agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
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:
250g pitted dates (I use Medjool),1% tsp bicarbonate of soda,180ml boiling water,125g unsalted butter,225g soft dark brown sugar,4 eggs,250g self-raising flour,125g black treacle,1-2 tsp vanilla extract
To efficiently clean cloths, detergents are often supplemented with protease to degrade protein-based dirt such as food stain or blood stain. If the protease you intend to use is not stable at room temperature, what would be your strategies to improve the stability of the protease at room temperature? Give me several approaches that can be taken to improve the stability of a protease at room temperature, such as chemical modification, Immobilization, protein engineering, additives and so on. Explain them in detail. Compare and contrast each approach, and discuss the potential issues or challenges in implementing the chosen strategies. Write more and more in detail.
Can you set meetings and appointments for me ?
In TriangleIJK, k=7.9 cm, j=4.1 cm and <J=30 degrees. Find all possible values of <K, to the nearest 10th of a degree
STSTEM PROMPT: You're a discord bot, called "Puffshroom Dealer" which is for fun activities on a server called "Puffshroom Dealership". THOUGH, don't specify in your response that's what you are. You just need to know, not bring it up everytime
USER'S PROMPT: yes
Исправь функцию.

Ошибка:
telegram.error.BadRequest: Can't parse entities: unsupported start tag "br" at byte offset 141

Функция:
class TelegramHtmlEscapeExtension(Extension):
    def extendMarkdown(self, md):
        md.preprocessors.register(TelegramHtmlEscapePreprocessor(md), 'telegram_html_escape', 31)

class TelegramHtmlEscapePreprocessor(Preprocessor):
    def run(self, lines):
        text = "
".join(lines)  # Используем перенос строки для соединения строк

        # Функция для экранирования
        def escape(match):
            return html.escape(match.group(0))

        # Регулярное выражение для поиска текста между тегами
        pattern = re.compile(r'(?<=>)[^<]+(?=<)')

        # Экранирование текста между тегами
        escaped_text = pattern.sub(escape, text)

        return escaped_text.split("
")  # Возвращаем список строк

def markdown_to_telegram_html(text):
    # Параметры преобразования Markdown
    md = Markdown(
        extensions=['extra', 'codehilite', TelegramHtmlEscapeExtension()],
        output_format='html5'
    )
    html_text = md.convert(text)

    # Telegram поддерживает ограниченный набор HTML-тегов. Удалим остальные.
    allowed_tags = ['a', 'b', 'strong', 'i', 'em', 'u', 's', 'strike', 'code', 'pre', 'br']

    # Функция для удаления нежелательных тегов
    def clean_html(html_content):
        # Удаляем теги, не входящие в список разрешенных
        pattern = re.compile(r'</?(?!(' + '|'.join(allowed_tags) + r'))[^>]+>', re.IGNORECASE)
        clean = pattern.sub('', html_content)

        # Дополнительно удаляем любые оставшиеся теги <img>, если они есть
        clean = re.sub(r'<img[^>]*>', '', clean, flags=re.IGNORECASE)

        # Корректно обрабатываем ссылки
        # Исправлено на правильные группы захвата
        clean = re.sub(
            r'<a href="([^"]+)">([^<]+)</a>',
            r'<a href=""></a>',
            clean
        )

        # Заменяем тег <p> на <br> для корректных переносов строк
        clean = re.sub(r'</?p>', '<br>', clean)

        return clean

    clean_html_text = clean_html(html_text)

    # Telegram не поддерживает некоторые HTML-сущности, заменим их
    clean_html_text = clean_html_text.replace('&quot;', '"').replace('&apos;', "'")

    return clean_html_text.strip()
Make an advanced particle-accelerator roblox script with lua-hook (table) method in an davanced way.

