DataHammer/scimrc huggingface dataset:
Question: Where do the concept sets come from?
Evidence: Towards empowering machines with the generative commonsense reasoning ability, we create a large-scale dataset, named CommonGen, for the constrained text generation task. We collect $37,263$ concept-sets as the inputs, each of which contains three to five common concepts. These concept-sets are sampled from several large corpora of image/video captions, such that the concepts inside them are more likely to co-occur in natural scenes. Through crowd-sourcing via Amazon Mechanical Turk (AMT), we finally obtain $89,028$ human-written sentences as expected outputs. We investigate the performance of sophisticated sequence generation methods for the proposed task with both automatic metrics and human evaluation. The experiments show that all methods are far from human performance in generative commonsense reasoning. Our main contributions are as follows: 1) We introduce the first large-scale constrained text generation dataset targeting at generative commonsense reasoning; 2) We systematically compare methods for this (lexically) constrained text generation with extensive experiments and evaluation. 3) Our code and data are publicly available (w/ the URL in the abstract), so future research in this direction can be directly developed in a unified framework.
Following the general definition in the largest commonsense knowledge graph, ConceptNet BIBREF11, we understand a concept as a common noun or verb. We aim to test the ability of generating natural scenes with a given set of concepts. The expected concept-sets in our task are supposed to be likely co-occur in natural, daily-life scenes . The concepts in images/videos captions, which usually describe scenes in our daily life, thus possess the desired property. We therefore collect a large amount of caption sentences from a variety of datasets, including VATEX BIBREF4, LSMDC BIBREF12, ActivityNet BIBREF13, and SNLI BIBREF15, forming 1,040,330 sentences in total.
We collect $37,263$ concept-sets as the inputs, each of which contains three to five common concepts. These concept-sets are sampled from several large corpora of image/video captions, such that the concepts inside them are more likely to co-occur in natural scenes.
The expected concept-sets in our task are supposed to be likely co-occur in natural, daily-life scenes . The concepts in images/videos captions, which usually describe scenes in our daily life, thus possess the desired property. We therefore collect a large amount of caption sentences from a variety of datasets, including VATEX BIBREF4, LSMDC BIBREF12, ActivityNet BIBREF13, and SNLI BIBREF15, forming 1,040,330 sentences in total.
Answer: The answers are shown as follows:
* These concept-sets are sampled from several large corpora of image/video captions
Question: What are the statistics about San Francisco International Airport?
Evidence: San Francisco International Airport (IATA: SFO, ICAO: KSFO, FAA LID: SFO) is an international airport in an unincorporated area of San Mateo County, 13 miles (21 km) south of Downtown San Francisco. It has flights to points throughout North America and is a major gateway to Europe, the Middle East, Asia, and Oceania.

SFO is the largest airport in the San Francisco Bay Area and the second-busiest in California, after Los Angeles International Airport (LAX). In 2017, it was the seventh-busiest airport in the United States and the 24th-busiest in the world by passenger count. It is the fifth-largest hub for United Airlines, which operates out of Terminal 3 and the International Terminal. SFO functions as United's primary transpacific gateway. Additionally, SFO is a major maintenance hub for United Airlines and houses the SFO Museum that was created in 1980, the first museum in an international airport. It also serves as a hub for Alaska Airlines, which operates in Terminal 2.

