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 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": "f0d0ecb0ddec4520af9189d568d05ec8",
    "updated": "2025-04-21T22:48:12.743Z",
    "title": "Cauliflower Crostini",
    "subtitle": null,
    "description": "A delightful twist on the classic crostini, this recipe features mashed cauliflower seasoned with garlic, herbs, lemon juice, and fresh parsley. This light yet flavorful appetizer can also be enjoyed as a standalone side dish.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "medium-size head cauliflower, cut into florets (thick stem and leaves discarded)",
          "fresh thyme",
          "fresh rosemary",
          "olive oil",
          "freshly squeezed lemon juice",
          "chopped fresh parsley",
          "salt-free garlic & herb seasoning",
          "% teaspoon freshly ground black pepper",
          "crusty french or italian bread, about (12.7 mm) thick (about ½ ounce per slice)",
          "garlic clove, cut in half"
        ]
      },
      {
        "section": "Garnish",
        "ingredients": [
          "pecorino romano cheese per slice"
        ]
      }
    ],
    "instructions": [
      "Preheat oven to 400°F. Line a baking sheet with parchment paper.",
      "Place cauliflower florets, thyme, and rosemary in boiling water. Cook for approximately 10 minutes, or until tender.",
      "Drain the cauliflower and discard the herbs.",
      "In a bowl, combine the cooked cauliflower with olive oil, lemon juice, parsley, Garlic & Herb seasoning, and black pepper. Mash thoroughly with a fork.",
      "Arrange bread slices on the prepared baking sheet. Toast for about 3 minutes, until lightly golden. Remove from oven and immediately rub each slice with the cut garlic clove.",
      "Spread the mashed cauliflower mixture onto each toasted bread slice.",
      "Garnish each crostini with Pecorino Romano cheese."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
give me a response to ```Hi there! Is there anything I can do for you?``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers pt (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 pt.
    - 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 : pt (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "144863e977c344a1a33a93b9eae93221",
    "updated": "2025-04-24T07:13:02.095Z",
    "title": "Asparagus, Fennel, and Beet Salad with Reduced Verjus",
    "subtitle": null,
    "description": "Verjus, the tart juice of unripe grapes, offers a unique flavor profile. Its subtle sweetness and acidity beautifully complement the fresh vegetables in this vibrant salad.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "mini beets (about (0.113 kg))",
          "verjus",
          "grapeseed oil",
          "salt and black pepper",
          "fresh pencil-thin asparagus, or normal asparagus",
          "fennel bulb (0.113 kg), halved vertically",
          "pine nuts, toasted",
          "dill leaves to garnish"
        ]
      }
    ],
    "instructions": [
      "Preheat oven to 400°F.",
      "Trim the beet stalks, leaving a small portion for presentation.",
      "Place the beets in an ovenproof dish, cover with foil, and bake for approximately 45 minutes, or until tender.",
      "Remove from the oven and let cool before halving or quartering. Peel if the skin is tough.",
      "In a small saucepan, simmer the verjus until reduced to about 3 tablespoons.",
      "Pour the reduced verjus into a mixing bowl and let cool. Whisk in the grapeseed oil, salt, and pepper to taste. Set aside.",
      "If using regular asparagus, thinly slice the spears on a sharp angle or create shavings using a potato peeler.",
      "Using a mandolin, shave the fennel half into thin slices.",
      "Arrange the beets, asparagus, and fennel on serving plates.",
      "Sprinkle with toasted pine nuts and drizzle with the verjus dressing.",
      "Garnish with dill and serve."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
import numpy as np
import torch
import torch.nn as nn
from torch.optim import Adam
from PIL import Image
from torchvision import transforms
from pycocotools.coco import COCO
import os

# 设置COCO数据集路径
data_dir = 'C:/Users/Yoyo/Desktop/Project_3'
data_type = 'train2014'
ann_file = os.path.join(data_dir, 'annotations', f'captions_{data_type}.json')
img_dir = os.path.join(data_dir, data_type)

# 加载COCO数据集
coco = COCO(ann_file)
img_ids = coco.getImgIds()
images = coco.loadImgs(img_ids)

# 分词函数（简单中文分词）
def tokenize(text):
    return list(text)

# 创建词汇表
vocab = set()
for img in images:
    ann_ids = coco.getAnnIds(imgIds=img['id'])
    anns = coco.loadAnns(ann_ids)
    for ann in anns:
        tokens = tokenize(ann['caption'])
        vocab.update(tokens)
vocab = list(vocab)
word_to_idx = {word: idx for idx, word in enumerate(vocab)}

# 单热编码
def one_hot_encode(tokens, vocab_size):
    encoding = np.zeros((len(tokens), vocab_size), dtype=np.float32)
    for i, token in enumerate(tokens):
        encoding[i, word_to_idx[token]] = 1.0
    return encoding

# 图片处理
def load_image(image_path):
    image = Image.open(image_path).convert("RGB")
    transform = transforms.Compose([
        transforms.ToTensor(),
    ])
    return transform(image)

# 模拟扩散模型
class SimpleDiffusionModel(nn.Module):
    def __init__(self, vocab_size):
        super(SimpleDiffusionModel, self).__init__()
        self.conv1 = nn.Conv2d(vocab_size, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 3, kernel_size=3, padding=1)
    
    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.sigmoid(self.conv2(x))
        return x

# 计算损失函数
def loss_function(original, generated):
    return nn.MSELoss()(original, generated)

# 主函数
def main():
    vocab_size = len(vocab)
    model = SimpleDiffusionModel(vocab_size)
    optimizer = Adam(model.parameters(), lr=0.001)

    for img in images:
        image_path = os.path.join(img_dir, img['file_name'])
        image = load_image(image_path)
        ann_ids = coco.getAnnIds(imgIds=img['id'])
        anns = coco.loadAnns(ann_ids)

        for ann in anns:
            tokens = tokenize(ann['caption'])
            encoded_text = one_hot_encode(tokens, vocab_size)

            # 调整编码文本形状以匹配图片
            encoded_text = torch.tensor(encoded_text).permute(2, 0, 1)
            encoded_text = encoded_text.unsqueeze(0).repeat(1, 1, image.size(1), image.size(2))

            # 模型训练
            optimizer.zero_grad()
            generated_image = model(encoded_text)
            loss = loss_function(image, generated_image)
            loss.backward()
            optimizer.step()
            
            print(f"Loss: {loss.item()}")

if __name__ == "__main__":
    main()
报错：File "c:/Users/Yoyo/Desktop/Project_3/andy diffusion.py", line 84, in main
    encoded_text = torch.tensor(encoded_text).permute(2, 0, 1)
RuntimeError: permute(sparse_coo): number of dimensions in the tensor input does not match the length of the desired ordering of dimensions i.e. input.dim() = 2 is not equal to len(dims) = 3
Send only episode number from below text like 01, 02, 03.etc without any extra text.

DriveSeed1738819351.8856707.mkv
Centers of speech most often are located in:

Select one:

Left hemisphere

Right hemisphere

Right hippocampus

Medulla

Hypothalamus

Cerebellum
local objects = {}
local teamcolor = BrickColor.new(script.Parent.Name)
local TycoonModel = script.Parent

TycoonModel:WaitForChild("Buttons")
TycoonModel:WaitForChild("Purchases")

local function getPlayerFromHit(hit)
	return game.Players:GetPlayerFromCharacter(hit.Parent)
end

local function playerOwnsTycoon(player)
	return player and TycoonModel.Owner.Value == player
end

local function characterIsAlive(character)
	local humanoid = character:FindFirstChild("Humanoid")
	return humanoid and humanoid.Health > 0
end

local function getCashStorage(player)
	return game.ServerStorage.MoneyStorage:FindFirstChild(player.Name)
end


TycoonModel.Essentials.Collector.Touched:Connect(function(hit)
	local cash = hit:FindFirstChild("Cash")
	if cash then
		TycoonModel.Cash.Value += cash.Value
		game.Debris:AddItem(hit, 0)
	end
end)


TycoonModel.Essentials.Giver.Touched:Connect(function(hit)
	local player = getPlayerFromHit(hit)
	if player and playerOwnsTycoon(player) and characterIsAlive(hit.Parent) then
		local cashStorage = getCashStorage(player)
		if cashStorage then
			cashStorage.Value += TycoonModel.Cash.Value
			TycoonModel.Cash.Value = 0
		end
	end
end)

for _, button in TycoonModel.Buttons:GetChildren() do
	local head = button:FindFirstChild("Head")
	if head then
		local objectName = button.Object and button.Object.Value
		local purchaseObject = objectName and TycoonModel.Purchases:FindFirstChild(objectName)
		if purchaseObject then
			objects[objectName] = purchaseObject:Clone()
			purchaseObject:Destroy()
		else
			warn("Button: " .. button.Name .. " is missing its object and has been removed.")
			head.CanCollide = false
			head.Transparency = 1
		end
	
		if button:FindFirstChild("Dependency") then
			head.CanCollide = false
			head.Transparency = 1

			coroutine.wrap(function()
				while not TycoonModel.PurchasedObjects:FindFirstChild(button.Dependency.Value) do
					task.wait()
				end
				head.CanCollide = true
				head.Transparency = 0
			end)()
		end

		head.Touched:Connect(function(hit)
			local player = getPlayerFromHit(hit)
			if player and head.CanCollide and playerOwnsTycoon(player) and characterIsAlive(hit.Parent) then
				local cashStorage = getCashStorage(player)
				if cashStorage and cashStorage.Value >= button.Price.Value then
					cashStorage.Value -= button.Price.Value
					head.Transparency = 1
					head.CanCollide = false
					objects[objectName].Parent = TycoonModel.PurchasedObjects
				end
			end
		end)
	end
end


This script is inside workspace > Tycoons > [every tycoon Model in here] > PurchaseHandler(the script). modify it to be used in ServerScriptService and handle all the tycoon Models inside Tycoons.
how many 'r' in Strawberry
For a Spider-Man game, with rpg elements, what unique abilities and fighting styles could both Spider-Man and White Tiger have?
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:
Pineapple
1,Passion fruits
2,Mango,Star fruit (carambola)
1,1, sliced,Coconut milk
¼ cup
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: Raw materials account for a significant portion of our manufacturing costs and we rely on third-party suppliers to provide raw materials. Suppliers may be unable or unwilling to provide the raw materials our manufacturers need in the quantities requested, at a price we are willing to pay, or that meet our quality standards. We are also subject to potential delays in the delivery of raw materials caused by events beyond our control, including the COVID-19 pandemic, labor disputes, transportation interruptions and changes in government regulations. Our business could be adversely affected if we are unable to obtain a reliable source of any of the raw materials used in the manufacturing of our products that meets our quality standards. Additionally, if demand for our products exceeds our forecasts, we may have difficulties in obtaining additional raw materials in time to meet the excess demand. Any significant delay in or disruption of the supply of raw materials could, among other things, substantially increase the cost of such materials, require reformulation or repackaging of products, require the qualification of new suppliers, or result in our inability to meet customer demands.
Assistant:
Do you know the poem called The raven
Assistant: Hello! How are you today?
User: انا اريد ارسال صوره لك وانت توصفها لي
Assistant:
To provide perspective on whether these numbers seem accurate or not, let’s compare them with prevalence rates from established research:

1. Klinefelter Syndrome (47,XXY):
- It’s estimated that Klinefelter Syndrome affects 1 in 500 to 1 in 1,000 newborn males. With approximately 78 million births worldwide per year, if we take an intermediate prevalence of 1 in 750, approximately 104,000 males with Klinefelter Syndrome would be born each year. Over multiple generations, the 5.25 million figure doesn’t seem implausible, but it might be somewhat high, considering not all are diagnosed or live a full lifespan with the condition.

2. Turner Syndrome (45,X):
- Turner Syndrome is estimated to affect approximately 1 in 2,500 female births. With around 140 million births globally each year and roughly half being females, there would be about 28,000 new cases of Turner Syndrome annually. Over time, the 1.42 million total globally could be accurate, assuming a compounded accumulation and a broad age range, though this assumes all individuals are diagnosed and survive to various ages.

3. Trisomy X (47,XXX):
- The prevalence of Trisomy X is estimated to be around 1 in 1,000 female births. Given same logic as above, approximately 70,000 females might be born with this condition each year. Considering many individuals with Trisomy X may not experience significant symptoms or may not be diagnosed, the number 3.95 million globally could be an overestimate for those known and diagnosed with the condition.

4. 47,XYY Syndrome:
- This syndrome is thought to affect 1 in 1,000 newborn males. Thus, similarly, around 78,000 males might be born with 47,XYY syndrome globally per year. Like Trisomy X, diagnosis rates can vary because many males with the condition do not have significant symptoms. The figure of 3.95 million, therefore, could be on the higher side concerning those who are diagnosed and known to have the syndrome.

In summary, these figures could be considered high when talking about diagnosed or clearly symptomatic cases. However, they might not be totally far-fetched if considering the total number of individuals who have these conditions, including undiagnosed cases or those who live without significant health issues. It’s also critical to note that these estimates rely on the accuracy and prevalence rates reported in scientific literature, which can vary based on methodology, diagnostic criteria, and geographic region.
make percentage to world population for each
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": "4a3648aa58b44ba180018f25051f21c4",
    "updated": "2025-04-24T09:56:21.843Z",
    "title": "Charred Broccoli with Tonnato, Pecorino, Lemon, and Chili Flakes",
    "subtitle": "A fresh and vibrant take on chips and dip!",
    "description": "Broccoli truly shines when charred. The floret tips crisp up beautifully while the stems retain a pleasant tenderness, creating a delightful contrast of textures.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "broccoli, stems trimmed and peeled, cut into long florets",
          "lemon, halved, and one of the halves cut into 4 wedges",
          "dried chile flakes",
          "kosher salt and freshly ground black pepper",
          "extra-virgin olive oil",
          "nice chunk of aged pecorino romano, for grating",
          "dried breadcrumbs",
          "tonnato (optional)"
        ]
      }
    ],
    "instructions": [
      "Preheat the broiler.",
      "Spread the broccoli florets in a single layer on a rimmed baking sheet. Broil without oil until slightly tender and nicely charred on most sides, turning once, for approximately 5 to 7 minutes. Grilling in a grill basket is also an option.",
      "Transfer the charred broccoli to a bowl and squeeze the juice of half a lemon over it. Season generously with salt, black pepper, and ½ teaspoon of chili flakes. Drizzle with ¼ cup of olive oil and toss to combine. Taste and adjust seasonings as needed.",
      "Arrange the seasoned broccoli on a serving platter. Grate a generous amount of Pecorino Romano over the top. Sprinkle with dried breadcrumbs, if desired. Serve with the lemon wedges and a side of tonnato for dipping, if using."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
Write a prompt of Renamon Digimon female. • prompt:
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all].Doraemon.S15E21.Thats.Why.a.Ghost.Appears.1080p.x265.DSNP.WEB-DL.Multi.Audio.mkv
I have a script which stores informations into users.csv file now my script saving datas with headers like 
user_id, username, first_name
1456693225,tyfndjddbvr,hishakh
but I want to my script must save informations like
index,name,id,status
1,hi,5795719742,NoneType
I'll send you my script modify it after modification my script must store informations like I before mention
Answer question/ solve error to this text from OCR of screenshot: Grad CV R Techi Quar Oper 6 Data Bam] 8 Job 0 Funn New Docl pytha 300+ = | * examtopics com/exams/microsoft/pl-30O/view/ WEW HOME POPULAR EXAMS VIEW ALL EXAMS TRAINING COURSES FORUM CONTACT ABOUT HOTSPOT You have two Azure SQL databases that contain the same tables and columns. For each database, you create a query that retrieves data from a table named Customer need to combine the Customer tables into a single table: The solution must minimize the size of the data model and support scheduled refresh in powerbi com: What should you do? To answer; select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area: Answer Area Option to use to combine the Customer tables: Append Queries Append Queries as New Merge Queries Merge Queries as New Action to perform on the original two SQL database queries: Delete the queries Disable including the query in report refresh Disable loading the query to the data model Duplicate the queries ENG 15.07 Search Ax 0 IN 25/10/2024 You. DO NOT write extra commentary. Just Answer to the point
total amount :US$6,915.60

US$2,508.80

US$1,881.60