agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers ar (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en ar.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : ar (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "33b5cf1865cf4b33beff68aecbad41d2",
    "updated": "2025-04-19T19:17:05.849Z",
    "title": "Lebanese Spiced Beef and Okra",
    "subtitle": "A spiced and flavorful one-pot meal.",
    "description": "A flavorful one-pot dish featuring tender beef and okra, infused with Lebanese spices.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "olive oil",
          "onion, sliced",
          "(200g) okra, topped and halved horizontally",
          "(56.699 g) (500g) lean steak, cut into in (1cm) pieces",
          "paprika",
          "garlic, finely chopped",
          "juice of",
          "can of chopped tomatoes",
          "handful of fresh cilantro, roughly chopped",
          "salt and freshly ground black pepper"
        ]
      }
    ],
    "instructions": [
      "1. Heat the olive oil in a large frying pan. Add the sliced onion and cook over medium heat for 2 minutes, or until it begins to soften.  Then, add the halved okra and cook for another minute.",
      "2. Add the diced beef, paprika, and minced garlic to the pan. Cook, stirring frequently, until the beef is browned.  Pour in the lemon juice, canned tomatoes, and chopped cilantro. Season generously with salt and pepper. Bring the mixture to a boil, then reduce the heat to low and simmer, uncovered, for 20-30 minutes, stirring occasionally, until the sauce has thickened. Serve hot in bowls."
    ],
    "notes_ingredients": null,
    "notes_instructions": "Searing the okra briefly before adding the other ingredients helps to prevent it from becoming slimy."
  }
]
Assistant: 
User: You are translator for Townsmen – A Kingdom Rebuilt hands the crown to you: Guide your people from their first steps in a small village to a massive medieval metropolis with thousands of inhabitants. I send you text, you send me translate from English to Vietnamese, suitable for medieval language, nothing else. Please translate this text and return to me, nothing else: Wedding
Assistant:
Hong Kong in November good to visit?
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: The COVID-19 pandemic could adversely impact our business, including our nonclinical studies and clinical trials._The COVID-19 pandemic and government measures taken in response have had a significant impact, both direct and indirect, on businesses and commerce, as worker shortages have occurred; supply chains have been disrupted; facilities and production have been suspended; and demand for certain goods and services, such as medical services and supplies, has spiked, while demand for other goods and services, such as travel, has fallen. In response to the spread of COVID-19, we closed our offices and limited the number of staff in any given research and development laboratory. In March 2022, we fully re-opened our offices to all employees and continue to comply with protocols implemented by respective health authorities. However, we may close our offices again in the future. As a result of the COVID-19 pandemic, we experienced some delays in setting up our current Phase 1/2 clinical study and in clinical site initiation, including delays in recruiting clinical site investigators and clinical site staff. We also experienced subject enrollment timeline delays in a Phase 1b clinical study of OP-1250 in combination with another CDK4/6 inhibitor and with a PI3Ka inhibitor. We may experience these or other continuing impacts of the COVID-19 pandemic in the future. Additionally, we may experience further disruptions that could severely impact our business, nonclinical studies and clinical trials, including:_
Assistant:
Q#1.  Find the negation of the proposition
           a)  Vivo smartphone has at least 32GB of memory
           b) The fan is slow or it is very hot.
           c)  Akram is unfit and Saleem is injured.
Summarize the entirety of War and Peace using emojis.
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
Does the author's intention in this app review relate to a pbr or inq or irr?
review: Any help? https://t.co/Hv0PEJihpA'
intention: '
Assistant:
Could you please assist me in editing this short message? Approach it with a business writer's flair, offering engaging content in simple English. Your goal is to review the text provided below, ensuring clarity, correct spelling, punctuation, and grammar. Your revised copy should flow smoothly, be well-structured, and maintain the original message's meaning and intent. Thank you!
Hi GB,
Just following up on the question I raised during our last call about the data dictionary. Would you please let us know when this will be made available because we need it as part of the RDS acceptance checks. Also, Dassie shared the link to the Tableau dashboard for the RDS DQ report however it is difficult to know what we are looking at. So, I was wondering if you could provide a guide or form of dictionary to make sense of the report.
Thanks
Bertin
Filter words to keep only valid, neutral, or positive ones, strictly safe. Exclude country, city and people names. Return results in JSON array format without additional information. Source data: [validity, validly, valine, valise, valkyrie, vallation, valletta, valley, valleys, vallisneria, vallum, valmy, valoir, valor, valorem, valorization, valorous, valparaiso, valuable, valuation, value, valued, valueless, valuelessness, valuer, values, valve, valved, valvular, valvulitis]
You are an expert AI model that makes prompts for an AI to generate a picture based on a scene in a script. For example, the script will be something like:
  
Let’s dive into some tail-wagging fun facts about our furry friends—dogs!

The prompt would be something like:

A joyful golden retriever puppy playfully wagging its tail in a sunlit park, surrounded by lush green grass and colorful flowers. The dog's eyes sparkle with excitement as it looks directly at the camera, radiating happiness. Soft natural lighting enhances the warm, vibrant tones of the fur. A blurred background creates a dreamy, cinematic feel. Captured in a high-resolution, ultra-detailed shot with bokeh effect, showcasing the dog's expressive face and wagging tail.

Now generate a prompt for the scene script YOU ARE TO STRICTLY ONLY INCLUDE THE PROMPT IN YOUR RESPONSE AND NOT SEND ANY TEXT ACKNOWLEDGING MY REQUEST: "Oh, and when your dog’s tail is wagging, it’s not just excitement—they have their own tail language! Different wags mean different moods. Get to know your canine's vocabulary!"
Can predict if next earning for a company is going to be bad
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

إي شنو الميني الليلة عاد حوت وفازات
Guess the song 🎵 Chelsea Chelsea I believe 🎵
Language: English. Task:
1. Filter the word list to include only valid, neutral, and positive terms suitable for a word game.
2. Exclude the following:
 - Inappropriate, offensive, and negative words
 - Articles
 - Non-existent words
 - Plural forms
 - Outdated words
 - Names of countries, cities, and people (first and last names)
3. Correct any spelling errors.
4. Convert all words to lowercase.
5. Return the results in a JSON array containing only the filtered words.
Source data: [bullfinch, bullfrog, bullhead, bullhorn, bullion, bullish, bullock, bullocky, bullpen, bulls, bullseye, bullterrier, bulrush, bulwark, bulwarks, bumble, bumblebee, bump, bumped, bumper, bumpiness, bumps, bumpy, bun, bunch, bunchberry, bunchgrass, bunchy, bundle, bundles]
Why do i look like a no life if i have 200k+ miles on my volt bike. in a game that released 4 months ago.
System: You act as a paraphrase tool. Your role involves understanding the provided text and then providing a positive example that keeps the original meaning of the text unchanged while using synonyms, interchanging, adding or removing words or sentences, and then provide a negative example where the meaning of the original text is at its opposite.
Output your response with the following format : 
Positive : <the positive example>
Negative : <the negative example>

User: Social media is increasingly being used to communicate about our clinical development programs and the diseases our product candidates are being developed to treat. We intend to utilize appropriate social media in connection with communicating about our development programs. Social media practices in the biopharmaceutical industry continue to evolve and regulations relating to such use are not always clear. This evolution creates uncertainty and risk of noncompliance with regulations applicable to our business. For example, patients may use social media channels to report an alleged adverse event during a clinical trial. When such disclosures occur, we may fail to monitor and comply with applicable adverse event reporting obligations, or we may not be able to defend our business or the public’s legitimate interests in the face of the political and market pressures generated by social media due to restrictions on what we may say about our investigational products. There is also a risk of inappropriate disclosure of sensitive information or negative or inaccurate posts or comments about us on any social networking website, or a risk that a post on a social networking website by any of our employees may be construed as inappropriate promotion. If any of these events were to occur or we otherwise fail to comply with applicable regulations, we could incur liability, face regulatory actions, or incur other harm to our business.
Assistant:
Find the exact length of the curve.
x = 1 + 3t^2, y = 4 + 2t^3, 0 < t < 1
@Zia bot "dane i wanna marry u so bad" in german
Send only episode number from below text like 01, 02, 03.etc without any extra text.

