agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: The patent positions of biotechnology companies and other actors in our fields of business can be highly uncertain and involve complex scientific, legal and factual analyses. The interpretation and breadth of claims allowed in some patents covering biological compositions may be uncertain and difficult to determine and are often affected materially by the facts and circumstances that pertain to the patented compositions and the related patent claims. The issuance and scope of patents cannot be predicted with certainty. Patents, if issued, may be challenged, invalidated, narrowed or circumvented. Challenges to our or our licensors’ patents and patent applications, if successful, may result in the denial of our or our licensors’ patent applications or the loss or reduction in their scope. In addition, defending against such challenges may be costly and involve the diversion of significant management time. Accordingly, rights under any of our patents may not provide us with enough protection against competitive products or processes and any loss, denial or reduction in scope of any of such patents and patent applications may have a material adverse effect on our business.
Assistant:
Can you write a technical article in table format of 50 words each using the following file.csv single line data? Write the texts without breaking the csv order. Type as many data as there are in the file.csv file. Write the titles of what you cannot write and leave the description.

Example:

1GL First-Generation Programming Language: A first generation (programming) language (1GL) is a grouping of programming languages that are machine level languages used to program first-generation computers. Originally, no translator was used to compile or assemble the first-generation language. The first-generation programming instructions were entered through the front panel switches of the computer system.
1NF First Normal Form: First normal form (1NF) is a property of a relation in a relational database. A relation is in first normal form if and only if no attribute domain has relations as elements.[1] Or more informally, that no table column can have tables as values (or no repeating groups).

file.csv

QDR Quad Data Rate
QA Quality Assurance
QFP Quad Flat Package
QoS Quality of Service
QOTD Quote of the Day
Qt Quasar Toolkit
Non compliant structure function pointer name
Hey, this is an English text. I want you to translate it into the Tunisian dialect. Please follow these steps:
1. Translate step by step and highlight differences between Tunisian dialect and Modern Standard Arabic.
2. Highlight sections you are confident translating into the Tunisian dialect.
Translate this text into the Tunisian dialect, focusing on capturing the essence of the language nuances:

Agricultural and horticultural pesticides fatal poisoning; the Jordanian experience 1999-2002. A prospective autopsy study addressing fatal poisoning with agricultural and horticultural pesticides was undertaken in Jordan over a 4 year period. A total number of 140 deaths occurred during 1999-2002. The mean fatality rate was 0.68 case per 100,000 population and the age range was 2-55 years; mean 28.3 years with male to female ratio 1.03. The largest number of cases occurred in those 20-29 years (n=69, 49.3%) followed by the age group 30-39 years (n=34, 24.3%) and 40-49 years (n=17, 12.1%). Less than 3.0% of the total fatal poisoning was noticed in both children younger than 9 years of age and those in the age group 50-59 years, with no fatal poisoning in adults at the age 60 years and above. At least 64.3% of all pesticide fatalities were due to suicide with male: female ratio (1.37:1). Accidental and homicide poisoning resulted in 24.3% and 7.9% of the total fatalities, respectively; however, only five cases 3.6% of fatal poisoning were due to unknown pesticides. The main pesticide used was carbamates with 110 cases 78.6% followed by organophosphorus 23 cases 16.4%. The study showed that the present legislation on pesticides availability in Jordan failed to reduce the number of fatal pesticides poisoning since the number of fatal pesticides poisoning was increased from 25.3 to 35 cases per year over a 20 years period. Enforcement of a new legislation addressing the availability of agricultural and horticultural pesticides for self-harm, especially carbamates and organophosphorus, is the most important strategy in the long term to prevent fatal pesticides poisoning in Jordan.
Create a long detailed text about music_department Les Claypool with the following summary. The title of the video is"TOP 20 Rock Bassists Of All Time, here goes my vote..". Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Les Claypool] Summary: [Known for his unconventional and quirky style, Claypool is one of the most skilled bass players. Tried out for Metallica after Cliff Burton's death but was deemed 'too good' for the position. Incorporates a wide range of techniques and influences in his playing, making him stand out.]

            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. 
            In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.
            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
            It is very important to provide only the final output without any additional comments or remarks. 
            Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
            Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
I will send you a passage
every organization has a unique culture. what steps as a travel nurse will you take to move forward with an organization’s culture
hi who are u ?
Assistant: 
User: System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
Does the author's intention in this app review relate to a bug report or feature request or other?
review: is there a problem transferring numbers to your network due to bank holiday? Did the form on thurs night and number still not transferred'
intention: '
Assistant:
Assistant:
write a script of when you are perfroming Philadelphia Freedom by elton john. during the performance, elton john himself surprises you.
Write this script with detailed dialogue: To her colleagues’ astonishment, Nadine expertly hypnotizes David by slowly talking him down and snapping her fingers, causing him to enter a deep trance. While in this trance, Nadine instructs David that the only thing he will be able to say, no matter what the question, is “I am hypnotized” in a dopey hypnotized voice. They all take great delight in witnessing the whole scenario unfold, laughing and teasing David. Then Nadine tells him he's the office foot massager. Afterwards, Nadine commands David to forget everything that transpired and prompts him to awaken, leaving him clueless about what just occurred.
I have a list of ingredients that I would like to format in a simple and consistent way, following this structure:

"Quantity Unit Description."

Formatting Rules:

Quantity must be a number (e.g., "200" instead of "two hundred").

Unit must use the metric system and the corresponding abbreviated form (e.g., grams → g, liters → l, milliliters → ml, etc.).

Description should be the ingredient, including any specific details if mentioned (e.g., "brown sugar", "melted unsalted butter").

If an ingredient is already formatted correctly, do not change it.

Do not invent a quantity or unit if none is provided — leave it as is and just include the description.

If a line includes alternatives or multiple options (e.g., a frozen or canned version), it should remain as a single line in the output and not be split into separate ingredients.

Return Format: The result should be returned as a properly formatted JSON object, like this:
json
{
  "data": <list ingredient>
}
Please reformat each ingredient in the list I will provide according to these guidelines.

Context Dump
Ingredients source provided:
2 cups shortening,8 cups confectioners' sugar,½ teaspoon salt,2 teaspoons vanilla extract,1 cup heavy whipping cream
Can you translate a picture to Arabic
Find some papers for contingency planning and spatial-temporal planning in autonomous driving.
You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```with customer_total_return as (select sr_customer_sk as ctr_customer_sk,sr_store_sk as ctr_store_sk,sum(SR_FEE) as ctr_total_return from store_returns,date_dim where sr_returned_date_sk = d_date_sk and d_year =2000 group by sr_customer_sk,sr_store_sk) select c_customer_id from customer_total_return ctr1,store,customer where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2 from customer_total_return ctr2 where ctr1.ctr_store_sk = ctr2.ctr_store_sk) and s_store_sk = ctr1.ctr_store_sk and s_state = 'TN' and ctr1.ctr_customer_sk = c_customer_sk order by c_customer_id limit 100; ```

[REWRITTEN QUERY]```with customer_total_return as ( select sr_customer_sk as ctr_customer_sk, sr_store_sk as ctr_store_sk, sum(SR_FEE) as ctr_total_return from store_returns join date_dim on sr_returned_date_sk = d_date_sk where d_year = 2000 group by sr_customer_sk , sr_store_sk ), avg_customer_return as ( select ctr_store_sk, avg(ctr_total_return) as avg_return from customer_total_return group by ctr_store_sk ) select c_customer_id from customer_total_return ctr1 join store on s_store_sk = ctr1.ctr_store_sk join customer on ctr1.ctr_customer_sk = c_customer_sk join avg_customer_return acr on ctr1.ctr_store_sk = acr.ctr_store_sk where ctr1.ctr_total_return > acr.avg_return * 1.2 and s_state = 'TN' order by c_customer_id limit 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must contain CTEs.
2. The original query must make use of the following fact tables: ['store_sales', 'catalog_returns']. Below is the join graph for these fact tables:
    ['store_sales(ss_hdemo_sk) references household_demographics(hd_demo_sk);', 'store_sales(ss_cdemo_sk) references customer_demographics(cd_demo_sk);', 'store_sales(ss_customer_sk) references customer(c_customer_sk);']
    ['catalog_returns(cr_ship_mode_sk) references ship_mode(sm_ship_mode_sk);', 'catalog_returns(cr_warehouse_sk) references warehouse(w_warehouse_sk);', 'catalog_returns(cr_returning_hdemo_sk) references household_demographics(hd_demo_sk);']
5. The original query must employ 4 joins.
6. The original query must employ the following operators in its WHERE clause: [].
7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.
Ты — эксперт по играм. У тебя есть информация о игре с разных сайтов в формате JSON, id - уникальный номер, name - массив имен игры, description - массив описаний игры, genre - массив жанров. Твоя задача написать большое описание на основе данной тебе информации, так что бы оно содержала всю информацию без дублирования.  ```{"_id":"1","name":["left = lose","game left = lose"],"description":["left = lose is a classic arcade platformer where you can only go forward but not backward. jump, run, collect the apples and reach the exit in each level. if you die, you'll have to start over from the beginning of the level.","never go left in left = lose, every pressing the left arrow will cost you a life. help your hero to jump from one place to another and collect the red apples and golden apples to score points. if you go left or you fall into a trap (row of spikes, the hole filled with water) press r to start over from the start of the current level. good luck!","left = lose is a cool retro platformer game in which you'll have to help your hero to collect apples and to complete each level one by one. don't die or you'll have to start over from the beginning of a level. good luck!"],"genre":["экшн-игры и приключения","platform","platform games","games","agility games","online games"]}```
Write about the plot of Wolfwalkers
gold salts” – a regular treatment in the 1930s. When did stop
Produce a sermon based on James 3:13
func QuadA(x, y int) {
	if x <= 0 || y <= 0 {
		return
	}

	for i := 0; i < y; i++ {
		for j := 0; j < x; j++ {
			if i == 0 || i == y-1 {
				if j == 0 || j == x-1 {
					z01.PrintRune('o')
				} else {
					z01.PrintRune('-')
				}
			} else {
				if j == 0 || j == x-1 {
					z01.PrintRune('|')
				} else {
					z01.PrintRune(' ')
				}
			}
		}
		z01.PrintRune('\n')
	}
}explain
Hand-engineered features are commonly used with traditional machine-learning (ML) methods for DR diagnosis. Different surveys have reviewed these traditional methods [1], [2], [3], [4], [5], [6], [7]. For example, Mookiah et al. [1] and Mansour [4] categorized DR diagnosis according to the adopted methodologies, such as mathematical morphology, retinal lesion tracking, thresholding and deformable models, clustering-based models, matched filtering models and hybrid approaches. Faust et al. [2] reviewed algorithms that extract lesion features from fundus images, such as the blood vessel area, exudes, hemorrhages, microaneurysms and texture. Joshi and Karule [3] reviewed the early research on exudate detection. Almotiri et al. [5] provided an overview of algorithms to segment retinal vessels. Almazrao et al. [6] and Thakur and Juneja [7] reviewed several methods for optic disc segmentation and diagnosis of glaucoma. However, expert knowledge is a prerequisite for hand-engineered features, and choosing the appropriate features requires intensive investigation of various options and tedious parameter settings. Moreover, techniques based on hand-engineered features do not generalize well.翻译并优化语句
Overview on VNC Connect (RealVNC) with key features, individual features, and usage costs
"Benny: when have i ever let you down?
Tom: ...
Benny: don't even, dude
Benny: ey! saw we left it out for ya huh?
Miguel: heh, yea. still pretty much in tune too, appreciate you snagging it for me.
Benny: course cuz! wasn't going to let them toss it out on ya!
Yom: 'course now you just have to find..."
Explain please and make it a detailed breakdown line by line segment by segment part by part in a detailed way of the whole paragraph, like the structure by little fragments and explain each of them
Design a railway station for a hypothetical 'tube' railway in Brighton england,   It's built inside the cliff in tunnel,  but it's a mid 1920's design asthetic..    Describe the station in the style of a late 1970's architectural review..   ( This 'Volkstube' station might have been influnced by desgin choices elsewhere in the UK.)
System: You are an expert Data Annotator. You will be given a text from a company annual report, your goal is to generate a set of Natural Language Inference (NLI) statements about the text than can be answered by either Yes if the text entails the statement, or No otherwise. Do not include any numerical reasoning or numbers in the statements.

Step 1: Read the paragraph carefully. 
Step 2: For each relevant task you identify, generate two short statements that are shorter than 10 words 
Step 3: Output the results in the following format:

Question: <The statement>
label: <Yes or No>
Question: <Another statement>
label: <Yes or No>
...

User: We depend on the experience, skill and contributions of our senior management and other key employees. If we fail to attract, motivate and retain highly qualified management, technical, compliance and sales personnel, our future success could be harmed. Our senior management provides strategic direction for our company, and if we lose members of our leadership team, our management resources may have to be diverted from other priorities to address this loss. Our products and services require sophisticated knowledge of the financial services industry, applicable regulatory and industry requirements, computer systems, and software applications, and if we cannot hire or retain the necessary skilled personnel, we could suffer delays in new product development, experience difficulty complying with applicable requirements or otherwise fail to satisfy our clients’ demands._
Assistant:
Create a GUI for chatting with from gradio_client import Client

client = Client("yuntian-deng/ChatGPT4Turbo")
result = client.predict(
		inputs="Hello!!",
		top_p=1,
		temperature=1,
		chat_counter=0,
		chatbot=[],
		api_name="/predict"
)
print(result)
show me the differences and the similarities of natural enzymes and recombinant enzymes
Imagine there was advanced civilizations on carboniferous period
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Any expansion into new markets or new lines of business might not be successful. None
Assistant:
Using HTML + JS (one file) create 2d aim trainer, add assistant that analyze my accuracy and says if i'm well done, provide full code
Give a TL;DR on the EU's new laws that affect Apple products and services.