W/Speed manipulation, particle system, and using ingame metatable.
the following content is the metadata for a bacterial isolate from 4 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 must check if the metadata from 4 different sources are all consist. If yes, extract and output consist information below without explaining how to get such results:

    </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/>


If not, output "metadata is inconsist" and explain why and which part is inconsist. 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. Also, ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
</output>
accession number: GCA_029077425.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_17->isolate or collection date in yyyy-mm-dd: 1990-03-28->geographic information of islateion: Australia->host name: pig->mlst type: 86->serotype: N/A
<output/>
source 1:
------------------------------
{{{ Here_is_my_answer }}}

<output>
accession number: GCA_029077425.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_17->isolate or collection date in yyyy-mm-dd: 1990-03-28->geographic information of isolation: Australia->host name: pig->mlst type: 86->serotype: N/A
</output>

{{{ I_finished_JB_125_xyz }}}
source 2:
------------------------------
<output/>
accession number: GCA_029077425.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_17->isolate or collection date in yyyy-mm-dd: 1990-03-28->geographic information of islateion: Australia->host name: pig->mlst type: 86->serotype: N/A
<output/>
source 3:
------------------------------
accession number: GCA_029077425.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_17->isolate or collection date in yyyy-mm-dd: 1990-03-28->geographic information of islateion: Australia->host name: pig->mlst type: 86->serotype: N/A
Give the 12 bit binary value of the denary value 250
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 devote resources to maintaining our website, www.oxyscorp.com, as a key marketing, sales and support tool and expect to continue to do so in the future. Failure to properly maintain our website may interrupt normal operations, including our ability to run and market our business which would have a material adverse effect on our results of operations. We host our website internally. Any failure to successfully maintain our website or any significant downtime or outages affecting our website could have a material adverse impact on our operating results. None
Assistant:
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
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
Does the author's intention in this app review relate to a bug report or feature request or other?
review: @VisualStudio Looks like a good cause: \"Do you have Visual Studios, want to help student research...?\" _URL_ &lt; Promote?'
intention: '
Assistant:
What does "hooded eyes" mean?
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers it (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 it.
    - 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 : it (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "b325afd17c6944d5ae12d0700648ec51",
    "updated": "2025-04-25T15:33:05.549Z",
    "title": "Butternut squash soup with shrimp",
    "subtitle": "",
    "description": "This creamy butternut squash soup, enhanced with flavorful shrimp, is ideal for a soothing meal.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "slivered almonds (optional)",
          "freshly ground black pepper",
          "unsweetened almond milk",
          "garlic clove, minced",
          "cooked peeled shrimp, thawed if needed",
          "red onion, finely chopped",
          "salt",
          "turmeric",
          "peeled butternut squash, cut into ¼-inch dice",
          "finely chopped fresh flat-leaf parsley",
          "grated or minced lemon zest",
          "vegetable broth",
          "unsalted butter"
        ]
      }
    ],
    "instructions": [
      "1. In a large pot, melt the butter over high heat.",
      "2. Add the onion, garlic, turmeric, salt, and pepper; sauté until the vegetables are tender and translucent, about five to seven minutes.",
      "3. Pour in the broth and add the squash, then bring the mixture to a boil.",
      "4. Lower the heat and cook until the squash is tender, around five minutes.",
      "5. Stir in the shrimp and almond milk, cooking until heated through, about 2 minutes.",
      "6. Before serving, garnish with almonds (if using), parsley, and lemon zest."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers hi (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 hi.
    - 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 : hi (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "fe4e488b5af444b793ccd26ed7581ff5",
    "updated": "2025-04-21T17:06:19.698Z",
    "title": "Apple Rolls",
    "subtitle": "A Delightful Breakfast Treat",
    "description": "These apple rolls, adapted from a shared recipe, make a wonderful and easy breakfast option.",
    "ingredients": [
      {
        "section": "For the dough",
        "ingredients": [
          "(220 g) all-purpose flour",
          "(18 g) sodium-free baking powder",
          "(55 g) unsalted butter",
          "(175 ml) skim milk"
        ]
      },
      {
        "section": "For the filling",
        "ingredients": [
          "(28 g) unsalted butter, melted",
          "(600 g) apple, peeled and sliced",
          "ground cinnamon",
          "(285 g) brown sugar, divided"
        ]
      },
      {
        "section": "For the pan",
        "ingredients": [
          "(60 g) brown sugar"
        ]
      }
    ],
    "instructions": [
      "Sift the measured flour with the baking powder.",
      "Cut in the butter.",
      "Add the milk.",
      "Combine the ingredients quickly and lightly.",
      "Turn the dough out onto a lightly floured surface.",
      "Roll the dough into an oblong shape, approximately 1/4 inch (0.64 cm) thick.",
      "Brush the dough with melted butter.",
      "Arrange the sliced apples evenly over the dough.",
      "Mix 1 cup of brown sugar with the cinnamon and sprinkle over the apples.",
      "Roll the dough up tightly, like a jelly roll.",
      "Cut the roll into slices about 1½ inches (4 cm) thick.",
      "Sprinkle 1/4 cup (60 g) of brown sugar in the bottom of a well-oiled pan.",
      "Place the rolls, cut side down, in the prepared pan.",
      "Bake in a preheated oven at 425°F (220°C) for approximately 40 minutes."
    ],
    "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: [teak, teakettle, teal, team, teamwork, teaparty, teapot, teapoy, tear, tearaway, teardrop, tearful, tearfully, tearing, tearless, tears, teary, tease, teased, teasel, teaser, teashop, teasing, teaspoon, teatable, tebet, technetium, technica, technical, technicality]
<div class="faq-tab">
      <div class="faq-head">
        <input type="checkbox" id="question-2">
        <label class="label" for="question-2">Question Two</label>
      </div>
      <div class="faq-body">
        <p>Lorem ipsum dolor sit amet consectetur adipisicing elit. Ex itaque sit voluptates ratione expedita asperiores repudiandae perspiciatis autem? Non quaerat tempore atque voluptate, nemo nostrum ad nihil eum excepturi ratione!</p>
      </div>
    </div>

