DataHammer/scimrc huggingface dataset:
Question: Give me a brief about Manchester City
Evidence: Manchester City Football Club, commonly referred to as Man City, is an English football club based in Manchester that competes in the Premier League, the top division in the English football league system. It founded in 1880 as St. Mark's (West Gorton), then Ardwick Association Football Club in 1887 and Manchester City in 1894. The club's home ground is the Etihad Stadium in east Manchester, to which they moved in 2003, having played at Maine Road since 1923. Manchester City adopted their sky blue home shirts in 1894, in the first season with the current name. Since its inception, the club has won eight league titles, six FA Cups, eight League Cups, six FA Community Shields, and one European Cup Winners' Cup.

The club joined the Football League in 1892, and won their first major honour, the FA Cup, in 1904. The club had its first major period of success in the late 1960s and early 1970s, winning the league title, FA Cup, League Cup, and European Cup Winners Cup under the management of Joe Mercer and Malcolm Allison. After losing the 1981 FA Cup Final, Manchester City went through a period of decline, culminating in relegation to the third tier of English football for the only time in their history in 1998. Following promotion to the top tier in 2001–02, they have remained in the Premier League since 2002–03.
Answer: The Manchester City Football Club, commonly referred to as Man City, is an English football club based in Manchester that competes in the Premier League, the top division in the English football league system. It founded in 1880 as St. Mark's (West Gorton), then Ardwick Association Football Club in 1887 and Manchester City in 1894. The club's home ground is the Etihad Stadium in east Manchester, to which they moved in 2003, having played at Maine Road since 1923. Manchester City adopted their sky blue home shirts in 1894, in the first season with the current name. Since its inception, the club has won eight league titles, six FA Cups, eight League Cups, six FA Community Shields, and one European Cup Winners' Cup.

The club joined the Football League in 1892, and won their first major honour, the FA Cup, in 1904. The club had its first major period of success in the late 1960s and early 1970s, winning the league title, FA Cup, League Cup, and European Cup Winners Cup under the management of Joe Mercer and Malcolm Allison. After losing the 1981 FA Cup Final, Manchester City went through a period of decline, culminating in relegation to the third tier of English football for the only time in their history in 1998. Following promotion to the top tier in 2001–02, they have remained in the Premier League since 2002–03.
Question: What are the 13 villages in Newton, Massachusetts?
Answer: Unlike many cities that have a single downtown and town center, the City of Newton, Massachusetts is divided into thirteen distinct villages with its own center. The thirteen villages are: 

* Auburndale
* Chestnut Hill
* Newton Centre
* Newton Corner
* Newton Highlands
* Newton Lower Falls
* Newton Upper Falls
* Newtonville
* Nonantum 
* Oak Hill 
* Thompsonville
* Waban
* West Newton
Question: Why do people travel to islands for a holiday?
Answer: Islands are a great location for people to enjoy the sunny weather, beautiful locations, tropical fruits and usually wonderful food. This especially appeals to people who live in countries who experience four seasons and may look for better weather during winter and autumn seasons. Islands also usually host very nice beaches.
Question: Which lexicon-based models did they compare with?
Evidence: TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. We use the concatenation of the title and the body as the query to retrieve the candidate comment set by means of the similarity of the tf-idf value. The model is trained on unpaired articles and comments, which is the same as our proposed model.
NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic.
TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline.
NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 .
Answer: The answers are shown as follows:
* TF-IDF
* NVDM
Question: When in the year 44 BC was Julius Caesar killed?
Answer: Caesar was killed on the Ides of March in 44 BC
Question: Which two kings did Sir Oliver Ingham serve under?
Evidence: Sir Oliver Ingham (about 1287–1344) was an English knight and landowner who served as a soldier and administrator under King Edward II of England and his successor, King Edward III. He was responsible for the civil government and military defence of the Duchy of Aquitaine during the War of Saint-Sardos and the early part of the Hundred Years' War.

