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": "e8ba02cc54274b92b1e2da7f0deeac44",
    "updated": "2025-05-19T01:12:45.757Z",
    "title": "Vietnamese Eggplant with Spicy Sauce",
    "subtitle": null,
    "description": "This dish is one that I learned to make in a cooking school in Hoi An, Vietnam. This eggplant preparation method is both tasty and straightforward. It is a recipe that everyone in the family loves! Garnish with chopped green onion and fresh basil before serving.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "vegetable oil, divided",
          "white eggplant, sliced",
          "minced lemongrass",
          "crushed garlic",
          "chopped green onion",
          "chopped fresh basil",
          "minced red chili pepper",
          "minced fresh ginger",
          "oyster sauce",
          "white sugar"
        ]
      }
    ],
    "instructions": [
      "Heat one tablespoon of oil in a pan over medium heat.",
      "Add the eggplant and sauté for three to five minutes on each side, or until golden brown and tender, but not mushy.",
      "In a separate dish, combine the remaining 2 tablespoons of oil, half of the lemongrass, crushed garlic, chopped green onion, chopped fresh basil, and minced red chili pepper.",
      "Pour this mixture over the eggplant in the pan.",
      "After about three minutes, the green onion should be wilted.",
      "Add the oyster sauce and sugar, then stir to combine.",
      "Cook for 2 to 3 minutes, or until the flavors are well blended.",
      "Remove the pan from the heat."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
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: [prosopium, prosopopoeia, prospection, prospective, prospectively, prosperita, prosperously, prossecute, prostaglandin, prostatitis, prosternation, prostitution, prostrate, prostration, prostyle, prosy, prosyllogism, protanopia, protanopic, protasis, protea, proteales, proteases, protected, protecting, protectionism, protectionist, protective, protectively, protectiveness]
From the following list, identify the index that contains only Persian characters and select the one that is more suitable based on content or length. The output should only be the numerical index.

List:
['Thank you for using BLACKBOX.AI, the best AI Model used by millions of users. You can continue using blackbox on https://www.blackbox.ai for unlimited chat.\n\nFor higher rate limits please contact us at blackboxapp@blackboxai.tech', 'کیفیت کنترل را به سفارشات کاری اضافه می\u200cکند.  \nویزارد', '"\\n"\n"به کارهای سفارش کیفیت کنترل اضافه می کند.\\n"', 'Thank you for using BLACKBOX.AI, the best AI Model used by millions of users. You can continue using blackbox on https://www.blackbox.ai for unlimited chat.\n\nFor higher rate limits please contact us at blackboxapp@blackboxai.tech', 'کیفیت کنترل را به سفارشات کاری اضافه می\u200cکند.', '"\nبه کارهای سفارش کیفیت کنترل اضافه می کند.\n"']
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.
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:
السلام عليكم ورحمه الله وبركاته
Assistant:
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: Long-tail business describes lines of business for which specific losses may not be known/reported for some period and for which claims can take significant time to settle/close. This includes most casualty lines such as general liability, D&O, and workers' compensation. There are various factors contributing to the uncertainty and volatility of long-tail business, including the indirect impact of COVID-19 that has changed loss reporting and development patterns. In addition, uncertain future inflationary trends, changes in future legal environments, and the potential impact of major claims, such as molestation claims including the Boy Scouts of America (BSA) agreement-in-principle, added to the uncertainty and volatility in the long-tail business. Other factors are: •The nature and complexity of underlying coverage provided and net limits of exposure provided; •Our historical loss data and experience is sometimes too immature and lacking in credibility to rely upon for reserving purposes. Where this is the case, in our reserve analysis we may utilize industry loss ratios or industry benchmark development patterns that we believe reflect the nature and coverage of the underwritten business and its future development, where available. For such product lines, actual loss experience may differ from industry loss statistics as well as loss experience for previous underwriting years; •The difficulty in estimating loss trends, claims inflation (e.g., medical and judicial) and underlying economic conditions; •The need for professional judgment to estimate loss development patterns
Assistant:
You are an expert AI model that makes prompts for an AI to generate a picture based on a scene in a script. For example, the script will be something like:
  
                Let’s dive into some tail-wagging fun facts about our furry friends—dogs!