The airport is owned and operated by the City and County of San Francisco and lies in a ZIP code assigned to San Francisco. However, it is located outside of San Francisco in unincorporated San Mateo County. Between 1999 and 2004, the San Francisco Airport Commission operated city-owned SFO Enterprises, Inc., to oversee its business purchases and operations of ventures.
Answer: It is an international airport 13 miles south of San Francisco. It is the largest airport in San Francisco Bay Area and second largest in the state of California. It is a 7th business airport in United States and 24th in the world by passenger count. It is one of the largest hub for United Airlines. it houses the SFO Museum which was created in 1980. It is owned and operated by the city and county of San Francisco
Question: How big is dataset used for fine-tuning BERT?
Evidence: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. We extracted text from the agreements, tokenized it into sentences, and removed sentences without alphanumeric text. We selected the BERT-Base uncased pre-trained model for fine-tuning. To avoid including repetitive content found at the beginning of each agreement we selected the 31st to 50th sentence of each agreement. We ran unsupervised fine-tuning of BERT using sequence lengths of 128, 256 and 512. The loss function over epochs is shown in Figure FIGREF3.
To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.
Answer: The answers are shown as follows:
* hundreds of thousands of legal agreements
Question: Using valid, well-formatted JSON, generate a list of espresso spellings based on the text provided
Evidence: The spelling expresso is mostly considered incorrect, though some sources call it a less common variant. It is common in French and Portuguese. Italy uses the term espresso, substituting s for most x letters in Latin-root words; x is not considered part of the standard Italian alphabet. Italian people commonly refer to it simply as caffè (coffee), espresso being the ordinary coffee to order; the same happens in Portugal (café), with some regional variations (bica in Lisbon and cimbalino traditionally in Porto); in Spain, while café expreso is seen as the more "formal" denomination, café solo (alone, without milk) is the usual way to ask for it when at an espresso bar.
Answer: espresso, expresso, coffee, caffè, café, bica, cimbalino, café expreso, café solo
Question: What is Sogndal?
Evidence: Sogndal is a municipality in Vestland county, Norway. It is located on the northern shore of the Sognefjorden in the traditional district of Sogn. The village of Hermansverk is the administrative center of Sogndal municipality. Other villages include Kaupanger, Kjørnes, Fimreite, Nornes, and Fjærland. Sogndal Airport, Haukåsen is located 10 kilometres (6.2 mi) southwest of Kaupanger.
Answer: Sogndal is a small city in southwestern Norway with a population of about 12,000
Question: Are these things typically yellow, green or red: the sun, a banana, blood, lettuce, a tomato, grass, peas, and a heart emoji.
Answer: The items that are usually yellow are the sun and a banana. The red items are blood, a tomato, and a heart emoji. The green items are lettuce, grass and peas.
Question: What was Barthelemy Hus-Desforges famous for
Evidence: Barthélemy Hus, called Hus-Desforges (18 July 1699 in Bordeaux – 1 September 1786 in Lyon, aged 63) was an 18th-century French comedian and troupe leader.

The son of maître à danser  Jérôme Hus and Marguerite Pageot, called Desforges, he was the youngest member of the Hus family and with his brother François, directed a touring company known as the "Hus brothers troupe."

Around 1722, he married Marie Anne Daguerre Ascorette, actress born in Namur (1709-1736). The couple had at least seven children who would be part of the troupe.

On 27 January 1750, he married in Brussels his second wife, Louise Vivier de Courtenay, descendant of the famous Courtenay family.

Barthélemy Hus-Desforges was the grandfather of musician Pierre-Louis Hus-Desforges (1773-1838).
Answer: Barthelemy Hus, called Hus-Desforges (18 July 1699 in Bordeaux – 1 September 1786 in Lyon, aged 63) was an 18th-century French comedian and troupe leader.

The son of maitre a danser  Jerome Hus and Marguerite Pageot, called Desforges, he was the youngest member of the Hus family and with his brother Francois, directed a touring company known as the "Hus brothers troupe."

Around 1722, he married Marie Anne Daguerre Ascorette, actress born in Namur (1709-1736). The couple had at least seven children who would be part of the troupe.

On 27 January 1750, he married in Brussels his second wife, Louise Vivier de Courtenay, descendant of the famous Courtenay family.

Barthelemy Hus-Desforges was the grandfather of musician Pierre-Louis Hus-Desforges (1773-1838).
Question: Tell me what are these visual charts appropriate for? 

