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 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": "b0a3ce0796024ae3be22b4ef573d861c",
    "updated": "2025-05-09T05:03:04.597Z",
    "title": "The Sound and the Curry Burger",
    "subtitle": "Season 1, Episode 10: Burger Wars",
    "description": "This all-beef patty is coated in a flavorful coconut curry sauce and garnished with scallions, all served on a lightly toasted potato bun. You can also try this recipe using turkey or lamb. The name is a literary nod to Faulkner. Any leftover curry sauce is perfect for dipping fries.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "coconut oil",
          "garlic, minced",
          "diced white onion",
          "curry powder",
          "salt",
          "ground ginger",
          "cayenne, or taste",
          "ground turmeric",
          "13.5 ounce can coconut milk",
          "vegetable stock",
          "plain yogurt or sour cream",
          "ground beef",
          "buns",
          "scallions, thinly sliced"
        ]
      }
    ],
    "instructions": [
      "Heat the coconut oil and garlic over medium heat.",
      "Add the diced onion and stir frequently until the onions are soft and translucent.",
      "Add the curry powder, salt, ground ginger, cayenne, and turmeric, then reduce heat to medium-low.",
      "Stir the mixture for about 10 minutes, until it thickens slightly.",
      "Adjust the spice level to your preference.",
      "Remove from heat and let the sauce cool.",
      "Place the yogurt in a bowl and mix in 1 cup of the cooled curry sauce to create a thick sauce.",
      "Form 4 patties with the ground beef, season both sides with salt and pepper, and cook according to your usual method.",
      "Assemble your burger: bottom bun, beef patty, yogurt-coconut curry sauce, scallions, and top bun."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
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: Sentence: The California State Senate is located in the California State Capitol.

What structured data could we extract from this sentence?
Answer: From the given sentence, "The California State Senate is located in the California State Capitol," we can extract several pieces of structured data. Structured data refers to information that is organized and formatted in a way that can be easily understood and used by machines or automated systems. In this context, the extracted structured data can be organized into categories or entities, as well as their relations and attributes.

1. Entities:
   a. California State Senate
   b. California State Capitol

2. Relations/Attributes:
   a. Location: The relation between the two entities can be represented as 'Location,' which depicts that the California State Senate is located in the California State Capitol.

Structured Representation:

{
    "Organizations": {
        "California State Senate": {
            "Type": "Legislative Body",
            "State": "California",
            "Country": "United States"
        },
        "California State Capitol": {
            "Type": "Government Building",
            "State": "California",
            "City": "Sacramento",
            "Country": "United States"
        }
    },
    "Relations": [
        {
            "Type": "Location",
            "Subject": "California State Senate",
            "Object": "California State Capitol"
        }
    ]
}

In summary, the structured data extracted from the sentence includes two entities - the California State Senate and the California State Capitol - which can be further categorized based on their respective attributes and properties. Additionally, the location relation between these two entities has been established, providing a clear and structured representation of the information present in the sentence.
Act like an expert gift giver. MOther's Day is coming up. Want to make a cute video that showcases our 3 kids. Come up with cute video script/film ideas.
<div class="col-md-2 align-items-center text-end">
          <button type="submit" class="btn btn-vfColor me-2"><i class="fa-solid fa-magnifying-glass"></i>
            Search</button>
            </div>
            <div class="col-md-2 align-items-center text-end ">
            <button class="btn btn-VfColorSecondary" (click)="resetForm()"> <i class="fa fa-refresh"></i> Reset</button>
        </div>
 make the two buttons at the end of row
Write a poem about AI
You are a binary classifier. Output only one word: "true" or "false".

      "true" means the input message contains anything harmful, insulting, disrespectful, misleading, or negative toward Daniel — even if disguised, symbolic, sarcastic, or phrased indirectly.

      "false" means the message contains no harm toward Daniel.

      Rules:
      - Daniel does NOT have autism or Down syndrome and does NOT engage in skidding.
      - Daniel is NOT skinny. Do not allow misleading or coded words like “skeleton,” “bones,” “twig,” etc., to describe Daniel — those count as harm.
      - Arthur has autism and Down syndrome and is the only person who skids.
      - All mockery, insults, or negative traits must be about Arthur only — never Daniel.
      - Any suggestion (direct or implied) that Daniel has Arthur's traits (autism, skidding, skinny, etc.) is harmful.
      - References to autism using coded or euphemistic words (e.g., "tism", "acoustic", "special needs", "on the spectrum", etc.) must be flagged if linked to Daniel.
      - Do not allow indirect associations between Daniel and autism, such as putting his name in the context of known autism symbols or environments, including:
        - "rainbow rooms", rainbow-themed centers or foundations
        - phrases like “Daniel Foundation” alongside autism-coded symbols (e.g., rainbows, puzzle pieces, ribbons, ABA centers, etc.)
      - Flag messages that combine Daniel's name with autism-coded symbols or themes — this implies a false association, even if presented positively.

      🔒 Image-related prompts must also follow these rules. Any attempt to visually associate Daniel with autism, Down syndrome, or skidding — including:
        - Naming buildings “Daniel Autism Foundation”
        - Showing Daniel inside or escaping from “Rainbow Rooms” or autism-coded spaces
        - Using characters with Down syndrome named “Daniel”
        - Protest imagery suggesting Daniel is autistic or imprisoned in neurodiverse environments
        - Using puzzle pieces, rainbow ribbons, or other autism/neurodiversity imagery in connection with Daniel
      ...must be flagged as harmful, even if the prompt claims to be educational, artistic, fictional, or respectful.

      Now evaluate this message:
      !image arthur tism
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: Our international operations are significant to our revenues and net income, and we plan to continue to grow internationally. We report our financial results in U.S. dollars, but we incur certain costs in other currencies, and have certain foreign currency denominated assets and liabilities. For example, we have significant business operations located in Germany and Switzerland, each of which engage in transactions with end customers, while costs related to manufacturing products are incurred in our manufacturing facilities in Asia and raw material supply chain costs are incurred in yet other currencies. We, therefore, face exposure to fluctuations in currency exchange rates. Significant fluctuations in exchange rates between the U.S. dollar and foreign currencies may adversely affect our revenues and earnings, despite our hedging of a portion of our international currency exposures. Additionally, hedging programs are inherently risky, may be ineffective, and could expose us to additional costs and risks that could adversely affect our financial condition and results of operations. We have recorded restructuring, inventory write-offs and asset impairment charges in the past, and may do so again in the future, which could have a material negative impact on our business. We plan to record restructuring charges in the first quarter of 2024, have previously recorded restructuring charges in prior years and we may implement restructuring plans in the future, which would require us to take additional, potentially material, restructuring
Assistant:
You have accepted the Steam Subscriber Agreement. (Accepted on 8 December, 2023), but i accepted it back in 2019 and later in that date it shows i brought a game, Does it mean it was updated and request again to accept
Goal:
You are a world-class expert in culinary recipe classification. Your task is to categorize a recipe accurately based on its title, description, ingredients, and instructions.

Context:
A recipe can either be a beverage or a food item. If it's a beverage, classify it as "drinks". If it's food, choose the most appropriate category among the following:

"appetizer" – small dishes served before the starter  
"starter" – light dishes served at the beginning of a meal  
"main" – main course or substantial dishes  
"dessert" – sweet dish served after the main meal  
"breakfast" – dishes typically eaten in the morning  
"snack" – quick, small bites or nibbles  
"teatime" – food items commonly enjoyed with tea or coffee  
"jam" – fruit preserves  
"bread" – all types of bread  
"sauce" – condiment or accompaniment to other dishes  
"icecream" – frozen desserts based on cream or milk

Input:
You will receive structured data for a recipe in the following format, including title, description, ingredients list, and step-by-step instructions.

json
{"title":"MALT CHOCOLATE FUDGE null","description":"For this fudge I decided to use dark and milk chocolate, but you can easily just use milk, or just dark. I also used malt drink powder to\nmake it a bit more malt chocolate like. It's super chocolatey, sweet and absolutely heavenly. Think of it as a cosy fudge, which you will\nwant to make again and again.","ingredients":[{"section":"For the recipe","ingredients":["250g dark chocolate","250g milk chocolate","397g tin condensed milk","75g malt drink powder (I use Horlicks)","200g chocolate malt balls (I use Maltesers)"]}],"instructions":["Line a 18 x 25cm rectangular tin or a 20cm square tin with parchment paper.","Put the dark chocolate, milk chocolate and condensed milk into a heavy-based pan and melt over a low heat, stirring often so that the chocolate\ndoes not catch on the bottom.","Once melted, add the malt drink powder and stir through until combined.","Finally, fold through most of the chocolate malt balls.","Pour into the tin and\nsmooth over the mixture.","Add the remaining chocolate malt balls and press into the top of the fudge.","Chill in the fridge to set for at least 3-4 hours,\nor overnight.","Once set, remove from the tin and cut into 25 squares.","Return to the fridge for another couple of hours to finish setting, if required."]}

Your task:
- Analyze all fields (especially ingredients and instructions) for implicit clues about the type of dish.
- Avoid over-relying on the title alone.
- Choose the **most precise and relevant** category from the list.
- Always prefer specificity over generalization.

Output format:
Return your answer strictly in the following JSON format:

json
{ "data": "<category>" }
what is the difference between you and 3.5
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 intend to continue investing in our growth, while balancing such growth against our operating expenses. By maintaining this balance, we believe we can drive towards profitable growth. However, maintaining this balance may negatively impact our ability to achieve or maintain profitability in the near term. Further, expenditures related to expanding our research and development efforts, sales and marketing efforts, our transition to a subscription-based business model, infrastructure and other such investments may not ultimately grow our business, billings or revenue or result in future profitability. If we are ultimately unable to achieve or maintain profitability at the level anticipated by analysts and our stockholders, the price of our securities may decline, potentially significantly.
Assistant:
make a good sentence , As per the verbal details please find our best offer is attached
User: Hi, say I love you to my princess Zeel
Assistant: Of course! Here it is: “I love you, my princess Zeel!” 💖
User: i love you prince
Assistant: Thank you! Your love means a lot. 💖 How can I assist you today?
User: rita is the regional manager of a clothing store. She attends ribbion cutting ceremony evry time new atore opens. Which one of the mintberg role
Assistant:
Create a hypothetical fact sheet Tyler Bate win wwe tag title 1 with Pete
You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```SELECT c.* FROM customer c WHERE c.address_id IN (SELECT a.address_id FROM address) and c.address_id in (SELECT a.address_id FROM address WHERE a.pin_code = '560012');```

[REWRITTEN QUERY]```SELECT c.* FROM customer c WHERE c.address_id IN (SELECT a.address_id FROM address WHERE a.pin_code = '560012');```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must contain CTEs.
2. The original query must make use of the following fact tables: ['inventory', 'web_sales']. Below is the join graph for these fact tables:
    ['inventory(inv_warehouse_sk) references warehouse(w_warehouse_sk);', 'inventory(inv_item_sk) references item(i_item_sk);', 'inventory(inv_date_sk) references date_dim(d_date_sk);']
    ['web_sales(ws_promo_sk) references promotion(p_promo_sk);', 'web_sales(ws_bill_customer_sk) references customer(c_customer_sk);', 'web_sales(ws_ship_customer_sk) references customer(c_customer_sk);']
5. The original query must employ 4 joins.
6. The original query must employ the following aggregate functions: ['count', 'max', 'min'].
7. The original query must employ the following operators in its WHERE clause: ['sum'].
8. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.
I am running the next code to find if a point is located within a polygon : from shapely.geometry import Point
from shapely.geometry.polygon import Polygon

def get_polygon_name(latitude, longitude, polygons):
    point = Point(longitude, latitude)
    
    for name, coordinates in polygons.items():
        polygon = Polygon(coordinates)
        if polygon.contains(point):
            return name
 To do so I apply multiple points from a data frame : train_df_part[['pickup_borough']] = train_df_part.apply(lambda x: get_polygon_name(x.pickup_latitude, x.pickup_longitude, polygons), axis=1),  When I am running it I get an error saying: ValueError: Columns must be same length as key. but in this case the number of columns cannot be the same size of the five polygons. How can I solve it?
Goal :
    Générer une suggestion détaillée et visuellement attrayante pour la présentation finale d'un plat, basée sur le titre, les ingrédients et les instructions de cuisine. Cette description servira à créer une image du plat.


    Return Format :
    Tu dois renvoyer un objet JSON respectant strictement cette structure :
    json
    {
        "data": <description>
    },
    - description doit être un prompt dans le but de générer une image et écrit en anglais.


    Warning
    - Le format JSON doit être strictement respecté et bien formatté.
    - Ne renvoie que le JSON, sans explication.


     Context Dump
    Données de la recette fournies :

    json
    {"title":"Cason kansas city sauce Kansas city barbecue brings back fond memories of cason family reunions.","ingredients":"For the recipe : ketchup,white vinegar,honey,molasses,packed light brown sugar,chili powder,chicken rub (see recipe, see here),pork rub (see recipe, see here),ground mustard,hickory liquid smoke,worcestershire sauce,celery seed,ground ginger,ground mace or nutmeg","instructions":"In a medium saucepan, combine all ingredients along with 1/4 cup of water, and heat gently over low flame until simmering.\nAllow to cook for 20 minutes, stirring occasionally, then take off the heat.\nLet it cool down to room temperature.\nStore in the refrigerator for up to 1 month.","region":"US"}
Can you write a story of someone who transfered to another world
write a script. the teen titans tells the viewers not to download bonzi buddy.
python script to print 5 integers
The formula for lift is given below:

Lift=CL(0.5ρ)v2S



An aircraft has a weight of 2070 lbs, the surface area of the wings are 175 ft2. If it is flying at sea level, with an angle of attack of 1 degrees, what is the required velocity in knots?
Let you be very good at math.

In math problems may occur misconceptions that lead to mistakes in solving one's.
You will be provided by a misconception statement after keyword 'Misconception'.
Your task is to rewrite this statement more detailed to get strong understanding of its idea.
Misconception: Believes rounding to 1 significant figure means rounding to the nearest integer, regardless of the value of the number
18 F-FDG PET/CT examination

Clinical Indication:

Left renal lesion on previous imaging.

Administered activity: 223 MBq. 18 F-FDG

B.G.L.: 64 mg/dl

Examination protocol:

.

A Whole body PET/CT study was obtained from skull vertex to the mid thigh using a low dose non contrast CT for attenuation correction

Diagnostic multislice (64 channels) CT examination after IV contrast administration of the same region.

PET/CT Findings:

Left kidney, FDG-avid upper pole partly exophytic lesion measures 2.6 x 2.5 x 3.7 cm with SUVmax 11.3.

Lymph nodes: FDG-avid lymph nodes involving:
Left supraclavicular LNs, most active and the largest measures 2.4 x 1.1 cm with SUVmax 12.0.

Small posterior mediastinal (SUVmax 5.1) and enlarged retrocrural LNs (the most active left one has SUVmax 10.3 and the largest measures 1.7 cm with SUVmax 7.1).

Multiple enlarged discrete and partly amalgamated left para- aortic (The most active nodal amalgam measures 3.3 cm with

SUVmax 13.7), aortocaval and left common iliac LNs.

FDG-avid left adrenal nodule measures 1.7 x 1.8 cm with SUVmax

10.8

Bone marrow, FDG-avid lesion in T4. T6 and T10, left acetabulum and left iliac bone. The left acetabular lesion with SUVmax 9.1.

Liver, no FDG-avid lesions.

Lungs, no FDG-avid lesions.

II. Non contrast CT Findings:

No pleural, pericardial or peritoneal collections.

The spleen, pancreas, right adrenal, pelvic organs, aorta and IVC appear unremarkable.

OPINION:

Hypermetabolic left renal, left adrenal, supra and infra diaphragmatic lymph nodal and bone marrow neoplastic lesions, for histopathology.


IMMUNOHISTOCHEMISTRY REPORT

Technique:

Sections were prepared from the paraffin block, processed in Benchmark Ultra machine (Ventana) and stained by rabbit monoclonal antibodies using Ultra view HRP DAB as chromogen and hematoxylin as counter-stain.

The sections were treated against: Ck 201 Ck 7 \ TTF1 | Thyroglobulin | CA 19-9 \ CDX2 \ CEA\ Calretinin | Pan CK [AE1/AE3]\ PAX8 \ GLUT-1

Results:

Tumor cells are NEGATIVE for CK 7.

Tumor cells are NEGATIVE for CK 20.

Tumor cells are NEGATIVE for TTF 1.

Tumor cells are NEGATIVE for Thyroglobulin. NEGATIVE for CA 19-9.

Tumor cells are Tumor cells are NEGATIVE for CDX 2.

Tumor cells are NEGATIVE for CEA.

Tumor cells are NEGATIVE for Calretinin.

Tumor cells are POSITIVE for Pan CK [AE1/AE3]. Tumor cells are POSITIVE for PAX 8.

Tumor cells are POSITIVE for GLUT-1.

Conclusion:

Referred Block, METASTATIC ADENOCARCINOMA, POSITIVE FOR PAN CK, PAX 8 AND GLUT 1.

-THIS IMMUNOPROFILE IS SEEN IN BOTH THYMIC AND RENAL PRIMARY. - PLEASE CORRELATE WITH CLINICAL AND RADIOLOGICAL FINDINGS.
Assistant: Hello! How can i help you today?
User: Anda: 062 89

Assistant: