DataHammer/scimrc huggingface dataset:
Question: Is the model evaluated on the graphemes-to-phonemes task?
Evidence: To increase the benefits of our data for ASR systems, we also trained a grapheme-to-phoneme (g2p) model: Out-of-vocabulary words can be a problem for ASR system. For those out-of-vocabulary words we need a model that can generate pronunciations from a written form, in real time. This is why we train a grapheme-to-phoneme (g2p) model that generates a sequence of phonemes for a given word. We train the g2p model using our dictionary and compare its performance with a widely used joint-sequence g2p model, Sequitur BIBREF26. For the g2p model we are using the same architecture as for the p2g model. The only difference is input and output vocabulary. The Sequitur and our model are using the dictionary with the same train (19'898 samples), test (2'412 samples) and validation (2'212 samples) split. Additionally, we also test their performance only on the items from the Zurich and Visp dialect, because most of the samples are from this two dialects. In Table TABREF15 we show the result of the comparison of the two models. We compute the edit distance between the predicted and the true pronunciation and report the number of exact matches. In the first columns we have the result using the whole test set with all the dialects, and in the 2nd and 3rd columns we show the number of exact matches only on the samples from the test set that are from the Zurich and Visp dialect. For here we can clearly see that our model performs better than the Sequitur model. The reason why we have less matches in the Visp dialect compared to Zurich is because most of the our data is from the Zurich dialect.
Additionally, we also test their performance only on the items from the Zurich and Visp dialect, because most of the samples are from this two dialects. In Table TABREF15 we show the result of the comparison of the two models.
Answer: Yes.
Question: what genres do they songs fall under?
Evidence: FLOAT SELECTED: Table 1: The number of songs and artists by genre
From the Vagalume's music web page, we collect the song title and lyrics, and the artist name. The genre was collected from the page of styles, which lists all the musical genres and, for each one, all the artists. We selected only 14 genres that we consider as representative Brazilian music, shown in Table TABREF8. Figure FIGREF6 presents an example of the Vagalume's music Web page with the song “Como é grande o meu amor por você”, of the Brazilian singer Roberto Carlos. Green boxes indicate information about music that can be extracted directly from the web page. From this information, the language in which the lyrics are available can be obtained by looking at the icon indicating the flag of Brazil preceded by the “Original” word.
FLOAT SELECTED: Table 1: The number of songs and artists by genre
We selected only 14 genres that we consider as representative Brazilian music, shown in Table TABREF8.
Answer: Gospel, Sertanejo, MPB, Forró, Pagode, Rock, Samba, Pop, Axé, Funk-carioca, Infantil, Velha-guarda, Bossa-nova and Jovem-guarda
Question: What is the most intuitive way of collecting reading comprehension questions?
Evidence: As a starting point for our pilots, we made use of texts from the InScript corpus BIBREF10 , which provides stories centered around everyday situations (see Section SECREF7 ). We conducted three different pilot studies to determine the best way of collecting questions that require inference over commonsense knowledge:
The most intuitive way of collecting reading comprehension questions is to show texts to workers and let them formulate questions and answers on the texts, which is what we tried internally in a first pilot. Since our focus is to provide an evaluation framework for inference over commonsense knowledge, we manually assessed the number of questions that indeed require common sense knowledge. We found too many questions and answers collected in this manner to be lexically close to the text.
In a second pilot, we investigated the option to take the questions collected for one text and show them as questions for another text of the same scenario. While this method resulted in a larger number of questions that required inference, we found the majority of questions to not make sense at all when paired with another text. Many questions were specific to a text (and not to a scenario), requiring details that could not be answered from other texts. Since the two previous pilot setups resulted in questions that centered around the texts themselves, we decided for a third pilot to not show workers any specific texts at all. Instead, we asked for questions that centered around a specific script scenario (e.g. eating in a restaurant). We found this mode of collection to result in questions that have the right level of specificity for our purposes: namely, questions that are related to a scenario and that can be answered from different texts (about that scenario), but for which a text does not need to provide the answer explicitly.
The next section will describe the mode of collection chosen for the final dataset, based on the third pilot, in more detail.
The most intuitive way of collecting reading comprehension questions is to show texts to workers and let them formulate questions and answers on the texts, which is what we tried internally in a first pilot.
Answer: The most intuitive way of collecting reading comprehension questions is to show texts to workers and let them formulate questions and answers on the texts, which is what they tried internally in a first pilot.
Question: Does spark provide date & time when a particular event is received in the structured streaming?
Answer: Not directly.  The source of the data is responsible for event time, the “arrival” time is typically the timestamp from the message server source you are reading from (Kafka, Kinesis, etc.) and the “processing” time can be inserted when processing the incomming data stream using the functions package current_timestamp function. https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions$  This isn’t exactly when Spark received the data from the server but is close and will be grouped by trigger interval.  Note that this function can not be used when in continuous mode.  https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#supported-queries
Question: What is Davis, CA known for?
Answer: Being the bike capitol of the U.S. as well as one of the top agriculture cities. UC Davis has bike paths everywhere across its campus - more so than streets for cars! Additionally, there is a consistent smell of manure throughout the campus due to the high number of cows. It's a very nice city to bike around as well as grow a garden in!
Question: What automatic metrics are used?
Evidence: To evaluate the responses generated by all compared methods, we compute the following automatic metrics on our test set:
BLEU: BLEU-n measures the average n-gram precision on a set of reference responses. We report BLEU-n with n=1,2,3,4.
Distinct-1 & distinct-2 BIBREF5: We count the numbers of distinct uni-grams and bi-grams in the generated responses and divide the numbers by the total number of generated uni-grams and bi-grams in the test set. These metrics can be regarded as an automatic metric to evaluate the diversity of the responses.
To evaluate the responses generated by all compared methods, we compute the following automatic metrics on our test set:

BLEU: BLEU-n measures the average n-gram precision on a set of reference responses. We report BLEU-n with n=1,2,3,4.

Distinct-1 & distinct-2 BIBREF5: We count the numbers of distinct uni-grams and bi-grams in the generated responses and divide the numbers by the total number of generated uni-grams and bi-grams in the test set. These metrics can be regarded as an automatic metric to evaluate the diversity of the responses.
Answer: The answers are shown as follows:
* BLEU
* Distinct-1 & distinct-2
Question: Which country has topped the swimming medals list in the summer olympics?
Evidence: Summary
Games	Year	Events	Best Nation
1	1896	4	 Hungary
2	1900	7	 Great Britain
3	1904	9	 Germany
4	1908	6	 Great Britain
5	1912	9	 Germany
6	
7	1920	10	 United States
8	1924	11	 United States
9	1928	11	 United States
10	1932	11	 Japan
11	1936	11	 Japan
12	
13	
14	1948	11	 United States
15	1952	11	 United States
16	1956	13	 Australia
Games	Year	Events	Best Nation
17	1960	15	 United States
18	1964	18	 United States
19	1968	29	 United States
20	1972	29	 United States
21	1976	26	 United States
22	1980	26	 East Germany
23	1984	29	 United States
24	1988	31	 East Germany
25	1992	31	 United States
26	1996	32	 United States
27	2000	32	 United States
28	2004	32	 United States
29	2008	34	 United States
30	2012	34	 United States
31	2016	34	 United States
32	2020	37	 United States
Answer: United States
Question: What are the models evaluated on?
Evidence: We build the iSQuAD and iNewsQA datasets based on SQuAD v1.1 BIBREF0 and NewsQA BIBREF1. Both original datasets share similar properties. Specifically, every data-point consists of a tuple, $\lbrace p, q, a\rbrace $, where $p$ represents a paragraph, $q$ a question, and $a$ is the answer. The answer is a word span defined by head and tail positions in $p$. NewsQA is more difficult than SQuAD because it has a larger vocabulary, more difficult questions, and longer source documents.
iMRC: Making MRC Interactive ::: Evaluation Metric
Since iMRC involves both MRC and RL, we adopt evaluation metrics from both settings. First, as a question answering task, we use $\text{F}_1$ score to compare predicted answers against ground-truth, as in previous works. When there exist multiple ground-truth answers, we report the max $\text{F}_1$ score. Second, mastering multiple games remains quite challenging for RL agents. Therefore, we evaluate an agent's performance during both its training and testing phases. During training, we report training curves averaged over 3 random seeds. During test, we follow common practice in supervised learning tasks where we report the agent's test performance corresponding to its best validation performance .
We build the iSQuAD and iNewsQA datasets based on SQuAD v1.1 BIBREF0 and NewsQA BIBREF1.
iMRC: Making MRC Interactive ::: Evaluation Metric
Since iMRC involves both MRC and RL, we adopt evaluation metrics from both settings. First, as a question answering task, we use $\text{F}_1$ score to compare predicted answers against ground-truth, as in previous works. When there exist multiple ground-truth answers, we report the max $\text{F}_1$ score. Second, mastering multiple games remains quite challenging for RL agents. Therefore, we evaluate an agent's performance during both its training and testing phases. During training, we report training curves averaged over 3 random seeds. During test, we follow common practice in supervised learning tasks where we report the agent's test performance corresponding to its best validation performance .
Answer: They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA)
Question: what is a computer system?
Answer: A computer system is a nominally complete computer that includes the hardware, operating system (main software), and peripheral equipment needed and used for full operation. This term may also refer to a group of computers that are linked and function together, such as a computer network or computer cluster.
Question: When was Scarlett Johansson born?
Evidence: Scarlett Ingrid Johansson (/dʒoʊˈhænsən/; born November 22, 1984) is an American actress. The world's highest-paid actress in 2018 and 2019, she has featured multiple times on the Forbes Celebrity 100 list. Time named her one of the 100 most influential people in the world in 2021. Her films have grossed over $14.3 billion worldwide, making Johansson the highest-grossing box office star of all time. She has received various accolades, including a Tony Award and a British Academy Film Award, in addition to nominations for two Academy Awards and five Golden Globe Awards.
Answer: Scarlett Johansson was born November 22, 1984.
Question: Provide instructions on making a peanut butter and jelly sandwich
Answer: Making the perfect peanut butter and jelly sandwich is part art and part science. One must must be judicious in the quantity of ingredients while ensuring proper coverage to ensure each bite is equal to the one before and after it. Here, both measurement and distribution of ingredients serves as the scientific aspect of constructing your delicious and timeless lunchtime meal. Jars of peanut utter suggest a serving size as 2 tablespoons, which, when applied to the critical soft white bread, may prove too much to hold and cause soak through or tearing of the bread. We think 1.5 tablespoons is the proper amount to ensure there is coverage of the bread slice to the crust without compromising the integrity of the bread. Jelly application is even more precarious, as it holds more fluid and can soak through the bread quicker; equally, jelly has a tendency to congeal. To ensure proper application, heat the jar of jelly closer to room temperature if refrigerated, then apply using a clean butter knife or small spatula. Again, one must ensure it reaches to the crust and is evenly distributed. Once you have complete coverage of peanut butter on one slice and jelly on the other, place the peanut butter side of the first slice on top of the upward-facing jelly slice. This ensure none of the jelly slips or drips. Once together, gently compress the sandwich. Slice delicately from one corner to the opposite corner. Gently press the crust edge of each triangle, and plate for serving.
Question: What are the 12 AV approaches which are examined?
Evidence: As a basis for our experiments, we reimplemented 12 existing AV approaches, which have shown their potentials in the previous PAN-AV competitions BIBREF11 , BIBREF12 as well as in a number of AV studies. The methods are listed in Table TABREF33 together with their classifications regarding the AV characteristics, which we proposed in Section SECREF3 .
FLOAT SELECTED: Table 2: All 12 AVmethods, classified according to their properties.
The methods are listed in Table TABREF33 together with their classifications regarding the AV characteristics, which we proposed in Section SECREF3 .
FLOAT SELECTED: Table 2: All 12 AVmethods, classified according to their properties.
Answer: MOCC, OCCAV, COAV, AVeer, GLAD, DistAV, Unmasking, Caravel, GenIM, ImpGI, SPATIUM and NNCD
Question: What datasets have this method been evaluated on?
Evidence: We evaluate a multi-task approach and three algorithms that explicitly model the task dependencies. We perform experiments on document-level variants of the SQuAD dataset BIBREF1 . The contributions for our papers are:
We perform experiments on document-level variants of the SQuAD dataset BIBREF1 .
Answer: The answers are shown as follows:
* document-level variants of the SQuAD dataset
Question: Name some famous teams in the Premier League
Answer: Manchester United, Manchester City, Arsenal, Liverpool, Chelsea, Hotspur, etc
Question: Given a reference text about Jeremiah Mutwalante Twa-Twa, when and where was he born?
Evidence: Jeremiah Mutwalante Twa-Twa (born 29 January 1951) is a Ugandan physician, public health specialist, and politician. His last name is sometimes spelled "Twatwa". He is the elected Member of Parliament representing Iki-Iki County, Budaka District. He has served in that capacity since May 2011. Before that, he served as the director of the Division of Child Health at the Uganda Ministry of Health from 2005 until 2010.

Background and education
He was born in Budaka District on 29 January 1951. Twa-Twa attended Sekulo Primary School in Budaka District from 1959 until 1964. He then studied at Mbale College in Mbale District from 1965 until 1966. He undertook his O-Level studies (S1-S4) at Jinja College in Jinja District from 1967 until 1970. In 1971, he entered Namilyango College in Mukono District to pursue his A-Level (S5-S6) education, graduating in 1972.

He graduated from Makerere University Medical School in 1978 with a Bachelor of Medicine and Bachelor of Surgery degree. In 1981, he obtained a Diploma in Public Health from the Makerere University School of Public Health. In 1995, he received a Master of Arts degree in demography from the same university. He also holds a Certificate in Epidemiology, awarded in 1983 by the University of Nairobi.[
Answer: Jeremiah Mutwalante Twa-Twa was born in the Budaka District of Uganda on January 29, 1951.
