DataHammer/scimrc huggingface dataset:
Question: Give me a list of films in which Walter Whitman appeared while he was under the age of 60.
Evidence: Walter K. Whitman (April 25, 1859 – March 27, 1928) was an American character actor of the stage and screen who was active during Hollywood's silent era. He is not to be confused with the influential poet of the same name.

Whitman had a long career on the stage in cities like New York, Boston, and Chicago before he began appearing in Triangle films in the 1910s, at which point he was already an older man. He died on March 27, 1928, in Santa Monica, California.

"I was only a country lad, but I had seen so many real good shows at our local opera house that I made up my mind I would be an actor," he'd later tell reporters of his beginnings. He also recounted that in 1896, he leapt from the fifth floor of the Hotel Richelieu in Montreal when it caught on fire.

Selected filmography
Missing Daughters (1924)
Long Live the King (1923)
The Grub-Stake (1923)
The Love Letter (1923)
Wasted Lives (1923)
Hearts Aflame (1923)
A Question of Honor (1922)
The Fire Bride (1922)
The Girl from Rocky Point (1922)
The New Disciple (1921)
The Mysterious Rider (1921)
His Nibs (1921)
The Girl from God's Country (1921)
The Three Musketeers (1921)
The Home Stretch (1921)
The Mark of Zorro (1920)
Darling Mine (1920)
Passion's Playground (1920)
Dangerous Hours (1919)
John Petticoats (1919)
When Bearcat Went Dry (1919)
Pretty Smooth (1919)
Destiny (1919)
The Cry of the Weak (1919)
Whom the Gods Would Destroy (1919)
The Heart of Humanity (1918)
Desert Law (1918)
Daughter Angele (1918)
They're Off (1918)
The Price of Applause (1918)
False Ambition (1918)
Everywoman's Husband (1918)
His Enemy, the Law (1918)
The Last Rebel (1918)
Old Hartwell's Cub (1918)
Captain of His Soul (1918)
The Hopper (1918)
Without Honor (1918)
The Regenerates (1917)
The Firefly of Tough Luck (1917)
The Tar Heel Warrior (1917)
Polly Ann (1917)
Wee Lady Betty (1917)
The Girl, Glory (1917)
The Millionaire Vagrant (1917)
The Desert Man (1917)
Paddy O'Hara (1917)
The Boss of the Lazy Y (1917)
The Dark Road (1917)
The Last of the Ingrams (1917)
Princess of the Dark (1917)
The Sin Ye Do (1916)
The Criminal (1916)
The Honorable Algy (1916)
The Three Musketeers (1916)
The Mating (1915)
Answer: Walter Whitman, an American actor in silent films, was born in 1859, so he was age 60 in 1919. Here is a list of the films he appeared in before 1919:

The Heart of Humanity (1918)
Desert Law (1918)
Daughter Angele (1918)
They're Off (1918)
The Price of Applause (1918)
False Ambition (1918)
Everywoman's Husband (1918)
His Enemy, the Law (1918)
The Last Rebel (1918)
Old Hartwell's Cub (1918)
Captain of His Soul (1918)
The Hopper (1918)
Without Honor (1918)
The Regenerates (1917)
The Firefly of Tough Luck (1917)
The Tar Heel Warrior (1917)
Polly Ann (1917)
Wee Lady Betty (1917)
The Girl, Glory (1917)
The Millionaire Vagrant (1917)
The Desert Man (1917)
Paddy O'Hara (1917)
The Boss of the Lazy Y (1917)
The Dark Road (1917)
The Last of the Ingrams (1917)
Princess of the Dark (1917)
The Sin Ye Do (1916)
The Criminal (1916)
The Honorable Algy (1916)
The Three Musketeers (1916)
The Mating (1915)
Question: Where are most apples grown?
Answer: The United States is the second largest apple producer worldwide, behind China. U.S. production is followed by Poland, Italy and France. The United States grows approximately 200 unique apple varieties.
Question: Please provide some information about Worcester Park railway station from the given passage
Evidence: Worcester Park railway station serves the Worcester Park area in south-west London, England. It is 10 miles 53 chains (17.2 km) down the line from London Waterloo. It opened in 1859 when the London and South Western Railway completed the Epsom branch. It was originally known as "Old Malden" and was renamed "Worcester Park" in 1862. Following substantial local housing development, the station was refurbished in the 1930s.
Answer: 1. The Worcester Park neighbourhood in southwest London, England, is served by the Worcester Park railway station.
2. It is 10.53 chains (17.2 km) away from London Waterloo on the same line.
3. After the London and South Western Railway finished building the Epsom branch, it was inaugurated.
4. Worcester Park replaced the earlier designation of "Old Malden" in 1862.
5. The station was renovated in the 1930s after significant local housing construction.
Question: Where is Horsfieldia nervosa nervosa endemic to?
Evidence: Horsfieldia nervosa is a species of plant in the family Myristicaceae. It is a tree endemic to Borneo where it is confined to Sarawak.
Answer: Borneo
Question: Name some popular computer games from early this millenia.
Answer: World of Warcraft, Grand Theft Auto, Final Fantasy X and Guitar Hero were all popular games in the first decade of the 2000s.
Question: Without quoting directly from the text give me a summary of the differences between Iyengar Yoga and other yoga practices.
Evidence: According to the Iyengar Yoga Institute, unlike more experiential approaches where students are encouraged to independently "find their way" to the asanas by imitating the teacher, an Iyengar Yoga class is precise, with misalignments and errors actively explained and corrected. It states that the style "emphasizes precision and alignment", and prioritizes correct movement over quantity, i.e. moving a small amount in the right direction is preferred to moving more but in other directions. Postures are held for a relatively long period of time compared to other schools of yoga; this allows the muscles to relax and lengthen, and encourages awareness in the pose. Props including belts, blocks and blankets are freely used to assist students in correct working in the asanas.
Answer: Based on the given text, Iyengar yoga differs from other types of yoga in the following points:

By focusing on precision and alignment, Iyengar yoga gives you precise instructions and continuously explains and corrects the misalignments and errors. It also prioritizes the quality of the postures over the intensity of doing them. 

It also keeps the practitioners in the postures for a longer time in comparison to other yoga styles.

Also the practitioners are provided with different props like belts, blocks and blankets that they can use freely to help themselves to perform the asanas correctly.
Question: What is the state capitol of Nevada?
Answer: The state capitol of Nevada is Carson City which was founded in 1858 and is named after Kit Carson.
Question: What is contrafact?
Evidence: Parker's style of composition involved interpolation of original melodies over existing jazz forms and standards, a practice known as contrafact and still common in jazz today. Examples include "Ornithology" (which borrows the chord progression of jazz standard "How High the Moon" and is said to be co-written with trumpet player Little Benny Harris), and "Moose The Mooche" (one of many Parker compositions based on the chord progression of "I Got Rhythm"). The practice was not uncommon prior to bebop, but it became a signature of the movement as artists began to move away from arranging popular standards and toward composing their own material. Perhaps Parker's most well-known contrafact is "Koko," which is based on the chord changes of the popular bebop tune "Cherokee," written by Ray Noble.
Answer: Contrafact is a type of jazz composition where the composer takes the basic structure of a popular song, rearranges it and provides a new melody to the song. Charlie Parker was a jazz musician and composer who used this style of composition extensively. Some of the best known examples of Parker's use of contrafact are:
"Ornithology"- Based on the popular song "How High the Moon"
"Moose the Moose"- Based on the standard "I've Got Rhythm"
"Koko"-Based on the swing standard "Cherokee"
Question: Do they propose any further additions that could be made to improve generalisation to unseen speakers?
Evidence: There are various possible extensions for this work. For example, using all frames assigned to a phone, rather than using only the middle frame. Recurrent architectures are natural candidates for such systems. Additionally, if using these techniques for speech therapy, the audio signal will be available. An extension of these analyses should not be limited to the ultrasound signal, but instead evaluate whether audio and ultrasound can be complementary. Further work should aim to extend the four classes to more a fine-grained place of articulation, possibly based on phonological processes. Similarly, investigating which classes lead to classification errors might help explain some of the observed results. Although we have looked at variables such as age, gender, or amount of data to explain speaker variation, there may be additional factors involved, such as the general quality of the ultrasound image. Image quality could be affected by probe placement, dry mouths, or other factors. Automatically identifying or measuring such cases could be beneficial for speech therapy, for example, by signalling the therapist that the data being collected is sub-optimal.
There are various possible extensions for this work. For example, using all frames assigned to a phone, rather than using only the middle frame.
Answer: Yes.
Question: In the main four professional sports leagues, what professional sports teams don't end in "S?"
Answer: In total there are eleven teams across the major four professional sports leagues that do not end in "S." There are four NBA teams that do not end in "S." Those teams are the Miami Heat, Oklahoma City Thunder, Orlando Magic and Utah Jazz. There are four NHL teams. Those are the Colorado Avalanche, Minnesota Wild, Seattle Kraken and Tampa Bay Lightning. Two MLB teams do not end in "S" and those are the Boston Red Sox and Chicago White Sox. The Washington Football Team is the only NFL team that does not end in "S."
Question: What is a computer?
Answer: A computer is an electronic device that generates output from input by the user. A computer can have a visual display to show user input as well as output. Modern computers have lot of sophisticated parts which includes a monitor for display, a keyboard for typing input, a mouse for browsing and CPU for processing the input.
Question: How can you take good star photos?
Answer: Taking good star photos requires 3 critical elements: good observation conditions, good equipment and plenty of time. Picking a dark site without a lot of light pollution that's away from cities and other strong sources of light is a must. The weather also has to be clear, without clouds, and preferably on a night without full moon which is another source of light pollution. On the equipment side, if you're planning to use a cellphone, make sure that it has a night mode and you have a way to keep it immobile for the duration of the photo, which could take 4 or more minutes. You will get better results with a digital camera as those have larger lenses which can capture more light. Also remember that the longer the photo, the more the earth will turn, causing your starts to appear as trails in the photo - which may or may not be desired. For professional looking photos you'll need a telescope, and remember that no matter what you do, your photos won't look anything like those coming from Hubble and other large telescopes. Lastly, plan on spending plenty of time - the longer the exposure the better you can see the stars, and you might want to take several photos - try different parts of the sky, see what happens when you include the ground or not, and remember to let your eyes adjust to the dark and look around yourself, while you're taking the photos!
Question: Write a script about a dystopian past where wars are fought between religion and technology. A younger challenges the ways of the elders.
Answer: The Order of the Skvll is holding a sacred meeting, with all members present. As per tradition, they are wearing hooded cloaks and masks. The entire auditorium is packed, and the high council of elders is leading the dialogue. 

Quo: “The tides of war are shifting and we stand on the edge of a perilous era. We must adapt, or be left behind. We cannot afford to be complacent.” 

Masked Elder 1(Male):  “Centuries of warfare have honed our blades and sharpened our senses. This coming conflict shall be no different.”

Quo: “Blinded by pride, you fail to see the threat that looms ahead. The "machines" have evolved. They will soon possess powers that surpass our own. Our sole prospect for defeating them rests in our ability to match their strength, to attain a power that rivals their own.” 
 
Masked Elder 2 (Female): “It’s not through technology that we find our strength, for true power comes from the divine within us. It is found in our ability to connect and draw from its boundless source. The Gods have granted us protection and with their aid, we shall vanquish our foes, as we always have.“ 

Quo: “The idea of divine strength is a fascinating one, but the Gods cannot protect us from the harsh realities upon us. Our duty, as protectors of our realm and the outer worlds, is to remain vigilant against the many threats that seek to undermine our safety. My oath remains resolute.’

Masked elder3 leans in next to her and says: “Quo, I beg you, don’t do this..” With worry in his eyes.
Question: What performance did they obtain on the SemEval dataset?
Evidence: We implement our model in Python using Tensorflow on a single GPU. We experiment with six different BiLSTM based architectures. The three architectures correspond to BiLSTM based systems without primary attention i.e. only with secondary attention for sentiment analysis (S1), emotion analysis (E1) and the multi-task system (M1) for joint sentiment and emotion analysis. The remaining three architectures correspond to the systems for sentiment analysis (S2), emotion analysis (E2) and multi-task system (M2), with both primary and secondary attention. The weight matrices were initialized randomly using numbers form a truncated normal distribution. The batch size was 64 and the dropout BIBREF34 was 0.6 with the Adam optimizer BIBREF35. The hidden state vectors of both the forward and backward LSTM were 300-dimensional, whereas the context vector was 150-dimensional. Relu BIBREF36 was used as the activation for the hidden layers, whereas in the output layer we used sigmoid as the activation function. Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis.
We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.
FLOAT SELECTED: TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.
F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. 
We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18.
FLOAT SELECTED: TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.
Answer: F1 score of 82.10%
Question: What type of errors do the classifiers use?
Evidence: In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error:
the correct class can be directly inferred from the text content easily, even without background knowledge
the correct class can be inferred from the text content, given that event-specific knowledge is provided
the correct class can be inferred from the text content if the text is interpreted correctly
We can identify three main cases for which the model produces an error:

the correct class can be directly inferred from the text content easily, even without background knowledge

the correct class can be inferred from the text content, given that event-specific knowledge is provided

the correct class can be inferred from the text content if the text is interpreted correctly
Answer: The answers are shown as follows:
* correct class can be directly inferred from the text content easily, even without background knowledge
* correct class can be inferred from the text content, given that event-specific knowledge is provided
* orrect class can be inferred from the text content if the text is interpreted correctly
Question: Which neural network architectures are employed?
Evidence: To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
Answer: The answers are shown as follows:
* gated neural network
Question: How to do forecasting with small dataset?
Answer: Forecasting with small datasets can be challenging, as many traditional methods require larger amounts of data for accurate predictions.
Here are few approaches that can help you.

Simple moving average: This involves taking the average of a certain number of past data points, and using it as the forecast for the next period. This method can be effective for stable time series with no trend or seasonality.

Exponential smoothing: This method assigns exponentially decreasing weights to past observations, with more recent observations being given more weight. This method can be effective for time series with trend and/or seasonality.

ARIMA: The AutoRegressive Integrated Moving Average (ARIMA) model is a popular choice for time series forecasting. It can be effective for small datasets if the underlying data follows a pattern that can be modeled using an ARIMA model.
Question: What was discovered in 1922 by Howard Carter
Answer: Tutankamen tomb
Question: Without quoting directly from the text, give me a summary of the Voyager 1 space mission
Evidence: Voyager 1 is a space probe launched by NASA on September 5, 1977, as part of the Voyager program to study the outer Solar System and interstellar space beyond the Sun's heliosphere. Launched 16 days after its twin Voyager 2, Voyager 1 has been operating for 45 years, 7 months and 1 day as of April 6, 2023 UTC . It communicates through NASA's Deep Space Network to receive routine commands and to transmit data to Earth. Real-time distance and velocity data is provided by NASA and JPL. At a distance of 159.20 AU (23.816 billion km; 14.799 billion mi) from Earth as of March 27, 2023, it is the most distant human-made object from Earth.

The probe made flybys of Jupiter, Saturn, and Saturn's largest moon, Titan. NASA had a choice of either doing a Pluto or Titan flyby; exploration of the moon took priority because it was known to have a substantial atmosphere. Voyager 1 studied the weather, magnetic fields, and rings of the two gas giants and was the first probe to provide detailed images of their moons.

As part of the Voyager program and like its sister craft Voyager 2, the spacecraft's extended mission is to locate and study the regions and boundaries of the outer heliosphere and to begin exploring the interstellar medium. Voyager 1 crossed the heliopause and entered interstellar space on August 25, 2012, making it the first spacecraft to do so. Two years later, Voyager 1 began experiencing a third "tsunami wave" of coronal mass ejections from the Sun that continued to at least December 15, 2014, further confirming that the probe is indeed in interstellar space.

In a further testament to the robustness of Voyager 1, the Voyager team tested the spacecraft's trajectory correction maneuver (TCM) thrusters in late 2017 (the first time these thrusters had been fired since 1980), a project enabling the mission to be extended by two to three years. Voyager 1's extended mission is expected to continue until about 2025, when its radioisotope thermoelectric generators (RTGs) will no longer supply enough electric power to operate its scientific instruments.
Answer: The Voyager 1 space mission began on September 5, 1977 when the probe was launched with mission parameters to explore out solar system, planets, and outer solar system beyond the sun.  The mission is currently in it's 45th year and has provided significant learning about the atmosphere of planets like Jupiter and Saturn, while continuing to scientific data on regions of space never before encountered.
Question: What pretrained LM is used?
Evidence: We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model. GPT is a multi-layer Transformer decoder with a causal self-attention which is pre-trained, unsupervised, on the BooksCorpus dataset. BooksCorpus dataset contains over 7,000 unique unpublished books from a variety of genres. Pre-training on such large contiguous text corpus enables the model to capture long-range dialogue context information. Furthermore, as existing EmpatheticDialogue dataset BIBREF4 is relatively small, fine-tuning only on such dataset will limit the chitchat topic of the model. Hence, we first integrate persona into CAiRE, and pre-train the model on PersonaChat BIBREF3 , following a previous transfer-learning strategy BIBREF1 . This pre-training procedure allows CAiRE to have a more consistent persona, thus improving the engagement and consistency of the model. We refer interested readers to the code repository recently released by HuggingFace. Finally, in order to optimize empathy in CAiRE, we fine-tune this pre-trained model using EmpatheticDialogue dataset to help CAiRE understand users' feeling.
We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model. 
Answer: The answers are shown as follows:
* Generative Pre-trained Transformer (GPT)