DataHammer/scimrc huggingface dataset:
Question: List the states which are part of the western ghat
Evidence: The Western Ghats extend from the Satpura Range in the north, stretching from Gujarat to Tamil Nadu. It traverses south through the states of Maharashtra, Goa, Karnataka and Kerala. Major gaps in the range are the Goa Gap, between the Maharashtra and Karnataka sections, and the Palghat Gap on the Tamil Nadu and Kerala border between the Nilgiri Mountains and the Anaimalai Hills. The mountains intercept the rain-bearing westerly monsoon winds, and are consequently an area of high rainfall, particularly on their western side. The dense forests also contribute to the precipitation of the area by acting as a substrate for condensation of moist rising orographic winds from the sea, and releasing much of the moisture back into the air via transpiration, allowing it to later condense and fall again as rain.[citation needed]

The northern portion of the narrow coastal plain between the Western Ghats and the Arabian Sea is known as the Konkan, the central portion is called Kanara and the southern portion is called Malabar. The foothill region east of the Ghats in Maharashtra is known as Desh, while the eastern foothills of the central Karnataka state is known as Malenadu. The range is known as Sahyadri in Maharashtra and Karnataka. The Western Ghats meet the Eastern Ghats at the Nilgiri mountains in northwestern Tamil Nadu. The Nilgiris connect the Biligiriranga Hills in southeastern Karnataka with the Shevaroys and Tirumala hills. South of the Palghat Gap are the Anamala Hills, located in western Tamil Nadu and Kerala with smaller ranges further south, including the Cardamom Hills, then Aryankavu pass, and Aralvaimozhi pass near Kanyakumari. The range is known as Sahyan or Sahian in Kerala. In the southern part of the range is Anamudi (2,695 metres (8,842 ft)), the highest peak in the Western Ghats. Ooty is called the Queen of the Western Ghats.
Answer: Western ghats stretches from Gujurat to Tamil Nadu states of India. It traverses south through the states of Maharashtra, Goa, Karnataka and Kerala. The northern portion of western ghats is called Konkan, the central is called Kanara and the southern portion is called Malabar.
Question: Who is god
Answer: I think Sun is good, If sun does not rise we all will be die,
Question: Is the dataset publicly available？
Evidence: We present a new dataset for the task of machine comprehension focussing on commonsense knowledge. Questions were collected based on script scenarios, rather than individual texts, which resulted in question–answer pairs that explicitly involve commonsense knowledge. In contrast to previous evaluation tasks, this setup allows us for the first time to assess the contribution of script knowledge for computational models of language understanding in a real-world evaluation scenario.
We expect our dataset to become a standard benchmark for testing models of commonsense and script knowledge. Human performance shows that the dataset is highly reliable. The results of several baselines, in contrast, illustrate that our task provides challenging test cases for the broader natural language processing community. MCScript forms the basis of a shared task organized at SemEval 2018. The dataset is available at http://www.sfb1102.uni-saarland.de/?page_id=2582.
The dataset is available at http：//www. sfb1102.uni-saarland.de/?page_id=2582.
Answer: Yes.
Question: Which American president spoke Dutch as their first language?
Evidence: Van Buren received a basic education at the village schoolhouse, and briefly studied Latin at the Kinderhook Academy and at Washington Seminary in Claverack. Van Buren was raised speaking primarily Dutch and learned English while attending school; he is the only president of the United States whose first language was not English. Also during his childhood, Van Buren learned at his father's inn how to interact with people from varied ethnic, income, and societal groups, which he used to his advantage as a political organizer. His formal education ended in 1796, when he began reading law at the office of Peter Silvester and his son Francis.
Answer: President Van Buren spoke Dutch as his first language. He is the only president of the United States of America who spoke English as a second language.
Question: Are the rules dataset specific?
Evidence: We describe our rules for WikiSQL here. We first detect WHERE values, which exactly match to table cells. After that, if a cell appears at more than one column, we choose the column name with more overlapped words with the question, with a constraint that the number of co-occurred words is larger than 1. By default, a WHERE operator is INLINEFORM0 , except for the case that surrounding words of a value contain keywords for INLINEFORM1 and INLINEFORM2 . Then, we deal with the SELECT column, which has the largest number of co-occurred words and cannot be same with any WHERE column. By default, the SELECT AGG is NONE, except for matching to any keywords in Table TABREF8 . The coverage of our rule on training set is 78.4%, with execution accuracy of 77.9%.
Our rule for KBQA is simple without using a curated mapping dictionary. First, we detect an entity from the question using strict string matching, with the constraint that only one entity from the KB has the same surface string and that the question contains only one entity. After that, we get the connected relations of the detected entity, and assign the relation as the one with maximum number of co-occurred words. The coverage of our rule on training set is 16.0%, with an accuracy of 97.3% for relation prediction.
The pipeline of rules in SequentialQA is similar to that of WikiSQL. Compared to the grammar of WikiSQL, the grammar of SequentialQA has additional actions including copying the previous-turn logical form, no greater than, no more than, and negation. Table TABREF23 shows the additional word-level mapping table used in SequentialQA. The coverage of our rule on training set is 75.5%, with an accuracy of 38.5%.
We describe our rules for WikiSQL here.
Our rule for KBQA is simple without using a curated mapping dictionary.
The pipeline of rules in SequentialQA is similar to that of WikiSQL.
Answer: Yes.
Question: Which of these could we use to feed our kid: apple, sandwich, beer, xanax, carrot, rock.
Answer: You can feed a kid with an apple, a sandwich and a carrot. You should absolutely not feed a kid a rock, beer nor xanax.
Question: In the series A Song of Ice and Fire, who is the founder of House Florent?
Answer: Florys the Fox, who is sibling to the founders of House Gardener and House Beesbury
Question: Write an analysis of the meaning of the green light in the Great Gatsby
Answer: The green light is a well known symbol used in the book the Great Gatsby, which can be seen to symbolize many different things. The color of the light is notable because green is a deeply symbolic color, and it is often associated with money and greed. Because the Great Gatsby is ultimately a story about wealth and excess, as well as human greed, this color is a purposeful choice to associate the idea of wealth always being just out of grasp. Additionally, the light ultimately symbolizes something which is just out of grasp, and can be seen as a metaphor of Jay Gatbsy's continuous pursuit of Daisy which was always destined to fail.
Question: Which of the following are either characters that appear in books by George R.R. Martin or Robert Jordan: Tyrion Lannister, Brienne of Tarth, Sandor Clegane, Rhaegar Targaryen, Robert Baratheon, Aviendha, Siuan Sanche, Perrin Aybara, Padan Fain, Rand al'Thor
Answer: Characters in George R.R. Martin books: Tyrion Lannister, Brienne of Tarth, Sandor Clegane, Rhaegar Targaryen
Characters in Robert Jordan books: Aviendha, Siuan Sanche, Perrin Aybara, Padan Fain, Rand al'Thor
Question: What are benhmark datasets for paraphrase identification?
Evidence: We adopt the following two datasets:
Quora BIBREF1: The Quora Question Pairs dataset contains question pairs annotated with labels indicating whether the two questions are paraphrases. We use the same dataset partition as BIBREF5, with 384,348/10,000/10,000 pairs in the training/development/test set respectively.
MRPC BIBREF34: The Microsoft Research Paraphrase Corpus consists of sentence pairs collected from online news. Each pair is annotated with a label indicating whether the two sentences are semantically equivalent. There are 4,076/1,725 pairs in the training/test set respectively.
We adopt the following two datasets:

Quora BIBREF1: The Quora Question Pairs dataset contains question pairs annotated with labels indicating whether the two questions are paraphrases. We use the same dataset partition as BIBREF5, with 384,348/10,000/10,000 pairs in the training/development/test set respectively.

MRPC BIBREF34: The Microsoft Research Paraphrase Corpus consists of sentence pairs collected from online news. Each pair is annotated with a label indicating whether the two sentences are semantically equivalent. There are 4,076/1,725 pairs in the training/test set respectively.
Answer: The answers are shown as follows:
* Quora
* MRPC
Question: Extract the name of the universities Norman Alan Burges studied at from the text. List them using bullets.
Evidence: Norman Alan Burges CBE (5 August 1911 – 4 October 2002), was an Australian botanist who became the first vice-chancellor of the New University of Ulster in Coleraine, Northern Ireland.

He was born 5 August 1911, in East Maitland, New South Wales, and took his first degree in MSc at the University of Sydney, then studied for his PhD in mycology at Emmanuel College, Cambridge. After a short period as a Research Fellow at Emmanuel, at the outbreak of war in 1939 he joined the Royal Air Force serving in Bomber Command. After the war he returned to Australia and in 1947 became professor of botany at the University of Sydney, and later dean of the Faculty of Science and a Fellow of Senate there. he also acted as the honorary general secretary of the Australian and New Zealand Association for the Advancement of Science during that period.
Answer: - University of Sydney
- Emmanuel College, Cambridge
Question: What are the difficulties of this task?
Evidence: Given a sentence and a list of pre-defined schemas which define the relation P and the classes of its corresponding subject S and object O, for example, (S_TYPE: Person, P: wife, O_TYPE: Person), (S_TYPE: Company, P: founder, O_TYPE: Person), a participating information extraction (IE) system is expected to output all correct triples [(S1, P1, O1), (S2, P2, O2) ...] mentioned in the sentence under the constraints of given schemas. A largest schema-based Chinese information extraction dataset is released in this competition. Precision, Recall and F1 score are used as the basic evaluation metrics to measure the performance of participating systems.
From the example shown in Figure FIGREF1, we can notice that one entity can be involved in multiple triplets and entity spans have overlaps, which is the difficulties of this task.
From the example shown in Figure 1, we can notice that one entity can be involved in multiple triplets and entity spans have overlaps, which is the difficulties of this task.
Answer: One entity can be involved in multiple triplets and entity spans have overlaps.
Question: Tell me a few highlights about Madonna's impact on pop music.
Evidence: Madonna Louise Ciccone (born August 16, 1958) is an American singer, songwriter, and actress. Dubbed the "Queen of Pop", Madonna has been widely recognized for her continual reinvention and versatility in music production, songwriting, and visual presentation. She has pushed the boundaries of artistic expression in mainstream music while continuing to maintain control over every aspect of her career. Her works, which incorporate social, political, sexual, and religious themes, have generated both controversy and critical acclaim. A prominent cultural figure of the 20th and 21st centuries, Madonna remains one of the most "well-documented figures of the modern age", with a broad amount of scholarly reviews and literature works on her, as well as an academic mini subdiscipline devoted to her named Madonna studies.

