DataHammer/scimrc huggingface dataset:
Question: Extract key reported metrics and their values along with the year the metric is reported from the given text in the format {Metric} - {Year} - {value} and show them as a list
Evidence: Indian Railways (IR) is a statutory body under the ownership of the Ministry of Railways, Government of India that operates India's national railway system. It manages the fourth largest national railway system in the world by size, with a total route length of 68,043 km (42,280 mi), running track length of 102,831 km (63,896 mi) and track length of 128,305 km (79,725 mi) as of 31 March 2022. 50,394 km (31,313 mi) of all the gauge routes are electrified with 25 kV 50 Hz AC electric traction as of 1 April 2022.

In 2020, Indian Railways carried 808.6 crore (8.086 billion) passengers and in 2022, Railways transported 1418.1 million tonnes of freight. It runs 13,169 passenger trains daily, on both long-distance and suburban routes, covering 7,325 stations across India. Mail or Express trains, the most common types of trains, run at an average speed of 50.6 km/h (31.4 mph).
Answer: Passengers - 2020 - 808.6 crore 
Tonnes of Freight - 2022 - 1418.1 million tonnes
Total Passenger Trains - 2022 - 13,169 
Total Route Length - Year - 68,043 km
Running Track Length - 2022 - 102,831 km
Track Length - 2022 - 50,394 km
Electrified Gauge Route - 2022 - 50,394 km
Average Speed - - 31.4 mph
Question: Do they use multi-attention heads?
Evidence: The attentional encoder layer is a parallelizable and interactive alternative of LSTM and is applied to compute the hidden states of the input embeddings. This layer consists of two submodules: the Multi-Head Attention (MHA) and the Point-wise Convolution Transformation (PCT).
This layer consists of two submodules: the Multi-Head Attention (MHA) and the Point-wise Convolution Transformation (PCT).
Answer: Yes.
Question: What is the difference between speed and velocity?
Answer: Speed and velocity are different terms. Speed is a measurement of distance over time. Velocity is a measurement of distance over time in a direction. Speed is scalar and can only be positive. Velocity is a vector and can either be positive or negative. For example, an object moving at a speed of 50 km/h tells us only how quickly it is moving but not its direction. An object with a velocity of +50 km/h tells us its speed and that it is moving forwards. Conversely, an object with a velocity of -50 km/h tells us its speed and that it is moving backward.
Question: What is the tallest building in the city of San Francisco?
Evidence: Salesforce Tower, formerly known as Transbay Tower, is a 61-story skyscraper at 415 Mission Street, between First and Fremont Street, in the South of Market district of downtown San Francisco. Its main tenant is Salesforce, a cloud-based software company. The building is 1,070 feet (326 m) tall, with a top roof height of 970 feet (296 m).
Answer: Salesforce Tower is a 61-story skyscraper at 415 Mission Street, between First and Fremont Street, in the South of Market district of downtown San Francisco. The building is 1,070 feet (326 m) tall, with a top roof height of 970 feet (296 m).
Question: Provide a list of some Japanese art forms I've not heard of.
Answer: Here is a number of Japanese art forms that you may not have heard of; Ikebana is the art of flower arranging. Kabuki is a form of traditional Japanese theatre. Origami is the art of folding paper into various shapes, such as animals. Anime is a Japanese form of comic book art. Haiku is a form of Japanese poetry.
Question: Write a short paragraph about why you should not have both a pet cat and a pet bird.
Answer: Having both a pet bird and a pet cat is not a good idea because cats will try to hunt birds, which are naturally the prey of cats. This hunting instinct is completely normal, and it isn't fair to the cat to try to train it not to attack birds, as it is simply in its nature to do so. However, letting a cat hunt your pet bird is similarly not humane, as it will be very stressful to the bird and could lead to the bird being injured. Larger birds are also not a good idea, as the bird could cause injury to the cat if the cat tries to attack it. Overall, it just isn't a good idea to have both cats and birds, since they tend not to coexist well, and pet owners should just pick one species to have.
Question: What metrics are considered?
Evidence: One-Vs-The-Rest strategy was adopted for the task of multi-class classification and I reported F-score, micro-F, macro-F and weighted-F scores using 10-fold cross-validation. The F1 score is a weighted average of the precision and recall. In the multi-class case, this is the weighted average of the F1 score of each class. There are several types of averaging performed on the data: Micro-F calculates metrics globally by counting the total true positives, false negatives and false positives. Macro-F calculates metrics for each label, and find their unweighted mean. Macro-F does not take label imbalance into account. Weighted-F calculates metrics for each label, and find their average, weighted by support (the number of true instances for each label). Weighted-F alters macro-F to account for label imbalance.
One-Vs-The-Rest strategy was adopted for the task of multi-class classification and I reported F-score, micro-F, macro-F and weighted-F scores using 10-fold cross-validation. 
Answer: The answers are shown as follows:
* F-score
* micro-F
* macro-F
* weighted-F
Question: When did Hawaii become a state?
Answer: Hawaii became a state on August 21, 1959.
Question: What are some low fat snack options?
Answer: popcorn, watermelon, low fat cheese sticks, low fat cottage cheese, apples, grapes, carrots, sugar snap peas, edamame
Question: Give me a numbered list of non Indian cricket players who made their debut in Indian Premier League (IPL) before they played a single first class match
Answer: 1.	Dewald Brevis, South Africa
2.	Krishmar Santokie Jamaica 
3.	Mujeeb Ur Rahman, Afghanistan 
4.	Sandeep Lamichhane, Nepal 
5.	Chris Green, Australia  
6.	Tim David, Australia
Question: Where does the vocabulary come from?
Evidence: Our training data consists of 2.09M sentence pairs extracted from LDC corpus. Table 1 shows the detailed statistics of our training data. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set.
Our training data consists of 2.09M sentence pairs extracted from LDC corpus.
Answer: The answers are shown as follows:
* LDC corpus
Question: What are main vegetables in V8 juice?
Evidence: The original V8 is a savory juice. It is made mainly from water and tomato concentrate, and reconstituted vegetable juice blend: water and concentrate of eight vegetables, specifically: beets, celery, carrots, lettuce, parsley, watercress, spinach, and tomato. Campbell's has produced several varieties of the drink, such as Original, Spicy Hot, Hint of Black Pepper, Lemon, Picante, Low-Sodium, and Organic.

Three spin-offs of the V8 brand, "V8 Energy", "V8 Splash" and "V8 V•Fusion", are blends of fruit and vegetable (specifically carrot) juices. V8 Splash is a juice cocktail partially sweetened with high-fructose corn syrup and sucralose; diet versions of V8 Splash omit the high-fructose corn syrup. V8 Energy also classifies as a cocktail and is partially sweetened with sucralose. V•Fusion is made from 100% juice.
Answer: beets, celery, carrots, lettuce, parsley, watercress, spinach, and tomato
Question: Identify which animal is domesticated or wild: Arabian camel, European fallow
Answer: European fallow is wild, Arabian camel is domesticated
Question: What algorithm is used for a classification problem?
Evidence: In all experiments, we use Support Vector Machines (SVMs) for classification problems and Support Vector Regression (SVR) for regression problems to make predictions from our representations of geographic locations. In both cases, we used the SVM INLINEFORM0 implementation BIBREF53 . For each experiment, the set of locations INLINEFORM1 was split into two-thirds for training, one-sixth for testing, and one-sixth for tuning the parameters. All embedding models are learned with Adagrad using 30 iterations. The number of dimensions is chosen for each experiment from INLINEFORM2 based on the tuning data. For the parameters of our model in Equation EQREF7 , we considered values of INLINEFORM3 from {0.1, 0.01, 0.001, 0.0001} and values of INLINEFORM4 from {1, 10, 100, 1000, 10 000, 100 000}. To compute KL divergence, we need to determine a set of classes INLINEFORM5 for each experiment. For classification problems, we can simply consider the given categories, but for the regression problems we need to define such classes by discretizing the numerical values. For the scenicness experiments, we considered scores 3 and 7 as cut-off points, leading to three classes (i.e. less than 3, between 3 and 7, and above 7). Similarly, for each climate related features, we consider two cut-off values for discretization: 5 and 15 for average temperature, 50 and 100 for average precipitation, 10 000 and 17 000 for average solar radiation, 0.7 and 1 for average water vapor pressure, and 3 and 5 for wind speed. The smoothing parameter INLINEFORM6 was selected among INLINEFORM7 based on the tuning data. In all experiments where term selection is used, we select the top 100 000 tags. We fixed the radius INLINEFORM8 at 1km when counting the number of tag occurrences. Finally, we set the number of negative examples as 10 times the number of positive examples for each location, but with a cap at 1000 negative examples in each region for computational reasons. We tune all parameters with respect to the F1 score for the classification tasks, and Spearman INLINEFORM9 for the regression tasks.
In all experiments, we use Support Vector Machines (SVMs) for classification problems and Support Vector Regression (SVR) for regression problems to make predictions from our representations of geographic locations.
Answer: Support Vector Machines.
Question: When would a railway be considered a heritage railway?
Evidence: A heritage railway or heritage railroad (US usage) is a railway operated as living history to re-create or preserve railway scenes of the past. Heritage railways are often old railway lines preserved in a state depicting a period (or periods) in the history of rail transport. The British Office of Rail and Road defines heritage railways as follows:...'lines of local interest', museum railways or tourist railways that have retained or assumed the character and appearance and operating practices of railways of former times. Several lines that operate in isolation provide genuine transport facilities, providing community links. Most lines constitute tourist or educational attractions in their own right. Much of the rolling stock and other equipment used on these systems is original and is of historic value in its own right. Many systems aim to replicate both the look and operating practices of historic former railways companies.
Answer: Generally speaking, a railway can be considered a heritage railway when it assumes the appearance, characteristics, and operations of that railway from a period(s) in its past. These railways are often old and have been intentionally preserved to maintain its operating practices from prior times, purposely keeping equipment and processes original to a period in the past. Many of these heritage railways are preserved for educational purposes or as tourist attractions.
Question: How better is performance compared to competitive baselines?
Evidence: FLOAT SELECTED: Table 4: Accuracy (P@1) on Vecmap. The best results are bolded. †Results as reported in the original paper. For unsupervised methods, we report the average accuracy across 10 runs.
Table TABREF15 shows the final results on Vecmap. We first compare our model with the state-of-the-art unsupervised methods. Our model based on procrustes (Ours-Procrustes) outperforms Sinkhorn-BT on all test language pairs, and shows better performance than Adv-C-Procrustes on most language pairs. Adv-C-Procrustes gives very low precision on DE-EN, FI-EN and ES-EN, while Ours-Procrustes obtains reasonable results consistently. A possible explanation is that dual learning is helpful for providing good initiations, so that the procrustes solution is not likely to fall in poor local optima. The reason why Unsup-SL gives strong results on all language pairs is that it uses a robust self-learning framework, which contains several techniques to avoid poor local optima.
FLOAT SELECTED: Table 4: Accuracy (P@1) on Vecmap. The best results are bolded. †Results as reported in the original paper. For unsupervised methods, we report the average accuracy across 10 runs.
Table TABREF15 shows the final results on Vecmap.
Answer: Proposed method vs best baseline result on Vecmap (Accuracy P@1):
EN-IT: 50 vs 50
IT-EN: 42.67 vs 42.67
EN-DE: 51.6 vs 51.47
DE-EN: 47.22 vs 46.96
EN-FI: 35.88 vs 36.24
FI-EN: 39.62 vs 39.57
EN-ES: 39.47 vs 39.30
ES-EN: 36.43 vs 36.06
Question: When running a marathon and attempting to run a personal best time, categorize the following as either ‘useful’ or ‘not useful’, ‘dangerous’ or ‘neither. Fast digesting carbohydrates, a fire extinguisher, a weight vest, scissors, running shoes
Answer: Fast-digesting carbohydrates are useful to make sure your body has fuel through all 26.2 miles. A fire extinguisher is neither useful nor not useful. A weight vest is not useful if you’re attempting to run your personal best. The extra weight will slow you down. Scissors are dangerous, you should never run with scissors. Running shoes are very useful to protect your feet and enhance your biomechanics and the efficiency of your stride to run your best time.
Question: Summarize the rulers of the territory of Albania in this time period.
Evidence: The Illyrian Ardiaei tribe, centred in Montenegro, ruled over most of the territory of Albania. Their Ardiaean Kingdom reached its greatest extent under King Agron, the son of Pleuratus II. Agron extended his rule over other neighbouring tribes as well. Following Agron's death in 230 BC, his wife, Teuta, inherited the Ardiaean kingdom. Teuta's forces extended their operations further southwards to the Ionian Sea. In 229 BC, Rome declared war on the kingdom for extensively plundering Roman ships. The war ended in Illyrian defeat in 227 BC. Teuta was eventually succeeded by Gentius in 181 BC. Gentius clashed with the Romans in 168 BC, initiating the Third Illyrian War. The conflict resulted in Roman conquest of the region by 167 BC. The Romans split the region into three administrative divisions.
Answer: King Agron ruled the territory of Albania, followed by his wife Teuta from 230 BC, and then Gentius from 181 BC. The Romans conquered the region in 167 BC.
Question: How was a quality control performed so that the text is noisy but the annotations are accurate?
Evidence: Wikilinks can be seen as a large-scale, naturally-occurring, crowd-sourced dataset where thousands of human annotators provide ground truths for mentions of interest. This means that the dataset contains various kinds of noise, especially due to incoherent contexts. The contextual noise presents an interesting test-case that supplements existing datasets that are sourced from mostly coherent and well-formed text.
We prepare our dataset from the local-context version of Wikilinks, and resolve ground-truth links using a Wikipedia dump from April 2016. We use the page and redirect tables for resolution, and keep the database pageid column as a unique identifier for Wikipedia entities. We discard mentions where the ground-truth could not be resolved (only 3% of mentions).
Wikilinks can be seen as a large-scale, naturally-occurring, crowd-sourced dataset where thousands of human annotators provide ground truths for mentions of interest. This means that the dataset contains various kinds of noise, especially due to incoherent contexts.
We prepare our dataset from the local-context version of Wikilinks, and resolve ground-truth links using a Wikipedia dump from April 2016. We use the page and redirect tables for resolution, and keep the database pageid column as a unique identifier for Wikipedia entities. We discard mentions where the ground-truth could not be resolved (only 3% of mentions).
Answer: The authors believe that the Wikilinks corpus  contains ground truth annotations while being noisy. They discard mentions that cannot have ground-truth verified by comparison with Wikipedia.
Question: What was the US civil war about?
Answer: The US civil war had a complex start but the main aim was the preservation of slavery for the confederacy and the elimination of slavery for the union. Slavery was the principal political issue between the parties. 
There was also an aspect of the confederacy wanting to secede from the Union to preserve slavery. The union did not want the slave stares to secede. After war broke out, it is interesting to note, no foreign government recognized the confederate government. 
By the mid 1800s, the US had two distinct regions: free states with fast growing economies based on family farming, manufacturing, mining, business and transporting goods. The slave states were based on a plantation system and the only fast growth was in texas. These states were largely rural with few large cities and little industry except in close proximity to free states.
Question: Who was Kyle Van Zyl playing against when he scored 36 of hisa teams 61 points?
Evidence: Van Zyl joined the Eastern Province Kings Academy, where he played for the Eastern Province U19 side in the 2010 Under-19 Provincial Championship. He was a key player for the Eastern Province U21 side in the 2012 Under-21 Provincial Championship, scoring 71 points in eight appearances. Van Zyl was under the Top SARU Performers, scoring the most tries at 6 in the 2012 Provincial Under 21 in the Rugby Junior Provincials.

This included a record and a remarkable personal haul in their opening match, when he scored 36 of his team's points in a 61–3 victory over Boland U21, consisting of four tries and eight conversions and was awarded Man of the Match.
Answer: Kyle Van Zyl was playing against Boland U21 when he scored 36 points, leading his team to victory in a 61-3 win.
Question: When is Tax Day not on April 15?
Answer: Income taxes in the United States generally must be filed on or before April 15. But when April 15 falls on a weekend or holiday, the filing deadline is moved to next business day.
Question: How big is the dataset?
Evidence: Train-test split: The labelled dataset that was available for this task was very limited in number of examples and thus as noted above few data augmentation techniques were applied to boost the learning of the network. Before applying augmentation, a train-test split of 78%-22% was done from the original, cleansed data set. Thus, 700 tweets/messages were held out for testing. All model evaluation were done in on the test set that got generated by this process. The results presented in this report are based on the performance of the model on the test set. The training set of 2489 messages were however sent to an offline pipeline for augmenting the data. The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below:
FLOAT SELECTED: Table 3: Train-test split
The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below:
FLOAT SELECTED: Table 3: Train-test split
Answer: Resulting dataset was 7934 messages for train and 700 messages for test.
Question: What are the benefits of a carbon-fiber paddle for whitewater kayaking?
Answer: In addition to the reduced weight, carbon fiber paddles are a bit more rigid than the traditional fiberglass and resin paddles frequently used by whitewater paddlers.  This extra rigidity allows more of the energy exerted by the paddler to be transferred to the blades.  The result is faster turns and greater agility, all things any paddler needs when navigating more technical stretches of the river.
Question: Do I need diverter to charge my car using excess generated power from solar panels?
Answer: The need for a separate Solar PV diverter depends mostly on your EVSE (car charger). Some chargers already come with the capability to intelligently divert any extra power from your solar panels by monitoring your grid connection or a combination of solar panel output and your house consumption. Smart EVSE still requires the installation by a licensed electrician, but the overall system is much simpler than the alternatives.

Car chargers, which do not support monitoring your electrical grid, may still allow controlling the charging by an external system, but complicates the overall setup and requires multiple modules to work together to achieve the goal.

Car chargers, which neither can monitor the grid and doesn't support external control of the charging current, may not work well even with a Solar PV diverter depending on the mode of operation of the Solar PV diverter and the design of the car charger.
Question: Given this paragraph about Love Island UK Season 9, Kai Fagan and Sanam Harrinanan won the final vote by what percent?
Evidence: On 13 March 2023, the series was won by Kai Fagan and Sanam Harrinanan with 43.75% of the final vote, becoming the first couple formed during the Casa Amor twist to win the series. Lana Jenkins and Ron Hall finished as runners-up.
Answer: 43.75%
Question: What can POS tags help the model capture they find through their case study?
Evidence: Table TABREF22 shows two examples of quality prediction on the validation data of WMT2018 QE task for English-Czech. In the first example, the model without POS tags and baseline features is biased towards predicting “OK” tags, while the model with full features can detect the reordering error. In the second example, the target word “panelu” is a variant of the reference word “panel”. The target word “znaky” is the plural noun of the reference “znak”. Thus, their POS tags have some subtle differences. Note the target word “zmnit” and its aligned source word “change” are both verbs. We can observe that POS tags can help the model capture such syntactic variants.
In the second example, the target word “panelu” is a variant of the reference word “panel”. The target word “znaky” is the plural noun of the reference “znak”. Thus, their POS tags have some subtle differences. Note the target word “zmnit” and its aligned source word “change” are both verbs. We can observe that POS tags can help the model capture such syntactic variants.
Answer: Syntactic variants.