DataHammer/scimrc huggingface dataset:
Question: What language is explored in the language modeling?
Evidence: Language modeling is a probabilistic description of language phenomenon. It provides essential context to distinguish words which sound similar and therefore has one of the most useful applications in Natural Language Processing (NLP) especially in downstreaming tasks like Automatic Speech Recognition (ASR). Recurrent Neural Networks (RNN) especially Long Short Term Memory (LSTM) networks BIBREF0 have been the typical solution to language modeling which do achieve strong results. In spite of these results, their fundamental sequential computation constraint has restricted their use in the modeling of long-term dependencies in sequential data. To address these issues Transformer architecture was introduced. Transformers relies completely on an attention mechanism to form global dependencies between input and output. It also offers more parallelization and has achieved SOTA results in language modeling outperforming LSTM models BIBREF1.
In recent years,we have seen a lot of development based on this standard transformer models particularly on unsupervised pre-training(BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7 which have set state-of-the art results on multiple NLP benchmarks. One such model architecture has been the Bidirectional Encoder Representations from Transformers (BERT) model which uses a deep bidirectional transformer architecture.
Another architecture of interest would be the Transformer-XL, which introduces the notion of recurrence in a self-attention model.
The primary research focus though has been mostly on English language for which abundant data is present. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English.
In this project, we explore the implementation of Transformer-based models (BERT and Transformer-XL) in language modeling for Finnish. We will use the same training data as in BIBREF8 so that we can do fair comparisons with the performance of the LSTM models. Also, as the BERT model is a bi-directional transformer, we will have to approximate the conditional probabilities given a sequence of words. We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language. With smaller units, the modeled sequences are longer, and we hope that the recursive XL architecture can allow us to still model long term effects. To the best of our knowledge this is the first work with the Finnish language to use the following:
Approximation of perplexity using a BERT architecture
Using Transformer-XL architecture with sub-word units.
Comparison of Transformer and LSTM models as language models in the same comparable settings with an agglutinative language.
It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English. In this project, we explore the implementation of Transformer-based models (BERT andTransformer-XL) in language modeling for Finnish.
Answer: An agglutinative language like Finnish.
Question: Which language-pair had the better performance?
Evidence: FLOAT SELECTED: Table 5: Results obtained for the English-French and French-English translation tasks, scored on three test sets using BLEU and TER metrics. p-values are denoted by * and correspond to the following values: ∗< .05, ∗∗< .01, ∗∗∗< .001.
FLOAT SELECTED: Table 5: Results obtained for the English-French and French-English translation tasks, scored on three test sets using BLEU and TER metrics. p-values are denoted by * and correspond to the following values: ∗< .05, ∗∗< .01, ∗∗∗< .001.
Answer: French-English
Question: What is gravity?
Answer: Gravity is the force by which a planet or other body draws objects toward its center. The force of gravity keeps all of the planets in orbit around the sun.
Gravity (or the acceleration due to gravity) is 9.81 meters per second squared, on the surface of Earth, because of the size of Earth and the distance we are on its surface from its center.
Question: Which ones are metals and non metals: Copper, Hydrogen, Silver, Carbon, Gold, Nitrogen
Answer: Metals: Copper, Silver, Gold
Non Metals: Hydrogen, Carbon, Nitrogen
Question: Who designed Washington D.C. and what are some interesting attributes about the city?
Evidence: The plan specified that most streets would be laid out in a grid. To form the grid, some streets (later named for letters of the alphabet) would travel in an east–west direction, while others (named for numbers) would travel in a north–south direction. Broader diagonal grand avenues, later named after the states of the Union, crossed the north–south-east/west grid. These "grand avenues" intersected with the north–south and east–west streets at circles and rectangular plazas that would later honor notable Americans and provide open space.
Answer: Washington DC was designed by Major Pierre Charles L'Enfant at the request of George Washington.  L'Enfant was a civil engineer by training, from France, and based his basic design for DC on the streets of Paris.  DC is laid out as a grid, with east-west streets named alphabetically, starting with the letters themselves, and adding syllables as the streets went further out from the city center.  The north-south streets are numbered, increasing as they radiate from the Capitol.  At the intersection of the center-most streets are named for the direction as they radiate from the Capitol, i.e. North Capitol.  However, there is no West Capitol street, as that is the prominent plazas known as the mall.  Diagonally across the grid are streets named after the states and those state streets intersect in circles named after prominent Americans with plazas and open spaces.
Question: What is the style of play for Zlatan Ibrahimović?
Answer: Ibrahimović has been described as being "good in the air, quick, tall, strong and agile, he plays well with his back to goal and boasts some of the best finishing, vision, passing and ball control around. A versatile and well-rounded attacker, from a tactical standpoint, Ibrahimović is capable of playing anywhere along the front line, due to his ability to both create and score goals for his team, although he is most often deployed as a striker, due to his composure and eye for goal. He has also functioned in a more creative playmaking role at times, as a supporting forward or even as a number 10, in particular in his later career, after losing some of his pace and stamina with age; this deeper position allows him to drop into midfield to pick up the ball, where he can utilize his technical ability, vision, passing, and movement to create space and provide assists for teammates. While naturally right-footed, Ibrahimović is a powerful and accurate striker of the ball from both inside or outside the penalty area with either foot, and is also known for his accuracy from penalties and dead ball situations. An accurate finisher with his head as well as with his feet, his height, elevation, and strength often give him an advantage at winning aerial challenges, and also allow him to function as a "target man"; despite his large stature, Ibrahimović is uncommonly agile for a player of his size, and his athleticism and ability in the air have seen him score several goals from acrobatic strikes and volleys throughout his career, which earned him the moniker Ibracadabra in the Italian media. In spite of his size and physique, Ibrahimović possesses excellent technique and ball control, which, coupled with his balance, power, and physicality, enables him to hold up the ball well with his back to goal, retain possession, and link up with other players; he has also been praised by pundits for his creativity and dribbling skills. Although he is not gifted with exceptional pace, in particular over shorter distances, which became more evident in his later career as he slowed down with age, he is also a quick player and a fast sprinter, who possessed significant acceleration in his youth, and was able to clock top speeds over 30 km/h even into his 30s.
Question: What skills are required to become a data analyst
Answer: 1.	SQL
2.	Statistics
3.	Data Management
4.	Data Visualisation
5.	Good Communicator
6.	Critical thinking
Question: Given a reference text about Psychiatry: An Industry of Death, tell me when it opened and and who owns and operates it.
Evidence: Psychiatry: An Industry of Death is a museum in Hollywood, Los Angeles, California, that has also hosted several touring exhibitions. It is owned and operated by the Citizens Commission on Human Rights (CCHR), an anti-psychiatry organization founded by the Church of Scientology and psychiatrist Thomas Szasz. The museum is located at 6616 Sunset Boulevard, Los Angeles, California. Entry is free.

