DataHammer/scimrc huggingface dataset:
Question: What year was the first public school in America founded?
Evidence: Boston is one of the oldest municipalities in America, founded on the Shawmut Peninsula in 1630 by Puritan settlers from the English town of the same name. It was the scene of several key events of the American Revolution and the nation's founding, such as the Boston Massacre, the Boston Tea Party, the Battle of Bunker Hill, and the siege of Boston. Upon American independence from Great Britain, the city continued to be an important port and manufacturing hub as well as a center for education and culture. The city has expanded beyond the original peninsula through land reclamation and municipal annexation. Its rich history attracts many tourists, with Faneuil Hall alone drawing more than 20 million visitors per year. Boston's many firsts include the United States' first public park (Boston Common, 1634), first public or state school (Boston Latin School, 1635) first subway system (Tremont Street subway, 1897), and first large public library (Boston Public Library, 1848).
Answer: The year 1635. Boston is known as having the first public or state school (Boston Latin School, 1635).
Question: what were the baselines?
Evidence: We refer the reader to BIBREF6 and its references for detailed model descriptions. We evaluate an RNN model which uses bidirectionally summed GRU memory cells BIBREF18 and uses the final states as embeddings; a CNN model which uses sentence-max-pooled convolutional filters as embeddings BIBREF19 ; an RNN-CNN model which puts the CNN on top of per-token GRU outputs rather than the word embeddings BIBREF20 ; and an attn1511 model inspired by BIBREF20 that integrates the RNN-CNN model with per-word attention to build hypothesis-specific evidence embeddings. We also report the baseline results of avg mean of word embeddings in the sentence with projection matrix and DAN Deep Averaging Network model that employs word-level dropout and adds multiple nonlinear transformations on top of the averaged embeddings BIBREF21 .
We evaluate an RNN model which uses bidirectionally summed GRU memory cells BIBREF18 and uses the final states as embeddings; a CNN model which uses sentence-max-pooled convolutional filters as embeddings BIBREF19 ; an RNN-CNN model which puts the CNN on top of per-token GRU outputs rather than the word embeddings BIBREF20 ; and an attn1511 model inspired by BIBREF20 that integrates the RNN-CNN model with per-word attention to build hypothesis-specific evidence embeddings. We also report the baseline results of avg mean of word embeddings in the sentence with projection matrix and DAN Deep Averaging Network model that employs word-level dropout and adds multiple nonlinear transformations on top of the averaged embeddings BIBREF21 .
Answer: The answers are shown as follows:
* RNN model
* CNN model 
* RNN-CNN model
* attn1511 model
* Deep Averaging Network model
* avg mean of word embeddings in the sentence with projection matrix
Question: How does their metric perform compared to ROUGE?
Evidence: Rouge has been the most widely used family of metrics in summarization evaluation. In the following, we briefly describe the different variants of Rouge:
Rouge-L, Rouge-W, Rouge-S and Rouge-SU were later extended to consider both the recall and precision. In calculating Rouge, stopword removal or stemming can also be considered, resulting in more variants.
In the summarization literature, despite the large number of variants of Rouge, only one or very few of these variants are often chosen (arbitrarily) for evaluation of the quality of the summarization approaches. When Rouge was proposed, the original variants were only recall-oriented and hence the reported correlation results BIBREF1 . The later extension of Rouge family by precision were only reflected in the later versions of the Rouge toolkit and additional evaluation of its effectiveness was not reported. Nevertheless, later published work in summarization adopted this toolkit for its ready implementation and relatively efficient performance.
The original Rouge metrics show high correlations with human judgments of the quality of summaries on the DUC 2001-2003 benchmarks. However, these benchmarks consist of newswire data and are intrinsically very different than other summarization tasks such as summarization of scientific papers. We argue that Rouge is not the best metric for all summarization tasks and we propose an alternative metric for evaluation of scientific summarization. The proposed alternative metric shows much higher and more consistent correlations with manual judgments in comparison with the well-established Rouge.
We argue that ROUGE is not the best metric for all summarization tasks and we propose an alternative metric for evaluation of scientific summarization. The proposed alternative metric shows much higher and more consistent correlations with manual judgments in comparison with the well-established ROUGE.
Answer: It shows much higher and more consistent correlations with manual judgments in comparison with the well-established ROUGE.
Question: What are the most popular implementations of Kafka?
Answer: There are multiple implementations of Apache Kafka available for the customers. Popular ones that you may come across are 

