DataHammer/scimrc huggingface dataset:
Question: What is open website for cataloguing abusive language data?
Evidence: Most detection systems rely on having the right training dataset, reflecting one of the most widely accepted mantras in computer science: Garbage In, Garbage Out. Put simply: to have systems which can detect and classify abusive online content effectively, one needs appropriate datasets with which to train them. However, creating training datasets is often a laborious and non-trivial task – and creating datasets which are non-biased, large and theoretically-informed is even more difficult (BIBREF0 p. 189). We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com.
We address this issue by
We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com.
We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com.
Answer: The answers are shown as follows:
* hatespeechdata.com
Question: Which popular clustering methods did they experiment with?
Evidence: In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . We further compare our approach with some other non-biased neural networks, such as bidirectional RNN. More details are listed as follows:
In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . 
Answer: The answers are shown as follows:
* K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods
Question: What kind of game is Binding of Isaac and what platforms can I play it on?
Evidence: The Binding of Isaac is a roguelike video game designed by independent developers Edmund McMillen and Florian Himsl. It was released in 2011 for Microsoft Windows, then ported to OS X, and Linux. The game's title and plot are inspired by the Biblical story of the Binding of Isaac. In the game, Isaac's mother receives a message from God demanding the life of her son as proof of her faith, and Isaac, fearing for his life, flees into the monster-filled basement of their home where he must fight to survive. Players control Isaac or one of seven other unlockable characters through a procedurally generated dungeon in a roguelike manner, fashioned after those of The Legend of Zelda, defeating monsters in real-time combat while collecting items and power-ups to defeat bosses and eventually Isaac's mother.

The game was the result of a week-long game jam between McMillen and Himsl to develop a The Legend of Zelda-inspired roguelike that allowed McMillen to showcase his feelings about both positive and negative aspects of religion that he had come to discover from conflicts between his Catholic and born again Christian family members while growing up. McMillen had considered the title a risk but one he could take after the financial success of Super Meat Boy, and released it without much fanfare to Steam in September 2011, not expecting many sales. The game soon gained popularity partially as a result of various Let's Play videos showcasing the title. McMillen and Himsl released an expansion "Wrath of the Lamb" in May 2012, but were limited from further expansion due to limitations with the Flash platform. They had started working with Nintendo in 2012 to release a 3DS version, but Nintendo later backed out of the deal, citing controversy over the game's religious themes.

Developer Nicalis worked with McMillen in 2014 to complete a remake of the game, The Binding of Isaac: Rebirth, bringing additional features that McMillen had planned that exceeded Flash's limitation, as well as to improve the game's graphics and enable ports for other systems beyond personal computers, including PlayStation 4 and Vita, Xbox One, Wii U, Nintendo 3DS, and the Nintendo Switch. This remake has commonly been cited as one of the best roguelike games of all time.

McMillen later worked with James Id to develop The Legend of Bum-bo, which serves as a prequel to The Binding of Isaac.

The Binding of Isaac has been well-received, with critics praising the game's roguelike nature to encourage repeated playthroughs. By July 2014, McMillen reported over 3 million copies had been sold. The game has been said to contribute to renewed interest in the roguelike genre from both players and developers.
Answer: The Binding of Isaac is a roguelike video game that is available to play on several platforms including Microsoft Windows, OS X, Linux, PlayStation 4/Vita, Xbox One, Wii U, Nintendo 3DS, and the Nintendo Switch.
Question: Which subfield of machine learning do they research?
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.
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.
Answer: Deep learning.
Question: What are the components of the data set used by the author?
Evidence: The dataset from Twitter that we are using in our work, consists of a train set, a validation set and a test set. It was published for the "First workshop on categorizing different types of online harassment languages in social media". The whole dataset is divided into two categories, which are harassment and non-harassment tweets. Moreover, considering the type of the harassment, the tweets are divided into three sub-categories which are indirect harassment, sexual and physical harassment. We can see in Table TABREF1 the class distribution of our dataset. One important issue here is that the categories of indirect and physical harassment seem to be more rare in the train set than in the validation and test sets. To tackle this issue, as we describe in the next section, we are performing data augmentation techniques. However, the dataset is imbalanced and this has a significant impact in our results.
The dataset from Twitter that we are using in our work, consists of a train set, a validation set and a test set.
Answer: A train set, a validation set and a test set.
Question: what is the best profession?
Answer: No single job or career path is best for everyone. However, many of the best ones have a few attributes in common: They pay well, provide challenges and personal growth, match our talents and skills, aren't too stressful, offers room to advance throughout our careers, and provide an acceptable work-life balance. Of course the importance of each of these is specific to the individual and therefore the best profession is very subjective. There are several online resource to help evaluate these categories.
Question: Which Bond girl guest starred on the TV show Game of Thrones?
Answer: Diana Rigg, who played Teresa di Vicenzo in the James Bond movie "On Her Majesty's Secret Service," played Olenna Tyrell in Game of Thrones.
Question: What are the major elements found on earth?
Evidence: Earth's mass is approximately 5.97×1024 kg (5,970 Yg). It is composed mostly of iron (32.1% by mass), oxygen (30.1%), silicon (15.1%), magnesium (13.9%), sulfur (2.9%), nickel (1.8%), calcium (1.5%), and aluminum (1.4%), with the remaining 1.2% consisting of trace amounts of other elements. Due to gravitational separation, the core is primarily composed of the denser elements: iron (88.8%), with smaller amounts of nickel (5.8%), sulfur (4.5%), and less than 1% trace elements. The most common rock constituents of the crust are oxides. Over 99% of the crust is composed of various oxides
Answer: Earth's mass is made of primarily iron, oxygen, silicon, magnesium, sulfur, nickel, calcium and aluminum.
Question: Which of these words are nouns and which are adjectives. Fridge, wipe, bed, chair, jump, yell, sleep, fruit and talk.
Answer: Fridge, bed, chair and fruit are words that are nouns. Wipe, jump, yell, sleep and talk are words that are adjectives.
Question: Who is Frank Lloyd Wright, and why is he famous?
Evidence: Frank Lloyd Wright (June 8, 1867 – April 9, 1959) was an American architect, designer, writer, and educator.

He designed more than 1,000 structures over a creative period of 70 years. Wright played a key role in the architectural movements of the twentieth century, influencing architects worldwide through his works and hundreds of apprentices in his Taliesin Fellowship. Wright believed in designing in harmony with humanity and the environment, a philosophy he called organic architecture. This philosophy was exemplified in Fallingwater (1935), which has been called "the best all-time work of American architecture".

Wright was the pioneer of what came to be called the Prairie School movement of architecture and also developed the concept of the Usonian home in Broadacre City, his vision for urban planning in the United States. He also designed original and innovative offices, churches, schools, skyscrapers, hotels, museums, and other commercial projects. Wright-designed interior elements (including leaded glass windows, floors, furniture and even tableware) were integrated into these structures. He wrote several books and numerous articles and was a popular lecturer in the United States and in Europe. Wright was recognized in 1991 by the American Institute of Architects as "the greatest American architect of all time". In 2019, a selection of his work became a listed World Heritage Site as The 20th-Century Architecture of Frank Lloyd Wright.

Raised in rural Wisconsin, Wright studied civil engineering at the University of Wisconsin and then apprenticed in Chicago, briefly with Joseph Lyman Silsbee, and then with Louis Sullivan at Adler & Sullivan. Wright opened his own successful Chicago practice in 1893 and established a studio in his Oak Park, Illinois home in 1898. His fame increased and his personal life sometimes made headlines: leaving his first wife Catherine Tobin for Mamah Cheney in 1909; the murder of Mamah and her children and others at his Taliesin estate by a staff member in 1914; his tempestuous marriage with second wife Miriam Noel (m. 1923–1927); and his courtship and marriage with Olgivanna Lazović (m. 1928–1959).
Answer: Frank Lloyd Wright continues to be an inspiration in architecture around the United States and many of his originally designed buildings and structures stand today.
Question: Identify which car manufacturer is Italian or American: Fiat, Dodge
Answer: Dodge is American, Fiat is Italian
Question: What gated recurrent unit (GRU) did the authors use as encoders for the connectionist temporal classification (CTC) network?
Evidence: An end-to-end ASR model maps input feature vectors to an output sequence of vectors of posterior probabilities of tokens without using separate acoustic model, pronunciation model and language model. In this work we implemented two different types of state of art end to end ASR models used for the task of continuous speech recognition and the input feature vectors can be EEG features or concatenation of acoustic and EEG features. We used Google's tensorflow and keras deep learning libraries for building our ASR models.
In our work we used a single layer gated recurrent unit (GRU)  with 128 hidden units as encoder for the CTC network.
Answer: The authors used a single layer gated recurrent unit (GRU) BIBREF10 with 128 hidden units as encoders for the connectionist temporal classification (CTC) network.
Question: What are the four seasons of the year?
Answer: The four seasons are summer, fall (or autumn), winter and spring.
Question: Best beach vacation in the world
Answer: 1. Maldives (Best resorts with All inclusive )
2. Mauritius 
3. Cancun
Question: In which country is the Karachchi Divisional Council located?
Evidence: Karachchi Divisional Council is the local authority for Kandavalai and Karachchi DS Divisions in northern Sri Lanka. KDC is responsible for providing a variety of local public services including roads, sanitation, drains, housing, libraries, public parks and recreational facilities. It has 19 members elected using the open list proportional representation system.
Answer: Sri Lanka
Question: What is a vegan?
Answer: A vegan is one who doesn't eat any food product coming from or derived from animals. This includes eggs, fish, dairy, and to some, honey.
Question: How many word embedding layers (and output layers) does Fine-tuning Bilingual LM have?
Evidence: We create a bilingual LM by plugging foreign language specific parameters to the pre-trained English LM (Figure FIGREF7). The new model has two separate embedding layers and output layers, one for English and one for foreign language. The encoder layer in between is shared. We then fine-tune this model using English and foreign monolingual data. Here, we keep tuning the model on English to ensure that it does not forget what it has learned in English and that we can use the resulting model for zero-shot transfer (§SECREF3). In this step, the encoder parameters are also updated so that in can learn syntactic aspects (i.e., word order, morphological agreement) of the target languages.
We create a bilingual LM by plugging foreign language specific parameters to the pretrained English LM (Figure 1). The new model has two separate word embedding layers (and output layers), one for English and one for foreign language.
Answer: Two separate word embedding layers (and output layers).
Question: What is EDM?
Answer: EDM or Electronic Dance Music is a genre of Electronic music that is typically played at clubs, bars, pubs, and parties for groovy people to dance to while they have a great time.
Question: Describe a plan for a trip to San Francisco (Bay Area), California. Your plan should include a budget, a timeline, and a description of the sights and activities at spot
Answer: When you visit San Francisco, CA a few local and a few around SF spots are a must-see. Depending on the timeline you can pick and choose between them. I'll describe a few of my favorites for when my own friends and families visit. 

here is a MUST-SEE list within the San Francisco Bay Area 
- Golden Gate - An engineering marvel and a sight to behold, enjoy time with friends and family overlooking majestic views of nature and city alike. The Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. 

- Lombard Street – also popular as the zig zag street among tourists, San Francisco is very hilly and the city makes the most of it; Enjoy this one of a kind street in the world where a very zig zag steep street, rustling with flowers and beautiful landscaping in the summer, developed as a tourist must see. There are multiple ways to get there - get on the famous trams/cable car, walk up to it, ride a scooter or one of the buggies famous in SF 

- Alcatraz and Pier 39 - these are absolutely touristy places, folks might enjoy the boat ride and stroll an easy evening on Pier 39. Beware, you will find all the same shops that you will find at Navy Pier in Chicago too. 

- If you get a chance visit the Muir Woods in the north, Half Moon Bay in the peninsula, the 18 mile drive in the south and one of my top favorites Yosemite National Park.
Question: How many GPUs are used for the training of the neural network?
Evidence: In this section, we present the details about training our neural network. The neural network architecture are implemented using PyTorch framework BIBREF26. The training is performed on a single Nvidia Tesla P100 SXM2. We first run our experiment on BiLSTM, BiLSTM-CNN, BiLSTM-CRF BiLSTM-CNN-CRF using the hyper-parameters mentioned in Table TABREF30. The training and evaluation was done on sentence-level. The RNN variants are initialized randomly from $(-\sqrt{k},\sqrt{k})$ where $k=\frac{1}{hidden\_size}$.
First we loaded our dataset and built vocabulary using torchtext library. This eased our process of data loading using its SequenceTaggingDataset class. We trained our model with shuffled training set using Adam optimizer with hyper-parameters mentioned in table TABREF30. All our models were trained on single layer of LSTM network. We found out Adam was giving better performance and faster convergence compared to Stochastic Gradient Descent (SGD). We chose those hyper-parameters after many ablation studies. The dropout of 0.5 is applied after LSTM layer.
For CNN, we used 30 different filters of sizes 3, 4 and 5. The embeddings of each character or grapheme involved in a given word, were passed through the pipeline of Convolution, Rectified Linear Unit and Max-Pooling. The resulting vectors were concatenated and applied dropout of 0.5 before passing into linear layer to obtain the embedding size of 30 for the given word. This resulting embedding is concatenated with word embeddings, which is again concatenated with one-hot POS vector.
In this section, we present the details about training our neural network. The neural network architecture are implemented using PyTorch framework (Paszke et al., 2017). The training is per_x001f_formed on a single Nvidia Tesla P100 SXM2.
Answer: Only one.
Question: Give me a bulleted list of the seven most recent Chancellors of Federal Republic of Germany
Answer: 1. Olaf Scholz (Current)
2. Angela Merkel
3. Gerhard Schroeder
4. Helmut Kohl
5. Helmut Schmidt
6. Willy Brandt
7. Kurt Georg Kiesinger
Question: When was Rhual constructed?
Evidence: Rhual is a Grade I listed building in Flintshire. This small mansion has medieval origins and is surrounded by a large landscaped park. The present building was constructed in 1634 by Evan Edwards, a member of a well established Flintshire family which traced its descent from the Welsh king Hywel Dda. He most likely incorporated an older medieval house into the north wing of the current building. The house has since been built upon further, and the east and south entrances were created in the 19th century.
Answer: Rhual was constructed in 1634 by Evan Edwards.
Question: Is the encoder a LTSM encoder？
Evidence: Our underlying model architecture is a standard attentional encoder-decoder BIBREF1 . Let INLINEFORM0 and INLINEFORM1 denote the source and target sentences, respectively. We use a Bi-LSTM encoder to represent the source words as a matrix INLINEFORM2 . The conditional probability of the target sentence is given as DISPLAYFORM0 
where INLINEFORM0 is computed by a softmax output layer that receives a decoder state INLINEFORM1 as input. This state is updated by an auto-regressive LSTM, INLINEFORM2 , where INLINEFORM3 is an input context vector. This vector is computed as INLINEFORM4 , where INLINEFORM5 is a probability distribution that represents the attention over the source words, commonly obtained as DISPLAYFORM0 
where INLINEFORM0 is a vector of scores. We follow BIBREF14 and define INLINEFORM1 as a bilinear transformation of encoder and decoder states, where INLINEFORM2 is a model parameter.
We use a Bi-LSTM encoder to represent the source words as a matrix INLINEFORM2
Answer: Not excatly, it is a Bi-LSTM encoder
Question: What are the measures of "performance" used in this paper?
Evidence: We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task. We compare our model with HDP-LDA, a nonparametric variant of the author-topic model (ATM), and the original random function network model. We also perform ablation studies to show the importance of each component in the model. The results of the comparison and ablation studies are shown in Table 1 . We use two tweets corpus for experiments, first is a subset of Twitter7 dataset BIBREF18 , obtained by querying with certain keywords (e.g. finance, sports, politics). we remove tweets that are not English with langid.py BIBREF19 and filter authors who do not have network information and who authored less than 100 tweets. The corpus consists of 60370 tweets by 94 authors. We then randomly select 90% of the dataset as training documents and use the rest for testing. Second tweets corpus is obtained from BIBREF20 , which contains a total of 781186 tweets. We note that we perform no word normalization to prevent any loss of meaning of the noisy text.
We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures.
Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task.
Answer: The answers are shown as follows:
* test-set perplexity, likelihood convergence and clustering measures
* visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task