DataHammer/scimrc huggingface dataset:
Question: What is the purpose of designing sub-block architecture for Jasper?
Evidence: Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words. Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system.
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6 . We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers. Liptchinsky et al. BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout.
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks. Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10 . We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR. As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs.
It is possible to increase the capacity of the Jasper model by stacking these operations. Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters). We use residual connections to enable this level of depth. We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR).
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2.95% WER and SOTA results among end-to-end models on LibriSpeech test-other. We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S). Using only greedy decoding without a language model we achieve 3.86% WER on LibriSpeech test-clean.
This paper makes the following contributions:
The sub-block architecture of Jasper was designed to facilitate fast GPU inference.
Answer: The sub-block architecture of Jasper was designed to facilitate fast GPU inference.
Question: It's your child's birthday, write a message on social media to express your happiness and celebrate their special day
Answer: Today is a very special day for my family - it's my kid's birthday! I wanted to make the day extra special by baking a cake myself, but with work keeping me busy, it was a bi of a challenge. As a mom, I know that spending with family is just as important as any professional responsibilities I may have. So here's to my little one on her special day, and to all the parents out there trying to juggle work and family life. We're doing the best we can, and that's something to be proud of.
Question: What should I consider when deciding between electric car or gas car?
Answer: Buying a car can be challenging, especially when trying to find the right balance between the upfront cost of buying/financing a car and the cost of operating that car. Electric cars do cost a premium vs gas-powered equivalent. Things to consider when deciding between both 
- Electric cars on average cost 50% less on maintenance and repairs. This is due to the fact that it is powered by a battery pack vs a complex gas engine. So fewer points of failure and less maintenance such as oil changes etc.
- Look for electric cars that still have rebates and tax incentives that can significantly lower the upfront cost of the car
- Identify your annual usage and calculate how much money you will save. you need to consider the cost of electricity in your location and if you have solar installed at home to lower the electric cost further.
Question: What does a typical meal look like
Answer: Most meals start with an appetizer, then people eat a main course and finally people end with dessert
Question: Give me a list of things to do in Washington, DC
Answer: Go see the White House, Washington Monument, Lincoln Memorial, and some of the other great landmarks. If that doesn't interest you, you can head anywhere on U-street for some great food.
Question: What is the difference between a rose and a sunflower?
Answer: Both a rose and a sunflower are types of flowers but a rose may have thorns and it’s petals are typically red, pink or white. A sunflower is a tall plant that is often yellow, does not have thorns, and has a large number of seeds that are edible for humans.
Question: What neural architectures are used?
Evidence: Feature-based models. We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). Several classical machine learning classifiers were tested with several feature combinations, among them Random Forest (RF) achieved the best result with all features. Neural model with monolingual embeddings. We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29. For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English . For the three languages, the size of the embeddings is 300 and the embeddings were fine-tuned during the training process. The CNN network was tuned with 20% of the training corpus using the $Hyperopt$ library.
