DataHammer/scimrc huggingface dataset:
Question: What model are the text features used in to provide predictions?
Evidence: Bag-of-words feature vectors were used to train a multinomial logistic regression model. Let INLINEFORM0 be the true label, where INLINEFORM1 is the total number of labels and INLINEFORM2 is the concatenation of the weight vectors INLINEFORM3 associated with the INLINEFORM4 th party then DISPLAYFORM0
Bag-of-words feature vectors were used to train a multinomial logistic regression model. Let INLINEFORM0 be the true label, where INLINEFORM1 is the total number of labels and INLINEFORM2 is the concatenation of the weight vectors INLINEFORM3 associated with the INLINEFORM4 th party then DISPLAYFORM0
Answer: The answers are shown as follows:
*  multinomial logistic regression
Question: When is the best time to transplant bearded iris?
Answer: Bearded iris are one of the most popular perennial flowers in the American landscape.  They have attractive spiky foliage that is evergreen in many Zones and provide beautiful blooms multiple times a year.  They are cold hardy and are easily propagated and transplanted.

The best time of the year to transplant bearded iris is late summer.  This provides the plant with sufficient time to establish a new root structure before the winter.  Bearded iris grow from rhizomes, which quickly grow roots that provide the plant with the nutrients required to grow and form flowers.  The rhizomes should be removed from the soil using a garden fork making an attempt to keep as much root structure as possible.  The spiky leaves of the plant can be trimmed at this time to reduce the risk of the plant being disturbed by strong wind gusts when it is replanted.  Plant the rhizome in its new location so that only about a half of the rhizome is buried in the soil.  This enables the rhizome to receive direct sunlight and more vigorously replenish its stored starches and proteins.
Question: Classify these foods as either "italian", "french", "mexican" or "other". Crepes Suzette, Parmigiana di melanzane, La paella de marisco, Tahchin, Tacos al pastor, Soupe à l'oignon, Enchiladas, Pappardelle al ragù.
Answer: italian: Parmigiana di melanzane, Pappardelle al ragù
french: Crepes Suzette, Soupe à l'oignon
mexican: Tacos al pastor, Enchiladas
other: La paella de marisco, Tahchin
Question: While the proposed setting makes sense in imitating real world settings, would it not be useful to have high quality trajectories in order to best leverage the action choices that come from these high quality offline features. Why not stick with D4RL?
Evidence: In this work, we address an open challenge in Offline RL. In the resource-constrained setting that is motivated by real world applications, the features available during training offline may be different than the limited features available online during deployment. We highlighted a performance gap between offline RL agents trained using only the online features and agents trained using all the offline features. To bridge this gap, we proposed a student-teacher-based policy transfer learning approach. The proposed algorithm improves over the baseline significantly even when the dataset quality is lacking in the resource-constrained setting. The simplicity of the approach (with just one additional hyperparameter) makes it easy to extend it to other offline RL algorithms. It would be interesting to study other transfer learning approaches (e.g., policy transfer with other divergence regularizations for stochastic policies) in the future. Moreover, we observe that the proposed approach benefits especially when the dataset quality is low which is often the case with real world datasets. Despite this, the performance gap with the teacher is still high (in the low quality data regime) and this suggests more tailored approaches are required.
In this work, we address an open challenge in Offline RL. In the resource-constrained setting that is motivated by real world applications, the features available during training offline may be different than the limited features available online during deployment.
Answer: As the reviewer rightly points out, the setting of RC-D4RL imitates real-world settings, and is therefore more practical for evaluating the resource-constrained setting algorithms.
Question: Extract one of the franchise introduced in IPL 2022 from the text and who won the title during the previous session.
Evidence: The 2022 Indian Premier League, also known as IPL 15 or for sponsorship reasons, TATA IPL 2022, was the fifteenth season of the Indian Premier League (IPL), a professional Twenty20 cricket league established by the Board of Control for Cricket in India (BCCI) in 2007. The tournament was played from 26 March 2022 to 29 May 2022. The group stage of the tournament was played entirely in the state of Maharashtra, with Mumbai, Navi Mumbai and Pune hosting matches.
The season saw the expansion of the league with the addition of two new franchises. Chennai Super Kings were the defending champions, having won their fourth title during the previous season.
In the final one of the new franchises, Gujarat Titans, beat Rajasthan Royals by seven wickets to win their first title.
Answer: The IPL 2022 season saw the expansion of the league with the addition of two new franchises, and one of them was Gujarat Titans. Gujarat Titans, beat Rajasthan Royals by seven wickets to win their first title.
Also, Chennai Super Kings were the defending champions, having won their fourth title during the previous season.
Question: What is Merlot?
Evidence: Merlot (/ˈmɜːrloʊ/ MUR-loh) is a dark blue–colored wine grape variety, that is used as both a blending grape and for varietal wines. The name Merlot is thought to be a diminutive of merle, the French name for the blackbird, probably a reference to the color of the grape. Its softness and "fleshiness," combined with its earlier ripening, make Merlot a popular grape for blending with the sterner, later-ripening Cabernet Sauvignon, which tends to be higher in tannin.
Answer: Merlot (/ˈmɜːrloʊ/ MUR-loh) is a dark blue–colored wine grape variety, that is used as both a blending grape and for varietal wines.
Question: What are the top 3 items that I need when traveling by plane.
Answer: When packing for this trip, you should pack: 
1. An empty reusable water bottle to fill at the airport so you can stay hydrated. 
2. A packable airplane travel pillow so that you can sleep comfortably on the flight.
3. A snack in case you get hungry.
Question: how to get started with data science and machine learning?
Answer: To be a good data scientist, you need a solid understanding of statistics and machine learning algorithms like linear regression, logistic regression, and tree-based models. Knowledge and experiences with deep learning concepts are a big plus nowadays in the job market. In addition, business acumen and good communication skills can help you bridge the gap between technical and business stakeholders. Here are a few recommended study materials: Coursera course Machine Learning by DeepLearnin.AI; Introduction to Machine Learning with Python: A Guide for Data Scientists; Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. But most importantly, keep learning and start now!
Question: Who is the best surfer in the world
Evidence: Robert Kelly Slater (born February 11, 1972) is an American professional surfer, best known for being crowned World Surf League champion a record 11 times. Slater is widely regarded as the greatest professional surfer of all time, and holds 56 Championship Tour victories. Slater is also the oldest surfer still active on the World Surf League, winning his 8th Billabong Pipeline Masters title at age 49.
Answer: Kelly Slater
Question: Name five NBA teams in the Western Conference.
Answer: Five NBA teams in the Western Conference are the Phoenix Suns, Memphis Grizzlies, Portland Trail Blazers, San Antonio Spurs, and Oklahoma City Thunder.
Question: What was the name of Dorrie Nossiter's exhibition?
Evidence: Dorrie Nossiter (29 June 1893 – 1977) was an English jeweller and jewellery designer from Aston, near Birmingham.

