DataHammer/scimrc huggingface dataset:
Question: America’s cup comeback
Answer: In September, 2013 one of sports most iconic comeback took place on the San Francisco Bay.  

The incredible comeback was when Oracle Team USA overturned an 8-1 deficit to retain the Auld Mug.  

Winning eight straight races has to be ranked the best and most unlikely of all such sporting turnarounds.

With the Kiwis requiring just one more race for victory,  the Americans called up Ben Ainslie who was the driver for the reserve boat.  The Brit’s influence helped shift the momentum that the Kiwis were unable to stop.

San Francisco Bay is extremely challenging to sail with currents and winds above 25knts.  The regatta will forever be remembered as the best comeback of sailing all time.
.
Question: What is the purpose of using Terraform modules ?
Answer: In order to easily maintain, manage and scale their infrastructure, DevOps teams design their infrastructure using modular and reusable components called Terraform modules. Terraform modules allow you to easily share and reuse the same components across multiple use cases and environments. It also helps enforce a standardised approach of defining resources and adopting best practices across your organisation. Not only does consistency ensure best practices are followed, it also helps to enforce compliant deployment, avoid accidental misconfigurations which could lead to costly errors.
Question: Should I become an influencer?
Answer: It is important to consider several factors when thinking about becoming an influencer. Influencers usually have a defined niche, and can make up to six figures or more creating content about it. However, at least in the beginning, your income will likely not come in a steady manner, and you may even be investing more into getting set up than you may be earning in return. The work happens in a freelance manner - if you like setting your own schedule this could appeal to you, but the flip side would be that your work bleeds more into your weekends and evenings. Some people also would love the idea of pursuing their hobbies and interest full time, but when they have to treat it as their job, soon become disenchanted. 

In summary, your decision to become an influencer should be based on your financial situation, risk tolerance, and personal working style and interests.
Question: Identify which animal is domesticated or wild: Fancy rat, Sugar glider
Answer: Fancy rat is domesticated, Sugar glider is wild.
Question: Which party Sam Jones was part of?
Evidence: Samuel Barry Jones (8 July 1923 – 26 January 1999) was an Australian politician. He was a member of the New South Wales Legislative Assembly from 1965 until 1984 and a member of the ALP.

Early life
Samuel Barry Jones was born in Newcastle. He was the son of a steelworker and a brother of Charlie Jones. He was educated to intermediate level at Newcastle Boys' High School and trained as a fitter and turner. He was an office-holder in the Amalgamated Engineering Union.

Political career
Sam Jones joined the Labor Party in 1945 and served as vice president and secretary of Tighes Hill branch, vice president of Newcastle and Waratah State Electorate Councils and secretary of Newcastle Federal Electorate Council.
Answer: Sam Jones (Samuel Barry Jones) served as vice president and secretary of the Labor Party in 1945.
Question: What's the hardest mineral?
Answer: Diamond is the hardest mineral on earth.
Question: Which baseline performs best?
Evidence: Statistical significance is assessed for select results using bootstrapping with 1000 samples in each of 100 iterations. This test shows that conditioning on the introduction of the source document improves performance compared to conditioning on the abstract when using the SciGPT2 model. However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used.
However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used.
Answer: The answers are shown as follows:
* IR methods perform better than the best neural models
Question: Do the authors test their annotation projection techniques on tasks other than AMR?
Evidence: Cross-lingual techniques can cope with the lack of labeled data on languages when this data is available in at least one language, usually English. The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 . Another common thread of cross-lingual work is model transfer, where parameters are shared across languages BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37 .
The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .
Answer: No.
Question: What is the history of Tax Day in the United States?
Answer: Tax Day exists as a deadline for many United States residents to report earnings in order to pay income taxes.

The United States introduced the income tax in 1861 in order to fund the American Civil War. This was later deemed unconstitutional and repealed in 1872. This was later reenacted in 1894 as part of the Revenue Act with its constitutionality settled in the Sixteenth Amendment to the Constitution in 1913. Tax day was initially set for March in 1913 and was eventually moved to April 15th in 1955.
Question: How old is the Earth?
Evidence: The geologic time scale is a way of representing deep time based on events that have occurred throughout Earth's history, a time span of about 4.54 ± 0.05 Ga (4.54 billion years). It chronologically organizes strata, and subsequently time, by observing fundamental changes in stratigraphy that correspond to major geological or paleontological events. For example, the Cretaceous–Paleogene extinction event, marks the lower boundary of the Paleogene System/Period and thus the boundary between the Cretaceous and Paleogene Systems/Periods. For divisions prior to the Cryogenian, arbitrary numeric boundary definitions (Global Standard Stratigraphic Ages, GSSAs) are used to divide geologic time. Proposals have been made to better reconcile these divisions with the rock record.
Answer: Earth is about 4.54 billion years old.
Question: Why should I use fabric softener?
Evidence: Machine washing puts great mechanical stress on textiles, particularly natural fibers such as cotton and wool. The fibers at the fabric surface are squashed and frayed, and this condition hardens while drying the laundry in air, giving the laundry a harsh feel. Adding a liquid fabric softener to the final rinse (rinse-cycle softener) results in laundry that feels softer.
Answer: Machine washing puts great mechanical stress on textiles, so adding a liquid fabric softener to the final rinse results in laundry that feels softer.
Question: Should their approach be applied only when dealing with incomplete data?
Evidence: We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT.
The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. 
Answer: No.
Question: What are some games you can play in the middle of nature and a bunch of people?
Answer: There are the classic games you can always play, like tag, or I spy, or Truth or Dare, but there are plenty of ways to be more creative.

You can collect a bunch of rocks. Then about 10 feet away create a small stack of rocks. Each person takes turns with their own pebbles and tries to knock the stack of rocks over.

If you are near moving water, you can have a "stick boat" race. Everyone chooses a stick and places it at one point in the river, and you cheer and goad each other to see who's stick wins the race.

You can divide into teams and create "nature art" like Andy Goldsworthy who assembles leaves and sticks into beautiful images.

You can simulate games like Bocce or Frisbee Golf with things you find; rocks can be bocce balls, sticks can be frisbees.

You can make a game out of anything.
Question: Identify which car manufacturer is Japanese or American: Jeep, Suzuki
Answer: Jeep is American, Suzuki is Japanese
Question: Who came in first place in Formula One the last ten years?
Answer: There have been three drivers that have won the world championship over the last ten seasons of Formula One.  In 2013, it was Sebastian Vettel (Germany) racing for the RedBull team.  Then, Lewis Hamilton (United Kingdom) won seven seasons in a row from 2014-2017 racing for Mercedes.  Then, Max Verstappen (Netherlands) won the championship the last two years from 2021-2022 racing for Redbull.
Question: What are the baselines that Masque is compared against?
Evidence: FLOAT SELECTED: Table 2: Performance of our and competing models on the MS MARCO V2 leaderboard (4 March 2019). aSeo et al. (2017); bYan et al. (2019); cShao (unpublished), a variant of Tan et al. (2018); dLi (unpublished), a model using Devlin et al. (2018) and See et al. (2017); eQian (unpublished); fWu et al. (2018). Whether the competing models are ensemble models or not is unreported.
FLOAT SELECTED: Table 5: Performance of our and competing models on the NarrativeQA test set. aSeo et al. (2017); bTay et al. (2018); cBauer et al. (2018); dIndurthi et al. (2018); eHu et al. (2018). fResults on the NarrativeQA validation set.
FLOAT SELECTED: Table 2: Performance of our and competing models on the MS MARCO V2 leaderboard (4 March 2019). aSeo et al. (2017); bYan et al. (2019); cShao (unpublished), a variant of Tan et al. (2018); dLi (unpublished), a model using Devlin et al. (2018) and See et al. (2017); eQian (unpublished); fWu et al. (2018). Whether the competing models are ensemble models or not is unreported.
FLOAT SELECTED: Table 5: Performance of our and competing models on the NarrativeQA test set. aSeo et al. (2017); bTay et al. (2018); cBauer et al. (2018); dIndurthi et al. (2018); eHu et al. (2018). fResults on the NarrativeQA validation set.
Answer: BiDAF, Deep Cascade QA, S-Net+CES2S, BERT+Multi-PGNet, Selector+CCG, VNET, DECAPROP, MHPGM+NOIC, ConZNet, RMR+A2D
Question: How many models were trained for each language and each architecture?
Evidence: We analyse the incremental effect of the different features in our system, focusing on the low-resource setting in Track 1 and using development data.
Encoding the entire context with an LSTM highly increases the variance of the observed results. So we trained fifty models for each language and each architecture. Figure FIGREF23 visualises the means and standard deviations over the trained models. In addition, we visualise the average accuracy for the five best models for each language and architecture, as these are the models we use in the final ensemble prediction. Below we refer to these numbers only.
The results indicate that encoding the full context with an LSTM highly enhances the performance of the model, by 11.15% on average. This observation explains the high results we obtain also for Track 2.
Adding the auxiliary objective of MSD prediction has a variable effect: for four languages (de, en, es, and sv) the effect is positive, while for the rest it is negative. We consider this to be an issue of insufficient data for the training of the auxiliary component in the low resource setting we are working with.
We indeed see results improving drastically with the introduction of multilingual training, with multilingual results being 7.96% higher than monolingual ones on average.
We studied the five best models for each language as emerging from the multilingual training (listed in Table TABREF27 ) and found no strong linguistic patterns. The en–sv pairing seems to yield good models for these languages, which could be explained in terms of their common language family and similar morphology. The other natural pairings, however, fr–es, and de–sv, are not so frequent among the best models for these pairs of languages.
Finally, monolingual finetuning improves accuracy across the board, as one would expect, by 2.72% on average.
The final observation to be made based on this breakdown of results is that the multi-tasking approach paired with multilingual training and subsequent monolingual finetuning outperforms the other architectures for five out of seven languages: de, en, fr, ru and sv. For the other two languages in the dataset, es and fi, the difference between this approach and the approach that emerged as best for them is less than 1%. The overall improvement of the multilingual multi-tasking approach over the baseline is 18.30%.
So we trained fifty models for each language and each architecture.
Answer: Fifty models.
Question: What is Jenkins?
Answer: Jenkins is an open source automation server. It helps automate the parts of software development related to building, testing, and deploying, facilitating continuous integration and continuous delivery.
Question: Classify each of these as 'useful' vs 'not useful' when applying for college admissions:
GRE scores, SAT scores, dresses, prom dates, admission letter, recommendation letters, interview prep, networking, fees, headphones, number of friends, social media influencer
Answer: Useful: GRE scores, SAT scores, admission letter, recommendation letters, interview prep, networking, fees

Not useful: dresses, prom dates, headphones, number of friends, social media influencer score
Question: What's funny about the phrase "Why was Dumbo sad? He felt irrelephant."
Answer: The phrase "Why was Dumbo sad? He felt irrelephant" is funny because Dumbo is an elephant and that word sounds like irrelevant. The phrase is a pun.
Question: Who set the first Guinness World Record for swimming under ice?
Evidence: Swimming under ice. On 16 March 2000, Hof set the Guinness World Record for farthest swim under ice on his second attempt, with a distance of 57.5 metres (188.6 feet). Hof's first attempt the day before failed when he began his swim without goggles and his corneas froze solid and blinded him. A rescue diver pulled him to the surface after he passed out. The record has been broken several times since and is 265 feet (81 m) as of 2022."
Answer: On 16 March 2000, Wim Hof set the Guinness World Record for thre farthest swim under ice, with a distance of 57.5 metres (188.6 feet).
Question: What is the important function of the resource collected by the authors?
Evidence: Our resource has the potential to be the basis of computational research in Mapudungun across several areas. Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis. The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation. We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction.
Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community. In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems. The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun. Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications.
The potential applications of our resource, however, are not exhausted in language technologies. The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture.
In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8. As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work.
Our resource has the potential to be the basis of computational research in Mapudungun across several areas.
Answer: The resource has the potential to be the basis of computational research in Mapudungun across several areas.
Question: What is the law of sines?
Answer: In geometry, the law of sines is a formula to find the triangle measurements based on knowing two angles and a side of it
Question: What are baseline models on WSJ eval92 and LibriSpeech test-clean?
Evidence: More recently, acoustic representation learning has drawn increasing attention BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23 in speech processing. For example, an autoregressive predictive coding model (APC) was proposed in BIBREF20 for unsupervised speech representation learning and was applied to phone classification and speaker verification. WaveNet auto-encoders BIBREF21 proposed contrastive predictive coding (CPC) to learn speech representations and was applied on unsupervised acoustic unit discovery task. Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks.
Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion.
Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks.
Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion.
Answer: The answers are shown as follows:
* Wav2vec BIBREF22
* a fully-supervised system using all labeled data
Question: What dog breed is a mix between a Cocker Spaniel and a Poodle?
Answer: Cockapoo
Question: Give me a list of the last five european golden boots winner And tell me how many goals they scored.
Answer: 1. Robert Lewandowski, 2022-2021, 35 goals
2. Robert Lewandowski, 2021-2020, 41 goals
3. Ciro Immobile, 2020-2019, 36 goals
4. Lionel Messi, 2019-2018, 36 goals
5. Lionel Messi, 2018-2017, 34 goals
Question: What is CamemBERT trained on?
Evidence: We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens.
We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens.
Answer: The answers are shown as follows:
* unshuffled version of the French OSCAR corpus
Question: Identify the bird from the list: Elder, Eider, Either
Answer: Eider
Question: Name different types of paper you would see in a notebook
Answer: Blank
Dotted
Ruled
Squared
Question: Paul McCartney and John Lennon wrote which Rolling Stones song?
Answer: "I Wanna Be Your Man"
Question: What is a debit card?
Answer: A debit card, also known as a check card or bank card is a payment card that can be used in place of cash to make purchases. The term plastic card includes the above and as an identity document. These are similar to a credit card, but unlike a credit card, the money for the purchase must be in the cardholder's bank account at the time of a purchase and is immediately transferred directly from that account to the merchant's account to pay for the purchase.

Some debit cards carry a stored value with which a payment is made (prepaid card), but most relay a message to the cardholder's bank to withdraw funds from the cardholder's designated bank account. In some cases, the payment card number is assigned exclusively for use on the Internet and there is no physical card. This is referred to as a virtual card.
Question: What is Ramadan?
Evidence: disambiguation).
Ramadan
رَمَضَان
Ramadan montage.jpg
From top, left to right: A crescent moon over Sarıçam, Turkey, marking the beginning of the Islamic month of Ramadan. Ramadan Quran reading in Bandar Torkaman, Iran. Community Iftar meal in Dubai, United Arab Emirates, Tarawah prayers in a mosque in West Sumatra, Indonesia. Foods served at a traditional Iftar meal. Ramadan decorations in Jerusalem. Zakat donation boxes in Taipei, Taiwan.
Also called	
Albanian: Ramazani
Azerbaijani: Ramazan
Bosnian: Ramazan
Bengali: রমজান, romanized: Rômzan / Rômjan
Hindi: रमज़ान, romanized: Ramzān
Kurdish: ڕەمەزان, romanized: Remezan
Persian: رمضان, romanized: Ramazān
Malay: Puasa
Pashto: روژه, romanized: Rozha
Punjabi: رمضان / ਰਮਜ਼ਾਨ, romanized: Ramzān
Somali: Rabadaan or Rabmadaan
Thai: เราะมะฎอน, romanized: Romadon
Turkish: Ramazan
Sindhi: روزو
Urdu: رمضان, romanized: Ramzān
Zazaki: Remezan
Observed by	Muslims
Type	Religious
Celebrations	Community iftars and Community prayers
Observances	
Sawm (fasting)
Zakat and sadaqah (alms giving)
Commemorating Qadr Night
Reading the Quran
Abstaining from all bad deeds and staying humble
Taraweeh prayer (Sunni Muslims)
Begins	At the last night of the month of Sha'ban
Ends	At the last night of the month of Ramadan
Date	Variable (follows the Islamic lunar calendar)
2023 date	22 March – 21 April
Frequency	Annual (lunar calendar)
Related to	Eid al-Fitr, Qadr Night
Ramadan (Arabic: رَمَضَان, romanized: Ramaḍān [ra.ma.dˤaːn]; also spelled Ramazan, Ramzan, Ramadhan or Ramathan) is the ninth month of the Islamic calendar, observed by Muslims worldwide as a month of fasting (sawm), prayer, reflection and community. A commemoration of Muhammad's first revelation, the annual observance of Ramadan is regarded as one of the Five Pillars of Islam and lasts twenty-nine to thirty days, from one sighting of the crescent moon to the next.

Fasting from dawn to sunset is fard (obligatory) for all adult Muslims who are not acutely or chronically ill, travelling, elderly, breastfeeding, diabetic, or menstruating. The predawn meal is referred to as suhur, and the nightly feast that breaks the fast is called iftar. Although fatwas have been issued declaring that Muslims who live in regions with a midnight sun or polar night should follow the timetable of Mecca, it is common practice to follow the timetable of the closest country in which night can be distinguished from day.
Answer: Ramadan is the holy month of fasting observed by Muslims yearly all over the world.During this month Muslims abstain from food from dawn till dusk for 29 days. Ramadan is also one of the key pillars of Islam.