Born about 1287, he was the son and heir of Sir John Ingham (1260-1309) of Ingham, Norfolk, who had served in the wars of King Edward I against the Scots, and his wife Margery. In 1310 he not only inherited his father's lands in Norfolk, Suffolk, Wiltshire and Hampshire but was himself summoned by King Edward II for military service against Scotland. Appointed a household knight of the king, he received many royal grants including the custody of Ellesmere Castle in Shropshire, keeper for the counties of Cheshire and Flintshire, and official positions in Shropshire and Wiltshire. As a knight banneret, he served in Scotland with the king in August 1322.
Answer: King Edward II of England and his successor, King Edward III
Question: Identify which instrument is string or percussion: Karyenda, Charango
Answer: Charango is string, Karyenda is percussion.
Question: Do they show an example of usage for INFODENS?
Evidence: The framework can be used as a standalone toolkit without any modifications given the implemented features and classifiers. For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors. Extending the framework with new feature extractors or classifiers is as simple as a drag and drop placement of the new code files into the feature_extractor and classifer directories respectively. The framework will then detect the new extensions dynamically at runtime. In this section we explore how each use case is handled.
Since a main use case for the framework is extracting engineered and learned features, it was designed such that developing a new feature extractor would require minimal effort. Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length. More complicated features and learned features are provided in the repository which can be used as a guide for developers. Documentation for adding classifiers and format writers is described in the Wiki of the repository but is left out of this paper due to the limited space.
For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors.
Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length.
Answer: Yes.
Question: What is the highest score possible in ten pin bowling?
Answer: The highest possible score in ten pin bowling is 300.
Question: When is Thai New Year?
Answer: Thai New Year, is also known as Songkran, is celebrated on Thailand between April 13-15 every year.
Question: Think up five popular cities to visit on vacation in the United States
Answer: Five cities tourists like to visit are New York City, Los Angeles, San Francisco, Miami and Chicago
Question: What is the best way to file California State Tax for Spouse Abroad in community property jurisdiction countries like China? The goal is to minimize the tax for community property income.
Answer: For resident spouse in California (CA) and non-resident spouse living and working in China, you can file Federal tax jointly and California state tax separately to minimize the community property income. But since both the California state and China are community property jurisdiction, only 50% of the non-resident spouse's income can be exempt from CA state tax, i.e. 100% of the resident spouse's California source income needs to be filed (50% in resident's form 540, and another 50% in non-resident's form 540NR), and 50% of non-resident spouse's foreign income needs to be filed in resident's Form 540. 

Note that Foreign Tax Credit or Exemption cannot be applied to state tax like federal tax.
Question: Where did the marathon come from?
Answer: The marathon is a long distance foot race that goes for 42.195 km (26mi 385 yd). The name 'marathon' came from the legend of Philippides, a Greek messenger. The legend states that during the Battle of Marathon, Philippides saw a Persian vessel change course towards Athens. He say this as an attempt by the Persions to rush into the city and claim a fake win over the Greeks. Philippides was said to have run the entire distance to Athens. When he arrived he ran into the assembly and shouted νενικήκαμεν (nenikēkamen, "we have won!"). Then he collapsed and died. 

While there is debate over the accuracy of this legend, when the modern olympics began in 1896, organizers were looking for an event to recall the glory of ancient Greece. The winner of the first Olympic marathon was Spyridon Louis on April 10 1896.
Question: How is jazz music different than classical?
Answer: This is really hard to define, because like any art form definitions are wide across people and cultures. Some famous jazz musicians have said that you cannot define jazz.  However, some distinct features of jazz music are: 

- syncopated rhythms 
- complex harmony using scale extensions like 7ths, 11ths, and 13ths
- an acceptance of dissonant harmony 
- improvisation
Question: What is the best way to invest my money?
Answer: There is no one way to best invest your money, it is based on multiple factors that determines how you should allocate in different asset types. Here are some factors that influence your investment allocation.