Nossiter crafted precious jewellery of her own designs in the English Arts and Crafts Tradition in both sterling silver and gold. Her work is known for her use of colour and floral and curvature lines using gemstones in motifs. She was predominantly active during the 1930s.

Nossiter was educated at the Municipal School of Art in Birmingham from 1910 to 1914. Nossiter married Ernest Guy Robinson in 1922. By 1935 she was living in London where her work was shown in the "Art by Four Women" exhibition at Walker's Gallery, London. Nossiter would go on to exhibit there from 1935 to 1939.

Nossiter's work is often confused with that of another female jeweller and jewellery designer of the same period, Sibyl Dunlop.
Answer: Dorrie Nossiter showed her work in the "Art by Four Women" exhibition at the Walker's Gallery in London, England.
Question: Why does a boat float on water whereas some other objects sink?
Answer: Objects with less buoyant force will sink. Buoyancy is a force acting on an object that causes it to rise or move higher in the liquid. If the object is denser than the water then it sinks and if the object is less dense than the water then it floats.
Question: How does counterfactual data augmentation aim to tackle bias?
Evidence: One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21. For example, all instances of grandmother are swapped with grandfather.
One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21.
Answer: The training dataset is augmented by swapping all gendered words by their other gender counterparts
Question: What does the test suite consist of?
Evidence: Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis).
Answer: The test suite consists of pairs of source and target sentences.
Question: What makes The Blade Itself by Joe Abercrombie good?
Answer: I re-read this book 7 years later, after recently reading book 4 and book 5 of the series.

