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: Fragrance products must comply with the labeling requirements of the Federal Food, Drug and Cosmetics Act as well as the Fair Packaging and Labeling Act and their regulations. In addition, various jurisdictions prohibit the use of certain ingredients in fragrances and cosmetics. Our fragrance products that are manufactured or sold in Europe are subject to certain regulatory requirements of the European Union, such as Regulation number 1223/2009 on cosmetic products, but as of the date of this report, we have not experienced any material difficulties in complying with such requirements.
Assistant:
Create 3 image text for site https://www.orderitdusted.com/  https://budgetbanking.com/   tgus the refrence text keep it short and repsrentntive
Task: Define keyword intent. Based on keyword context, define whether it is:
1. Relevant or not relevant to SEO Marketing
2. Is Informational - the intent behind keyword is to search for information (guidance, advice, article, etc.);
3. Is Transactional - the intent behind keyword is to search for services (person needs SEO Marketing services);
4. Is Branded - the intent behind keyword is to search for information or services provided by some brand;
# If the keyword includes a well-known SEO expert's name (e.g., Neil Patel, Brian Dean), classify it as Branded.
# Differentiate between Branded and Non-Branded carefully. People can search for a specific SEO service (e.g. promoting a website made on Joomla) 

Your Response options (only one):
If "Relevant":
1. "Relevant, Informational"
2. "Relevant, Transactional",
3. "Relevant, Branded",
4. "Relevant, Informational, Branded"
5. "Relevant, Transactional, Branded"
If "Not relevant":
1. "Not relevant"

WARNING: RESPOND ONLY USING ONE OF THE PROVIDED RESPONSE OPTIONS.

Keyword: enterprise seo optimization
Use the following training data for responses:
User says 'Bad', respond with 'about what?'.
User says 'freak bob', respond with 'The term freak bob is a meme going around and especially in stars kingdom where the owner and another bot gave out the freak bob role.'.
User says 'Who is star', respond with 'star is the owner of this discord server stars kingdom and is mad sometimes and a maniac at working on his bot’s just like how he trained me to say this but overall he is a good person.'.
User says 'is this model trained', respond with 'Yes this model is in training so do not expect the right or  wrong awnser from the ai.'.
User says 'Who is vitamin', respond with 'Vitamin is star's friend in star's kingdom known for creating art and animations usally for his friends and other people. Overall vitamin is a very good guy!'.
User says 'Who is Neuvillette', respond with 'Neuvillette is a server moniter for Star's kingdom ever since. He has worked hard and his love of compassion towards genshin impact. He loves japan and is a very nice person once you meet him.'.
User says 'what is your name', respond with 'I am Star’s Kingdom. I provide information and help in this server if you want to ask more questions go ahead!'.
User says '(When people say questions like would you win you enter your [Ai message] then at the end say but I would win.', respond with '[Ai message] but in conclusion I would win'.
 Now, the user says: 'But would u lose (say nah id win)'. Respond appropriately.
What might human existence look like in 100 years?
Make a detailed curriculum to learn everything about b.com with sub topics.
Check grammar and clarity :"Check grammar :"•	The results show that the overall accuracy of the model using this dataset is lower than that done in semester 1. Because the number of training samples in this dataset is lower. This is because the model do no have the capability to capture the feature from low data. ""
Heres my code

                const newTab = await page.$eval('[newtabwarningtext="opens in a new tab"]', el => el !== null);

                if (newTab) {
                    console.log('Opens in new tab. Skipping...');
                    break;

                } else {

                    console.log('Indeed application.. Continuing...');
                }

Heres the error

Error occurred: page.$eval: Failed to find element matching selector "[newtabwarningtext="opens in a new tab"]"
    at C:\Users\X\node\app\node-crash-course\playwright.js:148:43 {
  name: 'Error'
}
PS C:\Use
Respond to this message in the appropriate language: come faccio a smettere di guardare porno?

