DataHammer/scimrc huggingface dataset:
Question: Which publicly available datasets are used?
Evidence: In this paper, we use three data sets from the literature to train and evaluate our own classifier. Although all address the category of hateful speech, they used different strategies of labeling the collected data. Table TABREF5 shows the characteristics of the datasets.
Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as “Harrassing” or “Non-Harrassing”; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader “Harrassing” category BIBREF9 .
In this paper, we use three data sets from the literature to train and evaluate our own classifier.
Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as “Harrassing” or “Non-Harrassing”; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader “Harrassing” category BIBREF9 .
Answer: The answers are shown as follows:
* BIBREF3
* BIBREF4
* BIBREF9
Question: Which baselines did they compare?
Evidence: The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.
The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.
Answer: The answers are shown as follows:
* The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.
Question: What was used to evaluate the performance?
Evidence: The dataset was thoroughly evaluated, see leitner2019fine for more details. As state of the art models, Conditional Random Fields (CRFs) and bidirectional Long-Short Term Memory Networks (BiLSTMs) were tested with the two variants of annotation. For CRFs, these are: CRF-F (with features), CRF-FG (with features and gazetteers), CRF-FGL (with features, gazetteers and lookup). For BiLSTM, we used models with pre-trained word embeddings BIBREF22: BiLSTM-CRF BIBREF23, BiLSTM-CRF+ with character embeddings from BiLSTM BIBREF24, and BiLSTM-CNN-CRF with character embeddings from CNN BIBREF25. To evaluate the performance we used stratified 10-fold cross-validation. As expected, BiLSTMs perform best (see Table ). The F$_1$ score for the fine-grained classification reaches 95.46 and 95.95 for the coarse-grained one. CRFs reach up to 93.23 F$_1$ for the fine-grained classes and 93.22 F$_1$ for the coarse-grained ones. Both models perform best for judge, court and law.
To evaluate the performance we used stratified 10-fold cross-validation.
Answer: The authors used stratified 10-fold cross-validation.
Question: How many series did Dance Mums air?
Evidence: Dance Mums with Jennifer Ellison is a British reality television series that made its debut on Lifetime on 20 October 2014. Created by Shiver Productions, it is set in Liverpool, England, at Jelli Studios and follows the early careers of children in dance and show business, as well as the participation of their mothers. It is a spin-off of the American TV series Dance Moms.

The second series began airing on 12 October 2015, but the show was not commissioned for a third series.
Answer: Two seasons, it was not commissioned for a third series.
Question: By how much do they improve the accuracy of inferences over state-of-the-art methods?
Evidence: We first compare the perplexity of CWVAE with baseline methods. Perplexity measures the probability of model to regenerate the exact targets, which is particular suitable for evaluating the model performance on one-to-many problem BIBREF20. Further, we employ BLEU score to evaluate the accuracy of generations BIBREF21, and the number of distinct n-gram to evaluate the diversity of generations BIBREF6. The distinct is normalized to $[0, 1]$ by dividing the total number of generated tokens.
FLOAT SELECTED: Table 4: Average perplexity and BLEU score (reported in percentages) for the top 10 generations under each inference dimension of Event2Mind. The the best result for each dimension is emboldened.
FLOAT SELECTED: Table 6: Average perplexity and BLEU scores (reported in percentages) for the top 10 generations under each inference dimension of Atomic. The the best result for each dimension is emboldened.
Further, we employ BLEU score to evaluate the accuracy of generations BIBREF21, and the number of distinct n-gram to evaluate the diversity of generations BIBREF6. 
FLOAT SELECTED: Table 4: Average perplexity and BLEU score (reported in percentages) for the top 10 generations under each inference dimension of Event2Mind. The the best result for each dimension is emboldened.
FLOAT SELECTED: Table 6: Average perplexity and BLEU scores (reported in percentages) for the top 10 generations under each inference dimension of Atomic. The the best result for each dimension is emboldened.
Answer: ON Event2Mind, the accuracy of proposed method is improved by  absolute BLUE  2.9,  10.87, 1.79 for xIntent, xReact and oReact respectively.
On Atomic dataset, the accuracy of proposed method is improved by  absolute BLUE 3.95.   4.11, 4.49 for xIntent, xReact and oReact.respectively.
