DataHammer/scimrc huggingface dataset:
Question: tell me whether these are girl names or boy names: Ali, Billie, Cameron
Answer: They are gender neutral names
Question: Which are the top 3 countries I should visit in South East Asia or ASEAN?
Answer: Each country is beautiful, very different and has its own uniqueness. English is widely spoken in Singapore, the Philippines and Malaysia as such, navigating around these countries with English is easy. Tourism is big in ASEAN or South East Asia; hence, English is still understood in countries such as Thailand, Indonesia, Cambodia, Vietnam, etc, especially in the hospitality sector. Singapore is one of the world's most popular destinations as most airlines would fly into Singapore. It is a modern country with beautiful attractions. If one is into the outdoors, nature and beach holidays, then Malaysia, Thailand, Indonesia would be ideal countries to go to for their natural mountains and also islands.
Question: Which of these is not like the other?  Baseball, basketball, rock, puck,
Answer: rock
Question: What are previously reported models?
Evidence: We compare our model with several baseline models as described below. We note that the results of the first two are previously reported in BIBREF2.
Hasty Student BIBREF2 is a heuristics-based simple model which ignores the recipe and gives an answer by examining only the question and the answer set using distances in the visual feature space.
Impatient Reader BIBREF19 is a simple neural model that takes its name from the fact that it repeatedly computes attention over the recipe after observing each image in the query.
BiDAF BIBREF14 is a strong reading comprehension model that employs a bi-directional attention flow mechanism to obtain a question-aware representation and bases its predictions on this representation. Originally, it is a span-selection model from the input context. Here, we adapt it to work in a multimodal setting and answer multiple choice questions instead.
BiDAF w/ static memory is an extended version of the BiDAF model which resembles our proposed PRN model in that it includes a memory unit for the entities. However, it does not make any updates on the memory cells. That is, it uses the static entity embeeddings initialized with GloVe word vectors. We propose this baseline to test the significance of the use of relational memory updates.
We compare our model with several baseline models as described below. We note that the results of the first two are previously reported in BIBREF2.

Hasty Student BIBREF2 is a heuristics-based simple model which ignores the recipe and gives an answer by examining only the question and the answer set using distances in the visual feature space.

Impatient Reader BIBREF19 is a simple neural model that takes its name from the fact that it repeatedly computes attention over the recipe after observing each image in the query.

BiDAF BIBREF14 is a strong reading comprehension model that employs a bi-directional attention flow mechanism to obtain a question-aware representation and bases its predictions on this representation. Originally, it is a span-selection model from the input context.
BiDAF w/ static memory is an extended version of the BiDAF model which resembles our proposed PRN model in that it includes a memory unit for the entities.
Answer: The answers are shown as follows:
* Hasty Student
* Impatient Reader
* BiDAF
* BiDAF w/ static memory
Question: 5 ways Voldemort could have killed Harry Porter
Answer: He shouldn't have waited until the end of the school year.
He Should Have Allowed The Death Eaters To Attack In The Graveyard .
He could have contain his obsession of Magical Heritage and conceal hocrux into normal objects.
He could have ignored the prophecy and continued his activity.
He could have asked a 2nd opinion to check harry In the forest himself
Question: Given a reference text about Puerto Rico, tell me if it is a State, are its resident US citizens, and if they pay US taxes.
Evidence: Puerto Rico (Spanish for 'rich port'; abbreviated PR; Taino: Boriken, Borinquen), officially the Commonwealth of Puerto Rico (Spanish: Estado Libre Asociado de Puerto Rico, lit. 'Free Associated State of Puerto Rico'), is a Caribbean island and unincorporated territory of the United States with official Commonwealth status. It is located in the northeast Caribbean Sea, approximately 1,000 miles (1,600 km) southeast of Miami, Florida, between the Dominican Republic and the U.S. Virgin Islands, and includes the eponymous main island and several smaller islands, such as Mona, Culebra, and Vieques. It has roughly 3.2 million residents, and its capital and most populous city is San Juan. Spanish and English are the official languages of the executive branch of government, though Spanish predominates.

Puerto Rico was settled by a succession of indigenous peoples beginning 2,000 to 4,000 years ago; these included the Ortoiroid, Saladoid, and Taíno. It was then colonized by Spain following the arrival of Christopher Columbus in 1493. Puerto Rico was contested by other European powers, but remained a Spanish possession for the next four centuries. An influx of African slaves and settlers primarily from the Canary Islands and Andalusia vastly changed the cultural and demographic landscape of the island. Within the Spanish Empire, Puerto Rico played a secondary but strategic role compared to wealthier colonies like Peru and New Spain. By the late 19th century, a distinct Puerto Rican identity began to emerge, centered around a fusion of indigenous, African, and European elements. In 1898, following the Spanish–American War, Puerto Rico was acquired by the United States.

Puerto Ricans have been U.S. citizens since 1917, and can move freely between the island and the mainland. However, as residents of an unincorporated territory, American citizens of Puerto Rico are disenfranchised at the national level, do not vote for the president or vice president, and generally do not pay federal income tax.[Note 1] In common with four other territories, Puerto Rico sends a nonvoting representative to the U.S. Congress, called a Resident Commissioner, and participates in presidential primaries; as it is not a state, Puerto Rico does not have a vote in Congress, which governs it under the Puerto Rico Federal Relations Act of 1950. Congress approved a local constitution in 1952, allowing U.S. citizens residing on the island to elect a governor. Puerto Rico's current and future political status has consistently been a matter of significant debate.
Answer: No, Puerto Rico is not a US state. Rather, it is a US territory. Puerto Rican citizens have automatic US citizenship. Due to this, residents of Puerto Rico and the United States can travel freely between the two, without passports. Puerto Rican residents do not pay federal income tax nor do they vote for the US president.
Question: What are the top five things to do on a weekend trip to New York City in the winter?
Answer: 1. Visit the tree at the Rockefeller Center
2. Shop at the holiday markets such as the one in Union Square
3. Take in the sights on the Statue of Liberty cruise
4. Visit the One World Trade Center memorial
5. Watch a play on broadway
Question: Given this paragraph, what is the strongest part of the scapholunate ligament?
Evidence: The scapholunate ligament is an intraarticular ligament binding the scaphoid and lunate bones of the wrist together. It is divided into three areas, dorsal, proximal and palmar, with the dorsal segment being the strongest part. It is the main stabilizer of the scaphoid. In contrast to the scapholunate ligament, the lunotriquetral ligament is more prominent on the palmar side.
Answer: Given this text, the dorsal segment is the strongest part of the scapholunate ligament.
Question: What is a rocking chair?
Answer: A rocking chair has two long curved legs rather than four straight legs so that it rocks forward and backward.
Question: Provide a bulleted list of the seven best Science Fiction television shows
Answer: - The Expanse
- Battlestar Galactica
- Star Trek
- The 100
- Fringe
- Westworld
- Lost
Question: Where is the village of Congosto
Evidence: Congosto (Spanish pronunciation: [koŋˈɡosto]) is a village and municipality located in the region of El Bierzo (province of León, Castile and León, Spain) . It is located near to Ponferrada, the capital of the region. The village of Congosto has about 350 inhabitants.

Its economy was traditionally based on agriculture, wine and coal mining. Nowadays, most of the inhabitants work on the surrounding area on activities such as wind turbine manufacturing or coal mining.

Congosto also a large reservoir in its vicinity, the Barcena reservoir, to which many tourists visit during the summer.
Answer: Congosto is a village and municipality located in the region of El Bierzo (province of Leon, Castile and Leon, Spain). It is located near to Ponferrada, the capital of the region. The village of Congosto has about 350 inhabitants.