- Time horizon: This is the amount of time you intend to have your money invested. This is important as different asset classes are impacted more or less by short term market conditions. Based on how long you wish to be exposed to the market choosing an asset type that aligns with your time horizon is very important in your investment strategy.

- Risk Profile:  Indicates how confortable you are with the impact fluctuating market conditions have in your investments. Higher risk assets offer the biggest returns but they also have a higher chance of going down or loosing your investment completely. Understanding your risk profile is the first step in investing your money well. There are different investments assets that fit into different risk levels, a combination of these will offer a balanced way of investing your money. 

- Active/Passive investor: Depending on how much you want to be involved in the asset selection and allocation you would be considered an active investor or passive investor. Active investors are investors that spend extensive amount of time analysing the market and choosing assets for their portfolio. These investors may invest directly into stocks or stock options. Passive investors are investors that want to be exposed to the market but don't want to spend extensive amount of time selecting their assets, these investors leverage ETFs or mutual funds, that bundle different asset classes in one asset, to be exposed to their desired market without the need of choosing their underlying assets.. 

Their is no right or wrong way of investing your money, it is important to understand what type of investor you are and choose what works best for you.
Question: Why are ragdoll cats called ragdolls?
Answer: Ragdoll cats are best known for their docile, placid temperament and affectionate nature. The name 'Ragdoll' is derived from the tendency to go limp and relax when picked up, just like an old fashioned doll made from and stuffed with rags. In fact, it is not uncommon to see a Ragdoll cat held by its owner as if it were a baby - on its back, legs in the air, limp and calm as can be.  Most cat breeds would never tolerate that. The breed is particularly popular in both the United Kingdom and the United States.
Question: Tell me a silly, short poem about sheep where the first letter of each new line when read from top to bottom spells out a secret message.
Answer: Harriet stands in the meadows,
I watch, with wiggly toes

The sheep sings sweetly,
Harriet, stares completely
Eating the sweet grasses,
Running, pitter-patter,
Emptying their bladder
Question: Given this paragraph about Vice-Admiral Alexander Colville, tell me when he died and what battles he fought in.
Evidence: Vice-Admiral Alexander Colville, 7th Lord Colville of Culross (also spelled Colvill) (28 February 1717 – 21 May 1770), served as the Commodore and Commander in Chief of His Majesty's Ships and Vessels in North America from 1757 to 1762. Colville wrote a well-preserved series of detailed, well-written letters to various other military leaders, his family, the King, and other influential people. These letters have provided more historical information about that time period than many other sources available. His writing was so prolific that many of his letters still surface in antique shops from London, to Halifax to New York City. He is a poorly remembered, but important, contributor to the UK control of the North American seas and the battles of the Seven Years' War of 1756–1763.
Answer: Vice-Admiral Alexander Colville fought in the battles of the Seven Years' War of 1756-1763 and died on May 21, 1770.
Question: Write a dialogue between two hikers, John and Jane, who are lost in the woods and can't decide what to do. End the argument with Jane winning and them deciding to turn around and retrace their steps.
Answer: John: I think we're lost, this trail doesn't look familiar
Jane: I think you're right. We must have taken a wrong turn at the fork a mile back. Let's turn around and trace our steps back
John: No, I think we should just keep going forward. We are still on a path, so we should just go and see if we can find our way back to a path we know
Jane: I'm worried its going to get dark, and we'll just be going further into the woods. We should turn around and repeat our steps
John: Okay, you're right. Let's go back to where we first got lost and take the other side of the fork.
Question: Which languages do they work with?
Evidence: We use experiments to evaluate the effectiveness of our proposed method on NER task. On three different low-resource languages, we conducted an experimental evaluation to prove the effectiveness of our back attention mechanism on the NER task. Four datasets are used in our work, including CoNLL 2003 German BIBREF9 , CoNLL 2002 Spanish BIBREF10 , OntoNotes 4 BIBREF11 and Weibo NER BIBREF12 . All the annotations are mapped to the BIOES format. Table TABREF14 shows the detailed statistics of the datasets.
Table TABREF22 shows the results on Chinese OntoNotes 4.0. Adding BAN to baseline model leads to an increase from 63.25% to 72.15% F1-score. In order to further improve the performance, we use the BERT model BIBREF20 to produce word embeddings. With no segmentation, we surpass the previous state-of-the-art approach by 6.33% F1-score. For Weibo dataset, the experiment results are shown in Table TABREF23 , where NE, NM and Overall denote named entities, nominal entities and both. The baseline model gives a 33.18% F1-score. Using the transfer knowledge by BAN, the baseline model achieves an immense improvement in F1-score, rising by 10.39%. We find that BAN still gets consistent improvement on a strong model. With BAN, the F1-score of BERT+BiLSTM+CRF increases to 70.76%.
Four datasets are used in our work, including CoNLL 2003 German BIBREF9 , CoNLL 2002 Spanish BIBREF10 , OntoNotes 4 BIBREF11 and Weibo NER BIBREF12 . 
Table TABREF22 shows the results on Chinese OntoNotes 4.0. 
Answer: The answers are shown as follows:
* German
* Spanish
* Chinese
Question: Identify which animal is domesticated or wild: Dog, Asian Elephant
Answer: Dog is domesticated, Asian Elephant is wild.
Question: Which Taylor Jenkins Reid books have been made into a production (movie or TV)?
Answer: One True Loves is being made into a movie. Daisy Jones & The Six just came out on Amazon Video as a television adaptation. The Seven Husbands of Evelyn Hugo is being made into a movie. Malibu Rising is being made into a television adaptation.
Question: What is aliasing?
Answer: Aliasing is a signal processing phenomenon caused by trying to measure a signal with too low of a sample rate. This means that the time between consecutive measured samples (the sample rate) is too infrequent to properly capture the information of the signal. An example of this is the "carriage wheel" effect, where on film, car tires may appear to go backwards when the car is traveling forwards.
Question: Extract the awards won by Tendulkar. Separate them with a comma
Evidence: Tendulkar has received several awards from the government of India: the Arjuna Award (1994), the Khel Ratna Award (1997), the Padma Shri (1998), and the Padma Vibhushan (2008). After Tendulkar played his last match in November 2013, the Prime Minister's Office announced the decision to award him the Bharat Ratna, India's highest civilian award. He was the first sportsperson to receive the reward, and as of 2021, he is the youngest recipient and was the first sportsperson to receive the award. In 2012, Tendulkar was nominated to the Rajya Sabha, the upper house of the Parliament of India.
Answer: Arjuna Award, Khel Ratna Award, Padma Shri, Padma Vibhushan, Bharat Ratna
Question: What data did they use?
Evidence: We pre-trained our model on the Dutch section of the OSCAR corpus, a large multilingual corpus which was obtained by language classification in the Common Crawl corpus BIBREF16. This Dutch corpus has 6.6 billion words, totalling 39 GB of text. It contains 126,064,722 lines of text, where each line can contain multiple sentences. Subsequent lines are however not related to each other, due to the shuffled nature of the OSCAR data set. For comparison, the French RoBERTa-based language model CamemBERT BIBREF7 has been trained on the French portion of OSCAR, which consists of 138 GB of scraped text.
