DataHammer/scimrc huggingface dataset:
Question: What is the architecture of the RNN/LSTM?
Evidence: Using our cleaned data set, we now built a recurrent neural network (RNN) with long short-term memory gates (LSTM). Our RNN/LSTM is trained to predict, given a description, whether a home corresponds to a high/medium/low popularity listing. The architecture of the RNN/LSTM employs Tensorflow’s Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the input’s text is converted to a GloVe vector. These GloVe vectors are learned via a global word-word co-occurrence matrix using our corpus of Airbnb listing descriptions [8]. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-step’s LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. We finally ensemble these predictions, to create the model’s complete output prediction.
The architecture of the RNN/LSTM employs Tensorflows Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the inputs text is converted to a GloVe vector. These GloVe vectors are learned via a global wordword co-occurrence matrix using our corpus of Airbnb listing descriptions [8]. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-steps LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. We finally ensemble these predictions, to create the models complete output prediction.
Answer: The architecture of the RNN/LSTM employs Tensorflows Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the inputs text is converted to a GloVe vector. These GloVe vectors are learned via a global wordword co-occurrence matrix using their corpus of Airbnb listing descriptions. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-steps LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. They finally ensemble these predictions, to create the models complete output prediction.
Question: What are the five smallest countries in Europe?
Answer: The five smallest countries in Europe are Andorra, Monaco, San Marino, Vatican City, and Liechtenstein.
Question: Where is the Gate of the Paradise
Evidence: Bab al-Faradis (Arabic: بَابُ الْفَرَادِيسِ, romanized: Bāb al-Farādīs; "The Gate of the Paradise") or Bab al-Amara is one of the seven ancient city-gates of Damascus, Syria. The other name, Bab Al-Amara, refers to a name of a district in the old city where people in the 14th century would meet. During the Roman era, the gate was dedicated to Mercury.
Answer: Although you may search for a more spiritual answer, the Gate of the Paradise or Bab al-Faradis and Bab al-Amara in Arabic is one of the seven ancient city-gates of Damascus, Syria. The other name, Bab Al-Amara, refers to a name of a district in the old city where people in the 14th century would meet. During the Roman era, the gate was dedicated to Mercury.
Question: In which country is the town of Kirchenlamitz located?
Evidence: Kirchenlamitz is a town in the district of Wunsiedel, in Bavaria, Germany. It is situated in the Fichtelgebirge, 13 km northwest of Wunsiedel and 19 km south of Hof.
Answer: Germany
Question: Which English football team have held the FA cup the longest?
Evidence: Portsmouth have the distinction of being the football club which has held the FA Cup trophy for the longest uninterrupted period - seven years. Portsmouth had defeated Wolverhampton Wanderers 4–1 in the 1939 FA Cup Final and were awarded the trophy as 1938–39 FA Cup winners. But with the outbreak of World War II in September 1939, the regular Football League and FA Cup competitions for the 1939–40 season were cancelled for the duration of the war. Portsmouth's manager Jack Tinn was rumoured to have kept the FA Cup trophy 'safe under his bed' throughout the duration of the war, but this is an urban myth. Because the naval city of Portsmouth was a primary strategic military target for German Luftwaffe bombing, the FA Cup trophy was actually taken ten miles to the north of Portsmouth, to the nearby Hampshire village of Lovedean, and there it resided in a quaint thatched roof country pub called The Bird in Hand for the seven years of the war. After the conclusion of World War II, the FA Cup trophy was presented back to the Football Association by the club in time for the 1946 FA Cup Final.
Answer: Portsmouth FC
Question: What kinds of datasets have been used in the experiment?
Evidence: This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.
Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.
We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user", “URL" and “hashtag" references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
Dataset 1 (Balanced Dataset) This dataset was created by (Ptacek et al., 2014). The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets. Dataset 2 (Imbalanced Dataset) Since sarcastic tweets are less frequently used (Ptacek et al., 2014),  we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from (Ptacek et al., 2014). It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets. Dataset 3 (Test Dataset) We have obtained this dataset from The Sarcasm Detector2 . It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
Answer: Balanced dataset, imbalanced dataset, and test dataset.
Question: What system do they use in EEG acquisition?
Evidence: Data acquisition took place in a sound-attenuated and dark experiment room. Participants were seated at a distance of 68cm from a 24-inch monitor with a resolution of 800x600 pixels. A stable head position was ensured via a chin rest. Participants were instructed to stay as still as possible during the tasks to avoid motor EEG artifacts. Participants were also offered snacks and water during the breaks and were encouraged to rest. All sentences were presented at the same position on the screen and could span multiple lines. The sentences were presented in black on a light grey background with font size 20-point Arial, resulting in a letter height of 0.8 mm. The experiment was programmed in MATLAB 2016b BIBREF10, using PsychToolbox BIBREF11. Participants completed the tasks sitting alone in the room, while two research assistants were monitoring their progress in the adjoining room. All recording scripts including detailed participant instructions are available alongside the data.
Eye position and pupil size were recorded with an infrared video-based eye tracker (EyeLink 1000 Plus, SR Research) at a sampling rate of 500 Hz. The eye tracker was calibrated with a 9-point grid at the beginning of the session and re-validated before each block of sentences.
High-density EEG data were recorded at a sampling rate of 500 Hz with a bandpass of 0.1 to 100 Hz, using a 128-channel EEG Geodesic Hydrocel system (Electrical Geodesics). The recording reference was set at electrode Cz. The head circumference of each participant was measured to select an appropriately sized EEG net. To ensure good contact, the impedance of each electrode was checked prior to recording, and was kept below 40 kOhm. Electrode impedance levels were checked after every third block of 50 sentences (approx. every 30 mins) and reduced if necessary.
EEG acquisition High-density EEG data were recorded at a sampling rate of 500 Hz with a bandpass of 0.1 to 100 Hz, using a 128-channel EEG Geodesic Hydrocel system (Electrical Geodesics).
Answer: A 128-channel EEG Geodesic Hydrocel system.
Question: Write a story with a moral lesson at the end?
Answer: You all know the Lion is the king of the forest. One lion king used to go everywhere in its kingdom with his advisors, the wolf and the fox. As lunch time approached, the king asked the wolf what we should do. The wolf said that the prey animals will be coming from the north. You wait hidden in the bush in the south. I shall wait in the west and the fox can wait in the east and ensure the prey do not escape to the east or west.
 
Good idea. so they all followed the plan. Along came a rabbit, ran helter skelter frightened  on seeing the fox and the wolf and straight into the lion, who with one swipe of its paw killed the rabbit. Next a donkey came by and met a similar fate. So did a deer. The lion felt they had sufficient food for their meal and asked the wolf of how we were going to share the meal. The wolf said that the fox can have the rabbit., the lion can have the donkey and he would partake of the deer. The lion knew the tastiest meat is that of the deer, and sensing the deviousness of the advisor wolf smacked him dead. Next he asked the fox for advice on sharing. The fox meekly replied that whatever way the lion wants to have the meal is fine and good. On hearing this the lion said you are a wise and good advisor, who is your guru? The fox humbly replied that the wolf was his guru.  Seeing the fate of the wolf the fox learnt from the wolf's mistake.
 
The moral of the story: You can and must learn from your mistakes. To err is human. But to repeat a mistake is foolish. However we need not keep making mistakes to learn life lessons. It is really wise to learn from other's mistake.
Question: What is the Winged Victory monument?
Evidence: The Winged Victory of Samothrace, or the Nike of Samothrace, is a votive monument originally found on the island of Samothrace, north of the Aegean Sea. It is a masterpiece of Greek sculpture from the Hellenistic era, dating from the beginning of the 2nd century BC. It is composed of a statue representing the goddess Niké (Victory), whose head and arms are missing, and its base in the shape of a ship's bow.

The total height of the monument is 5.57 meters including the socle; the statue alone measures 2.75 meters. The sculpture is one of a small number of major Hellenistic statues surviving in the original, rather than Roman copies. Winged Victory has been exhibited at the Louvre Museum in Paris, at the top of the main staircase, since 1884.
Answer: The Winged Victory is a Greek statue representing the Goddess Nike, minus her head and arms, the base of which is carved to look like a ship's bow.  This sculpture dates back to the beginning of the 2nd century BC, and is notable because it survives in its Greek original, not as a Roman copy. It is exhibited at the Louvre Museum in Paris, at the top of the main staircase.
Question: Who is Juliette Roche?
Evidence: Juliette Roche (1884–1980), also known as Juliette Roche Gleizes, was a French painter and writer who associated with members of the Cubist and Dada movements. She was married to the artist Albert Gleizes.

She was born in 1884 to a wealthy Parisian family. Her father, Jules Roche, was a prominent member of both the French government and avant-garde art world. Other strong connections to the art world were manifested in her relationships with her godmother, Élisabeth, Countess Greffulhe, and her father's godson, Jean Cocteau. Juliette Roche studied painting at the Académie Ranson in Paris, with the support of her father. There, she was introduced to the artistic style of Les Nabis. In her poetic and pictorial work she showed profiles of independent women capable of self-expression.

In 1913, she exhibited at the Salon des Indépendants and began writing poetry, inserting phrases, such as advertising slogans; experimenting with typographic elements. In 1914 she held her first solo exhibition at the Bernheim-Jeune gallery.

When the First World War broke out, she traveled to New York City with her soon to be husband, the Cubist artist Albert Gleizes, who she met through the intermediary of Ricciotto Canudo, a film theoretician who published an avant-garde magazine Montjoie!, promoting Cubism. Juliette Roche and Albert Gleizes were married in September 1915.

In New York, she took part in Dada activities with Marcel Duchamp and Francis Picabia, The Gleizes' then traveled to Barcelona to exhibit in the Galeries Dalmau before returning to New York. collaborating with Duchamp in the preparation of the first exhibition of the Society of Independent Artists of 1917, and Duchamp submitted his infamous readymade Fountain.

In 1919, she returned to Paris and began writing La minéralisation de Dudley Craving Mac Adam, published in 1924, a story that tells of the adventures of Ather Cravan and other artists in exile in New York.

In 1920-21, she wrote État... Colloidal, published by the Chilean journalist Vicente Huidobro in the magazine Creación.

In 1927, together with Albert Gleizes, they founded the Moly-Sabata , a residence of artists in Sablons, which offered studios and workshops. She continued to exhibit the rest of his life in group exhibitions.
Answer: Juliette Roche was born in 1884 and was a French painter and writer. She was married to the artist Albert Gleizes.
She was also the founder of Moly-Sabata, a residence of artists in Sablons.
Question: What do the experiments demonstrate?
Evidence: In this work, we incorporate a domain discriminator to align the mismatched domains between ASR hypotheses and reference transcriptions. The adversarial learning allows the end-to-end QA model to learn domain-invariant features and improve the robustness to ASR errors. The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score. 
The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
Answer: The proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.
Question: I'm planning a trip to India. How do I decide which places to visit?
Answer: India has 28 states and many states have their own distinct culture, traditions, history, and cuisine. It takes many months to properly sample all that India has to offer, and most people who visit India (or even Indians themselves) have probably visited only a fraction of the interesting places. So, pick a particular culture that's interesting to you (for example Rajasthani), and based on the available budget and time pick a few nearby places to visit. While visiting the places, don't forget to enjoy the local cuisine, though be careful to do so in restaurants that are well reviewed and to drink only bottled water to prevent water-borne stomach diseases.
Question: Does the authors' method achieve competitive performance on the test set 2?
Evidence: In this paper, we report our solution to the information extraction task in 2019 Language and Intelligence Challenge. We first analyze the problem and find that most entities are involved in multiple triplets. To solve this problem, we incorporate BERT into the multi-head selection framework for joint entity-relation extraction. Enhanced BERT pre-training, soft label embedding and NER pre-training are three main technologies we introduce to further improve the performance. Experimental results show that our method achieves competitive performance: F1 score 0.892 (1st place) on the test set 1 and F1 score 0.8924 (2nd place) on the test set 2.
Experimental results show that our method achieves competitive performance: F1 score 0.892 (1st place) on the test set 1 and F1 score 0.8924 (2nd place) on the test set 2.
Answer: Yes, it does.
Question: Give me a list of most influential people in the world.
Answer: Elon Musk, Bill Gates, Jeff Bezos, Vladimir Putin, Warren Buffett, Xi Jinping
Question: Classify each of the following water bodies as oceans and seas: Pacific, Arabian, Caspian, Atlantic
Answer: Pacific - ocean
Arabian - sea
Caspian - sea
Atlantic - ocean
Question: What are some of the most popular card games?
Answer: - Poker
- Bridge
- Blackjack
- Rummy
- Crazy Eights
- Spades
- Hearts
- War
- Solitaire
Question: Who is the best men's test cricket player to have come from New Zealand?
Answer: Despite a rich cricket tradition in New Zealand, the country only became a test playing nation during the season of 1929/30, when the MCC toured the country playing 4 tests. Australia, the nearest test playing country to New Zealand, refused to grant Test status to any games played between the two nations until well after World War 2. This lack of test opportunities led to a now common path of talented New Zealanders moving off shore for the chance to play test cricket- most notably in the pre WWII period of Clarrie Grimmet, who took over 200 test wickets playing for Australia. Since then, Andrew Caddick (234 test wickets at an average of 29.91) and Ben Stokes (5000+ test runs and 196 test wickets) have played leading roles for England in tests.