 We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29. 
Answer: The answers are shown as follows:
* Convolutional Neural Network (CNN)
Question: What dataset do they use?
Evidence: SMT techniques require a parallel corpus in thr source and thr target language. A text-code parallel corpus similar to Fig. FIGREF12 is used in training. This parallel corpus has 18805 aligned data in it . In source data, the expression of each line code is written in the English language. In target data, the code is written in Python programming language.
A text-code parallel corpus similar to Fig. FIGREF12 is used in training. This parallel corpus has 18805 aligned data in it .
Answer: The answers are shown as follows:
*  text-code parallel corpus
Question: What are the categories of code-mixed puns?
Evidence: With India being a diverse linguistic region, there is an ever increasing usage of code-mixed Hindi-English language (along with various others) because bilingualism and even multilingualism are quite common. Consequently, we have also seen an increase in the usage of code-mixed language in online forums, advertisements etc. Code-mixed humour, especially puns have become increasingly popular because being able to use the same punning techniques but with two languages in play has opened up numerous avenues for new and interesting wordplays. With the increasing popularity and acceptance for the usage of code-mixed language, it has become important that computers are also able to process it and even decipher complex phenomena like humour. Traditional Word Sense Disambiguation (WSD) based methods cannot be used in target recovery of code-mixed puns, because they are no longer about multiple senses of a single word but about two words from two different languages. Code-switching comes with no markers, and the punning word may not even be a word in either of the languages being used. Sometimes words from the two languages can be combined to form a word which only a bilingual speaker would understand. Hence, this task on such data calls for a different set of strategies altogether. We approach this problem in two parts. First, we analyze the types of structures in code-mixed puns and classify them into two categories namely intra-sequential and intra-word. Second, we develop a four stage pipeline to achieve our goal - Language Identification, Pun Candidate Identification, Context Lookup and Phonetic Distance Minimization. We then test our approach on a small dataset and note that our method is successfully able to recover targets for a majority of the puns.
 First, we analyze the types of structures in code-mixed puns and classify them into two categories namely intra-sequential and intra-word. 
Answer: The answers are shown as follows:
* intra-sequential and intra-word
Question: What components is the QnAMaker composed of?
Evidence: As shown in Figure FIGREF4, humans can have two different kinds of roles in the system: Bot-Developers who want to create a bot using the data they have, and End-Users who will chat with the bot(s) created by bot-developers. The components involved in the process are:
QnAMaker Portal: This is the Graphical User Interface (GUI) for using QnAMaker. This website is designed to ease the use of management APIs. It also provides a test pane.
QnaMaker Management APIs: This is used for the extraction of Question-Answer (QA) pairs from semi-structured content. It then passes these QA pairs to the web app to create the Knowledge Base Index.
Azure Search Index: Stores the KB with questions and answers as indexable columns, thus acting as a retrieval layer.
QnaMaker WebApp: Acts as a layer between the Bot, Management APIs, and Azure Search Index. WebApp does ranking on top of retrieved results. WebApp also handles feedback management for active learning.
Bot: Calls the WebApp with the User's query to get results.
The components involved in the process are:

QnAMaker Portal: This is the Graphical User Interface (GUI) for using QnAMaker. This website is designed to ease the use of management APIs. It also provides a test pane.

QnaMaker Management APIs: This is used for the extraction of Question-Answer (QA) pairs from semi-structured content. It then passes these QA pairs to the web app to create the Knowledge Base Index.

Azure Search Index: Stores the KB with questions and answers as indexable columns, thus acting as a retrieval layer.

QnaMaker WebApp: Acts as a layer between the Bot, Management APIs, and Azure Search Index. WebApp does ranking on top of retrieved results. WebApp also handles feedback management for active learning.

Bot: Calls the WebApp with the User's query to get results.
Answer: The answers are shown as follows:
* QnAMaker Portal
* QnaMaker Management APIs
* Azure Search Index
* QnaMaker WebApp
* Bot
Question: Panchatantra stories were written by
Answer: Panchatantra stories were written by Kautilya in Sanskrit. The stories are about animals and humans living together in harmony. They teach valuable lessons about justice, compassion, and loyalty.
Question: When was Telangana created?
Evidence: Telangana (/ˌtɛlənˈɡɑːnə/ (listen); Telugu: [ˈtelaŋɡaːɳa], Urdu: [ˈtɪləŋɡɑːna] (listen)) is a state in India situated on the south-central stretch of the Indian peninsula on the high Deccan Plateau. It is the eleventh-largest state and the twelfth-most populated state in India with a geographical area of 112,077 km2 (43,273 sq mi) and 35,193,978 residents as per 2011 census. On 2 June 2014, the area was separated from the northwestern part of Andhra Pradesh as the newly formed state of Telangana, with Hyderabad as its capital. Its other major cities include Warangal, Nizamabad, Khammam, Karimnagar and Ramagundam. Telangana is bordered by the states of Maharashtra to the north, Chhattisgarh to the northeast, Karnataka to the west, and Andhra Pradesh to the east and south. The terrain of Telangana consists mostly of the Deccan Plateau with dense forests covering an area of 27,292 km2 (10,538 sq mi). As of 2019, the state of Telangana is divided into 33 districts.
Answer: 2014
Question: What programming languages is compatible with the models in the toolkit?
Evidence: Deep learning, a sub-field of machine learning research, has driven the rapid progress in artificial intelligence research, leading to astonishing breakthroughs on long-standing problems in a plethora of fields such as computer vision and natural language processing. Tools powered by deep learning are changing the way movies are made, diseases are diagnosed, and play a growing role in understanding and communicating with humans.
Such development is made possible by deep learning frameworks, such as Caffe BIBREF0 , Chainer BIBREF1 , CNTK BIBREF2 , Apache (incubating) MXNet BIBREF3 , PyTorch BIBREF4 , TensorFlow BIBREF5 , and Theano BIBREF6 . These frameworks have been crucial in disseminating ideas in the field. Specifically, imperative tools, arguably spearheaded by Chainer, are easy to learn, read, and debug. Such benefits make imperative programming interface quickly adopted by the Gluon API of MXNet (while can be seamlessly switched to symbolic programming for high performance), PyTorch, and TensorFlow Eager.
Leveraging the imperative Gluon API of MXNet, we design and develop the GluonCV and GluonNLP (referred to as GluonCV/NLP hereinafter) toolkits for deep learning in computer vision and natural language processing. To the best of our knowledge, GluonCV/NLP are the first open source toolkits for deep learning in both computer vision and natural language processing that simultaneously i) provide modular APIs to allow customization by re-using efficient building blocks; ii) provide pre-trained state-of-the-art models, training scripts, and training logs to enable fast prototyping and promote reproducible research; iii) leverage the MXNet ecosystem so that models can be deployed in a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala.
Leveraging the imperative Gluon API of MXNet, we design and develop the GluonCV and GluonNLP (referred to as GluonCV/NLP hereinafter) toolkits for deep learning in computer vision and natural language processing. GluonCV/NLP simultaneously i) provide modular APIs to allow customization by re-using efficient building blocks; ii) provide pre-trained state-of-the-art models, training scripts, and training logs to enable fast prototyping and promote reproducible research; iii) provide models that can be deployed in a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala (via the MXNet ecosystem).
Answer: It supports a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala (via the MXNet ecosystem)
Question: What is a polygon?
Answer: A polygon is a form in Geometry.  It is a single dimensional plane made of connecting lines and any number of vertices.  It is a closed chain of connected line segments or edges.  The vertices of the polygon are formed where two edges meet.  Examples of polygons are hexagons, pentagons, and octagons.  Any plane that does not contain edges or vertices is not a polygon.  An example of a non-polygon is a circle.
Question: Given these paragraphs about the Tesla bot, what kind of tasks is it expected to perform?
Evidence: Optimus, also known as Tesla Bot, is a conceptual general-purpose robotic humanoid under development by Tesla, Inc. It was announced at the company's Artificial Intelligence (AI) Day event on August 19, 2021. CEO Elon Musk claimed during the event that Tesla would likely build a prototype by 2022. Musk is on record having said that he thinks Optimus "has the potential to be more significant than the vehicle business over time."

Tesla Bot is planned to measure 5 ft 8 in (173 cm) tall and weigh 125 lb (57 kg). According to the presentation made during the first AI Day event, a Tesla Bot will be "controlled by the same AI system Tesla is developing for the advanced driver-assistance system used in its cars" and have a carrying capacity of 45 lb (20 kg). Proposed tasks for the product are ones that are "dangerous, repetitive and boring", such as providing manufacturing assistance.

