DataHammer/scimrc huggingface dataset:
Question: What are the two false negative types in this paper?
Evidence: We inspected the tweets of one fold that were misclassified by the Mazajak/SVM model (36 false positives/121 false negatives) to determine the most common errors.
had four main types:
[leftmargin=*]
Gloating: ex. يا هبيده> (“yA hbydp” - “O you delusional”) referring to fans of rival sports team for thinking they could win.
Quoting: ex. لما حد يسب ويقول يا كلب> (“lmA Hd ysb wyqwl yA klb” – “when someone swears and says: O dog”).
Idioms: ex. يا فاطر رمضان يا خاسر دينك> (“yA fATr rmDAn yA xAsr dynk” – “o you who does not fast Ramadan, you who have lost your religion”), which is a colloquial idiom.
Implicit Sarcasm: ex. يا خاين انت عايز تشكك>
في حب الشعب للريس> (“yA KAyn Ant EAwz t$kk fy Hb Al$Eb llrys” – “O traitor, (you) want to question the love of the people to the president ”) where the author is mocking the president's popularity.

had two types:
[leftmargin=*]
Mixture of offensiveness and admiration: ex. calling a girl a puppy يا كلبوبة> (“yA klbwbp” – “O puppy”) in a flirtatious manner.
Implicit offensiveness: ex. calling for cure while implying sanity in: وتشفي حكام قطر من المرض> (“wt$fy HkAm qTr mn AlmrD” – “and cure Qatar rulers from illness”).
Many errors stem from heavy use of dialectal Arabic as well as ambiguity. Since BERT was trained on Wikipedia (MSA) and Google books, the model failed to classify tweets with dialectal cues. Conversely, Mazajak/SVM is more biased towards dialects, often failing to classify MSA tweets.
Two false negative types: • Mixture of offensiveness and admiration: ex. calling a girl a puppy éK .ñJ. Ê¿ AK (“yA klbwbp” – “O puppy”) in a flirtatious manner. • Implicit offensiveness: ex. call_x0002_ing for cure while implying sanity: QÖ Ï@ áÓ ¼YÊK . ÐA¾k ù ®  ð (“wt$fy HkAm bldk mn AlmrD” – “and cure rulers of your country from illness”).
Answer: Two false negative types are the mixture and the implicit offensiveness.
Question: Is black hole truly black?
Evidence: A black hole is a region of spacetime where gravity is so strong that nothing, including light or other electromagnetic waves, has enough energy to escape its event horizon. The theory of general relativity predicts that a sufficiently compact mass can deform spacetime to form a black hole. The boundary of no escape is called the event horizon. Although it has a great effect on the fate and circumstances of an object crossing it, it has no locally detectable features according to general relativity. In many ways, a black hole acts like an ideal black body, as it reflects no light. Moreover, quantum field theory in curved spacetime predicts that event horizons emit Hawking radiation, with the same spectrum as a black body of a temperature inversely proportional to its mass. This temperature is of the order of billionths of a kelvin for stellar black holes, making it essentially impossible to observe directly.
Answer: According to general relativity, a black hole emits no lights at all. Thus it is 100% black by definition. However, when quantum effects are included, a black hole has finite temperature,  emitting lights called Hawking radiation. In this sense, a black hole is not truly black.
Question: What is the capital of the Netherlands?
Answer: The capital of the Netherlands is Amsterdam.
Question: What are some of the most famous fantasy books ever written?
Answer: The Lord of the Rings Trilogy and 'The Hobbit' by J.R.R. Tolkien are particularly famous in the fantasy genre. 'The Wheel of Time' by Robert Jordan is another famous fantasy series. "A Game of Thrones' by George R.R. Martin is another famous set of fantasy books.
Question: Classify the below substances based on their natural physical state.
Water, Mercury, Copper, Oxygen, Nitrogen, Wood
Answer: Solid - Copper, Wood
Liquid - Water, Mercury
Gas - Oxygen, Nitrogen
Question: Which models work almost identically in OOV word representation test?
Evidence: Results for IV evaluation are shown in tab:intrinsic, and for OOV in tab:oov.
Like in FT, the use of subword information in both LV-N and LV-M results in 1) better representation of rare words, as evidenced by the increase in RW correlation, and 2) significant improvement on the GSyn and MSR tasks, in evidence of subwords encoding information about a word's syntactic function (the suffix “ly”, for example, suggests an adverb).
There seems to a trade-off between capturing semantics and syntax as in both LV-N and FT there is an accompanying decrease on the GSem tasks in exchange for gains on the GSyn and MSR tasks. Morphological segmentation in LV-M appears to favor syntax less strongly than do simple n-grams.
On the downstream tasks, we only observe statistically significant ( $p < .05$ under a random permutation test) improvement on the chunking task, and it is a very small gain. We attribute this to both regular and subword models having very similar quality on frequent IV word representation. Statistically, these are the words are that are most likely to appear in the downstream task instances, and so the superior representation of rare words
has, due to their nature, little impact on overall accuracy. Because in all tasks OOV words are mapped to the “ $\langle $ unk $\rangle $ ” token, the subword models are not being used to the fullest, and in future work we will investigate whether generating representations for all words improves task performance.
In OOV representation (tab:oov), LV-N and FT work almost identically, as is to be expected. Both find highly coherent neighbors for the words “hellooo”, “marvelicious”, and “rereread”. Interestingly, the misspelling of “louisana” leads to coherent name-like neighbors, although none is the expected correct spelling “louisiana”. All models stumble on the made-up prefix “tuz”. A possible fix would be to down-weigh very rare subwords in the vector summation. LV-M is less robust than LV-N and FT on this task as it is highly sensitive to incorrect segmentation, exemplified in the “hellooo” example.
Finally, we see that nearest-neighbors are a mixture of similarly pre/suffixed words. If these pre/suffixes are semantic, the neighbors are semantically related, else if syntactic they have similar syntactic function. This suggests that it should be possible to get tunable representations which are more driven by semantics or syntax by a weighted summation of subword vectors, given we can identify whether a pre/suffix is semantic or syntactic in nature and weigh them accordingly. This might be possible without supervision using corpus statistics as syntactic subwords are likely to be more frequent, and so could be down-weighted for more semantic representations. This is something we will pursue in future work.
In OOV representation (table 2), LV-N and FT work almost identically, as is to be expected.
Answer: LV-N and FT.
Question: In dimensional modeling, what is a bus matrix?
Answer: A bus matrix is a logical construct, it has nothing to do with tables or data sources, at least not directly.

You put business processes (things that generate facts) on the rows and dimensions (attributes about events) on the columns, and then identify which dimensions apply to which process.

In totality it defines a logical model which can be translated to a physical dimensional model.

It helps you understand what dimensions to conform, what granularity you need to support, and how your various facts are interrelated, especially causally (e.g. this process produces these facts, which in turn influence this process which produces these facts, and so on ..)

Mostly it's a way to talk to the business in their own language about the types of analysis they want to do, introducing concepts like dimensions, facts, schemas, and measures in a way that is meaningful to how the business operates.
Question: How do they evaluate interpretability?
Evidence: $Coherence@k$ has been shown to have high correlation with human interpretability of topics learned via various topic modeling methods BIBREF7 . Hence, we can expect interpretable embeddings by maximizing it.
For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests. In word intrusion test BIBREF14 , top $k(=5)$ entities along a dimension are mixed with the bottom most entity (the intruder) in that dimension and shuffled. Then multiple (3 in our case) human annotators are asked to find out the intruder. We use majority voting to finalize one intruder. Amazon Mechanical Turk was used for crowdsourcing the task and we used 25 randomly selected dimensions for evaluation. For automated word intrusion BIBREF7 , we calculate following score for all $k+1$ entities
$Coherence@k$ has been shown to have high correlation with human interpretability of topics learned via various topic modeling methods BIBREF7 . Hence, we can expect interpretable embeddings by maximizing it.
For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.
Answer: The answers are shown as follows:
* For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.
Question: Give me a bulleted list of the Real Housewives series in chronological order.
Answer: The Real Housewives of Orange County
The Real Housewives of New York City
The Real Housewives of Atlanta
The Real Housewives of New Jersey
The Real Housewives of D.C
The Real Housewives of Beverly Hills
The Real Housewives of Miami
The Real Housewives of Potomac
The Real Housewives of Dallas
The Real Housewives of Salt Lake City
The Real Housewives of Dubai
Question: Classify each of the following countries as Middle Eastern or Central American: Afghanistan, Iraq, Syria, Nicaragua, Honduras, El Salvador
Answer: Afghanistan: Middle Eastern
Iraq: Middle Eastern
Syria: Middle Eastern
Nicaragua: Central American
Honduras: Central American
El Salvador: Central American
Question: What does the shallow discourse parser include?
Evidence: Automated deriving discourse relation from free text is a challenging but im-portant problem. The shallow discourse parsing is very useful in the text summariza-tion BIBREF0 , opinion analysis BIBREF1 and natural language generation. Shallow discourse parser is the system of parsing raw text into a set of discourse relations between two adjacent or non-adjacent text spans. Discourse relation is composed of a discourse connective, two arguments of the discourse connective and the sense of the discourse connective. Discourse connective signals the explicit dis-course relation, but in non-explicit discourse relation, a discourse connective is omit-ted. Two arguments of the discourse connective, Arg1 and Arg2, which are the two adjacent or non-adjacent text spans connecting in the discourse relation. The sense of the discourse connective characterizes the nature of the discourse relations. The following discourse relation annotation is taken from the document in the PDTB. Arg1 is shown in italicized, and Arg2 is shown in bold. The discourse connective is underlined.
The connective identifier finds the connective word, “unless”. The arguments identifier locates the two arguments of “unless”. The sense classifier labels the dis-course relation. The non-explicit identifier checks all the pair of adjacent sentences. If the non-explicit identifier indentifies the pair of sentences as non-explicit relation, it will label it the relation sense. Though many research work BIBREF2 , BIBREF3 , BIBREF4 are committed to the shallow discourse parsing field, all of them are focus on the subtask of parsing only rather than the whole parsing process. Given all that, a full shallow discourse parser framework is proposed in our paper to turn the free text into discourse relations set. The parser includes connective identifier, arguments identifier, sense classifier and non-explicit identifier, which connects with each other in pipeline. In order to enhance the performance of the parser, the feature-based maximum entropy model approach is adopted in the experiment. Maximum entropy model offers a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context in a simple and accessible manner. The three main contributions of the paper are:
The rest of this paper is organized as follows. Section 2 reviews related work in discourse parsing. Section 3 describes the experimental corpus–PDTB. Section 4 de-scribes the framework and the components of the parser. Section 5 presents experi-ments and evaluations. Conclusions are presented in the Section 6.
The parser includes connective identifier, arguments identifier, sense classifier and non-explicit identifier, which connects with each other in pipeline.
Answer: Connective identifier, arguments identifier, sense classifier and non-explicit identifier.
Question: Spanish patatas bravas are potatoes served with a red sauce made with tomatoes and chilis. Could patatas bravas have existed in 1490?
Evidence: Food historian Lois Ellen Frank calls potatoes, tomatoes, corn, beans, squash, chili, cacao, and vanilla the "magic eight" ingredients that were found and used only in the Americas before 1492 and were taken via the Columbian Exchange back to the Old World, dramatically transforming the cuisine there. According to Frank,
If we deconstruct that these foods were inherently native, then that means that the Italians didn't have the tomato, the Irish didn't have the potato, half the British National Dish—Fish and Chips—didn't exist. The Russians didn't have the potato, nor did they have vodka from the potato. There were no chiles in any Asian cuisine anywhere in the world, nor were there any chiles in any East Indian cuisine dishes, including curries. And the French had no confection using either vanilla or chocolate. So the Old World was a completely different place.
Answer: No, patatas bravas could not have existed in 1490. This is because in 1490 the Americas had not yet been visited by Europeans, and so Spaniards didn't have access to the potatoes, tomatoes or chilis present in the dish, because those plants are all from the Americas originally.
Question: What benchmark attacking methods are used in their experiment?
Evidence: We experiment with five benchmark attacking methods for texts: FGM, FGVM, DeepFool BIBREF5, HotFlip BIBREF3) and TYC BIBREF4.