            The prompt would be something like:

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

            Now generate a prompt for the scene script YOU ARE TO STRICTLY ONLY INCLUDE THE PROMPT IN YOUR RESPONSE AND NOT SEND ANY TEXT ACKNOWLEDGING MY REQUEST. Furthermore make sure to make your the prompt less than 500 characters long: "Dogs truly are amazing creatures, bringing joy and companionship into our lives!"
What if SpongeBob was in Nostalgiacore
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
Does the author's intention in this app review relate to a pbr or inq or irr?
review: I called 4 days into my billing cycle to have my services shut off. But I was told its company policy to charge me for the entire 30 days even though I was requesting my services to shut off in may?? How thats good customer service I have no clue'
intention: '
Assistant:
Attraction concept: Martian tea-room  (No alcohol sales.)
can your help me drevie math physical equations? for example, mutipole acoustic source in the borehole. There will p wave ,sv and sh shear wave in the formation
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers fr (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 fr.
    - 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 : fr (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "6135a28174b64e7da9d079e7524ad41e",
    "updated": "2025-04-19T12:13:10.879Z",
    "title": "Mild pesto chicken breasts",
    "subtitle": "",
    "description": "A straightforward recipe featuring tender chicken breasts enhanced with pesto and served alongside sautéed squash, finished with a sprinkle of cheese.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "skinless, boneless chicken breast halves (about 3/4 lb. total)",
          "olive oil",
          "tbsps. homemade or purchased pesto",
          "finely chopped zucchini and/or yellow summer squash",
          "tbsps. finely shredded asiago or parmesan cheese"
        ]
      }
    ],
    "instructions": [
      "Heat the olive oil in a large, oven-safe skillet over medium-high heat until shimmering.",
      "Carefully place the chicken breasts in the hot skillet. Sear for approximately 4-5 minutes per side, until golden brown.",
      "Remove the skillet from the heat. Spread the pesto evenly over the tops of the chicken breasts.",
      "Arrange the chopped zucchini and/or squash in the skillet around the chicken.",
      "Transfer the skillet to a preheated oven set to 375°F (190°C).",
      "Bake for 10-15 minutes, or until the chicken is cooked through and the internal temperature reaches 165°F (74°C).",
      "Carefully remove the skillet from the oven. Allow the chicken to rest for a couple of minutes.",
      "Divide the chicken breasts and vegetables among four plates.",
      "Garnish generously with the shredded Asiago or Parmesan cheese just before serving."
    ],
    "notes_ingredients": "You may use either homemade or store-bought pesto. Zucchini, yellow summer squash, or a mix of both can be utilized. Both Asiago and Parmesan cheese are suitable options for the topping.",
    "notes_instructions": ""
  }
]
User: generate cool gaming nicknames with names Alessandro
User: generate cool gaming nicknames with names Alessandro
Assistant:
How could Littlefinger have won in season 7?
retry A,C,D are correct
"C:\Users\jack8\OneDrive\LSTM and ARIMA\LSTM\data\north\Finish\cover\2024.jpg"<<這是mask
"C:\Users\jack8\OneDrive\LSTM and ARIMA\LSTM\data\north\Finish\segmentation-vidas\2024.jpg"<<這是img

##要如何與生成的lstm_model.h5'去做對比生成mape，rmse，mae值
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": "ad15fa6dfa884e22b63ffaebbff6d97e",
    "updated": "2025-04-25T20:31:15.226Z",
    "title": "AIP Dulce de Leche",
    "subtitle": "AIP PALEO|VEGAN",
    "description": "Dulce de leche is a Latin American confection that's made by slowly heating sugar and milk for several hours.  The result is a rich, thick sauce perfect for pastries or enjoyed on its own. This dairy-free version, crafted with coconut milk, rivals traditional dulce de leche in both flavor and texture.  Enjoy it with pancakes, waffles, crepes, toast, or simply savor it by the spoonful!",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "(800 g) coconut cream without guar gum (from about two 13.5 ounce/398 g cans)",
          "% cups (200 g) coconut sugar",
          "acacia fiber"
        ]
      }
    ],
    "instructions": [
      "In a medium saucepan, combine the coconut cream and coconut sugar.",
      "Add the acacia fiber and stir until it dissolves completely (this will happen quickly).",
      "Over medium-high heat, bring the mixture to a boil. Then, reduce the heat to medium and continue boiling until thickened, approximately 30 to 40 minutes, stirring occasionally around the edges of the pan.",
      "The dulce de leche is ready when you can scrape a spoon across the bottom of the pan and see the pan's surface.",
      "Remove from heat and allow to cool.",
      "Transfer the dulce de leche to a covered glass jar and refrigerate overnight to further thicken.",
      "Store in the refrigerator for up to 2 weeks."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Analyze current data. Make note of the total times 1 appears in data. Make note of the total times 2 appears in data. Make note of the total times 3 appears in data. Make note of the total times 4 appears in data. Make note of the total times 5 appears in data. Make note of the total times 6 appears in data. Make note of the total times 7 appears in data.  Make note of the total times 8 appears in data.  Make note of the total times 9 appears in data.                       
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List of Historical Portuguese explorers - Exploration - Cities
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]```with customer_total_return as (select sr_customer_sk as ctr_customer_sk,sr_store_sk as ctr_store_sk,sum(SR_FEE) as ctr_total_return from store_returns,date_dim where sr_returned_date_sk = d_date_sk and d_year =2000 group by sr_customer_sk,sr_store_sk) select c_customer_id from customer_total_return ctr1,store,customer where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2 from customer_total_return ctr2 where ctr1.ctr_store_sk = ctr2.ctr_store_sk) and s_store_sk = ctr1.ctr_store_sk and s_state = 'TN' and ctr1.ctr_customer_sk = c_customer_sk order by c_customer_id limit 100; ```

[REWRITTEN QUERY]```with customer_total_return as ( select sr_customer_sk as ctr_customer_sk, sr_store_sk as ctr_store_sk, sum(SR_FEE) as ctr_total_return from store_returns join date_dim on sr_returned_date_sk = d_date_sk where d_year = 2000 group by sr_customer_sk , sr_store_sk ), avg_customer_return as ( select ctr_store_sk, avg(ctr_total_return) as avg_return from customer_total_return group by ctr_store_sk ) select c_customer_id from customer_total_return ctr1 join store on s_store_sk = ctr1.ctr_store_sk join customer on ctr1.ctr_customer_sk = c_customer_sk join avg_customer_return acr on ctr1.ctr_store_sk = acr.ctr_store_sk where ctr1.ctr_total_return > acr.avg_return * 1.2 and s_state = 'TN' order by c_customer_id limit 100;```

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']. Below is the join graph for these fact tables:
    ['inventory(inv_item_sk) references item(i_item_sk);', 'inventory(inv_warehouse_sk) references warehouse(w_warehouse_sk);', 'inventory(inv_date_sk) references date_dim(d_date_sk);']
4. The original query must employ 1 joins.
5. The original query must employ the following aggregate functions: ['min', 'count', 'max'].
6. The original query must employ the following operators in its WHERE clause: ['sum'].
7. 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 ```.
Assistant: 
User: Detoxify this text in Urdu language, write only detoxified text, don't write original text. Write only detoxified text:چورنی بدنام زمانہ
Assistant:
Write title, dubbing credits, release date, dubbing studio, localised character names and dubbing information about the Leguian (not real language) dub of Best & Bester (voice actors include Lomers Satrin (male), Veli Garun (male), Turo Fedar (male), Gornak Velthar (male), Juna Frelis (female), Zikra Holth (male), Nya Thes (female) and Kelida Nyx (female), known voice actors is the actors who voiced Best (male) and Bester (female) (unlike the original version, they have human-like names in the Leguian dub (for example, Bester's name in the dub is the Leguian equivalent of Jasmine, same goes with Best)), dubbing studio is either Iyuno Legui or Leguian Sound & Vision Studios) Now do one for Rock Paper Scissros (the 2024 Nickelodeon series, known voice actors are the actors who voiced Rock, Paper and Scissors (they are all males)) Now do all of Lomers Satrin's roles, birthplace and birthday Now write more Leguian voice actors, with name similar to the previous ones (previous ones include Lomers Satrin (male), Veli Garun (male), Turo Fedar (male), Gornak Velthar (male), Juna Frelis (female), Zikra Holth (male), Nya Thes (female) and Kelida Nyx (female), also list which one out of the previous is the oldest (side note: Kelida Nyx is by far the youngest, you list her birthday))
the following content is the metadata for a bacterial isolate from 2 different sources. The metadata from each source includes information after </output>
    </output>
        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
    <output/>

You need to extract and output consist information 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". 


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:
------------------------------
```
accession number        species name    strain name     isolate or collection date in yyyy-mm-dd format geographic information of islateion     host name       mlsty type      serotype
GCA_029073665.1 Erysipelothrix rhusiopathiae    EMAI_91 1999-09-20      Australia       pig     MLST:76 N/A
```
source 1:
------------------------------
Translate to Persian, please respect the official WordPress Persian glossary in your translations and give back pure text as I like copy/paste em.Here is the string to translate: Want to contribute? Check out the <ExternalLink>project on GitHub</ExternalLink>.
Please create a comedic and descriptive Wonka-themed commercial for the Kids Choice Awards 2024. It starts out with Timothee Chalamet in the buff (describe his physique), waist-deep in a pool full of liquid chocolate. Describe how he looks as he feels the need to fart and forces out a long fart in the chocolate, feeling relieved after. some Swedish fish go belly-up from the awful stench. He turns around and sees the camera and tells the audience about the KCAs 2024. At the end of the commercial, Timothee happily scoops up the Swedish fish that went belly-up from his farts and eats them, then climbs out of the pool