https://en.wikipedia.org/wiki/Optimus_(robot)
Answer: Proposed tasks for the Tesla Bot are ones that are "dangerous, repetitive and boring", such as providing manufacturing assistance.
Question: Who are some of the top goal scorers of all time for Manchester City?
Answer: Some of the top goal scorers of all time for Manchester City are Sergio Aguero, Eric Brook, Tommy Johnson, Colin Bell, and Joe Hayes.
Question: What is the downside of using mulch for your landscape?
Answer: Mulch attracts termites and other insects. Mulch has to be replaced every other year.
Question: What is the Ritterhaus Uerikon
Evidence: There is an old mansion (Ritterhuus) built in 1531 that bases on a tiny castle of 1492 serving as place of the former local administration of the Einsiedeln Abbey. It is located at the lake shore.
Answer: There is an old mansion (Ritterhuus) built in 1531 that bases on a tiny castle of 1492 serving as place of the former local administration of the Einsiedeln Abbey. It is located at the lake shore in Uerikon, Switzerland.
Question: List 7 exotic fruits that I should try.
Answer: 1. Dragon fruit
2. Passion fruit 
3. Cherimoya
4. Soursop
5. Star fruit
6. Papaya
7. Mangosteen
Question: Why is aluminum wiring in a home dangerous?
Answer: When current is passed through a 10ft aluminum wire in a home, the wire can expand up to 3 inches. When the current ends, the wire can shrink up to 3 inches. This can cause wires to pull from electrical boxes and connections, as well as wear through their protective coating when touching parts of the house. Wires are typically hidden within walls and ceilings, and if connections are broken or wires become exposed, the electrical current can start fires.
Question: Which components of QA and QG models are shared during training?
Evidence: We proposed a neural machine comprehension model that can jointly ask and answer questions given a document. We hypothesized that question answering can benefit from synergistic interaction between the two tasks through parameter sharing and joint training under this multitask setting. Our proposed model adopts an attention-based sequence-to-sequence architecture that learns to dynamically switch between copying words from the document and generating words from a vocabulary. Experiments with the model confirm our hypothesis: the joint model outperforms its QA-only counterpart by a significant margin on the SQuAD dataset.
We hypothesized that question answering can benefit from synergistic interaction between the two tasks through parameter sharing and joint training under this multitask setting.
Answer: The answers are shown as follows:
* parameter sharing
Question: Are there syntax-agnostic SRL models before?
Evidence: Typically, SRL task can be put into two categories: constituent-based (i.e., phrase or span) SRL and dependency-based SRL. This paper will focus on the latter one popularized by CoNLL-2008 and 2009 shared tasks BIBREF5 , BIBREF6 . Most conventional SRL systems relied on sophisticated handcraft features or some declarative constraints BIBREF7 , BIBREF8 , which suffers from poor efficiency and generalization ability. A recently tendency for SRL is adopting neural networks methods attributed to their significant success in a wide range of applications BIBREF9 , BIBREF10 . However, most of those works still heavily resort to syntactic features. Since the syntactic parsing task is equally hard as SRL and comes with its own errors, it is better to get rid of such prerequisite as in other NLP tasks. Accordingly, marcheggiani2017 presented a neural model putting syntax aside for dependency-based SRL and obtain favorable results, which overturns the inherent belief that syntax is indispensable in SRL task BIBREF11 .
Accordingly, marcheggiani2017 presented a neural model putting syntax aside for dependency-based SRL and obtain favorable results, which overturns the inherent belief that syntax is indispensable in SRL task BIBREF11 .
Answer: Yes.
Question: Which dataset do they use?
Evidence: For the pre-translation, we used a PBMT system. In order to analyze the influence of the quality of the PBMT system, we use two different systems, a baseline system and a system with advanced models. The systems were trained on all parallel data available for the WMT 2016. The news commentary corpus, the European parliament proceedings and the common crawl corpus sum up to 3.7M sentences and around 90M words.
The systems were trained on all parallel data available for the WMT 2016. The news commentary corpus, the European parliament proceedings and the common crawl corpus sum up to 3.7M sentences and around 90M words.
Answer: The answers are shown as follows:
* parallel data available for the WMT 2016
Question: How do influencers make money?
Answer: Social media influencers make money in a variety of ways. Brand affiliation is one of the popular ways where they endorse a brand and get paid for their partnership. Paid subscription to access content is another popular option. Any advertising on original content will yield a share depending on the platform they use (youtube, instagram etc). Creators fund program provides them with a share of money for the views they generate on the content.
Question: Name some books that Jane Austen is best known for.
Answer: Jane Austen’s novels were not popular during her lifetime. Today, her most popular novels are ‘Pride and Prejudice’ (1813) and ‘Sense and Sensibility’ (1811). ‘Pride and Prejudice’ follows the relationship between Elizabeth Bennet, a country woman who makes hasty judgements, and Fitzwilliam Darcy, a proud aristocrat, as they overcome their differences and fall in love. ‘Sense and Sensibility’ follows the three Dashwood sisters who are uprooted from their home and must strike a balance between pragmatism and romance to find love and happiness.