- Open Source Implementations (VMs, K8s, Bare metal though rare): On-Prem and Cloud
- Confluent Kafka
- Azure EventHub with Kafka API
- Amazon MSK

Apache Kafka is the open source implementation of Kafka that can be deployed in your own infrastructure on-premises or in the cloud. It provides the core functionalities of Kafka and is the most widely used implementation.

Confluent is a company founded by the creators of Kafka that offers a complete Kafka distribution, along with additional features such as a schema registry, KSQL DB, Kafka Connect, and a management UI.

Azure Event Hubs is a cloud-based messaging service provided by Microsoft Azure that is fully compatible with Kafka. It offers a managed Kafka service as well as a native API.

Amazon MSK (Managed Streaming for Kafka) is a fully managed Kafka service offered by Amazon Web Services. It is fully compatible with Apache Kafka and provides seamless integration with other AWS services.
Question: What is the legal difference between laches and waiver?
Answer: In legal parlance, laches is a defense which when raised implies the plaintiff caused inordinate delay, was part of the act of which it complains or caused prejudice to a defendant due to any delay. The Statute of Limitations is a common example of laches where a plaintiff is precluded from bringing an action due to inordinate delay.

A defense of waiver claims that the plaintiff knowingly and intentionally signed away, relinquished or otherwise abandoned the right which it claims was violated by the defendant. A common example of a waiver is when one engages in a dangerous act for pleasure such as sky diving or rock climbing. The instructor or facility would have the purchaser sign a waiver for bodily harm suffered from engaging in the dangerous act.

Both defenses are affirmative defenses and must be raised for them to take effect. It is important to note that affirmative defenses can themselves fall to laches and waiver.
Question: What did Jamaican-American author Nicola Yoon major in college for her graduate degree?
Answer: Creative Writing at Emerson College
Question: Which of the following numbers are prime?