If however we define the question as "the best test cricket player to have played for New Zealand", then there are perhaps only 3 or 4 candidates that feature. Of the current generation, there is no doubt that Kane Williamson will one day be regarded as the greatest player to have come from New Zealand- with over 8000 test runs at an average north of 50. With many years in front of him, Williamson will no doubt hold the crown of best test cricketer in years to come. 

Martin Crowe must likewise be regarded in the pantheon of test greats to have played for New Zealand- despite a slow start to his career, and injuries curtailing his time in the team, he amassed over 5000 runs at an average of over 45.

However, most will agree that the most influential cricketer ever to play test cricket for New Zealand was Sir Richard Hadlee- the first cricketer to be knighted whilst still playing cricket for his country. Hadlee's bowling average of 22.29 is one of best in test cricket history, and at the time of his retirement he held the record for test wickets, at 431. Gaining 5 wickets in an innings 36 times was also a record, and led New Zealand to some of it's most famous test victories, notably victory in a series in Australia for the first time in 1985/86, with his best test figures of 15 for 123 coming in the first test at the Gabba. Added to his bowling, his destructive lower order batting made his one of the preeminent all rounders of the game., and truly NZ's best test cricketer.
Question: Classify the movies based on whether they belong to Fantasy or Mystery genre.
AVATAR, SHAZAM, MISSING, FREDDY
Answer: Mystery - MISSING, FREDDY
Fantasy - AVATAR, SHAZAM
Question: When did the global population reach 8 billion people?
Evidence: The UN estimated that the world population reached one billion for the first time in 1804. It was another 123 years before it reached two billion in 1927, but it took only 33 years to reach three billion in 1960. Thereafter, it took 14 years for the global population to reach four billion in 1974, 13 years to reach five billion in 1987, 12 years to reach six billion in 1999 and, according to the United States Census Bureau, 13 years to reach seven billion in March 2012. The United Nations, however, estimated that the world population reached seven billion in October 2011.

According to the UN, the global population reached eight billion in November 2022, but because the growth rate is slowing, it will take another 15 years to reach around 9 billion by 2037 and 20 years to reach 10 billion by 2057. Alternative scenarios for 2050 range from a low of 7.4 billion to a high of more than 10.6 billion. Projected figures vary depending on underlying statistical assumptions and the variables used in projection calculations, especially the fertility and mortality variables. Long-range predictions to 2150 range from a population decline to 3.2 billion in the "low scenario", to "high scenarios" of 24.8 billion. One extreme scenario predicted a massive increase to 256 billion by 2150, assuming the global fertility rate remained at its 1995 level of 3.04 children per woman; however, by 2010 the global fertility rate had declined to 2.52.

There is no estimation for the exact day or month the world's population surpassed one or two billion. The points at which it reached three and four billion were not officially noted, but the International Database of the United States Census Bureau placed them in July 1959 and April 1974 respectively. The United Nations did determine, and commemorate, the "Day of 5 Billion" on 11 July 1987, and the "Day of 6 Billion" on 12 October 1999. The Population Division of the United Nations declared the "Day of Seven Billion" to be 31 October 2011. The United Nations marked the birth of the eight billionth person on 15 November 2022.
Answer: According to the UN, the global population reached eight billion in November 2022