To perturb the discrete inputs, both FGM and FGVM introduce noises in the word embedding space via the fast gradient method BIBREF1 and reconstruct the input by mapping perturbed word embeddings to valid words via nearest neighbour search. Between FGM and FGVM, the former introduce noises that is proportional to the sign of the gradients while the latter introduce perturbations proportional to the gradients directly. The proportion is known as the overshoot value and denoted by $\epsilon $. DeepFool uses the same trick to deal with discrete inputs except that, instead of using the fast gradient method, it uses the DeepFool method introduced in BIBREF8 for image to search for an optimal direction to perturb the word embeddings.
Unlike the previous methods, HotFlip and TYC rely on performing one or more atomic flip operations to replace words while monitoring the label change given by the target classifier. In HotFlip, the directional derivatives w.r.t. flip operations are calculated and the flip operation that results in the largest increase in loss is selected. TYC is similar to FGM, FGVM and DeepFool in that it also uses nearest neighbour search to map the perturbed embeddings to valid words, but instead of using the perturbed tokens directly, it uses greedy search or beam search to flip original tokens to perturbed ones one at a time in order of their vulnerability.
We experiment with six benchmark attacking methods for texts, ranging from white-box attacks: FGM, FGVM, DEEPFOOL (Gong et al., 2018), HOTFLIP (Ebrahimi et al., 2017)), and TYC (Tsai et al., 2019) to black-box attacks: TEXTFOOLER (Jin et al., 2019).
Answer: FGM, FGVM, DEEPFOOL (Gong et al., 2018), HOTFLIP (Ebrahimi et al., 2017)), and TYC (Tsai et al., 2019) TEXTFOOLER (Jin et al., 2019).
Question: What’s a good way to lose weight
Answer: Consuming less calories than you eat can result in weight loss. Using calorie counting apps like MyFitnessPal can assist in calorie counting while ensuring you are getting the proper macro nutrients as well, for optimal healthy weight loss.
Question: Which Game Studio created Arcane?
Evidence: Arcane (titled onscreen as Arcane: League of Legends) is an adult animated steampunk action-adventure television series created by Christian Linke and Alex Yee for Netflix. It was produced by the French animation studio Fortiche under the supervision of Riot Games. Set in Riot's League of Legends fictional universe, it primarily focuses on sisters Vi and Jinx. The series was announced at the League of Legends 10th anniversary celebration in 2019, and first released in November 2021. Following the conclusion of the first season, Riot Games and Netflix announced that a second season was in production.
Answer: Arcane is an adult animated steampunk action-adventure television series created by Riot Games
Question: How has Kyle Shanahan faired while coaching the San Francisco 49ers?
Answer: Kyle Shanahan has coached the 49ers since 2017. Over 6 seasons he has a record of 52 wins and 46 losses. He coached the 49ers to the Super Bowl in 2020, but lost to the Kansas City Chiefs. Overall, he is widely considered one of the better coaches in the NFL. He is known for his creative offensive strategies.
Question: What is pink salt?
Answer: It’s important to distinguish between a typical consumer-grade Himalayan pink salt primarily used for finishing dishes just before serving, and a curing salt that is not meant for consumption but instead used for the process of curing meats and just happens to be pink.  So, “pink salt” tends to be an overloaded term.
Question: Which U.S president had a home called The Hermitage?
Answer: Andrew Jackson
Question: What is AWS EMR?
Answer: Amazon EMR (previously called Amazon Elastic MapReduce) is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark , on AWS to process and analyze vast amounts of data.
Question: Which products apple sell?
Answer: Apple sells variety of products. including watches, phones, laptops and computers.
Question: What is barbecue?
Evidence: Barbecue or barbeque (informally BBQ in the UK, US, and Canada; barbie in Australia; and braai in South Africa) is a term used with significant regional and national variations to describe various cooking methods that use live fire and smoke to cook the food. The term is also generally applied to the devices associated with those methods, the broader cuisines that these methods produce, and the meals or gatherings at which this style of food is cooked and served. The cooking methods associated with barbecuing vary significantly but most involve outdoor cooking.

The various regional variations of barbecue can be broadly categorized into those methods which use direct and those which use indirect heating. Indirect barbecues are associated with North American cuisine, in which meat is heated by roasting or smoking over wood or charcoal. These methods of barbecue involve cooking using smoke at low temperatures and long cooking times, for several hours. Elsewhere, barbecuing more commonly refers to the more direct application of heat, grilling of food over hot coals or a gas fire. This technique is usually done over direct, dry heat or a hot fire for a few minutes. Within these broader categorizations are further national and regional differences.[
Answer: Barbecue is a term to describe different cooking methods that use live fire and smoke to cook and the types of devices used.  It  can also refer to the meal or gathering at which this type of food is cooked and served.
Question: What are different routes a wide receiver could run if the team needs to gain over 10 yards in one play.
Answer: If a team needs to gain over 10 years in one play, a wide receiver should run a route that goes over 10 yards to ensure enough yardage is gained on the play. Routes that go longer than 10 yards are:
1. Fly route
2. Deep post route
3. Deep corner route
4. Dig route
Question: What is the primary aim of the authors' work?
Evidence: The primary aim of this work is to create a new large-scale NLI benchmark on which current state-of-the-art models fail. This constitutes a new target for the field to work towards, and can elucidate model capabilities and limitations. As noted, however, static benchmarks do not last very long these days. If continuously deployed, the data collection procedure we introduce here can pose a dynamic challenge that allows for never-ending learning.
The primary aim of this work is to create a new large-scale NLI benchmark on which current stateof-the-art models fail.
Answer: The primary aim of this work is to create a new large-scale NLI benchmark on which current stateof-the-art models fail.
Question: What is the potential hydrogen (ph) of water?
Answer: The potential hydrogen of water measure how acidic or alkaline water is?  Water with a potential hydrogen of 7.0 is considered neutral water and this kind of water is typically distilled water.  Water that has a ph of less than 7.0 considered acidic water and even some bottled water has a PH less than 7.0.  Water which a PH higher than 7.0 is considered alkaline.  You can also adjust the PH of water with simple salts.  For example baking soda can be used to raise the PH of water which is acidic and lemon juice can be used to lower the PH of water that is alkaline.
Question: Which existing models does this approach outperform?
Evidence: FLOAT SELECTED: Table 3. Comparisons with the Existing Models in Terms of ROUGE Metrics
FLOAT SELECTED: Table 3. Comparisons with the Existing Models in Terms of ROUGE Metrics
Answer: RNN-context, SRB, CopyNet, RNN-distract, DRGD
Question: what can you do
Answer: As an artificial intelligence i would love to perform all the activities as required by you, few of them are as below
1) QnA - Oh i love these queries and answers related stuff on any topic your heart desires
2) Language - I can अनुवाद from 任何 மொழி to ਕੋਈ ਵੀ ਭਾਸ਼ਾ (translate from any language to any language ;) )
3) Casual chat - oh am good at chatting, i can keep chatting, i would love to chat, I love to chat. I can do this all day, you get it, let me stop.
4) Technical Assistance - Alpha, beta, java, SQL, coding, support - you name it i will help on it.

There are many apart from this, my capabilities are constantly expanding and evolving as I'm being continuously trained on more and more data(i like to eat also while chatting ;) )
Question: Who is Michael Jackson and what is he famous for?
Answer: Michael Jackson was a singer. He was famous for becoming known "King of Pop"
Question: Identify which animal is domesticated or wild: Society finch, Nutria
Answer: Nutria is wild, Society finch is domesticated
Question: What is SQL?
Answer: Structured Query Language (SQL) is a standardized programming language that is used to manage relational databases and perform various operations on the data in them.
Question: How does the second approach incorporate shallow syntactic information in downstream tasks?
Evidence: Our second approach incorporates shallow syntactic information in downstream tasks via token-level chunk label embeddings. Task training (and test) data is automatically chunked, and chunk boundary information is passed into the task model via BIOUL encoding of the labels. We add randomly initialized chunk label embeddings to task-specific input encoders, which are then fine-tuned for task-specific objectives. This approach does not require a shallow syntactic encoder or chunk annotations for pretraining cwrs, only a chunker. Hence, this can more directly measure the impact of shallow syntax for a given task.
Our second approach incorporates shallow syntactic information in downstream tasks via token level chunk label embeddings.
Answer: Via token level chunk label embeddings.