The opening event on December 17, 2005, was attended by well-known Scientologists such as Priscilla Presley, Lisa Marie Presley, Jenna Elfman, Danny Masterson, Giovanni Ribisi, Catherine Bell, and Anne Archer, as well as former Scientologist Leah Remini.

The museum is dedicated to criticizing what it describes as "an industry driven entirely by profit". It has a variety of displays and exhibits that highlight physical psychiatric treatments, such as restraints, psychoactive drugs, electroconvulsive therapy and psychosurgery (including lobotomy, a procedure abandoned in the 1960s).

The exhibition is also well-known for being the site of a heated confrontation between BBC Panorama reporter John Sweeney, and the Church's then-spokesman Tommy Davis in March 2007, during the filming of Sweeney's documentary Scientology and Me.
Answer: The Psychiatry: An Industry of Death museum opened on December 17, 2005 and is owned and operated by the Citizens Commission on Human Rights.
Question: Given this paragraph about Wabuska, Nevada, when was a post office first opened in the town?
Evidence: Wabuska is an unincorporated community in Lyon County, Nevada, United States. The zip code is 89447, which it shares with nearby Yerington.

Wabuska (Washo language, White Grass) was established in the early 1870s. A post office was opened on September 18, 1874. In 1881, the town served as the principal Mason Valley supply center on the newly constructed Carson and Colorado Railroad of a line that went from Hazen to Mina. When copper was discovered in Mason Valley, the town became the northern terminus of the new Nevada Copper Belt Railroad, built 1909–1911. Wabuska waned with declining mining activity in the 1920s.

Several buildings from Wabuska, most notably the Wabuska Railroad Station, were relocated to Carson City and incorporated as the Nevada State Railroad Museum.
Answer: September 18, 1874
Question: From the passage find spacecrafts which are manufactured by SpaceX. Display the results in comma separated format.
Evidence: The Space Exploration Technologies Corporation (SpaceX) is an American spacecraft manufacturer, launcher, and a satellite communications corporation headquartered in Hawthorne, California. It was founded in 2002 by Elon Musk with the stated goal of reducing space transportation costs to enable the colonization of Mars. The company manufactures the Falcon 9, Falcon Heavy, and Starship launch vehicles, several rocket engines, Cargo Dragon and Crew Dragon spacecraft, and Starlink communications satellites.