Question: Classify these as a synthetic or natural fiber
Answer: wool, polyester, cotton, silk, nylon, aramid, jute
Question: Are there any highlights in their work?
Evidence: This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed.
The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models.
Answer: Yes. The greek version of the spaCy platform was added to the source code and was used for building the models.
Question: What's an AGI?
Evidence: An artificial general intelligence (AGI) is a hypothetical intelligent agent which can understand or learn any intellectual task that human beings or other animals can. AGI has also been defined alternatively as autonomous systems that surpass human capabilities at the majority of economically valuable work. Developing AGI is a primary goal of some artificial intelligence research and for several AI companies such as OpenAI, DeepMind, and Anthropic, as well as a common topic in science fiction and futures studies.
Answer: AGI (artificial general intelligence) refers to a hypothetical intelligent agent which can understand or learn any intellectual task that human beings or other animals can. It has also been defined as autonomous agents that could surpass human capabilities at the majority of economically valuable work.
Question: Identify which instrument is string or percussion: Frame drum, Citole
Answer: Citole is string, Frame drum is percussion.
Question: How can I drive from South Bay to San Francisco?
Answer: To drive from South Bay to San Francisco you will want to take either Interstate 280 or US Route 101. 101 tends to be a more direct drive but also tends to have more traffic congestion through the day. 280 is longer and hillier but tends to have less traffic and has the added bonus of nice scenery.
Question: Give me a bulleted list of different ways to get around a city without a car
Answer: The following are different ways to get around a city without a car:
1. Walk
2. Take public transportation such as the bus or subway
3. Ride a bike
4. Use Uber
5. Ride a scooter
6. Ask a friend with a car
7. Hitchhike
Question: Pick a title for the following:
The purpose of coaching is to form a partnership that enables you to draw more consistently on your wisdom, positive energy, and resourcefulness in decision-making, living a full life, and achieving your personal and professional goals by expanding your perspectives, being introspective, reducing blind spots, effectively managing self-limiting beliefs and feelings that block you, and developing new skills, habits, and practices.
Answer: Personal and Professional Coaching Goals
Question: Tell me which of these are NFL teams: Bengals, Pumas, Seahawks, Falcons, Roadrunners, Eagles, Coyotes, Sheep, Bears.
Answer: Bengals, Seahawks, Falcons, Eagles, Bears.
Question: What was the The McIntosh County Seat War
Evidence: The McIntosh County Seat War was a dispute in Oklahoma over the location of the McIntosh County seat that took place between 1907 and 1909. Following a pair of elections that resulted in the town of Checotah being designated as the new county seat, the people of Eufaula refused to hand over the county records. As a result, a group of heavily armed men from Chectotah attempted to seize the records, but were forced to surrender during the gunbattle that ensued. One year later, after another close election, Eufaula became the permanent county seat.
Answer: The McIntosh County Seat War was a dispute in Oklahoma over the location of the McIntosh County seat that took place between 1907 and 1909. Following a pair of elections that resulted in the town of Checotah being designated as the new county seat, the people of Eufaula refused to hand over the county records. 

As a result, a group of heavily armed men from Chectotah attempted to seize the records, but were forced to surrender during the gunbattle that ensued. One year later, after another close election, Eufaula became the permanent county seat.
Question: Do they test their word embeddings on downstream tasks?
Evidence: Outlier Detection. The Outlier Detection task BIBREF0 is to determine which word in a list INLINEFORM0 of INLINEFORM1 words is unrelated to the other INLINEFORM2 which were chosen to be related. For each INLINEFORM3 , one can compute its compactness score INLINEFORM4 , which is the compactness of INLINEFORM5 . INLINEFORM6 is explicitly computed as the mean similarity of all word pairs INLINEFORM7 . The predicted outlier is INLINEFORM8 , as the INLINEFORM9 related words should form a compact cluster with high mean similarity.
Sentiment analysis. We also consider sentiment analysis as described by BIBREF31 . We use the suggested Large Movie Review dataset BIBREF32 , containing 50,000 movie reviews.
Outlier Detection. The Outlier Detection task BIBREF0 is to determine which word in a list INLINEFORM0 of INLINEFORM1 words is unrelated to the other INLINEFORM2 which were chosen to be related. For each INLINEFORM3 , one can compute its compactness score INLINEFORM4 , which is the compactness of INLINEFORM5 .
Sentiment analysis. We also consider sentiment analysis as described by BIBREF31 .
Answer: Yes.
Question: Is Vietnamese an isolating language?
Evidence: Vietnamese, like many languages in continental East Asia, is an isolating language and one branch of Mon-Khmer language group. The most basic linguistic unit in Vietnamese is morpheme, similar with syllable or token in English and “hình vị” (phoneme) or “tiếng” (syllable) in Vietnamese. According to the structured rule of its, Vietnamese can have about 20,000 different syllables (tokens). However, there are about 8,000 syllables used the Vietnamese dictionaries. There are three methods to identify morphemes in Vietnamese text BIBREF10 .
Morpheme is the smallest meaningful unit of Vietnamese.
Morpheme is the basic unit of Vietnamese.
Morpheme is the smallest meaningful unit and is not used independently in the syntax factor.
In computational linguistics, morpheme is the basic unit of languages as Leonard Bloomfield mentioned for English BIBREF11 . In our research for Vietnamese, we consider the morpheme as syllable, called “tiếng” in Vietnamese (as Nguyen’s definition BIBREF12 ).
The next concept in linguistics is word which has fully grammar and meaning function in sentences. For Vietnamese, word is a single morpheme or a group of morphemes, which are fixed and have full meaning BIBREF12 . According to Nguyen, Vietnamese words are able classified into two types, (1) 1- syllable words with fully meaning and (2) n-syllables words whereas these group of tokens are fixed. Vietnamese syllable is not fully meaningful. However, it is also explained in the meaning and structure characteristics. For example, the token “kỳ” in “quốc kỳ” whereas “quốc” means national, “kỳ” means flag. Therefore, “quốc kỳ” means national flag.
Consider dictionary used for evaluating the corpus, extracting features for models, and evaluating the systems, there are many Vietnamese dictionaries, however we recommend the Vietnamese dictionary of Hoang Phe, so called Hoang Phe Dictionary. This dictionary has been built by a group of linguistical scientists at the Linguistic Institute, Vietnam. It was firstly published in 1988, reprinted and extended in 2000, 2005 and 2010. The dictionary currently has 45,757 word items with 15,901 Sino-Vietnamese word items (accounting for 34.75%) BIBREF13 .
Vietnamese, like many languages in continental East Asia, is an isolating language and one branch of Mon-Khmer language group.
Answer: Yes.
Question: Why should we eat organic food?
Answer: Organic food doesn't mean you can eat endless amounts of it. It actually doesn't even mean it's healthy for you. It simply means no chemicals and pesticides are sprayed all over the crop. Some crops absorb this worse than others or even pull it up through what exists in the soil. These chemicals can be detrimental to our physical health but also to our mental health.
Question: I'm putting together an exercise plan that needs to have both cardio and strength exercises, but I want to separate exercises into each category. Running, deadlifts, cycling, pushups, swimming, jump rope,  lunges and squats.
Answer: Running, cycling, swimming and jump rope are all forms of cardio exercise. Deadlifts, pushups, lunges and squats are forms of strength exercise.
Question: What dataset is used for the evaluation of the toolkits?
Evidence: This research gathers the results of Vietnamese word segmentation of several methods into one table as show in Table II. It is noted that they are not evaluated on a same corpus. The purpose of the result illustration is to provide an overview of the results of current Vietnamese word segmentation systems based on their individual features. All studies mentioned in the table have accuracy around 94-97% based on their provided corpus.
This study also evaluates the Vietnamese word segmentation based on existing toolkits using the same annotated Vietnamese word segmentation corpus. There are two available toolkits to evaluate and to segment. To be neutral to both toolkits, we use the EVBNews Vietnamese corpus, a part of EVBCorpus, to evaluate Vietnamese word segmentation. The EVBNews corpus contains over 45,000 segmented Vietnamese sentences extracted from 1,000 general news articles (as shown in Table III) BIBREF16 . We used the same training set which has 1000 files and 45,531 sentences. vnTokenizer outputs 831,455 Vietnamese words and 1,206,475 tokens. JVnSegmenter outputs 840,387 words and 1,201,683. We correct tags (BIO), and compare to previous outputs, we have rate from vnTokenizer is 95.6% and from JVnsegmenter is 93.4%. The result of both vnTokenizer and JVnSegmenter testing on the EVBNews Vietnamese Corpus are provided in Table IV.
This study also evaluates the Vietnamese word segmentation based on existing toolkits using the same annotated Vietnamese word segmentation corpus. There are two available toolkits to evaluate and to segment. To be neutral to both toolkits, we use the EVBNews Vietnamese corpus, a part of EVBCorpus, to evaluate Vietnamese word segmentation.
Answer: EVBNews Vietnamese corpus.
Question: How data engineers are different from software engineers?
Answer: Data engineers build data systems and databases while software engineers create applications, software, and other products.
Question: What are some different things you should bring with you on a day hiking trip?
Answer: You should bring water, a snack, sunscreen, hiking boots, a first aid kit and a cellphone
Question: Who is Octavia Butler?
Evidence: Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author and a multiple recipient of the Hugo and Nebula awards. In 1995, Butler became the first science-fiction writer to receive a MacArthur Fellowship. Born in Pasadena, California, Butler was raised by her widowed mother. Extremely shy as a child, Butler found an outlet at the library reading fantasy, and in writing. She began writing science fiction as a teenager. She attended community college during the Black Power movement. While participating in a local writer's workshop, she was encouraged to attend the Clarion Workshop, then held in Pennsylvania, which focused on science fiction.
Answer: Octavia Butler was a black Hugo and Nebula awards winning science fiction author from Pasadena, California.
Question: What was the algorithm initially used to?
Evidence: In this paper, we describe our approach and present the results for our participation in the task 1, i.e. multilingual information extraction, of the CLEF eHealth 2018 challenge BIBREF0 . More precisely, this task consists in automatically coding death certificates using the International Classification of Diseases, 10th revision (ICD-10) BIBREF1 .
We addressed the challenge by matching ICD-10 terminology entries to text phrases in death certificates. Matching text phrases to medical concepts automatically is important to facilitate tasks such as search, classification or organization of biomedical textual contents BIBREF2 . Many concept recognition systems already exist BIBREF2 , BIBREF3 . They use different approaches and some of them are open source. We developed a general purpose biomedical semantic annotation tool for our own needs. The algorithm was initially implemented to detect drugs in a social media corpora as part of the Drugs-Safe project BIBREF4 . We adapted the algorithm for the ICD-10 coding task. The main motivation in participating in the challenge was to evaluate and compare our system with others on a shared task.
The algorithm was initially implemented to detect drugs in a social media corpora as part of the Drugs-Safe project.
Answer: It was initially used to detect drugs in a social media corpora as part of the Drugs-Safe project
Question: How can the classifier facilitate the annotation task for human annotators?
Evidence: In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class).
This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class).
Answer: The answers are shown as follows:
* quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets
Question: Where is Smuggler Cove Shell Midden located?
Evidence: The Smuggler Cove Shell Midden (Smithsonian trinomial: 35TI46) is an archeological site located in Oswald West State Park near Neahkahnie Beach, Oregon, United States. First documented by archeologists in 1976, the midden has been found to contain remains of mussels, barnacles, and chiton to a depth of approximately 30 centimeters (12 in). Radiocarbon dating of a single sample of shell debris indicates that the Smuggler Cove campsite was occupied around 1660 CE, approximately the same date as the supposed wreck of a European ship at nearby Nehalem Spit, suggesting the site may preserve information from both before and after first contact between local people and Europeans. The site has been heavily damaged by construction of recreational facilities and coastal erosion, but a significant portion remains with potential to contribute to future research. Data generated from the site may help answer questions related to environmental change in the Oregon Coast region, settlement and subsistence patterns, emergence of ethnographic patterns among coastal people, the change in cultural patterns from before to after contact with European Americans, and other topics.
Answer: Smuggler Cove shell Midden is located in Oswald West State Park near Neahkahnie Beach in Oregon, United States.