DataHammer/scimrc huggingface dataset:
Question: Is hyperparameter Selection important in Neural network Systems?
Evidence: This work analyses, empirically, optimal combinations of hyper-parameters for embeddings, specifically for word2vec. It further shows that for downstream tasks, like NER and SA, there's no silver bullet! However, some combinations show strong performance across tasks. Performance of embeddings is task-specific and high analogy scores do not necessarily correlate positively with performance on downstream tasks. This point on correlation is somewhat similar to results by BIBREF24 and BIBREF14. It was discovered that increasing dimension size depreciates performance after a point. If strong considerations of saving time, energy and the environment are made, then reasonably smaller corpora may suffice or even be better in some cases. The on-going drive by many researchers to use ever-growing data to train deep neural networks can benefit from the findings of this work. Indeed, hyper-parameter choices are very important in neural network systems (BIBREF19).
Future work that may be investigated are performance of other architectures of word or sub-word embeddings, the performance and comparison of embeddings applied to languages other than English and how embeddings perform in other downstream tasks. In addition, since the actual reason for the changes in best model as corpus size increases is not clear, this will also be suitable for further research.
The work on this project is partially funded by Vinnova under the project number 2019-02996 "Språkmodeller för svenska myndigheter"
The on-going drive by many researchers to use ever-growing data to train deep neural networks can benefit from the findings of this work. Indeed, hyper-parameter choices are very important in neural network systems.
Answer: Yes.
Question: What was Dick Lammi's most famous work?
Evidence: Dick Lammi (January 15, 1909 – November 29, 1969) was an American jazz tubist and bassist associated with Dixieland jazz. Lammi played violin and banjo early in his career, and played as a banjoist in various groups in the Pacific Northwest in the late 1920s. He settled in Portland, Oregon in the early 1930s, and played bass in a group there; after a move to San Francisco in 1936, he began playing tuba alongside bass. His best-known work was as a member of Lu Watters's band, the Yerba Buena Jazz Band. Lammi played in the ensemble from 1941 to 1950, including on virtually all of their recordings.
Answer: Dick Lammi was most famous for his work as a member of Lu Watters's band, the Yerba Buena Jazz Band.
Question: What do they intend to explore in the future?
Evidence: In this paper, we investigated the application of deep neural network architectures for the task of hate speech detection. We found them to significantly outperform the existing methods. Embeddings learned from deep neural network models when combined with gradient boosted decision trees led to best accuracy values. In the future, we plan to explore the importance of the user network features for the task.
In the future, we plan to explore the importance of the user network features for the task.
Answer: The importance of the user network features for the task.
Question: What is a Tommy John Surgery?
Evidence: Ulnar collateral ligament reconstruction, colloquially known as Tommy John surgery (TJS), is a surgical graft procedure where the ulnar collateral ligament in the medial elbow is replaced with either a tendon from elsewhere in the patient's body, or with one from a deceased donor. The procedure is common among collegiate and professional athletes in several sports, particularly in baseball.
Answer: A surgical graft procedure where the ulnar collateral ligament in the medial elbow is replaced with either a tendon from elsewhere in the patient's body, or with one from a deceased donor.
Question: Why is the traffic so bad in Seattle Tuesday through Thursday?
Answer: One possible explanations is that they are common commute days in a week. The traffic is bad in Seattle attributes to several factors: it's a major metropolitan city with almost 2 million people in the area; its geographic features that prevent building roads more extensively esp connecting segments/patches across lakes and rivers; big companies such as Boeing, Microsoft, Amazon have big footprints in Seattle and the locations of their offices spread the area can also force people to commute around in workdays.
Question: Suggest some sports I can do solo?
Answer: You can run, swim, cycle, dance - all by yourself.
Question: Why are small datasets chosen for the experiment？
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods.
Answer: Their reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for their methods.
Question: Who is best Michael Schumacher or Hamilton
Answer: Michael Schumacher is the best player in the world.The time he drove the car is more dangerous and was not regulated like the modern F1
Question: Name the popular children’s books by Roald Dahl.
Evidence: Roald Dahl (13 September 1916 – 23 November 1990) was a British popular author of children's literature and short stories, a poet, and wartime fighter ace. His books have sold more than 300 million copies worldwide. Dahl has been called "one of the greatest storytellers for children of the 20th century".