SpaceX is developing a satellite internet constellation named Starlink to provide commercial internet service. In January 2020, the Starlink constellation became the largest satellite constellation ever launched, and as of December 2022 comprises over 3,300 small satellites in orbit. The company is also developing Starship, a privately funded, fully reusable, super heavy-lift launch system for interplanetary and orbital spaceflight. It is intended to become SpaceX's primary orbital vehicle once operational, supplanting the existing Falcon 9, Falcon Heavy, and Dragon fleet. It will have the highest payload capacity of any orbital rocket ever built on its debut, which is scheduled for early 2023 pending a launch license.

SpaceX has made several achievements in space exploration. These include making the first privately developed liquid-propellant rocket that reached orbit, the first private company to launch, orbit, and recover a spacecraft, the first private company to send a spacecraft to the International Space Station, the first to achieve vertical propulsive landing of an orbital rocket booster, the first to reuse such a booster, and the first private company to send astronauts to orbit and to the International Space Station. In addition, SpaceX's Falcon 9 rockets have been flown and landed over one hundred times.
Answer: Falcon 9, Falcon Heavy,  Starship launch vehicles, several rocket engines, Cargo Dragon, Crew Dragon spacecraft, Starlink communications satellites
Question: Who is the all time leading goal scorer in the NHL?
Answer: The all time leading goal scorer in the NHL is Wayne Gretzky with 894 goals., Gretzky played in the NHL for 21 seasons, spending most of his time with the Edmonton Oilers and the Los Angeles Kings, but he also played for the New York Rangers and briefly, for the St Louis Blues.
Question: Why is backcountry skiing fun?
Answer: Backcountry skiers get to enjoy a moderate hike uphill in a beautiful winter environment. Then they get to shred untracked powder.
The only reason why backcountry skiing is not world class is because of the potentially deadly danger of avalanches.
Question: What is a good gift for a seven year old boy?
Answer: Football, basketball, Pokémon cards, sports cards
Question: Write the first paragraph of a blog post describing the appeal of Cape Cod as a vacation destination.
Answer: Welcome to Cape Cod! A short 1 hour drive from Boston's Logan airport.  As soon as you cross one of the 2 bridges to Cape Cod an immediate sense of calm and relaxation will wash over your body.  You'll find yourself surrounded by the perfect combination of coastal charm and modern food, beverage and accommodation.  The biggest decision of the week will be staying near golf, beach or one of the swanky islands just off Cape . You can have it all - the best of both worlds!  Dining is easy and plentiful with causal options ranging from burgers, pizza and salads or go upscale with oysters & fresh seafood. A trip to the cape is not complete with out trying one of New England's crown jewels, the Maine Lobster served boiled or in a classic lobster roll.  So kick your feet up, relax and enjoy all that the Cape has to offer!
Question: What non-annotated datasets are considered?
Evidence: The performance of the joint learning architecture was evaluated on the two datasets described in the previous section. The joint learning model requires a paired and an unpaired dataset, so each of the two datasets was split into several parts. E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. The unpaired database was composed of two sets, one containing MRs only and the other containing natural texts only. This process resulted in 3 training sets: paired set, unpaired text set and unpaired MR set. The original development set (4.7K) and test set (4.7K) of the E2E dataset have been kept.
The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. As a result of this process, 43K companies were retained. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). Of course, there was no intersection between these sets.
The training set was also partitioned in order to obtain the paired and unpaired datasets. Because of the loose correlation between the MRs and their corresponding text, the paired dataset was selected such that it contained the infobox values with the highest similarity with its reference text. The similarity was computed using “difflib” library, which is an extension of the Ratcliff and Obershelp algorithm BIBREF19. The paired set was selected in this way (rather than randomly) to get samples as close as possible to a carefully annotated set. At the end of partitioning, the following training sets were obtained: paired set (10.5K), unpaired text set (24.5K) and unpaired MR set (24.5K).
E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. 
The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words.
The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K).
The training set was also partitioned in order to obtain the paired and unpaired datasets. 
Answer: The answers are shown as follows:
* E2E NLG challenge Dataset
* The Wikipedia Company Dataset
Question: Order the following animals in terms of average weight: finch, elephant, goat, gazelle, and a rabbit.
Answer: The ordering of animals in terms of weight from lightest to heaviest is finch, rabbit, gazelle, goat and elephant. The weights of gazelles and goats can be similar, however, on averages an adult goat will weigh more than and an adult gazelle.
Question: The Weeknd samples which '80s megahit in "Blinding Lights?"
Answer: "Take On Me" by A-ha
Question: In the series A Song of Ice and Fire, who is the founder of House Plowman?
Answer: Pate the Plowman
Question: what language models do they use?
Evidence: We calculate the probability of a sentence with a long-short term memory (LSTM, hochreiter1997long) LM, i.e., a special type of RNN LM, which has been trained on a large corpus. More details on LSTM LMs can be found, e.g., in sundermeyer2012lstm. The unigram probabilities for SLOR are estimated using the same corpus.
We train our LSTM LMs on the English Gigaword corpus BIBREF15 , which consists of news data.
We calculate the probability of a sentence with a long-short term memory (LSTM, hochreiter1997long) LM, i.e., a special type of RNN LM, which has been trained on a large corpus.
We train our LSTM LMs on the English Gigaword corpus BIBREF15 , which consists of news data.
Answer: The answers are shown as follows:
* LSTM LMs
Question: What are three neural machine translation (NMT) benchmark datasets used for evaluation?
Evidence: FlowSeq is a flow-based sequence-to-sequence model, which is (to our knowledge) the first non-autoregressive seq2seq model utilizing generative flows. It allows for efficient parallel decoding while modeling the joint distribution of the output sequence. Experimentally, on three benchmark datasets for machine translation – WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear.
 Experimentally, on three benchmark datasets for machine translation – WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear.
Answer: The answers are shown as follows:
* WMT2014, WMT2016 and IWSLT-2014
Question: Which kind of models do the authors perform early stopping for?
Evidence: We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs: English INLINEFORM0 Czech, English INLINEFORM1 German, English INLINEFORM2 Romanian and English INLINEFORM3 Russian. Our systems are based on an attentional encoder-decoder BIBREF0 , using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary BIBREF1 . We experimented with using automatic back-translations of the monolingual News corpus as additional training data BIBREF2 , pervasive dropout BIBREF3 , and target-bidirectional models.
We perform early stopping for single models, and use the 4 last saved models (with models saved every 30000 minibatches) for the ensemble results.
Answer: Single models.
Question: What cluster identification method is used in this paper?
Evidence: The trained Doc2Vec model is subsequently used to infer high-dimensional vector descriptions for the text of each document in our target analysis set. We then compute a matrix containing all the pairwise (cosine) similarities between the Doc2Vec document vectors. This similarity matrix can be thought of as the adjacency matrix of a full, weighted graph with documents as nodes and edges weighted by their similarity. We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. MS uses a diffusive process on the graph to reveal the multiscale organisation at different resolutions without the need to choose a priori the number or type of clusters.
We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity.
Answer: A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18
Question: Write critical success factors for a proposal to a company called ABC for an Enterprise Application Integration (EAI) effort
Answer: CRITICAL SUCCESS FACTORS 
The following factors will be critical to the successful integration of the ABC's Operational 
Support Systems:
Project Management – Strong project management and planning will ensure an 
organized and cohesive solution. Excellent communication, aggressive issue resolution 
management, and planning materials will ensure deadlines are met, objectives remain 
focused, and costs are managed. The variety of vendors, system and process owners 
involved in an EAI effort requires a significant coordination effort to ensure success.

Knowledge of Telecommunications Operational Business Practices – ABC
must employ the services of an experienced Telecommunications consulting company to 
ensure business needs are met during the OSS EAI effort. Understanding of ABC 
business needs in implementing this EAI project is critical in ensuring the appropriate 
implementation strategy and successful rollout of the integration.

Enterprise Application Integration Expertise – It is critical that the selected vendor 
have extensive experience in planning, designing, and developing integration 
approaches, architectures, and frameworks to integrate complex Telecommunication 
OSS systems.

Knowledge Transfer – In order to maintain a consistent level of operation and support, 
it is critical that ABC employees are imparted with the necessary knowledge to 
successfully transition into the primary roles that support their systems and future 
integration efforts. Documentation, personal attention, and other resources are vital to 
this knowledge transfer and critical to ABC’s ability to succeed as a growing service 
provider.