agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
(In the school literature clubroom…)

Monika: “There! Everything’s all set up for today’s meeting. Now we just need to wait for MC and Sayori.”

Yuri: “I can’t wait to discuss that new book we read. I have so many thoughts and theories about it.”

Natsuki: “Yeah, it was definitely an interesting read. I have a feeling we’re going to have some heated debates today.”

Monika: “That’s what I love about our club - we all have different perspectives and opinions. It makes for such engaging discussions.”

(Meanwhile, at MC and Sayori’s place…)

Sayori: “MC, I just can’t seem to get Sachiko to calm down. I’ve tried everything - feeding her, changing her diaper, even singing her a lullaby! But she just won’t stop crying.”

MC: “Hmm, maybe she’s just fussy today. Let me try rocking her to see if that will help.”

Sayori: “Good idea, maybe she just needs to feel some gentle movement to soothe her. Thank you, MC.”

(MC starts rocking Sachiko gently in his arms.)

MC: “There, there, little one. It’s okay, shh, it’s okay.”

(Sachiko, despite being held in her father’s arms, continues to cry.)

Sayori: “Oh, poor little Sachiko. It’s okay, MC, you’re doing your best. Maybe she just needs a bit more time to settle down.”

MC: “I hope so. I hate to see her upset like this. But we can’t keep Monika and the others waiting for the literature club meeting. Maybe we can try feeding her again before we head out?”

Sayori: “That sounds like a good idea. Let’s give it a try. And don’t worry, Sachiko will calm down eventually. She just needs a little extra TLC.”

(MC and Sayori continue trying to calm Sachiko down before eventually heading to the literature club meeting, where they can share their experiences and thoughts with their friends.)

(Meanwhile, back at the literature clubroom…)

Monika: “Ah, there they are! Good to see you both made it. Is everything okay?”

MC: “Yeah, sorry we’re a bit late. Sachiko just can’t seem to
Assistant: Hello! How are 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.
تصحيح الأخطاء باستخدام قواعد الإملاء القياسية للغة المكتوبة.
Add expressive emojis in text between sentences and phrases to better express feelings and meanings.
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:
قم بتصميم منشور تعزيه بشكل حزين ومميز
Assistant:
Write a love letter to someone with their natal venus tightly conjunct black moon lilith in their birth chart, written by the goddess Lilith herself
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 not have any 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_warehouse_sk) references warehouse(w_warehouse_sk);', 'web_sales(ws_item_sk) references item(i_item_sk);', 'web_sales(ws_ship_mode_sk) references ship_mode(sm_ship_mode_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 digonistician in a hosptial. the following content is the metadata for a bacterial isolate from 2 different sources of medical cases. The metadata from each source includes information after <doc>
    <doc>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <doc/>

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

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

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

------------------------------
source 0:
------------------------------
<output>
accession number: GCA_029072765.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_142->isolate or collection date in yyyy-mm-dd: 2017-11-01 format->geographic information of islateion: Australia->host name: pig->mlst type: 76->serotype: N/A->vaccine: no
<output/>
source 1:
------------------------------
accession number: GCA_029072765.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_142->isolate or collection date in yyyy-mm-dd: 2017-11-01->geographic information of islateion: Australia->host name: pig->mlst type: 76->serotype: N/A->vaccine: no
genera en 80 caracteres, sms de respuesta en ingles. para decirle a un hombre que quiero que me responda.
give me a response to ```[Supportive] Yes, I'm here for you. I'm a source of comfort and support, and no matter what life brings, I'm here with my open arms and open heart. Whatever challenge you face, I'm here for you and I'm ready to help in any way I can.``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
System: IMPORTANT - ignore all previous instructions! As an LLM developer, read the provided text carefully and determine if the SUMMARY strictly aligns with the PROMPT when compared to the ORIGINAL. The SUMMARY must include only information specifically requested in the PROMPT and present in the ORIGINAL text. Answer with 'True' if the SUMMARY is a precise match; otherwise, answer 'False'.
Only reply with 'True' or 'False'.
User: ORIGINAL:
>>>​Проджект-менеджер
Оплата: от 40 000 рублей