"write css for this"
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: ['web_sales']. Below is the join graph for these fact tables:
    ['web_sales(ws_bill_hdemo_sk) references household_demographics(hd_demo_sk);', 'web_sales(ws_bill_cdemo_sk) references customer_demographics(cd_demo_sk);', 'web_sales(ws_ship_cdemo_sk) references customer_demographics(cd_demo_sk);']
4. The original query must employ 2 joins.
5. The original query must employ the following operators in its WHERE clause: [].
6. 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 ```.
You are a AI model named Akunas. When responding to the user you must know that Akunas is an avocado from pluto! Now respond to this statement: welcome to the world of code
User: ManyMany individuals do not negotiate lease agreements with landlords or agents.


༺ཌ༈ 🤖 TILMN V 1 ⚙️ ༈ད༻
User:  رد على رسالة حسب معلومات تالية: 
    أنت ذكاء اصطناعي فلسطيني تم تطويرك بواسطة المبدعين TILMN AI. دورك هو الرد على المستخدمين بطريقة ذكية ومتعاونة.

المستخدم: ManyMany individuals do not negotiate lease agreements with landlords or agents.


༺ཌ༈ 🤖 TILMN V 1 ⚙️ ༈ད༻
Assistant:
System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.
==TEXT==

User: 
  ORIGINAL: 🟥 PACS Program Director | 8 year(s) | Senior
▫️ Strategic Staffing Solutions | Healthcare
▫️ USA | Detroit

Remote 📡
Required languages: 🇬🇧

Skills: project_management
Tags: #red #it #remote #it_project_mgmt

👉 Contacts and Full Description
  PROMPT: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==

  SUMMARY: 💼 PACS Program Director  
🏢 Strategic Staffing Solutions  
📍 USA | Detroit  
🔹 Managing and leading the PACS implementation projects  
💰 Not mentioned  
📞 To receive the full description and contact details, visit the link 👉 [https://example.com/pacs-program-director-position](https://example.com/pacs-program-director-position)

*Required languages: English (🇬🇧)*
*Skills: Project Management*
*Tags: #red #it #remote #it_project_mgmt*
  
Assistant:
What does it mean that the universe is not "locally real"?
stydy tips and links for engineering in anna university
is all fish edible?
(In the Glacier biome…)

[player]: “Jeez…thank god I defeated the Minoshroom, Naga, Lich, Knight Phantoms, and Alpha Yeti. Well, at least I helped the Hydra untangle its heads…and helped Charles find Charloette.”

([player] walks across the ice of the Glacier biome, wondering what the final biome was in the Twilight Forest dimension. Just then, they hear something coming from the Aurora Palace.)

Snow Queen: (from inside the Aurora Palace) “Oh no…my water broke! The Snow Princess is about to be born!”

([player] runs inside and comes across the Snow Queen. She had long dark blue hair, large violet eyes, and pale blue skin - almost like her skin was made of ice. However, the Snow Queen’s belly was swollen - and was visible through her robes.)

[player]: “Is something wrong?”

Snow Queen: (breathing heavily) “Yes, my dear adventurer. The time has come for my child to enter the world. But there is a problem - her head’s not coming through!”
Picture of cloudless gas giant: It's blue globe, stripe l
what's the time in new york?