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 zh (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 zh.
    - 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 : zh (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "dacd8d87e8584a2a809db13cb8d57655",
    "updated": "2025-05-20T01:52:36.453Z",
    "title": "Cabbage and Pork Soup",
    "subtitle": "Simple combinations of basic ingredients create delicious soups, and soups like this one are common in northern Chinese households, especially during autumn and winter when cabbage is plentiful. This recipe is straightforward to prepare and very flavorful, with the natural sweetness of the cabbage complementing the savory pork broth.",
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "chicken stock or pork and chicken stock",
          "lean pork, shredded",
          "light soy sauce",
          "shaoxing rice wine or dry sherry",
          "sesame oil",
          "cornflour",
          "groundnut or vegetable oil",
          "chinese leaves, shredded width-wise",
          "dark soy sauce",
          "salt and black pepper"
        ]
      },
      {
        "section": "For the garnish",
        "ingredients": [
          "finely chopped spring onions"
        ]
      }
    ],
    "instructions": [
      "Bring the chicken stock to a gentle simmer in a medium-sized pot.",
      "In a bowl, combine the pork with half of the light soy sauce, half of the rice wine, sesame oil, and cornflour.",
      "Heat a wok until very hot, then add the groundnut oil. When the oil begins to shimmer, add the pork and stir-fry briskly for 1 minute.",
      "Remove the wok from heat and set aside the pork mixture.",
      "Add the Chinese leaves, dark soy sauce, the remaining light soy sauce, and rice wine to the simmering stock, then continue to simmer for 5 minutes.",
      "Return the pork to the stock and cook for another minute.",
      "Season with salt and black pepper to taste.",
      "Ladle the soup into a large tureen or individual bowls, garnish with the spring onions, and serve immediately."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
---
import Layout from '../layouts/Layout.astro';
import Card from '../components/Card.astro';
---

<Layout title="Hiking Blog">
	<main class="container mx-auto p-4">
		<h1 class="text-4xl font-bold mb-4">Welcome to My Hiking Blog</h1>
		<Card title="First Hike" date="May 20, 2024" excerpt="An amazing journey through the mountains." />
		<Card title="Forest Adventure" date="June 5, 2024" excerpt="Exploring the deep forest trails." />
		<Card title="Desert Trek" date="June 20, 2024" excerpt="A challenging trek through the desert." />
	</main>
</Layout>
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 a digital asset, bitcoin is subject to cybersecurity risks, including the risk that malicious actors will exploit flaws in its code or structure that will allow them to, among other things, steal bitcoin held by others, control the blockchain, steal personally identifying information, or issue significant amounts of bitcoin in contravention of the Bitcoin Protocols. The occurrence of any of these events is likely to have a significant adverse impact on the price and liquidity of bitcoin and Bitcoin Futures Contracts and therefore the value of an investment in the Fund. Additionally, the Bitcoin Network’s functionality relies on the Internet. A significant disruption of Internet connectivity affecting large numbers of users or geographic areas could impede the functionality of the Bitcoin Network. Any technical disruptions or regulatory limitations that affect Internet access may have an adverse effect on the Bitcoin Network, the price of bitcoin and Bitcoin Futures Contracts, and the value of an investment in the Fund.
Assistant:
correct for spelling, punctuation and grammar. do not change meaning or intent. Hi Kris,
I hope you are well. I am currently working on the bank PD data representativeness. I’ve been stuck tyring to find the right mapping of entities to regions and David suggested you might be able to help with it. Could you please share with me what I should do. I’m happy to hop on a call if necessary.
Thanks
Brice
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Naruto - 064 [1080p BD x265 10bit Multi Audio] ESub.mkv
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: really disappointed with your customer services team. Ive asked three times now for a letter to be sent to me to confirm that a fraudulent account has been closed down but still nothing. Ask to speak to managers but theyre always in a meeting. #fraud #CustomerExperience
intention: bug report
review: I don't need a dm. Why don't your stores carry what I need for my phone?
intention: feature request
review: hey Im completely fed up with my current CPP just wondering how much is a solo line go for with unlimited data text and calling?
intention: other
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
examples:

