DataHammer/scimrc huggingface dataset:
Question: How are the offline and online datasets used, are they just concatenated?
Evidence: Next, we introduce a simple but effective online-offline replay buffer (OORB) to distinguish between near-on-policy online interaction data, and the offline data. OORB consists of two replay buffers.
One is the online buffer that collects the online interaction data. Besides, to ensure the data in the online buffer is near-on-policy, we set it to be very small, and fresh online interaction data are stored into it by following a first-in-first-out rule. The other is the offline buffer consisting of the newly generated online interaction data and the offline dataset which may come from any policies.
Data are sampled from OORB following a Bernoulli distribution, which means that with a probability p, they are sampled from the online buffer, and with probability 1 − p, they are sampled from the offline buffer. To benefit from both online and offline data in a balanced way, we empirically set p to 0.5, and its effect on the final performance is further tested via ablation studies in Section 5.4. Results show that p = 0.5 works best overall, which confirms our claim that offline data and online interaction data are both crucial for policy learning.
Next, we introduce a simple but effective online-offline replay buffer (OORB) to distinguish between near-on-policy online interaction data, and the offline data. OORB consists of two replay buffers. One is the online buffer that collects the online interaction data. Besides, to ensure the data in the online buffer is near-on-policy, we set it to be very small, and fresh online interaction data are stored into it by following a first-in-first-out rule. The other is the offline buffer consisting of the newly generated online interaction data and the offline dataset which may come from any policies. Data are sampled from OORB following a Bernoulli distribution, which means that with a probability p, they are sampled from the online buffer, and with probability 1 − p, they are sampled from the offline buffer. To benefit from both online and offline data in a balanced way, we empirically set p to 0.5, and its effect on the final performance is further tested via ablation studies in Section 5.4. Results show that p = 0.5 works best overall, which confirms our claim that offline data and online interaction data are both crucial for policy learning
Answer: As explained in section 4.3, we employ a two-level replay buffer: online buffer and offline buffer. The online buffer stores near-on-policy data collected by interaction with the environment, while the offline buffer stores all data, including the offline dataset provided by the D4RL [REF4] benchmark. When updating the policy, we randomly sample from these two buffers. In our experiments, we sample from the online buffer following a possibility of 0.5.
Question: Why is FIFA world cup is so popular?
Answer: Soccer is by far the most popular sport on the planet. To the fans, the FIFA world cup is like a big festival as they can enjoy the exciting games and cheer for their favorite players and teams.
Question: Did they experiment on this dataset?
Evidence: In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.
At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.
In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.
At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.
Answer: Yes.
Question: Is there any ethical consideration in the research?
Evidence: Last but not least, ethics and fairness are important considerations, that deserve to be studied. In that sense, detection of individual and global bias should be prioritized in order to give useful feedbacks to practitioners. Furthermore we are considering using adversarial learning as in BIBREF33 in order to ensure fairness during the training process.
Last but not least, ethics and fairness are important considerations, that deserve to be studied. In that sense, detection of individual and global bias should be prioritized in order to give useful feedbacks to practitioners. Furthermore we are considering using adversarial learning as in BIBREF33 in order to ensure fairness during the training process.
Answer: No.
Question: What baselines are used in the experiment？
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
As baselines, we use softmax attention, as well as two recently proposed coverage models: • COVPENALTY (Wu et al., 2016, §7). At test time, the hypotheses in the beam are rescored with a global score that includes a length and a coverage penalty.5 We tuned α and β with grid search on {0.2k}5k=0, as in Wu et al. (2016). • COVVECTOR (Tu et al., 2016).
Answer: They use softmax attention, COVPENALTY (Wu et al., 2016, §7) and COVVECTOR (Tu et al., 2016).
Question: Who is the highest run getter in cricket?
Answer: Sachin Tendulkar is the highest run getter in cricket with a total of 18,426 runs. He also has the highest number of centuries and half-centuries.
Question: What are hateful tweets on Twitter?
Evidence: With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.).
Answer: Those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.).
Question: What is linear regression?
Answer: Linear regression is an approach for modeling the relationship between and independent and dependent variables.  It is a linear relationship that is described by a positive, negative, or a 0 slope.  The slope describes the velocity of the relationship being measured by the Linear Regression.  The variables measured using linear regression have to be continuous.  If any of your measurements are discrete you can take a log transform of those variables into order to make then continuous and be able to use Linear Regression.
Question: Anderson Peters (gold winner) hits the 90 metre mark with a throw of
Answer: 90.31m
Question: Classify each of the following as either a Carnivore or a Herbivore: Cow, Shark, Zebra, Lion, Wolf, Elephant, Tiger, Deer, Leopard, Cheetah
Answer: Herbivore:Cow,Zebra,Elephant,Deer
Carnivores:Lion,Wolf,Leopard,Cheetah,Tiger,Shark
Question: How do they quantify alignment between the embeddings of a document and its translation?