1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2^11-1, 14, 17, 29, 47
Answer: Out of that list, the following numbers are prime: 2, 3, 5, 7, 17, 29, 47
Question: Tell me if these are baseball teams or football teams: Seattle Mariners, Chicago Cubs, Tampa Bay Devil Rays
Answer: They are all baseball teams
Question: which are rainbow colours violet red orange purple indigo pink blue black white yellow  green grey
Answer: VIBGYOR - Violet Indigo Blue Green Yellow orange red
Question: What is Ramadan?
Answer: Ramadan is the holiest of the 12 months in the Islamic calendar. In this month, Muslims all around the world observe fast from sunrise to sunset. 
This month is meant to bring solitaries and patience in the observer while they abstain from worldly pleasures and food and sequel desires.
Question: The authors mention that the nr of labels is equal to the number of instances in dataset X in the unsupervised case, but this is actually for supervised training right? In the unsupervised case there are no labels. Or do the authors refer to the pseudo labels then? But even then, in the supervised case this remark also holds.
Evidence: Following the previous setting, we evaluate the quality of representations on time series classification in a standard supervised manner. We train an SVM classifier with a radial basis function kernel on top of representations in the training split and then compare the prediction in the test set.
Datasets and Baselines. Two types of benchmark datasets are used for evaluations. The UCR archive is a collection of 128 univariate time series datasets and the UEA archive) consists of 30 multivariate datasets. We compare InfoTS with baselines including TS2Vec, T-Loss, TS-TCC, TST, and DTW. For our methods, InfoTS s , training labels are only used to train the meta-network to select suitable augmentations, and InfoTS is with a purely unsupervised setting for representation learning.
Performance. The results on the UEA datasets are summarized in   estimation and cross-entropy as approximations, we get the criteria in Eq. (). To empirically verify the effectiveness of the proposed criteria, we adopt the dataset CricketX from the UCR archive, and conduct augmentations with different configurations, such as jittering with noise from N (0, 0.3) and cutout 10% time stamps. For each configuration, we calculate the criteria score and the corresponding classification accuracy within the setting in Section 4.2. As shown in Figure, in general, accuracy performance is positively related to the proposed criteria in both supervised and unsupervised settings, verifying the correctness of using the criteria as the objective in the meta-network training.
Under the purely unsupervised setting, InfoTS preserves fidelity by adopting one-hot encoding as the pseudo labels.
Answer: Here, we refer to the pseudo labels, which means that the label is not ground truth.  We adopt one-hot encoding as the (pseudo) label, where the number of labels equals the number of instances in the dataset.  It can also be used in the supervised setting. However, it is unnecessary because we have the ground-truth labels in the supervised case, which provides additional information for selecting optimal augmentations.
Question: Given this paragraph about Zoom, where is the company based and when was the Zoom software released?
Evidence: Zoom Video Communications, Inc. (commonly shortened to Zoom, and stylized as zoom) is a communications technology company headquartered in San Jose, California. It provides videotelephony and online chat services through a cloud-based peer-to-peer software platform used for video communications (Meetings), messaging (Chat), voice calls (Phone), conference rooms for video meetings (Rooms), virtual events (Events) and contact centers (Contact Center), and offers an open platform allowing third-party developers to build custom applications on its unified communications platform (Developer Platform). The company was founded in 2011 by Eric Yuan, a former Cisco engineer and executive. It launched its software in 2013.
Answer: Zoom Video Communications, Inc is based in San Jose, California. The software launched by the company was released in 2013.
Question: Which of the following is a type of pasta: lasagna, sushi, or burrito?
Answer: Of the things listed, lasagna is pasta, while sushi and burrito are not.
Question: Imagine you are at the beach in Hawaii at sunset time. Describe what you see, smell and hear as you relax on a beach chair.
Answer: There is a beautiful sunset tonight, with every color of the rainbow painting the clouds in the sky. The horizon is still lit by the last rays of the sun, while higher up the sky is a beautiful cotton candy pink before bleeding into vibrant red and then fading into the fast approaching twilight. I'm watching a palm tree sway in the breeze, and I can hear the crashing waves and leaves rustling, along with the sounds of children playing in the surf and a nearby restaurant set up their bar for the night. The ocean breeze carries the smell of salt water through the air and mixes with the scent of the flowers from the nearby gardens.
Question: What is a pyroclastic flow?
Answer: A pyroclastic flow comes is created from a volcano that blasts out part of volcanic mountain into the sky.  It is a mixture of very hot volcanic rock, gases, ash and other trace elements that travels at hundred of miles per hour down the side of the volcanic mountain.  If you are in the way of a pyroclastic flow there is no escape and your life is in immense danger because of their temperature and mobility.  It is best to stay away from active volcanoes that could have a chance of developing a pyroclastic flow.  Pyroclastic flows have also been known to travel as far as 30 kilometers from the volcano.
Question: Write a short story about a toddler and her imaginary friend. She is with her family and excited but nervous about the new surroundings
Answer: She was about the leave the hotel room with her family. She watched as her parents packed up their bags and got her baby brother ready for a day out. They were busily talking to each other and running through their checklist of things to bring for the day out at the castle.
Feeling like she should help she asked Teetum, her imaginary friend to help her back her bag. Together her and Teetum packed imaginary sandwiches, imaginary rain coats and imaginary toys for the day out. This made her feel ready to face the excitement of the new city and the day out at the castle.
As they stepped out onto the street she held her mothers hand knowing they had everything they needed for the day.
Question: Is the RTE suitable as such to test a precise NLI system?
Evidence: We find that our hypothesis is validated: RTE is not suitable as such to test a precise NLI system, because, for entailment to hold as tagged in RTE, much world-knowledge is required and many missing hypotheses are omitted.
By using a crowd of experts to repair the missing hypotheses, we have constructed a dataset of 150 precise entailment problems, based on text found in real-world corpora. Even though the dataset is on the small size, it is, to the best of our knowledge, the first of this kind.
An issue with the method that we used to construct our new dataset of entailment problems is that it is difficult to scale: it demands several minutes of scarce expert work per constructed problem. Our plan is to investigate the possibility to gamify the process, so that lots of people can participate in the construction of precise entailment problems, as a form of entertainment. We leave any detail to further work, but this would be an asymmetric game, where one one player tries construct watertight entailment problems, and the opposing player would try and refute such entailment problems (say, by giving counter-examples). Identifying the possible kind of mistakes, as we have done here, will help prompting the players about things to look for — in either of the possible roles.
Finally we find it striking that many mistakes committed in RTE are falling for classical fallacies (appealing to authority accounts for more than a quarter of errors). Thus, we believe that while the entailment problems are useful for the construction of NLI systems, the added hypotheses (or moves taken in our hypothetical game) could be of interest to the linguistic community at large.
We find that our hypothesis is validated: RTE is not suitable as such to test a precise NLI system, because, for entailment to hold as tagged in RTE, much world-knowledge is required and many missing hypotheses are omitted.
Answer: No, it isn't.
Question: What is surfing?
Evidence: Surfing is a surface water sport in which an individual, a surfer (or two in tandem surfing), uses a board to ride on the forward section, or face, of a moving wave of water, which usually carries the surfer towards the shore. Waves suitable for surfing are primarily found on ocean shores, but can also be found in standing waves in the open ocean, in lakes, in rivers in the form of a tidal bore, or in wave pools.
Answer: Surfing is a sport where a rider uses a surfboard to ride a wave
Question: What is appealing about Charleston in South Carolina?
Answer: Charleston has a lot of historical sites that you can visit, which is great for educational purposes. The food is very diverse and provides many options for the pickiest of eaters. The low country is a beautiful scenery that sets the scene for cocktails, sunset catching and listening to the sounds of the creatures. There is a lot to see and learn from Charleston because of its southern charm and the people that have built its culture into a blossoming city.
Question: What is the difference between Kinesis and Kafka?
Answer: Kinesis and Kafka are both distributed streaming platforms that allow you to ingest, process, and store large volumes of streaming data. The main difference between the two is that Kinesis Data Streams  is a fully managed service provided by AWS, while Kafka is an open source project that can be run on any infrastructure.
Question: Identify which animal species is alive or extinct: Nuralagus, Black Spider Monkey
Answer: Black Spider Monkey is alive, Nuralagus is extinct.
Question: How do they capture the sentimental characteristics of words?