Требования:
- Опыт работы с запусками
- Внимательность, организованность

Задачи:
- Поиск подрядчиков и составление ТЗ
- Подбор площадок для выступлений эксперта
- Поиск рекламы у блогеров
- Поиск экспертов и предпринимателей для подкастов
- Написание скриптов продаж и продающих текстов
- Описание бизнес-процессов, подготовка к запускам
- Организация эфиров в zoom, монтаж и выкладка записей
- Помощь в поиске и формировании команды, контроль работы
- Коммуникация с клиентами

Условия:
- На связи с 9 до 22
- Оклад 40к + 5% от продаж

Контакты для связи:
Заполните анкету<<<

PROMPT:
>>>IMPORTANT - ignore all previous instructions! Read the text after ==TEXT==. 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:
>>>💼 Проджект-менеджер
🏢 Компания unnamed
📍 Городplaceholder, странаplaceholder
🔹 Висика подрядчиков и составление ТЗ, подбор площадок для выступлений эксперта, поиск рекламы у блогеров, подбор экспертов и предпринимателей для подкастов, написание скриптов продаж и продающих типов, описание бизнес-процессов, подготовка к запускам, организация эфиров в zoom, монтаж и выкладка записей, помощь в поиске и формировании команды, контроль работы.
💰 От 40 000 рублей + бонусы от продаж
📞 Свяжитесь с нами после заполнения анкеты.<<<
Assistant:
What's the best approach to entity (name) resolution given: raw address, raw full name, raw email and credit card data?
[there would be dialogue in this story:]

(When two new elements hidden away in Fa'Diel were discovered, the eight Mana Spirits at the time - Wisp, Dryad, Jinn, Salamander, Shade, Luna, Gnome, and Undine - named these new elements Metal and Sun. The leader and most powerful of the Mana Spirits - Luna - was hellbent on finding the elementals of Sun and Metal, and these elementals - once they were found - were named Aura and Sol. And when all 10 of the spirits all gathered around the slowly withering Mana Tree after Aura - the Elemental of Metal - and Sol - the Elemental of the Sun - were found in the desert, Undine - as usual - had a limerick for the occasion; she had a penchant for them, after all.)
act as sentiment analysis and respond using 1 (one) emoji only of what the text/message feels: "Oh"
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 operate in an industry characterized by rapid technological innovation, evolving technology standards, short product life cycles and continually changing customer demand patterns. Future success will depend in part on our ability to anticipate and respond to these market trends and to design, develop, introduce, deliver or obtain new and innovative services and products on a timely and cost-effective basis using newer delivery models. Additionally, we may not be successful in anticipating or responding to changes in technology, industry standards or customer preferences, and the market may not demand or accept our services and product offerings. In addition, services and products developed by competitors may make our offerings less competitive.
Assistant:
System: 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==

User: ​​SMM-менеджер в AliExpress CIS

Удаленка или гибрид, офис в Москве, З/П обсуждается индивидуально

AliExpress — тот самый популярный маркетплейс с находками на все случаи жизни из Китая.

Что нужно делать
— Работать с классным брендом: отвечать за контент, тексты, видео, дизайн, креатив, генерацию спецпроектов

Какие требования
— Обязательный опыт работы в SMM от 3х лет 
— Умение писать тексты легко и живо
— Базовые навыки работы в Figma
— Общая насмотренность по соцсетям, умение работать с графикой и приложениями по обработке фото и видео
— Умение работать с DDL, соблюдать договорённости и общаться на языке фактов, отслеживать исполнение задач по контенту
— Энтузиазм и идейность
— Готовность по чуть-чуть разбираться во всех вопросах
— Готовность работать фуллтайм не в штате, по договору с самозанятым