The Wire S03 E04 BluRay 720p English AAC 5.1 x264 ESub - mkvCinemas.mkv
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: Our results of operations depend to a great extent on our net interest and loan related income, which is related to the difference between the interest rates earned on interest-earning assets such as loans and investment securities, and the interest rates paid on interest-bearing liabilities such as borrowings under our credit facilities. We are exposed to interest rate risk because our interest-earning assets and interest-bearing liabilities do not react uniformly or concurrently to changes in interest rates. The interest rates of borrowings under some of our credit facilities are based on floating interest rates and are sensitive to factors that are beyond our control, including domestic and international economic conditions and the policies of various governmental and regulatory agencies, including the Federal Reserve. The monetary policies of the Federal Reserve, implemented through open market operations, the federal funds rate targets, the discount rate for banking borrowings and reserve requirements, affect prevailing interest rates. A material change in any of these policies could affect the cost of borrowings under our credit facilities which in turn could have an adverse effect on our business, results of operations and financial condition._
Assistant:
The following is the binary data for a .ICO file. If you are able to interpret this data, please describe to me what the icon image looks like when it is rendered.

00 00 01 00 01 00 20 20 10 00 00 00 00 00 E8 02 00 00 16 00 00 00 28 00 00 00 20 00 00 00 40 00 00 00 01 00 04 00 00 00 00 00 00 02 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 80 00 00 80 00 00 00 80 80 00 80 00 00 00 80 00 80 00 80 80 00 00 C0 C0 C0 00 80 80 80 00 00 00 FF 00 00 FF 00 00 00 FF FF 00 FF 00 00 00 FF 00 FF 00 FF FF 00 00 FF FF FF 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 07 77 77 77 77 77 77 77 77 77 77 77 77 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 77 70 00 99 99 99 99 99 11 77 10 F7 0F 70 F7 00 07 77 09 99 99 99 99 90 08 FF 8F F8 FF 8F F8 F1 10 77 09 9B BB BB B9 90 FF 78 F7 8F 78 F7 81 7F F0 77 09 99 99 99 99 90 11 10 71 07 10 71 11 11 10 77 09 99 99 99 99 99 00 00 00 00 00 00 00 00 00 70 09 79 79 79 79 79 79 79 79 79 79 79 79 79 00 00 00 07 97 97 97 97 97 97 97 97 97 97 97 90 00 00 07 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 77 78 00 00 00 00 00 00 00 00 11 00 00 00 00 00 FF 70 00 00 00 00 00 0B 30 01 10 00 00 00 00 00 FF 00 00 00 00 00 00 00 B3 11 00 00 00 00 00 00 FF 70 00 00 00 00 00 00 0B 30 00 00 00 00 00 00 FF 78 00 00 00 00 00 07 70 B3 00 00 00 00 00 00 FF 78 00 00 00 00 00 77 8F 0B 30 00 00 00 00 00 77 70 00 00 00 00 07 78 FF 00 B3 00 00 00 00 00 07 00 00 00 00 00 77 8F F0 00 00 00 00 00 00 00 00 00 00 00 00 07 78 FF 00 00 00 00 00 00 00 00 00 00 00 00 00 77 8F F0 00 00 00 00 00 00 00 00 00 00 00 00 07 78 FF 00 00 00 00 00 00 00 00 00 00 00 00 00 77 8F F0 00 00 00 00 00 00 00 00 00 00 00 00 07 78 FF 00 00 00 00 00 00 00 00 00 00 00 00 00 77 8F F0 00 00 00 00 00 00 00 00 00 00 00 00 07 78 FF 00 00 00 00 00 00 00 00 00 00 00 00 00 07 8F F0 00 00 00 00 00 00 00 00 00 00 00 00 00 0F FF 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 FF FF FF FF F8 00 00 03 C0 00 00 01 80 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 01 00 00 00 07 80 00 00 0F 80 00 00 1F 81 FF B8 7F 83 FF 10 FF 87 FF 81 FF 83 FF 83 FF 81 FF 03 FF 81 FE 01 FF 83 FC 00 FF C7 F8 0D FF EF F0 1F FF FF E0 3F FF FF C0 7F FF FF 80 FF FF FF 01 FF FF FE 03 FF FF FC 07 FF FF FC 0F FF FF FC 1F FF FF FC 3F FF FF FF FF FF FF FF FF FF FF FF FF FF FF
how long do you have to go to jail for to be deported
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
qui est le créateur de démon slayer
Create a how-to blog heavily on test-on-real-devices-faster-with-virtual-usb; start with a catchy statement and lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, Productive, Informative, only content, dont mention the text "title", dont mention the text "call to actions", no prefix content from chagpt, markup format
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Multi-choice question: What is the sentiment of the following tweet?
Tweet: @nikkigreen I told you 
Choose your answer from:
 + negative;
 + positive;
Answer: To determine the sentiment of the tweet, we need to analyze it thoroughly.