Question: According to this paragraph, what is UVA's overall ranking among national universities?
Evidence: U.S. News & World Report ranks UVA tied for 25th among national universities overall, 3rd among public universities and tied for 110th among global universities in its 2022 report, 8th best business program and 3rd best business management program.
Answer: UVA tied for 25th among national universities.
Question: Summarise from the given text, Why do people like pets?
Evidence: Pets provide their owners, or guardians, both physical and emotional benefits. Walking a dog can provide both the human and the dog with exercise, fresh air, and social interaction. Pets can give companionship to people who are living alone or elderly adults who do not have adequate social interaction with other people. There is a medically approved class of therapy animals that are brought to visit confined humans, such as children in hospitals or elders in nursing homes. Pet therapy utilizes trained animals and handlers to achieve specific physical, social, cognitive, or emotional goals with patients.
Answer: 1. Pets benefit their owners/guardians both physically and emotionally.
2. Talking a dog for a stroll helps both human and dog through physical exercise, inhaling fresh air and social interaction.
3. For people who live alone, aged people in nursery homes, children in hospitals etc who don't interact with other people, There is a class of medically approved therapy animals that are brought to visit to gain specific type of physical, social, cognitive, or emotional benefits
Question: Are the most accurate word recognition models always the most robust against adversarial attacks when used as a defense mechanism?
Evidence: As character and word-piece inputs become commonplace in modern NLP pipelines, it is worth highlighting the vulnerability they add. We show that minimally-doctored attacks can bring down accuracy of classifiers to random guessing. We recommend word recognition as a safeguard against this and build upon RNN-based semi-character word recognizers. We discover that when used as a defense mechanism, the most accurate word recognition models are not always the most robust against adversarial attacks. Additionally, we highlight the need to control the sensitivity of these models to achieve high robustness.
We discover that when used as a defense mechanism, the most accurate word recognition models are not always the most robust against adversarial attacks.
Answer: No.
Question: Why is Utah a great place to visit?
Answer: Utah is a great state to visit especially for nature lovers. There are 5 famous national parks in Utah also known as The Mighty Five: Zion, Bryce Canyon, Arches, Canyonlands and Capitol Reef. Many parks are close to well developed cities which provide great lodging, dining and entertaining options. Park facilities are well maintained. There are numerous recreational activities in the park for all levels and ages and the views are absolutely stunning.
Question: How many continents are there on Earth?
Answer: There are 7 continents - Africa, Antarctica, Asia, Australia, Europe, North America, and South America.
Question: Name some popular American wine brands.
Answer: Josh, E&J Gallo, Justin, Chateau Ste. Michelle, Little Penguin, Franzia, Sutter Home
Question: Whether the proposed model can generate Chinese classical poems of major types?
Evidence: Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form-stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University (Guo et al., 2019).
Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy.
Answer: Yes.
Question: When is the best time of year to visit Seattle?
Answer: Seattle is a wonderful city with a variety of different tourist activities. The summer is the most popular time to visit Seattle. Seattle receives the most sun in the summer, which allows for outdoor activities like hiking, boating, and sight seeing. The winters are often cold, dark, and overcast, which most tourists typically avoid. However, if you enjoy winter sports like skiing and snow shoeing, it may be the best time to visit.
Question: How many subscribers are inside the United States?
Evidence: The English-language print publication has a circulation of 30,000 qualified subscribers, of which 7,000 are outside the United States.
Answer: 30,000 - 7,000 = 23,000
Question: Give me some tips for increase my savings
Answer: To increase the savings, one needs to always strike a mindful balance between wants and needs and more importantly, it also depends on individual circumstances. Here are some things that one can start doing to increase the savings

1. Try to pay off the debt as soon as possible.
2. The principal of "Pay oneself first". It doesn't mean spend this money on wishes, but on the contrary, first put the fixed amount in the savings accounts and then try to go by your expenses.
3. Track your expenses. Sometimes we just don't know that we have so many useless subscriptions that are squeezing your hard earned money under your own eyes.
4. Try to cook at home rather than going to the restaurants or ordering food online on frequent basis.
Question: If you find yourself being pursued by a hungry bear in the woods, label each of these items in your backpack as either 'useful', 'not useful', or 'an unfortunate choice to pack':  a fish, a gun, a book, a hamburger.
Answer: In such a situation, being pursued by a hungry bear in the woods, a fish would be 'not useful', a gun would be 'useful', a book would be 'not useful', and a hamburger would be 'an unfortunate choice to pack'.