleck and the Flecktones, itself a play on Dick Dale and the Del-Tones.
Answer: This Is Extreme, Survive! (If I Let You), Huka Blues
Question: What is the CGRO that detects photons?
Evidence: The Compton Gamma Ray Observatory (CGRO) was a space observatory detecting photons with energies from 20 keV to 30 GeV, in Earth orbit from 1991 to 2000. The observatory featured four main telescopes in one spacecraft, covering X-rays and gamma rays, including various specialized sub-instruments and detectors. Following 14 years of effort, the observatory was launched from Space Shuttle Atlantis during STS-37 on April 5, 1991, and operated until its deorbit on June 4, 2000. It was deployed in low Earth orbit at 450 km (280 mi) to avoid the Van Allen radiation belt. It was the heaviest astrophysical payload ever flown at that time at 17,000 kilograms (37,000 lb).
Answer: The CGRO is the Compton Gamma Ray Observatory (CGRO) and was a space observatory detecting photons with energies from 20 keV to 30GeV in earth from 1991 to 2000.
Question: Classify each of these as a type of instrument or a type of school subject: Mathematics, Saxophone, french horn, history, physical education, Oboe, science, drums, geography, flute, tuba
Answer: Types of instruments are: french Horn, oboe, drums, flute, and tuba. Types of school subjects are: mathematics, history, physical education, science, and geography.
Question: If we were playing a game where we had to identify things that can be found inside a house, which of these would we call out: car, chair, table, park, cloud, microwave.
Answer: Inside a house you can find a chair, a table and a microwave. Cars, clouds and parks are found outside of houses.
Question: What are the key elements of a sauna?
Answer: Saunas are a room with a heat source that will hold room temperature between 150 and 220 degrees Fahrenheit. The heat source is electric or wood fire. Typically saunas have an 8’ ceiling, and the size of the room is optimized for the number of people that are expected to use it concurrently. Saunas also need benches. The optimal bench depth is 24” to allow users to lay down comfortably. Bench and wall paneling is typically western red cedar, although other wood types that can withstand high swings in temperature are suitable as well. Proper wall, ceiling, and even floor insulation is critical, and given there will be water vapor in the room, there needs to be a vapor barrier between the wall panels and insulation. A floor drain is also highly encouraged.
Question: What architecture has the neural network?
Evidence: Our neural network model mainly consists of three parallel LSTM BIBREF21 layers. It has three different inputs, including the target comment, its news title and its username. Comment and news title are encoded into a sequence of word embeddings. We use pre-trained word embeddings in word2vec. Username is encoded into a sequence of characters. We use one-hot encoding of characters.
Our neural network model mainly consists of three parallel LSTM BIBREF21 layers.
Answer: The answers are shown as follows:
* three parallel LSTM BIBREF21 layers
Question: What is Mexico?
Answer: Mexico (Spanish: México), officially the United Mexican States, is a country in the southern portion of North America. It is bordered to the north by the United States; to the south and west by the Pacific Ocean; to the southeast by Guatemala, Belize, and the Caribbean Sea; and to the east by the Gulf of Mexico. Mexico covers 1,972,550 km2 (761,610 sq mi), making it the world's 13th-largest country by area; with a population of over 126 million, it is the 10th-most-populous country and has the most Spanish-speakers. Mexico is organized as a federal republic comprising 31 states and Mexico City, its capital. Other major urban areas include Monterrey, Guadalajara, Puebla, Toluca, Tijuana, Ciudad Juárez, and León.
Question: How do they incorporate human advice?
Evidence: While most relational learning methods restrict the human to merely annotating the data, we go beyond and request the human for advice. The intuition is that we as humans read certain patterns and use them to deduce the nature of the relation between two entities present in the text. The goal of our work is to capture such mental patterns of the humans as advice to the learning algorithm. We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients. This allows the system to trade-off between data and advice throughout the learning phase, rather than only consider advice in initial iterations. Advice, in particular, become influential in the presence of noisy or less amout of data. A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 . Note that some of the rules are “soft" rules in that they are not true in many situations. Odom et al. odomAAAI15 weigh the effect of the rules against the data and hence allow for partially correct rules.
A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 . 
We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients.
Answer: by converting human advice to first-order logic format and use as an input to calculate gradient
Question: You have to Classify each of the following as either a gluten product or a gluten free product and display it according to their categories: sourdough bread, zucchini, barbecue sauce, hummus, amaranth, commercial chocolate milk
Answer: Gluten is a protein found in wheat, barley, and rye. Some people have a gluten intolerance or celiac disease, which means they must avoid gluten in their diet to prevent uncomfortable or harmful symptoms. Gluten-free products are made without these grains and can be a safe alternative for those with gluten intolerance or celiac disease. However, for those without a gluten intolerance or celiac disease, a gluten-free diet may not provide any additional health benefits. 

Here are the those products which are classified as gluten products from the provided list:
1. sourdough bread 
2. barbecue sauce
3. commercial chocolate milk

Here are the those products which are classified as gluten free products from the list provided:
1. zucchini
2. hummus
3. amaranth
Question: If you were a vegetable, what vegetable would you be?
Answer: Only vegetables are vegetables. Computers are not vegetables. Algorithms are not vegetables. Vegetables are healthy and delicious. They are nutritious and full of vitamins. It is possible to use your imagination to consider what kind of vegetable you would like to be. If I was a vegetable, because I am named after a sheep, I would like to be a vegetable that sheep enjoy eating. Sheep enjoy eating winter squash, so I would like to be a winter squash if I had to choose a vegetable to be.
Question: Why do we use passwords?
Answer: Passwords are used in today's digital environment to protect and secure personal accounts.  They are a line of defense against domestic and abroad criminals trying to get access to your personal information in order to steal your identity, open credit cards in your name or steal your hard earned money from your bank accounts.   Password management is hard for a lot of people because they use the same password for every account, they write them down and cannot remember all of them, and have no way to effectively manage their passwords or secure them.
Question: write short essay on Neil Armstrong experience on moon
Answer: On July 20th, 1969, Neil Armstrong, an American astronaut, made history by becoming the first human to set foot on the Moon. As he stepped onto the lunar surface, he famously uttered the words, "That's one small step for man, one giant leap for mankind."

Armstrong's experience on the Moon was a momentous occasion that captured the world's attention. He spent just over two hours outside the spacecraft, collecting samples, conducting experiments, and taking photographs. He was joined by fellow astronaut Buzz Aldrin, who followed him out of the lunar module a short while later.

Despite the excitement of the moment, Armstrong later described his time on the Moon as a serene and otherworldly experience. The Moon's surface was eerily quiet, with no atmosphere to carry sound. Armstrong described the sky as "completely black," and the Earth as a "magnificent, lonely" sight in the distance.

Armstrong's mission to the Moon was a triumph of human ingenuity and perseverance. It marked a new era in space exploration and cemented America's position as a leader in the field. His bravery and dedication to science continue to inspire generations of scientists and explorers to this day.
Question: Why do people purchase ski passes?
Answer: People buy ski passes so that they have unlimited access to a mountain resort. If they visit the resort enough times in a season, they can save money by using a pass rather than buying individual tickets for each day. Also, using a pass makes it faster to enter the resort without having to stop at a ticket counter.
Question: Extract from the text the list of actors and the characters they play.  List answer as bullet list in in format:  {actor name}:  {character name}
Evidence: The Mandalorian is an American space Western television series created by Jon Favreau for the streaming service Disney+. It is the first live-action series in the Star Wars franchise, beginning five years after the events of Return of the Jedi (1983), and stars Pedro Pascal as the title character, a lone bounty hunter who goes on the run to protect the Force-sensitive child Grogu. Starting in the third season, Katee Sackhoff also stars as Mandalorian Bo-Katan Kryze, following her appearance in the second season.
Answer: - Pedro Pascal: Mandelorian
- Katee Sackhoff: Bo-Katan Kryze
Question: Which of the following are stringed instruments and which are wind instruments: horn, violin, cello, trumpet, flute, clarinet, guitar, sitar, ukulele, bouzouki, oboe, saxophone
Answer: Horn, trumpet, flute, clarinet, oboe and saxophone are wind instruments. Violine, cello, guitar, sitar, ukulele and bouzouki are string instruments.
Question: Name all of the field positions in baseball in a bulleted list.
Answer: The names of the field positions in baseball are:
- Pitcher
- Catcher
- First Base
- Second Base
- Third Base
- Shortstop
- Right Field
- Center Field
- Left Field
Question: What type of babies did Regalado say Jiankui was making?
Evidence: On 25 November 2018, Regalado posted on MIT Technology Review website that He Jiankui was making CRIPSR babies. As it was publicised, He was prompted to announce his experiment and posted the news of the birth of twins, nicknamed Lulu and Nana, on YouTube in five videos the same day. He formally presented the experiment at the Second International Summit on Human Genome Editing organized at the University of Hong Kong on 28 November 2018. He explained that the experiment was to make the babies resistant to HIV infection as they were (as embryos) obtained from an HIV-positive father. He specifically used a mutant gene named CCR5-Δ32 that is known to confer innate resistance to HIV. The twins were born in secrecy in October 2018, and a third baby (revealed in 2022 as Amy) was then almost born, as He reported.
Answer: Regalado said that Jiankui was making CRIPSR babies.
Question: Extract from the text the name of the movie in which Matilde Ciccia had a role
Evidence: Matilde Ciccia (born 6 October 1952 in Monasterace) is an Italian former ice dancer. Competing with Lamberto Ceserani, she won the gold medal at the Italian Figure Skating Championships several times. They finished fifth at the 1975 World Championships and sixth at the 1976 Winter Olympics. Ciccia had a role in the 1980 movie Zappatore.
Answer: Zappatore
Question: What are the different towns that make up New England?
Answer: New England is a region comprising six states in the Northeastern United States: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont.
Question: Is some other metrics other then perplexity measured?
Evidence: The evaluation of chatbots remains an open problem in the field. Recent work BIBREF25 has shown that the automatic evaluation metrics borrowed from machine translation such as BLEU score BIBREF26 tend to align poorly with human judgement. Therefore, in this paper, we mainly adopt human evaluation, along with perplexity, following the existing work.
The evaluation of chatbots remains an open problem in the field. Recent work BIBREF25 has shown that the automatic evaluation metrics borrowed from machine translation such as BLEU score BIBREF26 tend to align poorly with human judgement. Therefore, in this paper, we mainly adopt human evaluation, along with perplexity, following the existing work.
Answer: No.
Question: What is a good Indian restaurant in Redmond, WA
Answer: Kanishka
Question: Names some of the famous bravolebrities from Million Dollar Listing Los Angeles
Answer: Tracy Tutor, Josh Flagg, Heather Altman and Josh Altman
Question: Categorize these car models as either 'fully electric', 'hybrid', 'plug-in', 'gasoline': Toyota Camry 2017 SE, Tesla Model 3, Toyota Prius, 2022 Ford Escape Plug-In Hybrid, Rivian
Answer: Tesla cars and Rivian cars are fully electric. Toyota Prius is a hybrid car. 2022 Ford Escape Plug-In Hybrid is plug-in hybrid. Toyota Camry 2017 SE is full gasoline.
Question: How is the intensity of the PTSD established?
Evidence: To provide an initial results, we take 50% of users' last week's (the week they responded of having PTSD) data to develop PTSD Linguistic dictionary and apply LAXARY framework to fill up surveys on rest of 50% dataset. The distribution of this training-test dataset segmentation followed a 50% distribution of PTSD and No PTSD from the original dataset. Our final survey based classification results showed an accuracy of 96% in detecting PTSD and mean squared error of 1.2 in estimating its intensity given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively. Table TABREF29 shows the classification details of our experiment which provide the very good accuracy of our classification. To compare the outperformance of our method, we also implemented Coppersmith et. al. proposed method and achieved an 86% overall accuracy of detecting PTSD users BIBREF11 following the same training-test dataset distribution. Fig FIGREF28 illustrates the comparisons between LAXARY and Coppersmith et. al. proposed method. Here we can see, the outperformance of our proposed method as well as the importance of $s-score$ estimation. We also illustrates the importance of $\alpha -score$ and $S-score$ in Fig FIGREF30. Fig FIGREF30 illustrates that if we change the number of training samples (%), LAXARY models outperforms Coppersmith et. al. proposed model under any condition. In terms of intensity, Coppersmith et. al. totally fails to provide any idea however LAXARY provides extremely accurate measures of intensity estimation for PTSD sufferers (as shown in Fig FIGREF31) which can be explained simply providing LAXARY model filled out survey details. Table TABREF29 shows the details of accuracies of both PTSD detection and intensity estimation. Fig FIGREF32 shows the classification accuracy changes over the training sample sizes for each survey which shows that DOSPERT scale outperform other surveys. Fig FIGREF33 shows that if we take previous weeks (instead of only the week diagnosis of PTSD was taken), there are no significant patterns of PTSD detection.
 Our final survey based classification results showed an accuracy of 96% in detecting PTSD and mean squared error of 1.2 in estimating its intensity given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively. 
Answer: Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity  is established as mean  squared error.
Question: Who is the fastest person on earth?
Answer: Usain Bolt. He currently holds the world record in both the 100 meter and 200 meter dash, 
widely considered to be the best measurement of outright speed.