DataHammer/scimrc huggingface dataset:
Question: How many campgrounds does Shenandoah National Park have?
Answer: Shenandoah National Park has five different campgrounds evenly dispersed throughout the park: Mathews Arm, Big Meadows, Lewis Mountain, Loft Mountain, and Dundo Group Campground. Mathews Arm is located in the northern section of the park. Big Meadows Campground is in the central portion of the park and is the most popular one. Lewis Mountain is similarly in the central part of the park, but is smaller, offering more privacy to people. Loft Mountain is the largest campground and is in the southern part of the park. Dundo Group Campground is also small and to the south, but only has group sites and the camp sits on the Appalachian Trail. The campsites offer different amenities such as food storage lockers and trash/recycling collection.
Question: Do they provide a framework for building a sub-differentiable for any final loss metric?
Evidence: We introduce a surrogate training objective that avoids these problems and as a result is fully continuous. In order to accomplish this, we propose a continuous relaxation to the composition of our final loss metric, INLINEFORM0 , and our decoder function, INLINEFORM1 : INLINEFORM2
Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . By introducing this new module, we are now able to construct our surrogate training objective: DISPLAYFORM0
We introduce a surrogate training objective that avoids these problems and as a result is fully continuous. 
Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 .
Answer: Yes.
Question: What were the results of the first experiment?
Evidence: We also observe that the best combination seems to consist in training our model on the original out-of-context dataset and testing it on the in-context pairs. In this configuration we reach an F-score (0.72) only slightly lower than the one reported in BIBREF3 (0.74), and we record the highest Pearson correlation, 0.3 (which is still not strong, compared to BIBREF3 's best run, 0.75). This result may partly be an artifact of the the larger amount of training data provided by the out-of-context pairs.
We also observe that the best combination seems to consist in training our model on the original out-of-context dataset and testing it on the in-context pairs. In this configuration we reach an F-score (0.72) only slightly lower than the one reported in BIBREF3 (0.74), and we record the highest Pearson correlation, 0.3 (which is still not strong, compared to BIBREF3 's best run, 0.75). 
Answer: Best performance achieved is 0.72 F1 score
Question: What kind of features are used by the HMM models, and how interpretable are those?
Evidence: We compare a hybrid HMM-LSTM approach with a continuous emission HMM (trained on the hidden states of a 2-layer LSTM), and a discrete emission HMM (trained directly on data).
We interpret the HMM and LSTM states in the hybrid algorithm with 10 LSTM state dimensions and 10 HMM states in Figures 3 and 3 , showing which features are identified by the HMM and LSTM components. In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters. The HMM and LSTM states pick up on spaces, indentation, and special characters in the data (such as comment symbols in Linux data). We see some examples where the HMM and LSTM complement each other, such as learning different things about spaces and comments on Linux data, or punctuation on the Shakespeare data. In Figure 2 , we see that some individual LSTM hidden state dimensions identify similar features, such as comment symbols in the Linux data.
FLOAT SELECTED: Figure 2: Visualizing HMM and LSTM states on Linux data for the hybrid with 10 LSTM state dimensions and 10 HMM states. The HMM and LSTM components learn some complementary features in the text related to spaces and comments.
We compare a hybrid HMM-LSTM approach with a continuous emission HMM (trained on the hidden states of a 2-layer LSTM), and a discrete emission HMM (trained directly on data).
We interpret the HMM and LSTM states in the hybrid algorithm with 10 LSTM state dimensions and 10 HMM states in Figures 3 and 3 , showing which features are identified by the HMM and LSTM components.
FLOAT SELECTED: Figure 2: Visualizing HMM and LSTM states on Linux data for the hybrid with 10 LSTM state dimensions and 10 HMM states. The HMM and LSTM components learn some complementary features in the text related to spaces and comments.
Answer: A continuous emission HMM uses the hidden states of a 2-layer LSTM as features and a discrete emission HMM uses data as features. 
The interpretability of the model is shown in Figure 2.
Question: "repair instead of replace" give five points to support this argument
Answer: 1) saves cost
2) reduces wastage
3) good for environment
4) promotes local employment 
5) efficient use of resources
Question: Why the answer generated from the model is simply “yes” or “no”?
Evidence: We have shown in Section UID47 a simple example consisting of only four clauses from which our model can identify the clause containing the emotion cause correctly. We notice that for some complex text passages which contain long distance dependency relations, negations or emotion transitions, our model may have a difficulty in detecting the correct clause containing the emotion causes. It is a challenging task to properly model the discourse relations among clauses. In the future, we will explore different network architecture with consideration of various discourse relations possibly through transfer learning of larger annotated data available for other tasks.
Another shortcoming of our model is that, the answer generated from our model is simply “yes” or “no”. The main reason is that the size of the annotated corpus is too small to train a model which can output natural language answers in full sentences. Ideally, we would like to develop a model which can directly give the cause of an emotion expressed in text. However, since the manual annotation of data is too expensive for this task, we need to explore feasible ways to automatically collect annotate data for emotion cause detection. We also need to study effective evaluation mechanisms for such QA systems.
The main reason is that the size of the annotated corpus is too small to train a model which can output natural language answers in full sentences.
Answer: The main reason is that the size of the annotated corpus is too small to train a model which can output natural language answers in full sentences.
Question: How many clinical patient phenotypes are labeled in their dataset?
Evidence: We have created a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes believed to be associated with risk of recurrent Intensive Care Unit (ICU) readmission per our domain experts (co-authors LAC, PAT, DAG) as well as the literature. BIBREF10 BIBREF11 BIBREF12
Each entry in this database of consists of a Subject Identifier (integer), a Hospital Admission Identifier (integer), Category (string), Text (string), 15 Phenotypes (binary) including “None” and “Unsure”, Batch Date (string), and Operators (string). These variables are sufficient to use the data set alone, or to join it to the MIMIC-III database by Subject Identifier or Hospital Admission Identifier for additional patient-level or admission-level data, respectively. The MIMIC database BIBREF8 was utilized to extract Subject Identifiers, Hospital Admission Identifiers, and Note Text.
Annotated discharge summaries had a median token count of 1417.50 (Q1-Q3: 1046.75 - 1926.00) with a vocabulary of 26454 unique tokens, while nursing notes had a median count of 208 (Q1-Q3: 120 - 312) with a vocabulary of 12865 unique tokens.
Table defines each of the considered clinical patient phenotypes. Table counts the occurrences of these phenotypes across patient notes and Figure contains the corresponding correlation matrix. Lastly, Table presents an overview of some descriptive statistics on the patient notes' lengths.
We have created a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes believed to be associated with risk of recurrent Intensive Care Unit (ICU) readmission per our domain experts (co-authors LAC, PAT, DAG) as well as the literature. (Kansagara et al., 2011) (Kansagara et al., 2012) (Kangovi et al., 2014)
Answer: 15 clinical patient phenotypes.
Question: How many titles have Liverpool won?
Evidence: Domestically, the club has won 19 League titles, eight FA Cups, a record nine League Cups and 16 FA Community Shields. In international competitions, the club has won six European Cups, three UEFA Cups, four UEFA Super Cups—all English records—and one FIFA Club World Cup.
Answer: Liverpool has won 19 League titles, 8 FA cups, 9 League cups, 16 FA community shields and 6 European cups.
Question: What are important elements to consider when putting together a team to execute your operating model for AI?
Answer: When building the right team to execute on your operating model for AI it is key to take into account the following elements:

1) How mature is my data foundation: Whether your data is still in silos, stuck in proprietary formats or difficult to access in a unified way will have big implications on the amount of data engineering work and data platform expertise that is required.

2) Infrastructure and platform administration: Whether you need to maintain or leverage as a service offerings can have a huge impact on your overall team composition. Moreover, if your Data and AI platform is made up of multiple services and components the administrative burden of governing and securing data and users and keeping all parts working together can be overwhelming especially at enterprise scale.

3) MLOps: To make the most of AI you need to be able to use it to impact your business. Hiring a full data science team without having the right ML engineering expertise or the right tools to package, test, deploy and monitor is extremely wasteful. There are several steps that go into running effective end-to-end AI applications and your operating model should reflect that in the roles that are involved and in the way model lifecycle management is executed from use case identification, to development to deployment, and perhaps most importantly utilisation.  

These three dimensions should inform your focus and the roles that should be part of your development team. Over time, the prevalence of certain roles might shift as your organisation matures along these dimensions and on the platform decisions that you make.
Question: Based on the given text, List some aspects of the pearl.
Evidence: A pearl is a hard, glistening object produced within the soft tissue (specifically the mantle) of a living shelled mollusk or another animal, such as fossil conulariids. Just like the shell of a mollusk, a pearl is composed of calcium carbonate (mainly aragonite or a mixture of aragonite and calcite) in minute crystalline form, which has deposited in concentric layers. The ideal pearl is perfectly round and smooth, but many other shapes, known as baroque pearls, can occur. The finest quality of natural pearls have been highly valued as gemstones and objects of beauty for many centuries. Because of this, pearl has become a metaphor for something rare, fine, admirable and valuable.
Answer: 1.	Pearl is a sparkling and hard object which is produced inside mollusk shell or other shells.
2.	Just like the shells pearls are also composed of calcium carbonate.
3.	The Ideal pearl is perfectly round and smooth, but it can come in different shapes like baroque pearls.
4.	The best quality natural pearls are valued as gemstones.
5.	Since pearls are fine, rare and valuable it has become a metaphor.
Question: What are the most useful features besides baseline features?
Evidence: Our experimental results show that the baseline features outperform the pre-trained features for sarcasm detection. However, the combination of pre-trained features and baseline features beats both of themselves alone. It is counterintuitive, since experimental results prove that both of those features learn almost the same global and contextual features. In particular, baseline network dominates over pre-trained network as the former learns most of the features learned by the latter. Nonetheless, the combination of baseline and pre-trained classifiers improves the overall performance and generalizability, hence proving their effectiveness in sarcasm detection. Experimental results show that sentiment and emotion features are the most useful features, besides baseline features (Figure FIGREF36 ). Therefore, in order to reach a better understanding of the relation between personality features among themselves and with other pre-trained features, we carried out Spearman correlation testing. Results, displayed in Table TABREF39 , show that those features are highly correlated with each other.
Experimental results show that sentiment and emotion features are the most useful features, besides baseline features (Figure 4).
Answer: Sentiment and emotion features.
Question: What is NEFT?
Answer: National Electronic Funds Transfer (NEFT) is an electronic funds transfer system maintained by the Reserve Bank of India (RBI). Started in November 2005, the setup was established and maintained by Institute for Development and Research in Banking Technology. NEFT enables bank customers in India to transfer funds between any two NEFT-enabled bank accounts on a one-to-one basis. It is done via electronic messages.

Unlike real-time gross settlement, fund transfers through the NEFT system do not occur in real-time basis. Previously, NEFT system settled fund transfers in hourly batches with 23 settlements occurring between 00:30 hrs. to 00:00 hrs.

From 16 December 2019, there would be 48 half-hourly batches occurring between 00.30 am to 00:00 am every day regardless of a holiday or otherwise.

As of 30 November 2019, NEFT facilities were available at 1,48,477 branches/offices of 216 banks across the country and online through the website of NEFT-enabled banks. NEFT has gained popularity due to the ease and efficiency with which the transactions can be concluded.

There is no limit – either minimum or maximum – on the amount of funds that can be transferred using NEFT.
Question: Which machine learning models are used?
Evidence: Our first goal is to train a sentiment analysis model (on training and validation datasets) in order to perform classification inference on event-based tweets. We experimented with different feature extraction methods and classification models. Feature extractions examined include Tokenizer, Unigram, Bigram, 5-char-gram, and td-idf methods. Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel). Model accuracies are reported in Table FIGREF3 .
 Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel). 
Answer: The answers are shown as follows:
* RNNs
* CNNs
* Naive Bayes with Laplace Smoothing
* k-clustering
* SVM with linear kernel
Question: When formalize the intuition for Expected Gradient Length (EGL), how to measure the informativeness?
Evidence: State-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize. Labeling thousands of hours of audio, however, is expensive and time-consuming. A natural question to ask is how to achieve better generalization with fewer training examples. Active learning studies this problem by identifying and labeling only the most informative data, potentially reducing sample complexity. How much active learning can help in large-scale, end-to-end ASR systems, however, is still an open question.
The speech recognition community has generally identified the informativeness of samples by calculating confidence scores. In particular, an utterance is considered informative if the most likely prediction has small probability BIBREF1 , or if the predictions are distributed very uniformly over the labels BIBREF2 . Though confidence-based measures work well in practice, less attention has been focused on gradient-based methods like Expected Gradient Length (EGL) BIBREF3 , where the informativeness is measured by the norm of the gradient incurred by the instance. EGL has previously been justified as intuitively measuring the expected change in a model's parameters BIBREF3 .We formalize this intuition from the perspective of asymptotic variance reduction, and experimentally, we show EGL to be superior to confidence-based methods on speech recognition tasks. Additionally, we observe that the ranking of samples scored by EGL is not correlated with that of confidence scoring, suggesting EGL identifies aspects of an instance that confidence scores cannot capture.
In BIBREF3 , EGL was applied to active learning on sequence labeling tasks, but our work is the first we know of to apply EGL to speech recognition in particular. Gradient-based methods have also found applications outside active learning. For example, BIBREF4 suggests that in stochastic gradient descent, sampling training instances with probabilities proportional to their gradient lengths can speed up convergence. From the perspective of variance reduction, this importance sampling problem shares many similarities to problems found in active learning.
where the informativeness is measured by the norm of the gradient incurred by the instance.
Answer: The informativeness is measured by the norm of the gradient incurred by the instance.
Question: What are the biggest cities in Germany?
Answer: The biggest cities in Germany are Berlin, Hamburg, Munich and Cologne. Berlin is the biggest city with almost 3.7 million inhabitants. The second biggest city is Hamburg with 1.9 million inhabitants.
Question: What are the places McCaw travelled to capture movements of the Sun?
List them in a comma separated format.
Evidence: McCaw was born in Daly City, California, in 1971. He has a BFA from the Academy of Art College in San Francisco.

McCaw is known for his large-format homemade cameras in which he uses expired gelatin silver photo paper and long exposures to make solarized paper negatives which often include the burned path of the sun within the frame, in a series named Sunburn. McCaw travels to remote places to capture different apparent movements of the sun, including the Arctic Circle in Alaska, the Galápagos Islands and the Mojave Desert. McCaw's earlier work used a 7×17 inch view camera to create large-format negatives from which he made platinum prints. Projects following Sunburn include work with a modified Cirkut camera, resulting in exposures that can take more than 24 hours. A series titled Poli-optic employs a homemade camera with a grid of lenses. Finally, the series Heliograph includes work in which there are multiple exposures of the sun on the same paper negative.
Answer: Arctic Circle in Alaska, the Galápagos Islands, the Mojave Desert.
Question: What does the author hope to do about his future work?
Evidence: We present an attention-based approach for the detection of harassment language in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classiﬁcation specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach and a few baseline methods. According to the results of our experiments and considering the F1 Score, the multi-attention method having a projected layer, achieved the highest performance. Also, we tackled the problem of the imbalance between the training, validation and test sets performing the technique of back-translation.
In the future, we would like to perform more experiments with this dataset applying different models using BERT BIBREF21. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media.
In the future, we would like to perform more experiments with this dataset applying different models using BERT [22]. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media.
Answer: They would like to perform more experiments with this dataset applying different models using BERT and to apply the models presented in this work, in other datasets about hate speech in social media.
Question: What is the mission of the Federal Bureau of Investigation?
Answer: The Federal Bureau of Investigation (FBI) is the domestic intelligence and security service of the United States and its principal federal law enforcement agency. Operating under the jurisdiction of the United States Department of Justice, the FBI is also a member of the U.S. Intelligence Community and reports to both the Attorney General and the Director of National Intelligence.  A leading U.S. counterterrorism, counterintelligence, and criminal investigative organization, the FBI has jurisdiction over violations of more than 200 categories of federal crimes.
Question: Whats the difference between a bass guitar and a regular guitar?
Answer: There are a few significant differences between a bass guitar and a regular guitar.  A bass guitar typically has 4 strings (though it can have more), is larger in body & neck, and has higher string gauges (string thickness).  A bass guitar is used for keeping the band on track by keeping the rhythm and hitting the root notes of the chords.  

A regular guitar typically has 6 strings, and can be used for a wider variety of purposes.  These include keeping the rhythm with chords, playing foundational riffs, or improvisational solos for instrumental layering.  The regular guitarists are almost always the more well-known of the two.
Question: Based on the paragraph about the Korean War, what is the name of the new sovereign state created in the north?
Evidence: In 1910, Imperial Japan annexed Korea, where it ruled for 35 years until its surrender at the end of World War II on 15 August 1945. The United States and the Soviet Union divided Korea along the 38th parallel into two zones of occupation. The Soviets administered the northern zone and the Americans administered the southern zone. In 1948, as a result of Cold War tensions, the occupation zones became two sovereign states. A socialist state, the Democratic People's Republic of Korea, was established in the north under the totalitarian communist leadership of Kim Il-sung, while a capitalist state, the Republic of Korea, was established in the south under the autocratic leadership of Syngman Rhee. Both governments of the two new Korean states claimed to be the sole legitimate government of all of Korea, and neither accepted the border as permanent.
Answer: Democratic People's Republic of Korea