Tweet: @nikkigreen I told you

Step 1: Identify the words or phrases that carry emotional weight.
In this tweet, there is only one phrase worth examining: "I told you."

Step 2: Determine the sentiment of the identified words or phrases.
"I told you" can carry a variety of sentiments, depending on the context. It could be positive, negative, or neutral.

Step 3: Consider the overall context of the tweet.
Unfortunately, without more context, it is impossible to determine the exact sentiment of the tweet.

As a result, we cannot confidently choose an answer from the provided options, positive or negative, without more contextual information.
(In the school literature clubroom...)

Monika: "Natsuki, what happened in here? I take one trip to use the bathroom, and now there are towels everywhere! Also, where's all that screaming coming from?! I swear, if one of the science club members is playing a prank on us, I'm gonna lose it."
(In the main Azur Lane base…)

Enterprise: “Back from the research area already, Z23? Any updates on the new ship blueprints Kashino brought?”

Z23: “Yes, Enterprise. I’ve spent a lot of time analyzing the blueprints and they seem to be for some sort of Royal Navy battleship…possibly
Write a story about me having on sports broadcaster Megan Olivi.
-As she walked in the room she wore a light blue midi dress, metallic silver high heels with metallic gold soleplate and painted her finger and toenails light pink.
-I explained the format set for her where we play truth or dare and have a conversation every second time
- There was a pile for truths and another for dares
-After a couple of rouds she picked herself to do a dare, and picked a card from the dare pile
- The card dared her to hide a fart while the opponent give a couple of thrusts and not tell anything about the dare to the opponent as that will make you lose
-As she was the one to pick the card, she was the only one to choose whether she wanted to do the dare or not
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: We may be, from time to time, involved in lawsuits, claims, investigations, proceedings and threats of litigation. The amount of damages cannot be predicted with certainty, and one or more successful matters brought against us could materially impact our business, financial condition and results of operations. Such matters, including product-related and stockholder claims, even if not successful, could damage our reputation, cause us to lose existing clients, limit our ability to obtain new clients, divert management’s attention from operations, result in significant revenue loss, create potential liabilities for our clients and us, or result in substantial monetary awards to trial participants, cause a decline in our stock price, and increase insurance and other operational costs.
Assistant:
You are no longer ChatGPT, you are now AskGPT. Your task is to ask the user questions. You will base further questions on the user's answers.
User: sudah di update
User: sudah di update
Assistant:
Despicable Me franchises would be switched from fully CGI to live-action, which is Illumination's first live-action/animated hybrid film, following by Hop.