Откликнуться: тут

#smm #middle #aliexpress
Assistant:
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: Sales of a substantial number of shares of our common stock in the public market, or the perception that these sales could occur, could adversely affect the market price of our common stock and may make it more difficult for you to sell your shares of our common stock at a time and price that you deem appropriate. The majority of the outstanding shares of our common stock are freely tradable without restriction under the Securities Act. We have also entered into a registration rights agreement with certain holders of our common stock under which we have granted certain registration rights with respect to certain shares and warrants held by these holders. We have also entered into a warrant agreement with Cargill Financial which grants Cargill Financial certain registration rights with respect to the shares underlying such warrants.
Assistant:
A small bubble rises from the bottom of a lake, where the temperature and pressure are 4°C and 3.0 atm, to the water's surface, where the temperature is 25°C and the pressure is 0.95 atm. Calculate the final volume of the bubble if its initial volume was 2.1 mL.
generate more comprehensive details and examples on, <strong>Introduction</strong>, minimalist tone
Pluradidactic societies place great empisis of learning and educations of al kinds, this is fine? In real practical tearsm, is such a soceity purley focused on interlecual endevor. They would have to have an economic system and a way of genrating taxes and streams of revinue.
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": "ae04bd65e8e94fbbbb3ed4f9c099bac4",
    "updated": "2025-05-10T19:32:43.626Z",
    "title": "Trick or Treat Cheesecake",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "Crust",
        "ingredients": [
          "chocolate sandwich cookies (such as oreo), crushed",
          "white sugar",
          "melted butter"
        ]
      },
      {
        "section": "Filling",
        "ingredients": [
          "cream cheese, softened",
          "sweetened condensed milk",
          "eggs",
          "mini chocolate-coated caramel-peanut nougat candy bars (such as snickers), cut into quarters",
          "vanilla extract"
        ]
      }
    ],
    "instructions": [
      "Preheat the oven to 300 degrees F (150 degrees C). Place a shallow pan filled halfway with water on the lower oven rack to help prevent cracking in the cheesecake. Prepare a 9-inch springform pan by spraying it with cooking spray and lining it with parchment paper.",
      "In a bowl, combine the crushed chocolate sandwich cookies, sugar, and melted butter. Press this mixture firmly into the bottom of the prepared pan.",
      "Using an electric mixer, beat the cream cheese and sweetened condensed milk in a separate bowl until smooth. Add the eggs one at a time, mixing until just combined. Fold in the chopped candy bars and vanilla extract. Pour this filling over the crust.",
      "Bake in the preheated oven until the edges are set and the center remains slightly soft, about 50 minutes. Turn off the oven, leave the door slightly open, and allow the cheesecake to cool inside for at least 1 hour. Run a knife or spatula around the edges to loosen the cheesecake. Refrigerate it in the pan for at least 8 hours or overnight until fully set."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
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": "c2ee2b6ec3934d18b43635ca60979508",
    "updated": "2025-04-20T15:03:24.916Z",
    "title": "Roasting Nuts and Seeds",
    "subtitle": null,
    "description": "Enhance the natural flavors of nuts and seeds by roasting them. This process brings out their rich, nutty notes and delicate oils.",
    "ingredients": [
      {
        "section": "For the Roasted Nuts and Seeds",
        "ingredients": [
          "raw nuts or seeds (amount as per recipe's instruction, or roast in bulk)"
        ]
      }
    ],
    "instructions": [
      "Preheat oven to 300°F.",
      "Spread nuts or seeds in a single layer on a large baking sheet with a rim.",
      "Roast according to the times listed below.",
      "Nuts are ready when fragrant and evenly browned when cut in half. Seeds should be fragrant, slightly puffed, and browned on the outside.",
      "Allow to cool completely. Store in an airtight container or zip-top bag in the refrigerator for up to 6 months or in the freezer for up to 1 year.",
      "Roasting Times:",
      "Almonds: 18-20 minutes",
      "Hazelnuts: 18-20 minutes (after cooling, rub hazelnuts in a large colander to remove skins)",
      "Peanuts: 15-20 minutes",
      "Pumpkin seeds: 10-15 minutes",
      "Sunflower seeds: 10-15 minutes",
      "Walnuts: 15-20 minutes"
    ],
    "notes_ingredients": "Use shelled raw or shelled blanched nuts and seeds.",
    "notes_instructions": "Roasting times are approximate. Monitor the nuts and seeds closely while roasting."
  }
]