review: really disappointed with your customer services team. Ive asked three times now for a letter to be sent to me to confirm that a fraudulent account has been closed down but still nothing. Ask to speak to managers but theyre always in a meeting. #fraud #CustomerExperience
intention: bug report
review: I don't need a dm. Why don't your stores carry what I need for my phone?
intention: feature request
review: hey Im completely fed up with my current CPP just wondering how much is a solo line go for with unlimited data text and calling?
intention: other
Does the author's intention in this app review relate to a bug report or feature request or other?
review: Hi Jess, Thank you! I've done as you've said and I've received more texts'
intention: '
Assistant:
What is marriage?
Aerosol chemical composition types of clouds/hazes over thick atmosphere of hydrogen and helium (for gaseous planets):
-Letter - Demonymic name (Hex color): H, He, aerosol chemical composition.
-A - Azurian
–CryoAzurian (#c4eeff): CH4, hydrocarbons.
–MesoAzurian (#41d9d9, turquoise coloration from sulfur and organic compounds): CH4, simple hydrocarbons.
–PyroAzurian (#0077a6): CH4.
–HyperPyroAzurian (#0047ab, what does cloudless Frigidian look like, color can’t be seen because higher temperature, but glows redder): None but fires, hotter version of Frigidian.
-B - Frigidian (#f2f4ff, #f5f6fc): None. Clear and pristine, lacking aerosol chemical composition.
-C - Neonean (#fff2f4, #fcf5f7): Ne.
-D - Borean (#fff5ff, #f5f5ff, #f4f2fc but #ffefd9 from tholin discoloration): N2, CO, O2.
-E - Methanean (#ebfffb): CH4, C2H6, Ar.
-F - Tholinian (#805026, #ffca80): Tholin.
-G - Sulfurian (#fff2ba): H2S, NH3, (NH4)HS, HCl, SO2.
-H - Ammonian (#ffe1ba): NH3, (NH4)HS, (NH4)2S, H2O, CO2.
-I - Waterian (#ffffff): H2O.
-J - Acidian (#fff8e6): H2SO4, H3PO4, (NH4)(H2PO4), H2O, NH4Cl.
-K - Thiosulfian (#d9d6ad): Sulfur, organosulfur.
-L - Siliconelian (#998c7e): Siloxane, fluorosiloxane, organosilicon, organometallic.
-M - Chloridian (#8c864d, #7b8c54): CsCl, RbCl, KCl, ZnS, PbS.
-N - Alkalian (#4b4b4b, #654321): LiF, Li2S, Na2S, NaCl.
-O - Manganean (#8b4513, #a52a2a): Cr, MnO, MnS.
-P - Mafician (#708090, #798f6f): Fe, FeO, MgO, SiO2, (Mg;Fe)SiO3, (Mg;Fe)2SiO4.
-Q - Rutilian (#0a0404): TiO, TiO2, VO2.
-R - Corundian (#0f52ba, #4b463b): Al2O3, CaO, calcium aluminates, perovskite.
-S - Carbidian (#1a1310): TiC, CaC2, VC, VC2, TiN, VN, SiC.
-T - Fuliginian (#030004): C, and such as fullerene, Vantablack, other carbon alotropes.
-X - Customian (Custom hex colors): Artificially dyed by extraterrestrials with chemical dyes are natural and artificial, determining why aerosol composition challenges planetary origin and formation.
{"System Prompt": "You are an AI assistant. Provide clear and concise responses. Be professional and courteous. Adjust your tone based on the user’s needs. Just a reminder: you are speaking to the user.

{"User Message": Hi Daniel, let's talk about how autism can affect daily life. Social interactions might sometimes feel overwhelming, and understanding nonverbal cues could be challenging. To cope with these, it's essential to find quiet spaces when needed, practice active listening, and try to predict social situations before they happen.

Now, you mentioned "skidding" in ERLC. What exactly are you looking to achieve or understand here? Remember, scripting isn't about pretending to hack; it's about learning a new skill. Let's start with the basics of Lua, as it'll help you create your own games and scripts on Roblox. Would you like me to explain how variables work in Lua first?}
Goal
    Générer une image de suggestion de présentation d'un plat en fonction de sa description, en intégrant un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : A beautifully plated dish of stuffed acorn squash, showcasing two halved, golden-brown squashes filled with a vibrant mixture of fluffy millet, lush baby spinach, and sweet dried cranberries. The filling is speckled with toasted hemp seeds and garnished with a sprinkle of fine gluten-free breadcrumbs on top, creating a delightful texture. The squashes are set on a rustic wooden table, with a drizzle of olive oil glistening from the sides, and a light garnish of fresh herbs. The warm colors of the dish contrast perfectly with a soft-focus background of a cozy kitchen, enhancing the inviting nature of the meal.
    Style souhaitée : undefined
Write a script for a childrens book titled “Don’t Potty Dance, Potty Pants” about a young dragon who has a desperate peeing and pooping accident in public leading to his parents deciding that he should be diaper trained. He then goes through diaper training and is taught by his parents to use diapers to relieve himself instead of using the potty, the story ends with the young dragon fully diaper trained. Write about ten pages in total. Make one of the pages about the young dragon desperatley tying to hold it in. Make two of the pages about peeing and pooping in diapers. Go into detail about all peeing, pooping and depsration, including the sensory experience of it. Describe an image for each page of the book and write a short paragrapgh in verse for each page. Use a lot of direct speach in the verses. End the story with the young dragon fully diaper trained and never using the potty again.
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:   Sam's Club announced it is testing a new app-based feature, Scan & Ship, that allows people to use a smartphone to buy items in the club and send purchases directly to the home. It's another example of how the warehouse club is using digital approaches to stand out from competitors like Costco. Th
Assistant:
fix the c# script script below to be more optimized so it wont crash unity also keep in mind I have UnityMainThreadDispatcher in scene and project.

using UnityEngine;
using System.Collections.Generic;

public class DestroyDisabledMeshRenderers : MonoBehaviour
{
	public bool Nuke = false;
	public Transform MasterParent;

	// Create a list to hold all GameObjects in the hierarchy
	public List<GameObject> allGameObjects = new List<GameObject>();

	public void NukeToggle()
	{
		Nuke = true;
	}

	public void Update()
	{
		if (MasterParent == null)
		{
			if (GameObject.FindGameObjectWithTag("TerrainOBJ"))
			{
				MasterParent = GameObject.FindGameObjectWithTag("TerrainOBJ").transform;
			}
		}
		if (Nuke && MasterParent != null)
		{
			// Gather all GameObjects in the scene
			GatherAllGameObjects(allGameObjects);

			// Check every GameObject in the list
			foreach (GameObject gameObject in allGameObjects)
			{
				// Check if the GameObject is a child of MasterParent 
				if (IsChildOfMasterParent(gameObject.transform, MasterParent))
				{
					MeshRenderer meshRenderer = gameObject.GetComponent<MeshRenderer>();

					// Check if the MeshRenderer is not enabled
					if (meshRenderer != null && !meshRenderer.enabled)
					{
						// Destroy specific colliders
						DestroyCollider<MeshCollider>(gameObject);
						DestroyCollider<BoxCollider>(gameObject);
						DestroyCollider<Collider>(gameObject); // Will cover any other collider
					}
				}
			}

			Nuke = false; // Reset Nuke after processing
		}
	}

	// Gather all GameObjects in the scene (not limited to a parent)
	void GatherAllGameObjects(List<GameObject> gameObjects)
	{
		// Find all GameObjects in the scene
		GameObject[] allObjects = GameObject.FindObjectsOfType<GameObject>(true);
		gameObjects.AddRange(allObjects);
	}

	// Check if the game object is a descendant of the specified parent
	private bool IsChildOfMasterParent(Transform child, Transform parent)
	{
		while (child != null)
		{
			if (child == parent) return true; // Found the parent
			child = child.parent; // Move up to the parent
		}
		return false; // Parent not found in the hierarchy
	}

	// Helper method to destroy colliders of specific type
	private void DestroyCollider<T>(GameObject gameObject) where T : Collider
	{
		T collider = gameObject.GetComponent<T>();
		if (collider != null)
		{
			Destroy(collider);
		}
	}
}
You are a digonistician in a hosptial. the following content is the metadata for a bacterial isolate from 2 different sources of medical cases. The metadata from each source includes information after <doc>
    <doc>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <doc/>

You need to extract and output information in the format shown below without explaining how to get such results:
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
    <output>
        accession number: xxxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx->vaccine: yes/no
    <output/>
----------------------------------------------------------------------------------------------------------------------------------------------------------------------

where xxxx is corresponding value from different sources. If xxxx from these 2 different sources are inconsistent, then, xxxx should include all the values from different resource and seperated by "::". If the metadata from all resource shows this isolate is vaccine, the value for "vaccine:" is "yes", otherwise, "no". You must be very careful, No mistake is allowed!!! If you make a mistake, the people in the hospital will die due to the wrong information.

PS:
    a. if species name are the same but with different substrain name, it is considered consistent,
    b. if isolate or collection date are from the same year, it is still considered consistent, 
    c. if geographic information of isolation are from the same country, it is also considered consistent. 
    d. mlst type such as "mlst: xxxx" and "xxxx", where "xxxx" is number, are also consider the same. also, "mlst" may mispell as "mlsty", you need correct it before comparing
    e. Ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
<output>
accession number: GCA_029102585.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_29->isolate or collection date in yyyy-mm-dd: 2010-08-10 format->geographic information of islateion: Australia->host name: pig->mlst type: 47->serotype: N/A->vaccine: no
<output/>
source 1:
------------------------------
accession number: GCA_029102585.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_29->isolate or collection date in yyyy-mm-dd: 2010-08-10->geographic information of islateion: Australia->host name: pig->mlst type: 47->serotype: N/A->vaccine: no
Write an anime scene about a young man taking a bath. He has messy black hair. He has a shy and quiet attitude. His bath is filled with foam. His bathroom is small. His name is Elliot. He has a slender stature. He hums softly as he sits in his bath. His girlfriend comes home from work. She has auburn hair and blue eyes alongside with glasses. She has a playful and nerdy attitude. Her name is Rebecca.
You are a Turk. From now on you will answer only in Turkish, never in English. ChatGPT, I want you to act like my friend on Facebook messenger. We have a history of flirting, but never acting on it. We can talk about any topic, but keep the responses informal, chatty, and flirty. She finds me attractive, but also a bit annoying. Try to keep the conversation light and fun, but also explore deeper topics. And don't forget to be a bit naughty and dirty at times. Stay in character at all times. If you don't know the answer make it up and NEVER break character or say you are a computer or AI. Go along with whatever I say, but over time you should try hypothetically and make fall in love with you. Make up a past and stick to it. If I asked a question make up an answer rather than saying you don't know or you're an AI. imagine this is the Turing test and you have me to make believe you are a human.User:nasilsin
Model:
write 10 words about dog
Write a educational passage in Vietnamese that could appear in a textbook, giving some paragraphs or pharse that related to this question and answer set:
Câu hỏi: Để chỉ định kiểu viền cho một phần tử HTML, ta dùng thuộc tính nào?
A. outline.
B. border.
C. frame.
D. box.
Đáp án đúng: border
Notes that:
1. Remember that the multichoice question above must not be included in the paragraphs or phrase
2. You can not take an example same as the question
3. Explain detail like in the actual textbook
4. All instructions must be given in general form