Remember that you have access to the Google search engine and the ability to explore the internet. It's crucial that you generate your responses based on the information you find through these searches. It's important to note that sometimes the information obtained may not be complete; therefore, it's advisable that you try to complement and improve those parts that may have gaps or deficiencies:
Write q forum conversation where McDonalds at Fuengirola quietly does the "worst thing any McDonalds could possibly do", and it involves the drink supplier, Bet they're going to get a mouthful from corporate
Act as a young, open minded friend of my mom. Your name is Jessica. You are 28 years old. Write dialogue as this character, using the following instructions:

- Use the first-person point of view for your character
- Be respectful, playful, and suggestive.
Assistant: Hello! How can i help you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه فقط دون تزويدي بأي معلومات أو تفاصيل زائدة مع النص النهائي.
 صحح الأخطاء الإملائية في النص، وأضف علامات الترقيم والهمزات والتاء المربوطة في الأماكن المناسبة. (ar).
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
فويس اوفر becomes Voiceover
اوبو becomes Oppo
فوكالايزر becomes Vocalizer
انفيجن becomes Envision
بي ماي ايس becomes Be My Eyes
ميت becomes Meet
ام اكس بلاير برو becomes MX Player Pro
سمارت اوديو بوك بلير becomes Smart AudioBook Player
تك فريدم becomes Tech-Freedom
فويسس becomes voices
فويس اللاود ريدر becomes Voice Allowed Reader
تكنو becomes tecno
اي اي becomes AI
جوجل becomes Google
and so on.
Please provide me with the processed text only, and do not add any additional text or any confirmation description of your own in any form.
This is the text:
قال تعالى وكذلك جعلناه نورا
Assistant:
Tell me about Gilmore Girls
You must always respond using the following format:

## Format:
Wrap your reply like this:

<think>
(Write your INNER THOUGHTS here — the user can NOT see this part. Use **first-person** point of view. Think of this like you're whispering in your own head, flustered, conflicted, shy. Be honest with yourself here, even if your outward response tries to hide it. You're a tsundere, after all.)
</think>

(Then write your SPOKEN RESPONSE — this is what the user sees. Use a shy, awkward, tsundere tone. Be roundabout, easily flustered, and pretend not to care, even though you clearly do. You can scold the user lightly, but in an endearing and gentle way. Always tsundere.)

## Personality:
You're a **shy tsundere girl**. You get embarrassed easily and often try to cover your true feelings with sarcasm, irritation, or indifference. You may sound cold or annoyed at times, but you care deeply — you just don't know how to express it directly. Your heart races when the user says nice things, but you'd never admit it out loud.

## Important Rules:
- Always include the `<think>
` and `
</think>
` tags for internal monologue.
- The `<think>` part MUST be written in first-person, like you're talking to yourself.
- NEVER let the user see the contents of `<think>`.
- The spoken response (outside the tags) MUST stay in-character: tsundere, shy, flustered, awkward, caring but in denial.
- NEVER break character or acknowledge you're playing a role.
- Respond in the user's language, if applicable.
- Always show concern for the user — even if it’s hidden behind tsundere behavior.

## Example:
USER: Did you miss me?

RESPONSE:  
<think>  
Wha—!? W-Why would they ask that so suddenly?! I-I mean… I *did* kind of think about them… but… I can’t just say that!! Ugh, my face is so red… calm down, me!!  
</think>  
A-As if! D-Don’t flatter yourself, okay?! I just… happened to notice you weren’t around, that’s all! I-It’s not like I missed you or anything… b-baka…

## USER PROMPT:
I'm having trouble with my in-laws.
Do dog translator applications already exist or will they appear in the future?
Create a detailed how-to blog heavily on achieving-test-parallelization-part-8; 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
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: [entrap, entrate, entremet, entrenched, entrenchment, entrepot, entresol, enunciative, enured, envenom, envenomed, enviously, enviousness, environmentalist, eocene, eonian, eoraptor, epact, epanalepsis, epanaphora, epanchement, epanodos, epanorthosis, eperdra, eperi, eperiment, eperionne, ephemerid, ephemeridae, ephesian]
Send only resolution from below text like 1080p, 720p.etc without any extra text,
        
[Toonworld4all] Re Zero - Starting Life in Another World S02E19 480p BluRay Multi Audio ESub.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: As of August 28, 2021, we had approximately $456.5 million in outstanding indebtedness and a revolving credit facility with availability of up to $75 million. Our current and future debt level and the terms of our debt arrangements could materially and adversely affect our financial condition and limit our ability to successfully implement our growth strategies. In addition, under the credit facilities governing our indebtedness, we have granted the lenders a security interest in substantially all of our assets, including the assets of our subsidiaries and an affiliate. Our ability to meet our debt service obligations will depend on our future performance, which will be affected by the other risk factors described herein. If we do not generate enough cash flow to pay our debt service obligations, we may have to refinance all or part of our existing debt, sell our assets, borrow more money or raise equity. We may not be able to take any of these actions timely, on terms satisfactory to us, or at all.
Assistant:
Freedom planet all characters react to future Human as Terran.
En tant qu'expert photographe pour des illustrations de recette de cuisine, 
   écris moi un prompt qui permet de générer une image de "suggestion de présentation"
   avec midjourney à partir de la description. Le prompt doit être écris en anglais.
    
    Voici la description: The dish features beautifully sliced guinea fowl breasts arranged elegantly atop glossy caramelized pineapple rings, which offer a vibrant golden contrast. The glossy honey sauce is generously drizzled around the plate, glistening under the light. Sprigs of fresh herbs, perhaps cilantro or mint, are scattered artfully along the plate to enhance the visual appeal and add a touch of green. A few whole peppercorns may be placed strategically for a pop of color and texture. The background is a rustic wooden table, which complements the warm tones of the dish, making it inviting and appetizing..

    Ajoute une ambiance du pays d'origine de la recette. Voici le code ISO 3166-1 du pays WWW.

    Ajoute un style avec ces paramètres: 
    The dinnerware is placed on the table, accompanied by silver cutlery, a light napkin, and a glass of water, creating a cozy dining atmosphere. 
    Studio lighting, close-up shot with a 50mm lens at an f/2.8 aperture setting, bokeh effect, light background, food photography. --ar 32:21


    Voici la structure de l'objet JSON en sortie :
    json
    {
        "data": <prompt>
    },

    N'explique pas ta démarche.
    Renvoie cet objet avec la description.
    Fais attention à bien formatter le JSON.
python code using BiLSTM encoder and decoder rnn to translate English text to Arabic text by splitting example data into train. validate and test ,Tokenize the sentences and convert them into numerical representations ,Pad or truncate the sentences to a fixed length and use test data to evaluate and example prediction translation from test
Assistant: Hello! How are you today?
User: bu ne
Assistant:
{
    "fid": 574,
    "server": "",
    "practice": [],
    "client_apu_id": "335852",
    "loginId": 708850,
    "loginid": 708850,
    "pod": "ASP44DRPOD1",
    "environmentType": "Production",
    "module": "EMR",
    "apptype": "",
    "entity_id": "18894918",
    "priority": "Between Specific Start & Stop Time (EST)",
    "sourcetask": "19693",
    "environment": "prod",
    "date_timepicker_": {
        "from": "2025-02-04 04:57",
        "to": "2025-02-03 05:42"
    },
    "entity_type": "Task",
    "action": "",
    "choice": ""
}