Dahl was born in Wales to affluent Norwegian immigrant parents, and spent most of his life in England. He served in the Royal Air Force (RAF) during the Second World War. He became a fighter pilot and, subsequently, an intelligence officer, rising to the rank of acting wing commander. He rose to prominence as a writer in the 1940s with works for children and for adults, and he became one of the world's best-selling authors. His awards for contribution to literature include the 1983 World Fantasy Award for Life Achievement and the British Book Awards' Children's Author of the Year in 1990. In 2008, The Times placed Dahl 16th on its list of "The 50 Greatest British Writers Since 1945". In 2021, Forbes ranked him the top-earning dead celebrity.
Dahl's short stories are known for their unexpected endings, and his children's books for their unsentimental, macabre, often darkly comic mood, featuring villainous adult enemies of the child characters. His children's books champion the kindhearted and feature an underlying warm sentiment. His works for children include James and the Giant Peach, Charlie and the Chocolate Factory, Matilda, The Witches, Fantastic Mr Fox, The BFG, The Twits, George's Marvellous Medicine and Danny, the Champion of the World. His works for older audiences include the short story collections Tales of the Unexpected and The Wonderful Story of Henry Sugar and Six More.
Answer: Roald Dahl’s works for children include James and the Giant Peach, Charlie and the Chocolate Factory, Matilda, The Witches, Fantastic Mr Fox, The BFG, The Twits, George's Marvellous Medicine and Danny, the Champion of the World.
Question: What is the best Florida Key
Answer: There are so many keys in Florida to choose from! If you are looking for lively nightlife, you want to check out Key West, as that is the busiest and most traveled Florida Key. If you would like something a bit more low-key but still with plenty to do, Key Largo or Marathon Key may be a better fit. Any Florida Key has plenty to do, and they are all close together and easy to drive between so you could visit many different locations while you are there.
Question: What is protein purification?
Answer: Protein purification is a series of processes intended to isolate one or a few proteins from a complex mixture, usually cells, tissues or whole organisms. Protein purification is vital for the specification of the function, structure and interactions of the protein of interest. The purification process may separate the protein and non-protein parts of the mixture, and finally separate the desired protein from all other proteins. Ideally, to study a protein of interest, it must be separated from other components of the cell so that contaminants won't interfere in the examination of the protein of interest's structure and function. Separation of one protein from all others is typically the most laborious aspect of protein purification. Separation steps usually exploit differences in protein size, physico-chemical properties, binding affinity and biological activity. The pure result may be termed protein isolate.
Question: What leads to further improvements that the author finds?
Evidence: Named entity typing is the task of detecting the type (e.g., person, location, or organization) of a named entity in natural language text. Entity type information has shown to be useful in natural language tasks such as question answering BIBREF0 , knowledge-base population BIBREF1 , BIBREF2 , and co-reference resolution BIBREF3 . Motivated by its application to downstream tasks, recent work on entity typing has moved beyond standard coarse types towards finer-grained semantic types with richer ontologies BIBREF0 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . Rather than assuming an entity can be uniquely categorized into a single type, the task has been approached as a multi-label classification problem: e.g., in “... became a top seller ... Monopoly is played in 114 countries. ...” (fig:arch), “Monopoly” is considered both a game as well as a product.
The state-of-the-art approach BIBREF8 for fine-grained entity typing employs an attentive neural architecture to learn representations of the entity mention as well as its context. These representations are then combined with hand-crafted features (e.g., lexical and syntactic features), and fed into a linear classifier with a fixed threshold. While this approach outperforms previous approaches which only use sparse binary features BIBREF4 , BIBREF6 or distributed representations BIBREF9 , it has a few drawbacks: (1) the representations of left and right contexts are learnt independently, ignoring their mutual connection; (2) the attention on context is computed solely upon the context, considering no alignment to the entity; (3) document-level contexts which could be useful in classification are not exploited; and (4) hand-crafted features heavily rely on system or human annotations.
To overcome these drawbacks, we propose a neural architecture (fig:arch) which learns more context-aware representations by using a better attention mechanism and taking advantage of semantic discourse information available in both the document as well as sentence-level contexts. Further, we find that adaptive classification thresholds leads to further improvements. Experiments demonstrate that our approach, without any reliance on hand-crafted features, outperforms prior work on three benchmark datasets.
Further, we find that adaptive classification thresholds leads to further improvements.
Answer: Adaptive classification thresholds.
Question: Provide some key points on Cardiac surgery from the given text
Evidence: Cardiac surgery, or cardiovascular surgery, is surgery on the heart or great vessels performed by cardiac surgeons. It is often used to treat complications of ischemic heart disease (for example, with coronary artery bypass grafting); to correct congenital heart disease; or to treat valvular heart disease from various causes, including endocarditis, rheumatic heart disease, and atherosclerosis. It also includes heart transplantation.
Answer: 1. Cardiac surgery, often known as cardiovascular surgery, is heart or large vessel surgery carried out by cardiac surgeons.
2. It is frequently used to cure congenital heart disease, treat valvular heart disease from many causes, including endocarditis, rheumatic heart disease, and atherosclerosis, or to address complications of ischemic heart disease (for instance, with coronary artery bypass grafting).
3. Heart transplantation is also a part of it.
Question: What would you suggest as to how someone could sell a bike?
Answer: There are a number of ways to sell a bike. The simplest way is to ask friends or family if someone would be interested in purchasing, next look at your closest community. Usually in apartments, or even in towns, there are community noticeboards that you could use to advertise your bike. Finally, there are many online community sites on platforms such as Facebook, where you could list your bike for sale.
Question: Based on the reference text, provide a bulleted list of the benefits workers receive from Proposition 22
Evidence: 2020 California Proposition 22

Proposition 22 was a ballot initiative in California that became law after the November 2020 state election, passing with 59% of the vote and granting app-based transportation and delivery companies an exception to Assembly Bill 5 by classifying their drivers as "independent contractors", rather than "employees". The law exempts employers from providing the full suite of mandated employee benefits (which include time-and-a-half for overtime, paid sick time, employer-provided health care, bargaining rights, and unemployment insurance) while instead giving drivers new protections:

120 percent of the local minimum wage for each hour a driver spends driving (with passenger or en route), but not for time spent waiting
$0.30/mile for expenses for each mile driven with passenger or en route
health insurance stipend for drivers who average more than 15 hours per week driving
requiring the companies to pay medical costs and some lost income for drivers hurt while driving or waiting
prohibiting workplace discrimination and requiring that companies develop sexual harassment policies, conduct criminal background checks, and mandate safety training for drivers.
The initiative became state law after passage in November 2020, was challenged in California state courts in 2021, and was upheld on appeal in 2023.
Answer: Under Proposition 22, workers receive the following benefits:
- 120% of the local minimum wage
- $0.30 per mile driven
- Health insurance stipend
- Covered medical costs and some lost income for injuries on the job
- Workplace discrimination protections
Question: What are some movies everyone should see since the year 2000?
Answer: Gladiator, Wedding Crashers, Old School, Green Book, CODA, Whiplash, Joker, Hangover, The Social Network, The Dark Knight, Gangs of New York
Question: How does running slow make you run faster?
Answer: There has been a recent shift in thinking about the types of training that makes a distance runner faster while mitigating the risk of injuries.  It has been found that running up to 80% of your  weekly miles at a very comfortable slow pace was optimal for the fastest improvement.  A slow pace can be measured by running at or below 70% of your maximum heart rate (MHR=220 beats per minute minus your age).  For those who don't have a heart rate monitor, another way to measure that you are in this zone is by being able to have a conversation without being out of breath. This “run slow to get faster" methodology has been embraced by the elite running community and some believe has contributed to the multiple recent world records for both men and women in the middle and long distances.  This has also been shown to have the same effect on beginner runners and had tremendous benefits that resulted in improved overall speed performance, while reducing the risk of injury in getting to your race day.
Question: Which of these flowers are annuals vs perennials: Petunias, Verbena, Alyssum, Zinnias, Digitalis Dalmatian, Amsonia, Clementine, Peonies.
Answer: Petunias, Verbena, Alyssum, and Zinnias are annual flowers, they bloom one season and then die. 

Digitalis Dalmatian, Amsonia, Clementine, and Peonies are perennials. They can bloom for many season.
Question: What are different types of grass?
Answer: There are more than 12,000 species of grass. The most common is Kentucky Bluegrass, because it grows quickly, easily, and is soft to the touch. Rygrass is shiny and bright green colored. Fescues are dark green and shiny. Bermuda grass is harder but can grow in drier soil.
Question: Prove that 2 + 2 = 5.
Answer: 2 + 2 does not equal 5, it equals 4.
Question: Which of the following are names of flowers: daffodil, rose, quinoa, lily, daisy, elm, pyrite, violet, jade, leather, polycarbonate, jasmine
Answer: daffodil, rose, lily, daisy, violet, jasmine
Question: What are the most common food allergies?
Evidence: Allergic reactions are abnormal immune responses that develop after exposure to a given food allergen. Food allergens account for about 90% of all allergic reactions. The most common food allergens include milk, eggs, peanuts, tree nuts, fish, shellfish, soy, and wheat, which are referred to as "the big eight". In April of 2021, President Joe Biden signed the FASTER Act into law. This landmark food allergy law recognizes sesame as the ninth mandatory food label allergen. It also encourages research that will improve the quality of life for food allergy families, and that could potentially lower the rates of future food allergies.
Answer: The most common allergies in humans are:
- Milk
- Eggs
- Peanuts
- Shellfish
- Wheat
- Soy
- Tree nuts
Question: What is COCOMO
Evidence: The Constructive Cost Model (COCOMO) is a procedural software cost estimation model developed by Barry W. Boehm. The model parameters are derived from fitting a regression formula using data from historical projects (63 projects for COCOMO 81 and 163 projects for COCOMO II).
Answer: COCOMO is a software cost/effort estimation tool built on regression algorithm
Question: How is the architecture fault-tolerant?
Evidence: KryptoOracle has been built in the Apache ecosystem and uses Apache Spark. Data structures in Spark are based on resilient distributed datasets (RDD), a read only multi-set of data which can be distributed over a cluster of machines and is fault tolerant. Spark applications run as separate processes on different clusters and are coordinated by the Spark object also referred to as the SparkContext. This element is the main driver of the program which connects with the cluster manager and helps acquire executors on different nodes to allocate resource across applications. Spark is highly scalable, being 100x faster than Hadoop on large datasets, and provides out of the box libraries for both streaming and machine learning.
Spark RDD has the innate capability to recover itself because it stores all execution steps in a lineage graph. In case of any faults in the system, Spark redoes all the previous executions from the built DAG and recovers itself to the previous steady state from any fault such as memory overload. Spark RDDs lie in the core of KryptoOracle and therefore make it easier for it to recover from faults. Moreover, faults like memory overload or system crashes may require for the whole system to hard reboot. However, due to the duplicate copies of the RDDs in Apache Hive and the stored previous state of the machine learning model, KryptoOracle can easily recover to the previous steady state.
KryptoOracle has been built in the Apache ecosystem and uses Apache Spark. Data structures in Spark are based on resilient distributed datasets (RDD), a read only multi-set of data which can be distributed over a cluster of machines and is fault tolerant. 
Spark RDD has the innate capability to recover itself because it stores all execution steps in a lineage graph. In case of any faults in the system, Spark redoes all the previous executions from the built DAG and recovers itself to the previous steady state from any fault such as memory overload. Spark RDDs lie in the core of KryptoOracle and therefore make it easier for it to recover from faults. Moreover, faults like memory overload or system crashes may require for the whole system to hard reboot. However, due to the duplicate copies of the RDDs in Apache Hive and the stored previous state of the machine learning model, KryptoOracle can easily recover to the previous steady state.


Answer: By using Apache Spark which stores all executions in a lineage graph and recovers to the previous steady state from any fault
Question: Classify the following animals as either plant-only eaters or meat eaters: kangaroo, camel, bear, bison, cat, hippo, wolf, raccoon, alligator, hawk, rabbit, koala, horse, zebra, giraffe, goat, shark, dog, chicken, snake.
Answer: Animals that eat only plants are called Herbivores. Those include: bison, camel, giraffe, goat, hippo, horse, kangaroo, koala, rabbit, zebra.
Animals that eat only meat are called carnivores. Animals that eat a combination of meat and plants are called omnivores. Carnivores and omnivores include: alligator, bear, cat, chicken, dog, hawk, raccoon, shark, snake, wolf.