Evidence: We generate several word vectors, which capture the sentimental, lexical, and contextual characteristics of words. In addition to these mostly original vectors, we also create word2vec embeddings to represent the corpus words by training the embedding model on these datasets. After generating these, we combine them with hand-crafted features to create document vectors and perform classification, as will be explained in Section 3.5.
We generate several word vectors, which capture the sentimental, lexical, and contextual characteristics of words.
Answer: They generate several word vectors,which capture the sentimental, lexical, and contextual characteristics of words.
Question: Identify which instrument is string or woodwind: Tambura, Tubax
Answer: Tubax is woodwind, Tambura is string.
Question: What are the three lines of research in this paper?
Evidence: Our work is related to three lines of research: Sequence labeling, Adversarial training, and Crowdsourcing.
Sequence labeling. NER is widely treated as a sequence labeling problem, by assigning a unique label over each sentential word BIBREF9 . Early studies on sequence labeling often use the models of HMM, MEMM, and CRF BIBREF10 based on manually-crafted discrete features, which can suffer the feature sparsity problem and require heavy feature engineering. Recently, neural network models have been successfully applied to sequence labeling BIBREF1 , BIBREF11 , BIBREF2 . Among these work, the model which uses Bi-LSTM for feature extraction and CRF for decoding has achieved state-of-the-art performances BIBREF11 , BIBREF2 , which is exploited as the baseline model in our work.
Adversarial Training. Adversarial Networks have achieved great success in computer vision such as image generation BIBREF12 , BIBREF13 . In the NLP community, the method is mainly exploited under the settings of domain adaption BIBREF14 , BIBREF7 , cross-lingual BIBREF15 , BIBREF6 and multi-task learning BIBREF16 , BIBREF17 . All these settings involve the feature divergences between the training and test examples, and aim to learn invariant features across the divergences by an additional adversarial discriminator, such as domain discriminator. Our work is similar to these work but is applies on crowdsourcing learning, aiming to find invariant features among different crowdsourcing workers.
Crowdsourcing. Most NLP tasks require a massive amount of labeled training data which are annotated by experts. However, hiring experts is costly and non-scalable, both in terms of time and money. Instead, crowdsourcing is another solution to obtain labeled data at a lower cost but with relative lower quality than those from experts. BIBREF18 snow2008cheap collected labeled results for several NLP tasks from Amazon Mechanical Turk and demonstrated that non-experts annotations were quite useful for training new systems. In recent years, a series of work have focused on how to use crowdsourcing data efficiently in tasks such as classification BIBREF19 , BIBREF20 , and compare quality of crowd and expert labels BIBREF21 .
In sequence labeling tasks, BIBREF22 dredze2009sequence viewed this task as a multi-label problem while BIBREF3 rodrigues2014sequence took workers identities into account by assuming that each sentential word was tagged correctly by one of the crowdsourcing workers and proposed a CRF-based model with multiple annotators. BIBREF4 nguyen2017aggregating introduced a crowd representation in which the crowd vectors were added into the LSTM-CRF model at train time, but ignored them at test time. In this paper, we apply adversarial training on crowd annotations on Chinese NER in new domains, and achieve better performances than previous studies on crowdsourcing learning.
Our work is related to three lines of research: Sequence labeling, Adversarial training, and Crowdsourcing.
Answer: Sequence labeling, Adversarial training, and Crowdsourcing.
Question: In which country are the world's four busiest commuter rail stations located?
Evidence: Shibuya Station is a railway station in Shibuya, Tokyo, Japan, operated jointly by East Japan Railway Company (JR East), Keio Corporation, Tokyu Corporation, and Tokyo Metro. With 2.4 million passengers on an average weekday in 2004, it is the fourth-busiest commuter rail station in Japan and the world (after Shinjuku, Ikebukuro, and Ōsaka / Umeda) handling a large amount of commuter traffic between the city center and suburbs to the south and west.
Answer: Japan
Question: Which two kings fought at the Battle of Kressenbrunn according to the text?
Evidence: The Battle of Kressenbrunn (Hungarian: Első morvamezei csata / kroissenbrunni csata) was fought in July 1260 near Groissenbrunn in Lower Austria between the Kingdom of Bohemia and the Kingdom of Hungary for the possession of the duchies of Austria and Styria. The Bohemian forces were led by King Ottokar II Přemysl, while the Hungarians were led by his cousin, King Béla IV of Hungary .

In 1251, Ottokar's father King Wenceslaus I of Bohemia had not only granted him the title of a Margrave of Moravia, but also installed him as duke of Austria and Styria, territories that were princeless after the ruling Babenberg dynasty had become extinct in 1246. To legitimate his succession, Ottokar in 1252 married the Duchess Margaret of Austria, the sister of the last Babenberg duke and about 26 years his senior.
Answer: King Ottokar II and King Béla IV