Choropleth map, heatmap, scatter plot
Density plot, horizon chart, line chart,  Geographic heatmap , Bar chart
Answer: Although any of these charts might be used for various purposes, here is a list of use cases and corresponding chart types;

Comparison: Bar chart, line chart
Correlation: heatmap, scatter plot
Distribution: Density plot, horizon chart
Geospatial: Choropleth map,  Geographic heatmap
Question: Identify all of the breeds of dogs in this list:

Banana
Poodle
Pekingese
Hunan
Apple
Peach
Lettuce
Arubian Cucunu
German Shepherd
Jack Russell Terrier
Wolfhound
Pariah Dog
Tamales
Coquito
Mezcal
Gingko
Papaya
Sequoia
Redwood
Maple
Dogwood
Afghan Hound
Airedale Terrier
Camphor Tree
Eucalyptus
Chihuaha
Basenji
Bichon Frise
Blue Spruce
Magnolia
Cacao
Black Locust
Tequila
Answer: The following are dog breeds that are found in the list: 
Poodle
Pekingese
Arubian Cucunu
German Shepherd
Jack Russell Terrier
Wolfhound
Afghan Hound
Airedale Terrier
Chihuahua
Basenji
Bichon Frise
Question: What do the experimental results of this paper indicate?
Evidence: In this paper, we proposed a model that utilizes chunked timelines of tweets and a recurrent neural model in order to infer the factuality of a Twitter news account. Our experimental results indicate the importance of analyzing tweet stream into chunks, as well as the benefits of heterogeneous knowledge source (i.e., lexica as well as text) in order to capture factuality. In future work, we would like to extend this line of research with further in-depth analysis to understand the flow change of the used features in the accounts' streams. Moreover, we would like to take our approach one step further incorporating explicit temporal information, e.g., using timestamps. Crucially, we are also interested in developing a multilingual version of our approach, for instance by leveraging the now ubiquitous cross-lingual embeddings BIBREF22, BIBREF23.
Our experimental results indicate the importance of analyzing tweet stream into chunks, as well as the benefits of heterogeneous knowledge source (i.e., lexica as well as text) in order to capture factuality.
Answer: The importance of analyzing tweet stream into chunks, as well as the benefits of heterogeneous knowledge source (i.e., lexica as well as text) in order to capture factuality.
Question: What is industrial espionage?
Answer: Industrial espionage, economic espionage, corporate spying, or corporate espionage is a form of espionage conducted for commercial purposes instead of purely national security. 
While political espionage is conducted or orchestrated by governments and is international in scope, industrial or corporate espionage is more often national and occurs between companies or corporations.
Question: Could you please describe the winter of Seattle?
Answer: The winters in Seattle can be very cold and unpredictable. Some days are rainy, some are snowy, and some are just plain cold. The winters used to be difficult to tolerate. My family had to escape to Hawaii or other places to get some sunshine in the mid of the winter. However, since I started to ski with my son, the winter became fun. I started to looking forward the coming of the winter.
Question: Where did xiaolongbao originate from and what are some key types
Evidence: "Xiaolongbao" originated in Changzhou, Jiangsu province, by Wan Hua Tea House in the years of Daoguang Emperor (1820 to 1850). Xiaolongbao evolved from the guantangbao (soup-filled dumplings/buns) from Kaifeng, Henan province, the capital city of Northern Song Dynasty (AD 960–1127).

There are numerous styles of xiaolongbao in Jiangsu cuisine. Shanghai-style xiaolongbao originated in Nanxiang, which was a neighboring village of Shanghai in Jiangsu that eventually ended up becoming an outer suburb of Shanghai's Jiading District. The inventor of xiaolongbao sold them in his first store in Nanxiang next to the town's notable park, Guyi Garden. From there the xiaolongbao expanded into downtown Shanghai and outward. The Suzhou and Wuxi styles are larger (sometimes twice as large as a Nanxiang-style soup dumpling) and have sweeter fillings. The Nanjing style is smaller with an almost translucent skin and less meat.
Answer: Xiaolongbao were first seen in Chengzhou which is located in Jiangsu province.
Since it's humble beginnings it has spread far and wide and there exist many variants.
A few common types are the:
- Shanghai Style
- Suzhou Style
- Wuxi Style
- Nanjing Style
Question: When was Galaga released?
Evidence: Galaga is a 1981 fixed shooter arcade video game developed and published by Namco. In North America, it was released by Midway Manufacturing. It is the sequel to Galaxian (1979), Namco's first major video game hit in arcades. Controlling a starship, the player is tasked with destroying the Galaga forces in each stage while avoiding enemies and projectiles. Some enemies can capture a player's ship via a tractor beam, which can be rescued to transform the player into a "dual fighter" with additional firepower.
Answer: Galaga was released in 1981 as a fixed shooter arcade video game developed and published by Namco.
Question: How was quality measured?
Evidence: Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)
To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Individual worker-vs-worker agreement yields 79.8 F1 over 10 experiments with 150 predicates, indicating high consistency across our annotators, inline with results by other structured semantic annotations (e.g. BIBREF6). Overall consistency of the dataset is assessed by measuring agreement between different consolidated annotations, obtained by disjoint triplets of workers, which achieves F1 of 84.1 over 4 experiments, each with 35 distinct predicates. Notably, consolidation boosts agreement, suggesting it is a necessity for semantic annotation consistency.
Dataset Quality Analysis ::: Dataset Assessment and Comparison
We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. To construct the expert set, we blindly merged the Dense set with our worker annotations and manually corrected them. We further corrected the evaluation decisions, accounting for some automatic evaluation mistakes introduced by the span-matching and question paraphrasing criteria. As seen in Table TABREF19, our gold set yields comparable precision with significantly higher recall, which is in line with our 25% higher yield.
Dataset Quality Analysis ::: Agreement with PropBank Data
It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. In Table TABREF22, we replicate the experiments in BIBREF4 for both our gold set and theirs, over a sample of 200 sentences from Wall Street Journal (agreement evaluation is automatic and the metric is somewhat similar to our UA). We report macro-averaged (over predicates) precision and recall for all roles, including core and adjuncts, while considering the PropBank data as the reference set. Our recall of the PropBank roles is notably high, reconfirming the coverage obtained by our annotation protocol.
Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)
To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate.
Dataset Quality Analysis ::: Dataset Assessment and Comparison
We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. 
Dataset Quality Analysis ::: Agreement with PropBank Data
It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. 
Answer: Inter-annotator agreement, comparison against expert annotation, agreement with PropBank Data annotations.
Question: How should you care for grapes?
Answer: Grapes like sandy soil that is not too wet, so make sure to let the ground dry between waterings. Grapes often grow far more fruit than they can sustainably produce. Prune them aggressively in the early spring before the sap starts to flow while it is still cold. One method of pruning is to leave two to four vine lengths with about 10-15 buds each from the last year's growth, and leave a joint for this years growth to grow. Wire these vine lengths up to your trellis, and enjoy the grapes in the fall!
Question: Whas is the performance drop of their model when there is no parsed input?
Evidence: The proposed models are compared against two baselines. The first, referred to here as seq2seq, is the standard RNN-based neural machine translation system with attention BIBREF0 . This baseline does not use the parsed data.