Its economy was traditionally based on agriculture, wine and coal mining. Nowadays, most of the inhabitants work on the surrounding area on activities such as wind turbine manufacturing or coal mining.

Congosto also a large reservoir in its vicinity, the Barcena reservoir, to which many tourists visit during the summer.
Question: Does BOW obtain slightly better results than character 4-grams?
Evidence: In Table 6 we show the results obtained by the described representations employing the Multiclass Classifier. As can be appreciated, the proposed low dimensionality representation improves more than 35% the results obtained with the state-of-the-art representations. BOW obtains slightly better results than character 4-grams, and both of them improve significantly the ones obtained with tf-idf 2-grams. Instead of selecting the most frequent $n$ -grams, our approach takes advantage from the whole vocabulary and assigns higher weights to the most discriminative words for the different language varieties as shown in Equation 10 .
 We highlight that our LDR obtains competitive results compared with the use of distributed representations. Concretely, there is no significant difference among them (Skip-gram $z_{0.05} = 0,5457 < 1,960$ and SenVec $z_{0.05} = 0,7095 < 1,960$ ). In addition, our proposal reduces considerably the dimensionality of one order of magnitude as shown in Table 6 .
BOW obtains slightly better results than character 4-grams, and both of them improve significantly the ones obtained with tf-idf 2-grams.
Answer: Yes, it does.
Question: List the business verticals of Reliance Industries Limited as per the passage. Display the result as a dashed list.
Evidence: Reliance Industries Limited is an Indian multinational conglomerate, headquartered in Mumbai. It has diverse businesses including energy, petrochemicals, natural gas, retail, telecommunications, mass media, and textiles. Reliance is one of the most profitable companies in India, the largest publicly traded company in India by market capitalisation, and the largest company in India as measured by revenue. It is also one of the largest employers in India, with over 300,000 employees in the world.The company is ranked 100th on the Fortune Global 500 list of the world's biggest corporations as of 2022. Reliance continues to be India's largest exporter, accounting for 7% of India's total merchandise exports and it has access to markets in over 100 countries. Reliance is responsible for almost 5% of the Government of India's total revenue from customs and excise duty. It is also the highest income tax payer in the private sector in India. The company has relatively little free cash flow and high corporate debt.
Answer: energy
petrochemicals
natural gas
retail
telecommunications
mass media
textiles
Question: What dataset is used for the experiment?
Evidence: Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
 INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
In order to train a neural g2p system， one needs a large quantity of pronunciation data. A standard dataset for g2p is the Carnegie Mellon Pronouncing Dictionary (Lenzo, 2007). However, that is a monolingual English resource, so it is unsuitable for our multilingual task. Instead, we use the multilingual pronunciation corpus[https://drive.google.com/drive/ folders/0B7R_gATfZJ2aWkpSWHpXUklWUmM] collected by Deri and Knight (2016) for all experiments.
Answer: They use the multilingual pronunciation corpus collected by Deri and Knight (2016) for all experiments.
Question: List down some points about military base from the given passage.
Evidence: A military base is a facility directly owned and operated by or for the military or one of its branches that shelters military equipment and personnel, and facilitates training and operations. A military base always provides accommodations for one or more units, but it may also be used as a command center, training ground or proving ground. In most cases, military bases rely on outside help to operate. However, certain complex bases are able to endure on their own for long periods because they are able to provide food, water and other necessities for their inhabitants while under siege. Bases for military aviation are called military air bases, or simply "air bases". Bases for military ships are called naval bases.
Answer: 1. A military base is a structure that is directly owned and run by or for the military or one of its branches, houses military people and equipment, and aids in operations and training.
2. A military base may serve as a command post, a training area, or a proving ground in addition to housing one or more units.
3. Military bases typically rely on outside assistance to function.
4. However, some sophisticated bases can survive on their own for extended periods of time because they can supply their residents with food, water, and other essentials even when they are under attack.
5. Military air bases, or simply "air bases," are bases for military aviation.
6. Naval bases are bases for military ships.
Question: How much transcribed data is available for for Ainu language?
Evidence: The corpus we have prepared for ASR in this study is composed of text and speech. Table 1 shows the number of episodes and the total speech duration for each speaker. Among the total of eight speakers, the data of the speakers KM and UT is from the Ainu Museum, and the rest is from Nibutani Ainu Culture Museum. All speakers are female. The length of the recording for a speaker varies depending on the circumstances at the recording times. A sample text and its English translation are shown in Table 2.
FLOAT SELECTED: Table 1: Speaker-wise details of the corpus
The corpus we have prepared for ASR in this study is composed of text and speech. Table 1 shows the number of episodes and the total speech duration for each speaker.
FLOAT SELECTED: Table 1: Speaker-wise details of the corpus
Answer: Transcribed data is available for duration of 38h 54m 38s for 8 speakers.
Question: What is Auto Loader?
Answer: Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup. Auto Loader has two modes: file notification mode and directory listing mode.
Question: Is model compared against state of the art models on these datasets?
Evidence: Deep convolutional neural networks (CNNs) with 2D convolutions and small kernels BIBREF1, have achieved state-of-the-art results for several speech recognition tasks BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6. The accuracy of those models grows with their complexity, leading to redundant latent representations. Several approaches have been proposed in the literature to reduce this redundancy BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11, and therefore to improve their efficiency.
Models: Our baseline CNN model BIBREF21 consists of 15 convolutional and one fully-connected layer. We use $3\times 3$ kernels throughout the network. We start with 64 output channels in the first layer and double them after 3 and 9 layers. We use batch normalization in every convolutional layer, and ReLU afterwards (unless a reverse order is noted). The initial learning rate is 0.001. We use early stopping for training.
Deep convolutional neural networks (CNNs) with 2D convolutions and small kernels BIBREF1, have achieved state-of-the-art results for several speech recognition tasks BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6.
r
Models: Our baseline CNN model BIBREF21 consists of 15 convolutional and one fully-connected layer.
Answer: Yes.
Question: Is Bullimore a first name?
Evidence: Bullimore is a surname. Notable people with the surname include:

Tony Bullimore (1939–2018), English sailor and businessman
Wayne Bullimore (born 1970), English footballer
Answer: No, it is a surname.
Question: Give me a bulleted list of the top five most followed Instagram profiles.
Answer: 1. @Instagram
2. @Cristiano
3. @leomessi
4. @selenagomez
5. @kyliejenner
Question: Which datasets were used?
Evidence: We chose three different datasets for the experiments: SNLI, MultiNLI and SICK. All of them have been designed for NLI involving three-way classification with the labels entailment, neutral and contradiction. We did not include any datasets with two-way classification, e.g. SciTail BIBREF14 . As SICK is a relatively small dataset with approximately only 10k sentence pairs, we did not use it as training data in any experiment. We also trained the models with a combined SNLI + MultiNLI training set.
The Stanford Natural Language Inference (SNLI) corpus BIBREF4 is a dataset of 570k human-written sentence pairs manually labeled with the labels entailment, contradiction, and neutral. The source for the premise sentences in SNLI were image captions taken from the Flickr30k corpus BIBREF15 .
The Multi-Genre Natural Language Inference (MultiNLI) corpus BIBREF5 consisting of 433k human-written sentence pairs labeled with entailment, contradiction and neutral. MultiNLI contains sentence pairs from ten distinct genres of both written and spoken English. Only five genres are included in the training set. The development and test sets have been divided into matched and mismatched, where the former includes only sentences from the same genres as the training data, and the latter includes sentences from the remaining genres not present in the training data.
SICK BIBREF6 is a dataset that was originally constructed to test compositional distributional semantics (DS) models. The dataset contains 9,840 examples pertaining to logical inference (negation, conjunction, disjunction, apposition, relative clauses, etc.). The dataset was automatically constructed taking pairs of sentences from a random subset of the 8K ImageFlickr data set BIBREF15 and the SemEval 2012 STS MSRVideo Description dataset BIBREF16 .
We chose three different datasets for the experiments: SNLI, MultiNLI and SICK.
The Stanford Natural Language Inference (SNLI) corpus BIBREF4 is a dataset of 570k human-written sentence pairs manually labeled with the labels entailment, contradiction, and neutral. 
The Multi-Genre Natural Language Inference (MultiNLI) corpus BIBREF5 consisting of 433k human-written sentence pairs labeled with entailment, contradiction and neutral.
SICK BIBREF6 is a dataset that was originally constructed to test compositional distributional semantics (DS) models. 
Answer: The answers are shown as follows:
* SNLI, MultiNLI and SICK
Question: How is D_f chosen and what does it represent ?
Evidence: We propose a novel model, which we denote by Cluster-bAsed iMportancE Learning fOr Timeseries (CAMELOT). Our proposed methodology is displayed in Figure Let N denote the number of patients and D f the number of input features. Input data consists of a set of patient trajectories
, where T n is the maximum number of temporal observations for patient n, and a set of patient outcomes Y = {y n } N n=1 . Input trajectory data for the n-th patient is represented as X n = [x n,1 , ..., x n,Tn ], where each x n,t ∈ R D f is referred to as an observation (vector), with a maximum of observed D f feature values. The corresponding patient outcome is a one-hot encoded vector y n ∈ R 4 (more generally, the dimension of y n equals to the number of possible outcomes).
Our DL model can be decomposed into 3 neural network blocks: an Encoder, Identifier and Predictor. We refer the action of each network respectively as E, I and P (for example, I(x) denotes the output of the Identifier given some input vector x). Both the Identifier and Predictor are Multilayer Perceptrons (MLP), networks of stacked feed-forward dense layers. On the other hand, the Encoder block can be further sub-divided into a) a stack of RNN layers and b) our proposed custom attention layer (see Section 3.3 for further details). Separately, we also consider a set of trainable cluster representation vectors, C = {c 1 , ..., c K }. We assign the outcome for cluster i as P (c i ).
A model call is as follows: Given the n-th patient input trajectory data X n , the Encoder network returns a latent representation z n := E(X n ) ∈ R l . Consequently, the Identifier network computes cluster assignment probabilities,   Input data for patient n, X n is fed as input to the RNN block, which outputs a sequence of latent output states (o n,t ) Tn t=1 . For t = 1, ..., T n , we compute D f feature representations in latent space as:
where
] is our collection of feature representations, σ is applied element-wise and A := D ⊙ x n,t is a matrix satisfying A i,j = D i,j (x n,t ) j . Equivalently, R i n,t is the output of a dense layer, U i with kernel D i , bias B i , activation σ and input (x n,t ) i . We approximate o n,t ≈
This approximation is minimised following a least squares criterion, which has a well-known solution, α t , and corresponding optimal approximation o n,t .
Given, ôn,t , a similar procedure is used to compute a context vector as z := t β t ôn,t , where weights β are learned to provide a more representative context vector.
Let N denote the number of patients and Df the number of input features.
Answer: We use it to refer to the number of input features.
Question: What is Apache Kafka?
Answer: Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala.
Question: What film released and set in 1985 features Doc Brown's Delorean time machine that takes Marty McFly back to 1955 where he inadvertently prevented his birth by intervening when his mother and father first met and fell in love?
Answer: Back to the Future
Question: Which data sources do they use?
Evidence: FLOAT SELECTED: Table 1: An approximate number of sentence pairs for each task.
FLOAT SELECTED: Table 1: An approximate number of sentence pairs for each task.
Answer: - En-Fr (WMT14)
- En-De (WMT15)
- Skipthought (BookCorpus)
- AllNLI (SNLI + MultiNLI)
- Parsing (PTB + 1-billion word)