We pre-trained our model on the Dutch section of the OSCAR corpus, a large multilingual corpus which was obtained by language classification in the Common Crawl corpus BIBREF16.
Answer: The answers are shown as follows:
* the Dutch section of the OSCAR corpus
Question: In the model section, does the author give a summary of the model?
Evidence: In this section, we introduce a novel RvNN architecture, called SATA Tree-LSTM (Structure-Aware Tag Augmented Tree-LSTM). This model is similar to typical Tree-LSTMs, but provides dynamic compositionality by augmenting a separate tag-level tree-LSTM which produces structure-aware tag representations for each node in a tree. In other words, our model has two independent tree-structured modules based on the same constituency tree, one of which (word-level tree-LSTM) is responsible for constructing sentence representations given a sequence of words as usual, while the other (tag-level tree-LSTM) provides supplementary syntactic information to the former.
In section 3.1, we first review tree-LSTM architectures. Then in section 3.2, we introduce a tag-level tree-LSTM and structure-aware tag representations. In section 3.3, we discuss an additional technique to boost the performance of tree-structured models, and in section 3.4, we describe the entire architecture of our model in detail.
In section 3.1, we first review tree-LSTM architectures. Then in section 3.2, we introduce a tag-level tree-LSTM and structure-aware tag representations. In section 3.3, we discuss an additional technique to boost the performance of tree-structured models, and in section 3.4, we describe the entire architecture of our model in detail.
Answer: Yes.
Question: Name some of the bravolebrities from The Real Housewives of Potomac
Answer: Ashley Darby, Gizelle Bryant, Karen Huger, Mia Thornton, Robyn Dixon, Wendy Osefo and Candiace Dillard Bassett
Question: Does KenLM report the actual probabilities of the N-grams?
Evidence: After training the N-gram language models, the next step was scoring. For each hashtag file that needed to be evaluated, the logarithm of the probability was assigned to each tweet in the hashtag file based on the trained language model. The larger the probability, the more likely that tweet was according to the language model. Table 1 shows an example of two scored tweets from hashtag file Bad_Job_In_5_Words.tsv based on the tweet data trigram language model. Note that KenLM reports the log of the probability of the N-grams rather than the actual probabilities so the value closer to 0 (-19) has the higher probability and is associated with the tweet judged to be funnier.
Note that KenLM reports the log of the probability of the N-grams rather than the actual probabilities so the value closer to 0 (-19) has the higher probability and is associated with the tweet judged to be funnier.
Answer: No, it reports the log of the probability of the N-grams.
Question: What embeddings do they use to initialize the word embeddings for their methods?
Evidence: We first discuss a few baseline methods and then discuss the proposed approach. In all these methods, an embedding is generated for a tweet and is used as its feature representation with a classifier.
Baseline Methods: As baselines, we experiment with three broad representations. (1) Char n-grams: It is the state-of-the-art method BIBREF0 which uses character n-grams for hate speech detection. (2) TF-IDF: TF-IDF are typical features used for text classification. (3) BoWV: Bag of Words Vector approach uses the average of the word (GloVe) embeddings to represent a sentence. We experiment with multiple classifiers for both the TF-IDF and the BoWV approaches.
Proposed Methods: We investigate three neural network architectures for the task, described as follows. For each of the three methods, we initialize the word embeddings with either random embeddings or GloVe embeddings. (1) CNN: Inspired by Kim et. al BIBREF3 's work on using CNNs for sentiment classification, we leverage CNNs for hate speech detection. We use the same settings for the CNN as described in BIBREF3 . (2) LSTM: Unlike feed-forward neural networks, recurrent neural networks like LSTMs can use their internal memory to process arbitrary sequences of inputs. Hence, we use LSTMs to capture long range dependencies in tweets, which may play a role in hate speech detection. (3) FastText: FastText BIBREF4 represents a document by average of word vectors similar to the BoWV model, but allows update of word vectors through Back-propagation during training as opposed to the static word representation in the BoWV model, allowing the model to fine-tune the word representations according to the task.
All of these networks are trained (fine-tuned) using labeled data with back-propagation. Once the network is learned, a new tweet is tested against the network which classifies it as racist, sexist or neither. Besides learning the network weights, these methods also learn task-specific word embeddings tuned towards the hate speech labels. Therefore, for each of the networks, we also experiment by using these embeddings as features and various other classifiers like SVMs and GBDTs as the learning method.
For each of the three methods, we initialize the word embeddings with either random embeddings or GloVe embeddings.
Answer: Random embeddings or GloVe embeddings.
Question: where do you stay ?
Answer: As I am an artificial intelligence language model, I suppose i stay in the software part of the internet and i do exist as a program. However, i would like to stay in your heart and answer all your heart desired queries :)