Evidence: We construct the embeddings for each document using BERT models finetuned on MLDoc. We mean-pool each document embedding to create a single vector per document. We then calculate the cosine similarity between the embeddings for the English document and its translation. In Table TABREF21 , we observe that the median cosine similarity increases dramatically with adversarial training, which suggests that the embeddings became more language-independent.
We construct the embeddings for each document using BERT models finetuned on MLDoc. We mean-pool each document embedding to create a single vector per document. We then calculate the cosine similarity between the embeddings for the English document and its translation. In Table TABREF21 , we observe that the median cosine similarity increases dramatically with adversarial training, which suggests that the embeddings became more language-independent.
Answer: The answers are shown as follows:
* median cosine similarity
Question: Which characters belong to DC or Marvel Universe? Namor, Aquaman
Answer: Aquaman is DC, Namor is Marvel
Question: Classify each of the following as either software or hardware: iPhone, iOS 13, Windows 10, Macbook, Windows Server 2012, iPad, Windows 11
Answer: iPhone: Hardware
iOS 13: Software
Windows 10: Software
Macbook: Hardware
Windows Server 2012: Software
iPad: Hardware
Windows 11: Software
Question: What are 3 ways I can do to feel better if I am in a bad mood?
Answer: I'm sorry you are in a bad mood.  You can try going outside for a walk, go to the gym and workout, or take a hot bath.  Feel well soon.
Question: What kind of dataset do they make?
Evidence: Sarcasm, a creative device used to communicate an intended meaning that is actually the opposite of its literal meaning, is notoriously difficult to convey and interpret through text, in part because doing so relies heavily upon shared contextual understandings that can be marked more easily by altered prosody (e.g., emphasis upon certain words) or non-verbal signals (e.g., rolling one's eyes). It is a complex process even for humans, and in fact an inability to detect sarcasm has been linked with a number of neurocognitive disorders, including dementia BIBREF0 . It is similarly a challenging open task in natural language processing, and has direct implications to a number of other critical application areas, such as sentiment analysis.
Most research on automatic sarcasm detection to date has focused on the Twitter domain, which boasts an ample source of publicly-available data, some of which is already self-labeled by users for the presence of sarcasm (e.g., with #sarcasm). However, Twitter is highly informal, space-restricted, and subject to frequent topic fluctuations from one post to the next due to the ebb and flow of current events—in short, it is not broadly representative of most text domains. Thus, sarcasm detectors trained using features designed for maximum Twitter performance are not necessarily transferable to other domains. Despite this, it is desirable to develop approaches that can harness the more generalizable information present in the abundance of Twitter data.
In this work, we develop a set of domain-independent features for sarcasm detection and show that the features generally perform well across text domains. Further, we validate that domain adaptation can be applied to sarcasm detection to leverage patterns in out-of-domain training data, even when results from training only on that source domain data are extremely bad (far below baseline results), to improve over training on only the target data or over training on the simply combined dataset. Finally, we make a new dataset of sarcastic and non-sarcastic tweets available online as a resource to other researchers.
Finally, we make a new dataset of sarcastic and non-sarcastic tweets available online as a resource to other researchers.
Answer: A new dataset of sarcastic and non-sarcastic tweets.
Question: According to the passage, mention some of Taylor’s early albums.
Evidence: Taylor Alison Swift (born December 13, 1989) is an American singer-songwriter. Her genre-spanning discography, songwriting abilities and artistic reinventions have received critical praise and wide media coverage. Born in West Reading, Pennsylvania, Swift moved to Nashville at age 14 to become a country artist. She signed a songwriting deal with Sony/ATV Music Publishing in 2004 and a recording contract with Big Machine Records in 2005. Her 2006 self-titled debut album made her the first female country artist to write a U.S. platinum-certified album.
Swift's next albums, Fearless (2008) and Speak Now (2010), explored country pop. The former's "Love Story" and "You Belong with Me" were the first country songs to top the U.S. pop and all-genre airplay charts, respectively. She experimented with rock and electronic styles on Red (2012), which featured her first Billboard Hot 100 number-one song, "We Are Never Ever Getting Back Together", and eschewed her country image in her synth-pop album, 1989 (2014), supported by chart-topping songs "Shake It Off", "Blank Space", and "Bad Blood". Media scrutiny inspired the urban-flavored Reputation (2017) and its number-one single "Look What You Made Me Do".
Exiting Big Machine, Swift signed with Republic Records in 2018 and released her seventh studio album, Lover (2019), followed by the autobiographical documentary Miss Americana (2020). She ventured into indie folk and alternative rock in her 2020 albums Folklore and Evermore, whose singles "Cardigan" and "Willow" topped the Hot 100. Swift began re-recording her first six albums after a dispute over their masters, re-releasing two in 2021—Fearless (Taylor's Version) and Red (Taylor's Version). The latter's "All Too Well (10 Minute Version)" became the longest song to top the Hot 100. Her 2022 album Midnights and single "Anti-Hero" broke all-time streaming records. Swift has directed music videos and films, such as All Too Well: The Short Film (2021), and played supporting roles in others.
Having sold over 200 million records globally, Swift is one of the best-selling musicians in history. She is the most streamed woman on Spotify and the only act to have five albums open with over one million copies sold in the US. Among her accolades are 12 Grammy Awards, including three Album of the Year wins; an Emmy Award; 40 American Music Awards; 29 Billboard Music Awards; three IFPI Global Recording Artist of the Year awards; and 92 Guinness World Records. Swift has been featured in rankings such as Rolling Stone's 100 Greatest Songwriters of All Time, Billboard's Greatest of All Time Artists, the Time 100 and Forbes Celebrity 100. Honored with titles such as Artist of the Decade and Woman of the Decade, Swift is an advocate for artists' rights and women's empowerment. Her music is credited with influencing a generation of singers and songwriters.
Answer: Taylor had the following early career albums:
​1.“Taylor Swift” (2006 self-titled debut album)
​2.”Fearless” (2008)
3.”Speak Now” (2010)
Question: Give me a list of five  healthy snacks for kids
Answer: Peanut butter, cheese, milk, fruit and popcorn
Question: What was the Triceratops diet?
Evidence: Triceratops were herbivorous, and because of their low slung head, their primary food was probably low growing vegetation, although they may have been able to knock down taller plants with their horns, beak, and bulk. The jaws were tipped with a deep, narrow beak, believed to have been better at grasping and plucking than biting.

Triceratops teeth were arranged in groups called batteries, of 36 to 40 tooth columns in each side of each jaw, with 3 to 5 stacked teeth per column, depending on the size of the animal. This gives a range of 432 to 800 teeth, of which only a fraction were in use at any given time (Tooth replacement was continuous throughout the life of the animal). They functioned by shearing in a vertical to near-vertical orientation. The great size and numerous teeth of Triceratops suggests that they ate large volumes of fibrous plant material, with some researchers suggesting palms and cycads, and others suggesting ferns, which then grew in prairies.
Answer: The triceratops were herbivores and are believed to have eaten plants such as palms, cycads and ferns.
Question: Where did the battle of the buldge take place?
Evidence: The Battle of the Bulge, also known as the Ardennes Offensive, was the last major German offensive campaign on the Western Front during World War II. The battle lasted for five weeks from 16 December 1944 to 28 January 1945, towards the end of the war in Europe. It was launched through the densely forested Ardennes region between Belgium and Luxembourg. It overlapped with the Alsace Offensive, subsequently the Colmar Pocket, another series of battles launched by the Germans in support of the Ardennes thrust.
Answer: The battle of the Buldge took place in the Ardennes region between Belgium and Luxembourg
Question: Who is the first king in China history?
Answer: Qin Shi Huang (Chinese: 秦始皇, 259–210 BC) was the founder of the Qin dynasty and the first emperor of a unified China. Rather than maintain the title of "king" (王 wáng) borne by the previous Shang and Zhou rulers, he ruled as the First Emperor (始皇帝) of the Qin dynasty from 221 to 210 BC. His self-invented title "emperor" (皇帝 huángdì) would continue to be borne by Chinese rulers for the next two millennia.
Question: Please list several after school activities my 8 year old daughter can participate in.
Answer: At eight years old, your daughter has many options for after school activities. She could consider an artistic activity like painting, dance, or music classes. She could also join a sports team. Many children her age enjoy soccer. Try to find an activity she can do with her friends, it will make it more fun for her.
Question: Why do kids like to play video games so much these days?
Answer: First, video games are fun and very addictive. The game is designed to transport us to new realities and satisfy our needs for achievement and recognition. Especially most of the young generation plays online games, they use that as a social activity to hang out and make new friends. In addition, kids now have fewer outdoor activities and stay home longer than before. so playing video games is the easiest way to entertain themselves and engage with their friends.
Question: Imagine you have won the lottery, and have 5 million dollars after tax to spend in San Francisco, where you currently rent a 2 bedroom apartment with three roommates who are your best friends but who you hate living. Describe how you would use the money, keeping in mind you don't have a high paying job so you want to do fun things and also set yourself up for the future.
Answer: First, I would use half the money to buy myself my own home in San Francisco. I wouldn't buy anything too fancy, but I would want my own place in a good neighborhood where I wouldn't have to share a room with a roommate. With the remaining 2.5 million I'd set aside 500 thousand in an account for future maintenance of the house and property taxes. Then I'd put aside 1 million for my retirement savings, putting it in a longterm investment account. With the remaining million, I'd use another 500k for short term savings, and put that in a more aggressive investment. I'd spend the rest of the money on a few fun items, like taking a big trip with all of my roommates and buying a new car and some nice clothing. For our trip, we'd live the life of luxury, taking first class flights and staying in nice resorts across the world, visiting places like Paris, Rome, Singapore and Tahiti.