Gosh I've forgotten how excellent the first book is. It's hilarious, grim/dark, witty, with a fantastic cast of characters and a nuanced worldbuilding that I'm dying to uncover.
Question: For English corpora, does their approach outperform the baseline?
Evidence: Sentiment analysis has recently been one of the hottest topics in natural language processing (NLP). It is used to identify and categorise opinions expressed by reviewers on a topic or an entity. Sentiment analysis can be leveraged in marketing, social media analysis, and customer service. Although many studies have been conducted for sentiment analysis in widely spoken languages, this topic is still immature for Turkish and many other languages.
Neural networks outperform the conventional machine learning algorithms in most classification tasks, including sentiment analysis BIBREF0. In these networks, word embedding vectors are fed as input to overcome the data sparsity problem and make the representations of words more “meaningful” and robust. Those embeddings indicate how close the words are to each other in the vector space model (VSM).
Most of the studies utilise embeddings, such as word2vec BIBREF1, which take into account the syntactic and semantic representations of the words only. Discarding the sentimental aspects of words may lead to words of different polarities being close to each other in the VSM, if they share similar semantic and syntactic features.
For Turkish, there are only a few studies which leverage sentimental information in generating the word and document embeddings. Unlike the studies conducted for English and other widely-spoken languages, in this paper, we use the official dictionaries for this language and combine the unsupervised and supervised scores to generate a unified score for each dimension of the word embeddings in this task.
Our main contribution is to create original and effective word vectors that capture syntactic, semantic, and sentimental characteristics of words, and use all of this knowledge in generating embeddings. We also utilise the word2vec embeddings trained on a large corpus. Besides using these word embeddings, we also generate hand-crafted features on a review basis and create document vectors. We evaluate those embeddings on two datasets. The results show that we outperform the approaches which do not take into account the sentimental information. We also had better performances than other studies carried out on sentiment analysis in Turkish media. We also evaluated our novel embedding approaches on two English corpora of different genres. We outperformed the baseline approaches for this language as well. The source code and datasets are publicly available.
The paper is organised as follows. In Section 2, we present the existing works on sentiment classification. In Section 3, we describe the methods proposed in this work. The experimental results are shown and the main contributions of our proposed approach are discussed in Section 4. In Section 5, we conclude the paper.
We also evaluated our novel embedding approaches on two English corpora of different genres. We outperformed the baseline approaches for this language as well. The source code and datasets are publicly available
Answer: Yes.
Question: How does this compare to traditional calibration methods like Platt Scaling?
Evidence: Compared to using external models for confidence modeling, an advantage of the proposed method is that the base model does not change: the binary classification loss just provides additional supervision. Ideally, the resulting model after one-round of training becomes better not only at confidence modeling, but also at assertion generation, suggesting that extractions of higher quality can be added as training samples to continue this training process iteratively. The resulting iterative learning procedure (alg:iter) incrementally includes extractions generated by the current model as training samples to optimize the binary classification loss to obtain a better model, and this procedure is continued until convergence. [t] training data $\mathcal {D}$ , initial model $\theta ^{(0)}$ model after convergence $\theta $ $t \leftarrow 0$ # iteration
A key step in open IE is confidence modeling, which ranks a list of candidate extractions based on their estimated quality. This is important for downstream tasks, which rely on trade-offs between the precision and recall of extracted assertions. For instance, an open IE-powered medical question answering (QA) system may require its assertions in higher precision (and consequently lower recall) than QA systems for other domains. For supervised open IE systems, the confidence score of an assertion is typically computed based on its extraction likelihood given by the model BIBREF3 , BIBREF5 . However, we observe that this often yields sub-optimal ranking results, with incorrect extractions of one sentence having higher likelihood than correct extractions of another sentence. We hypothesize this is due to the issue of a disconnect between training and test-time objectives. Specifically, the system is trained solely to raise likelihood of gold-standard extractions, and during training the model is not aware of its test-time behavior of ranking a set of system-generated assertions across sentences that potentially include incorrect extractions.
We follow the evaluation metrics described by Stanovsky:2016:OIE2016: area under the precision-recall curve (AUC) and F1 score. An extraction is judged as correct if the predicate and arguments include the syntactic head of the gold standard counterparts.
Compared to using external models for confidence modeling, an advantage of the proposed method is that the base model does not change: the binary classification loss just provides additional supervision.
For supervised open IE systems, the confidence score of an assertion is typically computed based on its extraction likelihood given by the model BIBREF3 , BIBREF5 
We follow the evaluation metrics described by Stanovsky:2016:OIE2016: area under the precision-recall curve (AUC) and F1 score.
Answer: No reliability diagrams are provided and no explicit comparison is made between confidence scores or methods.
Question: Who are the top 4 in EPL?
Evidence: The 2000s saw the rise of first Liverpool, and then Arsenal to real competitiveness, Chelsea finally breaking the duopoly by winning in 2004-05. The dominance of the so-called "Top Four" clubs. Arsenal, Chelsea, Liverpool and Manchester United saw them finish at the top of the table for the bulk of the decade, thereby guaranteeing qualification for the UEFA Champions League. Only three other clubs managed to qualify for the competition during this period: Newcastle United (2001–02 and 2002–03), Everton (2004–05) and Tottenham Hotspur (2009–10) – each occupying the final Champions League spot, with the exception of Newcastle in the 2002–03 season, who finished third.

Following the 2003–04 season, Arsenal acquired the nickname "The Invincibles" as it became the first, and to date, only club to complete a Premier League campaign without losing a single game.

In May 2008, Kevin Keegan stated that "Top Four" dominance threatened the division: "This league is in danger of becoming one of the most boring but great leagues in the world." Premier League chief executive Richard Scudamore said in defence: "There are a lot of different tussles that go on in the Premier League depending on whether you're at the top, in the middle or at the bottom that make it interesting."

Between 2005 and 2012 there was a Premier League representative in seven of the eight Champions League finals, with only "Top Four" clubs reaching that stage. Liverpool (2005), Manchester United (2008) and Chelsea (2012) won the competition during this period, with Arsenal (2006), Liverpool (2007), Chelsea (2008) and Manchester United (2009 and 2011) all losing Champions League finals. Leeds United were the only non-"Top Four" side to reach the semi-finals of the Champions League, in the 2000–01 season. There were three Premier League teams in the Champions League semi-finals in 2006–07, 2007–08, and 2008–09, a feat only ever achieved five times (along with Serie A in 2002–03 and La Liga in 1999–2000).

Additionally, between the 1999–2000 and 2009–10 seasons, four Premier League sides reached UEFA Cup or Europa League finals, with only Liverpool managing to win the competition in 2001. Arsenal (2000), Middlesbrough (2006) and Fulham (2010) all lost their finals.

Although the group's dominance was reduced to a degree after this period with the emergence of Manchester City and Tottenham, in terms of all-time Premier League points won they remain clear by some margin. As of the end of the 2021–22 season – the 27th season of the Premier League – Liverpool, in fourth place in the all-time points table, were over 300 points ahead of the next team, Tottenham Hotspur. They are also the only teams to maintain a winning average of over 50% throughout their entire Premier League tenures.
Answer: Arsenal, Liverpool, Manchester United and Chelsea are know as the "Top Four" in the English Premier league.
Question: What batting traits make a good baseball or softball hitter?
Answer: There are many traits that great baseball and softball batters share.  Some of the most important are extreme confidence, sound batting mechanics, and coordination developed though a combination of talent and practice.  

All great hitters share some consistencies in their batting mechanics such as their lower half mechanics, their hand and bat path, and their head and eye movement.

With their lower half mechanics they generate powerful torque from the ground up by timing the planting their feet in a balanced position and forcefully rotating and opening their hips.  Hitting powerful line drives relies heavily on the barrel of the bat meeting the ball flush.  Batters increase the odds of this by keeping their hands in, connected to the back shoulder, and having one palm facing up and one palm facing down for as long as possible as the bat path travels through the strike zone.  Finally, all great hitters maintain consistent head and eye position throughout the motion. 

There are many nuances of batting mechanics that all great hitters share but these are some of the most important.