With sales of over 300 million records worldwide, Madonna is the best-selling female recording artist of all time. She is the most successful solo artist in the history of the U.S. Billboard Hot 100 chart and has achieved the most number-one singles by a woman in Australia, Canada, Italy, Spain, and the United Kingdom. With a revenue of over U.S. $1.5 billion from her concert tickets, she remains the highest-grossing female touring artist worldwide. Forbes has named Madonna the annual top-earning female musician a record 11 times across four decades (1980s–2010s). She was inducted into the Rock and Roll Hall of Fame in 2008, her first year of eligibility. Madonna was ranked as the greatest woman in music by VH1, and as the greatest music video artist ever by MTV and Billboard. Rolling Stone also listed her among its greatest artists and greatest songwriters of all time.
Answer: Madonna is known worldwide as the top-selling female pop artist of all time. She has enjoyed global acclaim -- and criticism -- for her sometimes provocative work spanning over four decades. Her work includes recorded music, live performance, and several film credits.
Question: What functions are applied in the last layer?
Evidence: As the last layer, softmax functions are applied to achieve the classification of different tasks, which emits the prediction of probability distribution for the specific task $i$.
where $\hat{{\rm \textbf {y}}}_i$ is the predictive result, ${\rm \textbf {F}}_i$ is the concatenation of private features ${\rm \textbf {H}}_i$ of task $i$ and the outputs ${\rm \textbf {SSL}}_i$ of selected sharing layer for task $i$. ${\rm \textbf {W}}_i$ and ${\rm \textbf {b}}_i$ are trainable parameters.
Given the prediction of all tasks, a global loss function forces the model to minimize the cross-entropy of prediction and true distribution for all the tasks:
where $\lambda _i$ is the weight for the task $i$, and $N$ is the number of tasks. In this paper, $N=2$, and we give more weight $\lambda $ to the task of fake news detection.
As the last layer, softmax functions are applied to achieve the classification of different tasks, which emits the prediction of probability distribution for the specific task i.
Answer: Softmax functions.
Question: What is Halt and Catch Fire about?
Evidence: Halt and Catch Fire is an American period drama television series created by Christopher Cantwell and Christopher C. Rogers. It aired on the cable network AMC in the United States from June 1, 2014, to October 14, 2017, spanning four seasons and 40 episodes. It depicts a fictionalized insider's view of the personal computer revolution of the 1980s and the early days of the World Wide Web in the early 1990s. The show's title refers to computer machine code instruction Halt and Catch Fire (HCF), the execution of which would cause the computer's central processing unit to cease meaningful operation (and in an exaggeration, catch fire).
Answer: Halt and Catch Fire is an American period drama spanning four seasons and 40 episodes. It depicts a fictionalized insider's view of the personal computer revolution of the 1980s and the early days of the World Wide Web in the early 1990s.
Question: What smoothie ingredients should I use if I don’t like bananas?
Answer: Mango and non-fat yogurt. If you are allergic to dairy, avocados provide a smoother texture to your smoothie and fresh smell too.
Question: Why is free climbing called free climbing?
Evidence: Most of the climbing done in modern times is considered free climbing—climbing using one's own physical strength, with equipment used solely as protection and not as support—as opposed to aid climbing, the gear-dependent form of climbing that was dominant in the sport's earlier days. Free climbing is typically divided into several styles that differ from one another depending on the choice of equipment used and the configurations of their belay, rope and anchor systems.
Answer: Free climbing is named so because it is performed solely using one's own physical strength, "free" from any support from equipment other than as protection. That is, gear is never used to help a climber actually climb, only to catch them in case they fall.
Question: What is Ethereum?
Answer: Ethereum is a decentralized blockchain with smart contract functionality. Ether (is the native cryptocurrency of the platform. Among cryptocurrencies, ether is second only to bitcoin in market capitalization. It is open-source software.

Ethereum was conceived in 2013 by programmer Vitalik Buterin. Additional founders of Ethereum included Gavin Wood, Charles Hoskinson, Anthony Di Iorio and Joseph Lubin.
Question: What are performance compared to former models?
Evidence: Table TABREF11 presents the results on 14 treebanks from the CoNLL shared tasks. Our model yields the best results on both UAS and LAS metrics of all languages except the Japanese. As for Japanese, our model gives unsatisfactory results because the original treebank was written in Roman phonetic characters instead of hiragana, which is used by both common Japanese writing and our pre-trained embeddings. Despite this, our model overall still gives 1.0% higher average UAS and LAS than the previous best parser, BIAF.
Following BIBREF23, we report results on the test sets of 12 different languages from the UD treebanks along with the current state-of-the-art: BIAF and STACKPTR. Although both BIAF and STACKPTR parsers have achieved relatively high parsing accuracies on the 12 languages and have all UAS higher than 90%, our model achieves state-of-the-art results in all languages for both UAS and LAS. Overall, our model reports more than 1.0% higher average UAS than STACKPTR and 0.3% higher than BIAF.
Our model yields the best results on both UAS and LAS metrics of all languages except the Japanese. As for Japanese, our model gives unsatisfactory results because the original treebank was written in Roman phonetic characters instead of hiragana, which is used by both common Japanese writing and our pre-trained embeddings. Despite this, our model overall still gives 1.0% higher average UAS and LAS than the previous best parser, BIAF.
Although both BIAF and STACKPTR parsers have achieved relatively high parsing accuracies on the 12 languages and have all UAS higher than 90%, our model achieves state-of-the-art results in all languages for both UAS and LAS. Overall, our model reports more than 1.0% higher average UAS than STACKPTR and 0.3% higher than BIAF.
Answer: The answers are shown as follows:
* model overall still gives 1.0% higher average UAS and LAS than the previous best parser, BIAF
* our model reports more than 1.0% higher average UAS than STACKPTR and 0.3% higher than BIAF
Question: Do they just sum up all the loses the calculate to end up with one single loss?
Evidence: We have found it beneficial to apply the loss function at several intermediate layers of the network. Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \lbrace k_1, k_2, ..., k_L\rbrace \subseteq \lbrace 1,..,M-1\rbrace $. The total objective function is then defined as
where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. The coefficient $\lambda $ scales the auxiliary loss and we set $\lambda = 0.3$ based on our preliminary experiments. We illustrate the auxiliary prediction and loss in Figure FIGREF6.
Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \lbrace k_1, k_2, ..., k_L\rbrace \subseteq \lbrace 1,..,M-1\rbrace $. The total objective function is then defined as

where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. T
Answer: No.
Question: Whether the proposed architecture can replace stacked LSTM?
Evidence: In this paper, we proposed a method of stacking multiple LSTM layers for modeling sentences, dubbed CAS-LSTM. It uses not only hidden states but also cell states from the previous layer, for the purpose of controlling the vertical information flow in a more elaborate way. We evaluated the proposed method on various benchmark tasks: natural language inference, paraphrase identification, and sentiment classification. Our models achieve the new state-of-the-art accuracy on SNLI and Quora Question Pairs datasets and obtain comparable results on MultiNLI and SST datasets. The proposed architecture can replace any stacked LSTM under one weak restriction—the size of states should be identical across all layers.
For future work we plan to apply the CAS-LSTM architecture beyond sentence modeling tasks. Various problems e.g. sequence labeling, sequence generation, and language modeling might benefit from sophisticated modulation on context integration. Aggregating diverse contexts from sequential data, e.g. those from forward and backward reading of text, could also be an intriguing research direction.
The proposed architecture can replace any stacked LSTM only under one weak restriction—the size of states should be identical across all layers.
Answer: Yes, it can replace any stacked LSTM only under one theyak restriction—the size of states should be identical across all layers.
Question: From the text, please list which Tesla models when ordered received free supercharging and for how long?
Evidence: In 2012, Tesla began building a network of 480-volt fast-charging Supercharger stations. As of June 2022, Tesla operates 36,165 Superchargers in 3,971 stations worldwide (an average of 9 chargers per station). The Supercharger is a proprietary direct current (DC) technology that provides up to 250 kilowatts (kW) of power. All Tesla cars except the first generation Roadster come standard with hardware to charge at Superchargers. The navigation software in Tesla cars can recommend the fastest route for long-distance travel, incorporating charging stops.

Model S and X cars ordered before January 15, 2017, and between August 3, 2019, and May 26, 2020, received free unlimited supercharging. Model S and X cars ordered between January 15, 2017, and August 3, 2019, got 400 kWh (1,400 MJ) of free Supercharging credits per year, which provides a range of roughly 1,000 miles per year (1,600 kilometres per year). Subsequent Tesla models never received free unlimited supercharging.
Answer: Here is a list of Tesla cars that received free supercharging when newly ordered:

1. Tesla model S and X cars ordered before January 15, 2017 received free unlimited supercharging.
2. Tesla model S and X cars ordered between August 3, 2019 and May 26, 2020 also received free unlimited supercharging.
3. Tesla model S and X cars that were ordered between January 15, 2017 and August 3, 2019 received 400 KWh of free supercharging credits per year. It translates to roughly 1000 miles per year.

Subsequent Tesla models never received free unlimited supercharging.
Question: Remove vowels from this text:
Evidence: The men's individual competition of the triathlon events at the 2011 Pan American Games was held on October 23 at the API Maritime Terminal in Puerto Vallarta. The defending Pan American Games champion is Andy Potts of the United States.
Answer: Th mn's ndvdl cmpttn f th trthln vnts t th 2011 Pn mrcn Gms ws hld n ctbr 23 t th P Mrtm Trmnl n Prt Vllrt. Th dfndng Pn mrcn Gms chmpn s ndy Ptts f th ntd Stts.
Question: Identify which instrument is string or percussion: Bombo criollo, Imzad
Answer: Imzad is string, Bombo criollo is percussion.
Question: What is standard guitar tuning?
Answer: E, B, G, D, A, E