The multi-source systems improve strongly over both baselines, with improvements of up to 1.5 BLEU over the seq2seq baseline and up to 1.1 BLEU over the parse2seq baseline. In addition, the lexicalized multi-source systems yields slightly higher BLEU scores than the unlexicalized multi-source systems; this is surprising because the lexicalized systems have significantly longer sequences than the unlexicalized ones. Finally, it is interesting to compare the seq2seq and parse2seq baselines. Parse2seq outperforms seq2seq by only a small amount compared to multi-source; thus, while adding syntax to NMT can be helpful, some ways of doing so are more effective than others.
 The first, referred to here as seq2seq, is the standard RNN-based neural machine translation system with attention BIBREF0 . This baseline does not use the parsed data.
The multi-source systems improve strongly over both baselines, with improvements of up to 1.5 BLEU over the seq2seq baseline and up to 1.1 BLEU over the parse2seq baseline.
Answer: The answers are shown as follows:
*  improvements of up to 1.5 BLEU over the seq2seq baseline
Question: What has more caffeine between Coffee and Tea
Answer: While many teas do have caffeine like Green and Black, Coffee has about twice as much caffeine content.
Question: What is the Shiba Inu (crypto)?
Answer: Shiba Inu token (ticker: SHIB) is a decentralized cryptocurrency created in August 2020 by an anonymous person or group known as "Ryoshi". It is named after the Shiba Inu (柴犬), a Japanese breed of dog originating in the Chūbu region, the same breed that is depicted in Dogecoin's symbol, itself originally a satirical cryptocurrency based on the Doge meme. Shiba Inu has been characterized as a "meme coin" and a pump-and-dump scheme.
Question: Who are the pianists that are considered masters in playing Bach's music?
Answer: Glenn Gould is one of the most famous pianists playing Bach's music with his distinct style of articulating the contrapuntal texture of Bach's music. Zhu Xiao-mei's recording of Bach's The Goldberg Variations is acclaimed as one of the best recording of this piece. American pianist Murray Perahia has also built up a repertoire of Bach's piano music that's loved by many people.
Question: What is the source of the tables?
Evidence: The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns. InvoiceNo, CustomerID and StockCode are mostly 5 or 6-digit integers with occasional letters. Quantity is mostly 1 to 3-digit integers, a part of them being negative, and UnitPrice is composed of 1 to 6 digits floating values. InvoiceDate are dates all in the same format, Country contains strings representing 38 countries and Description is 4224 strings representing names of products. We reconstruct text mails from this data, by separating each token with a blank space and stacking the lines for a given invoice, grouped by InvoiceNo. We will use the column label as ground truth for the tokens in the dataset. For simplicity reasons we add underscores between words in Country and Description to ease the tokenization. Another slight modification has to be done: $25\%$ of the CustomerId values are missing, and we replace them by '00000'. A sample can be found in Fig. 4 .
The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns. InvoiceNo, CustomerID and StockCode are mostly 5 or 6-digit integers with occasional letters. Quantity is mostly 1 to 3-digit integers, a part of them being negative, and UnitPrice is composed of 1 to 6 digits floating values. InvoiceDate are dates all in the same format, Country contains strings representing 38 countries and Description is 4224 strings representing names of products. We reconstruct text mails from this data, by separating each token with a blank space and stacking the lines for a given invoice, grouped by InvoiceNo.
Answer: The answers are shown as follows:
* The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns.
Question: What is a Tide?
Answer: Tides are the rise and fall of sea levels caused by the combined effects of the gravitational forces exerted by the Moon.
Question: Why did Major League Baseball introduce new rules for 2023?
Answer: Major League Baseball has long grappled with a stigma that its games were boring because they were too long without much action.  

There were multiple reasons for games to swell to over 3 hours long on average, but notably were more pitching changes, long delays between pitches, batters who would step out of the batters box between pitches, evolving batter strategies to be home-run or strikeout, and increased coach mound visits.  

In 2023, Major League Baseball implemented a number of drastic rule changes in its top flight league that had shortened game times to 2 hours, 30 minutes in lower levels called the Minor Leagues.  These changes included a pitch clock, limiting the number of pickoff attempts.  To increase the amount of offense generated, they eliminated the defensive shift and increased the size of the bases, effectively shortening the distance between bases to encourage more stolen base attempts.  

Through the first week of the 2023 season, the average length of game is down to 2 hours, 30 minutes like the Minors, batting averages are up 15 points to .245, and the number of stolen bases doubled in comparison to the year before.