DataHammer/scimrc huggingface dataset:
Question: In this research, based on what to compare the performance of seven classifiers?
Evidence: Question classification (QC) deals with question analysis and question labeling based on the expected answer type. The goal of QC is to assign classes accurately to the questions based on expected answer. In modern system, there are two types of questions BIBREF0. One is Factoid question which is about providing concise facts and another one is Complex question that has a presupposition which is complex. Question Answering (QA) System is an integral part of our daily life because of the high amount of usage of Internet for information acquisition. In recent years, most of the research works related to QA are based on English language such as IBM Watson, Wolfram Alpha. Bengali speakers often fall in difficulty while communicating in English BIBREF1.
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers. Bengali questions have flexible inquiring ways, so there are many difficulties associated with Bengali QC BIBREF0. As there is no rich corpus of questions in Bengali Language available, collecting questions is an additional challenge. Different difficulties in building a QA System are mentioned in the literature BIBREF2 BIBREF3. The first work on a machine learning based approach towards Bengali question classification is presented in BIBREF0 that employ the Stochastic Gradient Descent (SGD).
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.
Answer: Based on their anticipated answers is compared.
Question: Given this reference text about Husinec, what was the highest temperature reached in Fahrenheit?
Evidence: Husinec is located about 6 kilometres (4 mi) north of Prague. It lies in the Prague Plateau. It it situated in a meander of the Vltava River, partly in the valley of the river and partly on a promontory above the valley.

The municipality is known for high average temperatures, which are caused by the specific relief of the landscape and the natural conditions of the river valley. Drought-tolerant and heat-tolerant plants typical of subtropical climates thrive here. On 19 June 2022, the highest June temperature in the Czech Republic was recorded here, namely 39.0 °C (102.2 °F).
Answer: 102.2 degrees Fahrenheit
Question: Which competitive relational classification models do they test?
Evidence: In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction. Relation prediction aims at predicting the relationship between entities with a given set of triples as training data; while relation extraction aims at extracting the relationship between two entities in a sentence.
We hope to design a simple and clear experiment setup to conduct error analysis for relational prediction. Therefore, we consider a typical method TransE BIBREF3 as the subject as well as FB15K BIBREF3 as the dataset. TransE embeds entities and relations as vectors, and train these embeddings by minimizing DISPLAYFORM0
For relation extraction, we consider the supervised relation extraction setting and TACRED dataset BIBREF10 . As for the subject model, we use the best model on TACRED dataset — position-aware neural sequence model. This method first passes the sentence into an LSTM and then calculate an attention sum of the hidden states in the LSTM by taking positional features into account. This simple and effective method achieves the best in TACRED dataset.
In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction. 
We hope to design a simple and clear experiment setup to conduct error analysis for relational prediction. Therefore, we consider a typical method TransE BIBREF3 as the subject as well as FB15K BIBREF3 as the dataset.
For relation extraction, we consider the supervised relation extraction setting and TACRED dataset BIBREF10 . As for the subject model, we use the best model on TACRED dataset — position-aware neural sequence model. 
Answer: For relation prediction they test TransE and for relation extraction they test position aware neural sequence model
Question: What is the introduced meta-embedding method introduced in this paper?
Evidence: To overcome the above-mentioned challenges, we propose a locally-linear meta-embedding learning method that (a) requires only the words in the vocabulary of each source embedding, without having to predict embeddings for missing words, (b) can meta-embed source embeddings with different dimensionalities, (c) is sensitive to the diversity of the neighbourhoods of the source embeddings.
Our proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space. Although the number of words in the vocabulary of a particular source embedding can be potentially large, the consideration of nearest neighbours enables us to limit the representation to a handful of parameters per each word, not exceeding the neighbourhood size. The weights we learn are shared across different source embeddings, thereby incorporating the information from different source embeddings in the meta-embedding. Interestingly, vector concatenation, which has found to be an accurate meta-embedding method, can be derived as a special case of this reconstruction step.
To overcome the above-mentioned challenges, we propose a locally-linear meta-embedding learning method that (a) requires only the words in the vocabulary of each source embedding, without having to predict embeddings for missing words, (b) can meta-embed source embeddings with different dimensionalities, (c) is sensitive to the diversity of the neighbourhoods of the source embeddings.

Our proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space.
Answer: The answers are shown as follows:
* proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space.
Question: What factors should I consider before becoming a dog owner?
Answer: Here are some questions you may ask yourself before becoming a dog owner:
1. Whether you have time to spend with the new family member daily. Dogs are social animals who need accompany just like human beings. You will need to spend time on walking, training and playing with your dogs every day.
2. Whether you have the financial ability to raise a dog. It's quite expensive to raise dogs. You will need to spend money on dog food, toys, pet necessities, vet bills, grooming, etc.
3. Whether you are prepared if you are a person who loves to travel. It will become harder to travel after having dogs.
4. Whether you are allergic to dogs.
5. Whether your current living condition is suitable for raising dogs. You also need to research what dog breeds are suitable for you, and dogs from different breeds have very different personalities. Is your home big enough for dogs of the breed you want to have? Are you living in a neighborhood that allows you to walk dogs easily and offers convenient access to facilities like dog parks? Do you have kids? 
6. Whether you are prepared for dog training. Dogs need proper training to have good manners, which takes time, patience and persistence.
Question: What evaluation settings are used for the proposed dataset?
Evidence: We evaluate the proposed model on the SimpleQuestions dataset BIBREF0 . The dataset consists of 108,442 single-relation questions and their corresponding (topic entity, predicate, answer entity) triples from Freebase. It is split into 75,910 train, 10,845 validation, and 21,687 test questions. Only 10,843 of the 45,335 unique words in entity aliases and 886 out of 1,034 unique predicates in the test set were present in the train set. For the proposed dataset, there are two evaluation settings, called FB2M and FB5M, respectively. The former uses a KB for candidate generation which is a subset of Freebase and contains 2M entities, while the latter uses subset of Freebase with 5M entities.
In our experiments, the Memory Neural Networks (MemNNs) proposed in babidataset serve as the baselines. For training, in addition to the 76K questions in the training set, the MemNNs use 3K training questions from WebQuestions BIBREF27 , 15M paraphrases from WikiAnswers BIBREF2 , and 11M and 12M automatically generated questions from the KB for the FB2M and FB5M settings, respectively. In contrast, our models are trained only on the 76K questions in the training set.
For our model, both layers of the LSTM-based question encoder have size 200. The hidden layers of the LSTM-based decoder have size 100, and the CNNs for entity and predicate embeddings have a hidden layer of size 200 and an output layer of size 100. The CNNs for entity and predicate embeddings use a receptive field of size 4, $\lambda =5$ , and $m=100$ . We train the models using RMSProp with a learning rate of $1e^{-4}$ .
In order to make the input character sequence long enough to fill up the receptive fields of multiple CNN layers, we pad each predicate or entity using three padding symbols $P$ , a special start symbol, and a special end symbol. For instance, $Obama$ would become $S_{start}PPP ObamaPPPS_{end}$ . For consistency, we apply the same padding to the questions.
For the proposed dataset, there are two evaluation settings, called FB2M and FB5M, respectively. The former uses a KB for candidate generation which is a subset of Freebase and contains 2M entities, while the latter uses subset of Freebase with 5M entities.
Answer: Two evaluation settings, called FB2M and FB5M, respectively.
Question: Give me a bulleted list of ingredients that I need to bake chewy chocolate chip cookies, and include volume measurements for any ingredient I have to measure out.
Answer: - 340 grams chocolate chips
- 250 grams all-purpose flour
- 210 grams packed brown sugar
- 170 grams softened butter
- 100 grams white sugar
- 15 grams vanilla extract
- 3 grams salt
- 3 grams baking soda
- 2 large eggs
Question: Is the supervised morphological learner tested on Japanese?
Evidence: We conduct two out of the three proposed approaches for Japanese-Vietnamese translation systems and the results are given in the Table TABREF15.
FLOAT SELECTED: Table 1: Results of Japanese-Vietnamese NMT systems
We conduct two out of the three proposed approaches for Japanese-Vietnamese translation systems and the results are given in the Table TABREF15.
FLOAT SELECTED: Table 1: Results of Japanese-Vietnamese NMT systems
Answer: No.
Question: What datasets are experimented with?
Evidence: We conducted our experiments on the CMU ARCTIC database BIBREF33, which contains parallel recordings of professional US English speakers sampled at 16 kHz. One female (slt) was chosen as the target speaker and one male (bdl) and one female (clb) were chosen as sources. We selected 100 utterances each for validation and evaluation, and the other 932 utterances were used as training data. For the TTS corpus, we chose a US female English speaker (judy bieber) from the M-AILABS speech dataset BIBREF34 to train a single-speaker Transformer-TTS model. With the sampling rate also at 16 kHz, the training set contained 15,200 utterances, which were roughly 32 hours long.
We conducted our experiments on the CMU ARCTIC database BIBREF33, which contains parallel recordings of professional US English speakers sampled at 16 kHz. One female (slt) was chosen as the target speaker and one male (bdl) and one female (clb) were chosen as sources. We selected 100 utterances each for validation and evaluation, and the other 932 utterances were used as training data. For the TTS corpus, we chose a US female English speaker (judy bieber) from the M-AILABS speech dataset BIBREF34 to train a single-speaker Transformer-TTS model. With the sampling rate also at 16 kHz, the training set contained 15,200 utterances, which were roughly 32 hours long.
Answer: The answers are shown as follows:
* the CMU ARCTIC database BIBREF33
*  the M-AILABS speech dataset BIBREF34
Question: Please give me a short bulleted list of what happened in the 2023 Covenant School shooting incident
Evidence: On March 27, 2023, a mass shooting occurred at The Covenant School, a private Presbyterian Church in America parochial school in the Green Hills neighborhood of Nashville, Tennessee. Local resident and former student of the school Aiden Hale (formerly Audrey Elizabeth Hale) killed three children and three adults. Hale, 28, was killed by two Metropolitan Nashville Police Department officers responding to the incident.

Hale was initially reported to be a woman and referred to by his birth name by police. Later, it was confirmed that Hale was a trans man who went by the name Aiden and used he/him pronouns.

Hale drove a Honda Fit to the school, arriving at 9:54 a.m. CDT and parking it in the lot. At 9:57, Hale sent an Instagram message to an old friend saying an earlier post he made was "basically a suicide note" and that he planned to die that day. His friend called a crisis hotline before contacting the Davidson County Sheriff's Office at 10:13.

At 10:11, Hale shot through a set of glass side doors and entered the building. He was armed with two rifles and a pistol. At 10:13, police received a call about an active shooter. He walked across the second floor of the school before opening fire. He fired into several classrooms; no one in the classrooms was injured because the teachers had fortified doors and sheltered the students. The police first arrived at the school at 10:24. A teacher told an officer that the students were in lockdown and two were missing.

Officers entered the building at 10:25 and they searched each room for Hale. They heard gunshots coming from the second floor. Five Metro Nashville police officers went upstairs and saw him in a lobby area, firing through a window at arriving police vehicles. Two of the officers fired four times each killing him at 10:27, 14 minutes after the initial 911 call was made
Answer: Hale, 28, a former student killed three children and three adults as part of a mass shooting that occurred at The Covenant School, a private Presbyterian Church in America parochial school in the Green Hills neighborhood of Nashville, Tennessee.
Question: What are the main differences between traditional fishing with a spinning rod or baitcasting rod and fly fishing?
Answer: The main differences between the two methods of fishing are the types of lures that are used and in the way the lure is delivered by a cast of the rod.  In traditional or spin fishing, the lure itself is weighted and this weight provides the leverage necessary to cast out into the water.  In contrast, fly fishing the lures are typically called flies and are very light.  Because they are so light, a heavier line is used and the weight of the line is what allows the fly to be cast out and delivered to the water.  Spin fishing lures are often measured in ounces of weight and are often made of metal or plastic.  They can range in size and design depending on the target species of fish.  Fly fishing flies are typically very lightweight hairs, threads, or other materials tied to a hook.  The fly fishing line weights are measured in grains.
Question: What is refinancing?
Answer: Refinancing is the replacement of an existing debt obligation with another debt obligation under a different term and interest rate. The terms and conditions of refinancing may vary widely by country, province, or state, based on several economic factors such as inherent risk, projected risk, political stability of a nation, currency stability, banking regulations, borrower's credit worthiness, and credit rating of a nation. In many industrialized nations, common forms of refinancing include primary residence mortgages and car loans.
Question: What do the encoder blocks used in QANet compose of?
Evidence: The used architecture of the QA model is briefly summarized below. Here we choose QANet BIBREF2 as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF BIBREF17 when reaching the same performance on SQuAD.
The network architecture is illustrated in Figure FIGREF2 . The left blocks and the right blocks form two QANets, each of which takes a document and a question as the input and outputs an answer. In QANet, firstly, an embedding encoder obtains word and character embeddings for each word in INLINEFORM0 or INLINEFORM1 and then models the temporal interactions between words and refines word vectors to contextualized word representations. All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention. The intuition here is that convolution components can model local interactions and self-attention components focus on modeling global interactions. The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words. After that, a model encoder layer containing seven encoder blocks captures the interactions among the context words conditioned on the question. Finally, the output layer predicts a start position and an end position in the document to extract the answer span from the document.
All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention.
Answer: All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention.
Question: Give me a list of the lines on the London Underground
Answer: - Bakerloo
- Central
- Circle
- District
- Hammersmith & City
- Jubilee
- Metropolitan
- Northern
- Piccadilly
- Victoria
- Waterloo & City
Question: Where can I buy leather pants for women?
Answer: You can find leather pants at a variety of retailers. For lower cost options, try stores like Zara, H&M, Old Navy, and Gap. If you have a higher price point, try stores like Nordstrom, Macy's, Abercrombie & Fitch, or online retailers Revolve or Lulus.
Question: How did Andy Warhol create the "piss paintings"?
Answer: Andy Warhol created paintings in the 1970s by  covering canvases with copper paint and having various staffers urinate on them. This created different colored splotches, pools, and lines.
Question: Why do people live in homes?
Answer: People live in homes because its a way to protect you from the elements of nature like storms, heavy rain, lightning, heat and humidity.  Its a place that offers comfort with air conditioning, offers a comfortable place to sleep, and offers a comfortable place to cook and eat and have entertaining activities like television, and spending time with your family.
Question: What neural configurations are explored?
Evidence: We compare medium-sized, small, and minimal networks to show if network size can be reduced to increase training and testing efficiency while retaining high accuracy. Networks over six layers have shown to be non-effective for this task. We tried many configurations of our network models, but report results with only three configurations of Transformers.
Transformer Type 1: This network is a small to medium-sized network consisting of 4 Transformer layers. Each layer utilizes 8 attention heads with a depth of 512 and a feed-forward depth of 1024.
Transformer Type 2: The second model is small in size, using 2 Transformer layers. The layers utilize 8 attention heads with a depth of 256 and a feed-forward depth of 1024.
Transformer Type 3: The third type of model is minimal, using only 1 Transformer layer. This network utilizes 8 attention heads with a depth of 256 and a feed-forward depth of 512.
We tried many configurations of our network models, but report results with only three configurations of Transformers.

Transformer Type 1: This network is a small to medium-sized network consisting of 4 Transformer layers. Each layer utilizes 8 attention heads with a depth of 512 and a feed-forward depth of 1024.

Transformer Type 2: The second model is small in size, using 2 Transformer layers. The layers utilize 8 attention heads with a depth of 256 and a feed-forward depth of 1024.

Transformer Type 3: The third type of model is minimal, using only 1 Transformer layer. This network utilizes 8 attention heads with a depth of 256 and a feed-forward depth of 512.
Answer: The answers are shown as follows:
* tried many configurations of our network models, but report results with only three configurations
* Transformer Type 1
* Transformer Type 2
* Transformer Type 3
Question: What was unique about Scrubs?
Evidence: Scrubs focuses on the unique point of view of its main character and narrator, Dr. John Michael "J.D." Dorian (Zach Braff) for the first eight seasons, with season nine being narrated by the new main character Lucy Bennett (Kerry Bishé). Most episodes feature multiple story lines thematically linked by voice-overs done by Braff, as well as the comical daydreams of J.D. According to Bill Lawrence, "What we decided was, rather than have it be a monotone narration, if it's going to be Zach's voice, we're going to do everything through J.D.'s eyes. It opened up a visual medium that those of us as comedy writers were not used to." Actors were given the chance to improvise their lines on set with encouragement by series creator Bill Lawrence, with Neil Flynn and Zach Braff being the main improvisors.
Answer: Most episodes featured voice-overs done by Zach Braff (who stars as the main character, J.D.), as well as the comical daydreams of J.D. This opened up a unique visual medium that the comedy writers were not used to. Also, actors were given the chance to improvise their lines on set.
Question: What is a hunting dog?
Answer: Hunting dogs are usually paired with a human to help in the sport of hunting.  They have been used for hundreds of years and are considered to improve the hunters abilities.  Hunting dogs are typically pointers or retrievers.  There are many breeds, such as German Shorthaired Pointers and labradors just to name a couple.
Question: Name some female characters from "The X-Files."
Answer: Dana Scully, Marita Covarrubias, Melissa Scully, Margaret Scully, Leyla Harrison, Teena Mulder, Samantha Mulder, Diana Fowley, Monica Reyes, Cassandra Spender
Question: What is MATLAB?
Answer: MATLAB is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.