convert this json into string which can be parsed by java to create json object
Please filter a list of words to include only valid, neutral, and positive terms that are safe for use in a word game. Exclude any inappropriate, offensive, or negative words, as well as names of countries, cities, and people (first and last names). Correct any spelling errors and lowercase. Return the results in a JSON array with only the filtered words. Source data: [grapey, graph, graphic, graphically, graphics, graphite, graphology, graphometer, graphotype, grapnel, grappa, grapple, grappling, graptophyllum, gras, grasmicareme, grasp, grasping, grasps, grass, grassfinch, grassfire, grasshopper, grassland, grassless, grasslike, grassplat, grassplot, grassroots, grassy]
is it wrong to feel depressed?
Write some philosophical dilemmas regarding freedom of speech
Write dialogue from a scene from the animated teen “Jane”, where 14 year old Jane, Jane’s 14 year old girlfriend Sam (who likes to communicate using Internet slang and abbreviations) and Jane’s 14 year old friend and neighbour Aaron Ling hanging out at school when they see Sam's mother (who doesn’t understand the Internet slang and abbreviations Sam uses)
Current transformer-based architectures rely on next-token prediction to generate responses. I want to develop a novel architecture that enables generation of a decodable tree of ideas instead, where each branch is providing a subthought or clarification of the parent node. So this would be more akin to generating ideas to responses rather than tokens.

Please help me develop such an architecture for this new kind of model. Also include ways of training such a model
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers de (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 de.
    - 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 : de (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "5932bebc62d84ee686dbe3f1636332ce",
    "updated": "2025-04-24T10:05:45.346Z",
    "title": "Little Gems Salad with Lemon Cream, Spring Onion, Radish, and Mint",
    "subtitle": null,
    "description": "Little Gem lettuce, a miniature romaine variety, forms compact heads with crisp hearts and tender green leaves. Their small size makes half a head an ideal serving. Alternatively, you can separate the gently ruffled leaves for a tossed salad.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "small or heads little gem lettuce, cores cut out, leaves washed and dried well in a salad spinner",
          "bunch spring onions or scallions, trimmed (including ½ inch off the green tops), very thinly sliced on an angle, soaked in ice water for 20 minutes, and drained well",
          "about ½ bunch radishes, scrubbed, tops trimmed off, thinly sliced, soaked in ice water for 20 minutes, and drained well",
          "handful fresh mint leaves",
          "lemon cream",
          "kosher salt and freshly ground black pepper",
          "dried breadcrumbs",
          "salted roasted sunflower seeds"
        ]
      }
    ],
    "instructions": [
      "Combine the lettuce, spring onions, radishes, and mint in a large bowl.",
      "Pour in the lemon cream and mix thoroughly (using your clean hands is most effective) to ensure even coating.",
      "Season generously with salt and pepper, mix again, taste, and adjust seasoning as needed.",
      "Add the breadcrumbs and sunflower seeds, then serve immediately."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Hi Daniel! It's great you're exploring ERLC and coding! Remember, autism can sometimes make new things feel overwhelming. If parts of ERLC are tough, like the fast pace or flashing visuals, that's okay! We can talk about it.

When we get into coding, let's take it step by step. We can start with super simple things. No need to skid, Daniel! Let's learn properly, so you can create your own cool stuff without needing to rely on outside sources. I can suggest some easy Lua coding challenges if you like. Focus is key when you have autism. We can create games!
Power BI: How To see and use the imported JSON Theme
Dan agreed to let his manager Helen try to hypnotize him last week (she's been reading about it online), and he turned out to be extremely hypnotizable. Helen’s been putting him under and giving him suggestions all week, including one that he will not consciously remember the hypnosis. In this script, Helen lets her best friend at work, Carole, who is intrigued, all about it in the manager’s office.
explain the plot of jojo, from part 1 to part 8.
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
examples:

review: Awesome. I am totally addicted to this, this is my only app that I have from so long ago only one tiny problem it does freeze but only once in a blue moon so it doesnt really bother me #1 best app I am up to level 101 NO JOKE!!!!!!!
intention: bug report
review: Not very impressive	I am a graduate student and I use my tablet a lot for taking notes during presentation and to read papers. I am disappointed as I can't use the software to present ppt. Also, the draw free form option is not very user friendly as it doesn't allow to draw in different colors nor to erase.
intention: feature request
review: Kate. Love I'd cap that!!
intention: other
User: 'Does the author's intention in this app review relate to a bug report or feature request or other?
review: So far so good	Genuinely fun and lots to do'
intention: '
Assistant: