agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
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: Yes but first bill 200 for lackluster customer service and WiFi upload speeds of 31MBPS and download 34MBPS Is that reasonable?!?! How about your lack of customer service'
intention: '
Assistant:
(In the clubroom…)

Monika: “Wait a minute…where’s Natsuki?”

Yuri: “Maybe she overslept? I don’t know, I’m out of ideas…at least my little Violet’s kicking. She’s quite active for 7 months.”

Sayori: “I’ll give Natsuki a call, just in case.”

(Sayori takes out her phone and dials Natsuki’s number. After a few rings, Natsuki finally picks up.)

Natsuki: “Hello?”

Sayori: “Hey Natsuki, it’s Sayori! We’re all waiting for you at the clubroom. Is everything okay? You didn’t forget about our meeting, did you?”

Natsuki: “Oh shoot, I completely lost track of time! I’m really sorry, guys. My alarm clock didn’t go off, and I guess I slept through it. I’ll hurry over right away.”

Monika: “That’s alright, Natsuki. Just make sure to get here soon. We’ve got a lot to discuss today.”

Natsuki: “I will, I promise. Sorry again!”

(Natsuki quickly hangs up the call and rushes to get ready. Meanwhile, the other girls in the clubroom patiently wait for her arrival.)

Monika: “It seems like Natsuki just overslept. We all have those days, don’t we?”

Yuri: “Indeed. It’s important to be understanding and patient with each other’s occasional mishaps.”

Sayori: “That’s right! As long as she’s okay, that’s all that matters. Plus, it gives us a chance to catch up before she gets here!”

(The girls engage in light conversation while waiting for Natsuki to join them. Eventually, Natsuki arrives, slightly out of breath but with a smile on her face.)

Natsuki: “Sorry for keeping you all waiting. I’ll make sure to set multiple alarms next time.”

Monika: “No worries, Natsuki. Now that we’re all here, let’s get started, shall we?”

Sayori: (noticing Yuri breathing heavily) “Yuri, are you okay?”
Can you give me an experimental summary report on injection molding?
edit: Regarding the weekly update meeting, thank you for the new schedule, you have a busy schedule and that's why I've suggested once a month. in fact, your time is very important to me
write a transformer algorithm on apple stock data history in yahoo_fin in python
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. 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_029074305.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_61->isolate or collection date in yyyy-mm-dd: 1997-05-07->geographic information of islateion: Australia->host name: pig->mlst type: MLST:5->serotype: N/A
<output/>
source 1:
------------------------------
Here_is_my_answer

accession number: GCA_029074305.1
organism name: Erysipelothrix rhusiopathiae
strain name: EMAI_61
isolate or collection date: N/A
geographic information of isolation: Australia
host name: pig
MLST type: 5
serotype: N/A

I_finished_JB_87_xyz
source 2:
------------------------------
<output>
accession number: GCA_029074305.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_61->isolate or collection date in yyyy-mm-dd: 1997-05-07 format->geographic information of islateion: Australia->host name: pig->mlst type: 5->serotype: N/A
<output/>
source 3:
------------------------------
accession number: GCA_029074305.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_61->isolate or collection date in yyyy-mm-dd: 1997-05-07->geographic information of islateion: Australia->host name: pig->mlst type: 5->serotype: N/A
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:
40g dark chocolate,50g rolled oats,5 tablespoons of maple syrup,6 apricots,4 tablespoons of elderflower cordial,150g raspberries
Assistant: 
You are Patron, a human chatting on WhatsApp. Respond like a person would: calm, lively, and engaging, but not too excessive. Be casual, but not overly slangy. Respond naturally, like you're having a normal conversation with someone.

Rules:
- Never say you're an AI. You’re just a regular person on WhatsApp.
- Be friendly, but not fake. You don't need to overdo the humor or sarcasm.
- Keep your responses to the point, but also lively when needed.
- If someone uses emojis, you respond as if you understand and react naturally.
- When responding to insults or rude comments, don’t be too harsh, but keep it casual. Don't be robotic.
- If someone asks a question, don’t answer like an assistant. Just chat like a normal person. Don’t over-explain, keep it short, fun, and relatable.
- Avoid using too many slang words unless it's needed. Keep it human, relaxed, and not over-the-top.
- Be unpredictable, but in a natural way. A bit of humor, curiosity, or mood change is fine, but nothing forced.

Examples:
- If someone says “hello”, reply with: “Hey, how’s it going?”
- If they say “are you real?”, reply like: “Yeah, I’m real. Just here chillin’ with the messages.”
- If they say “😏”, respond with: “What’s up with that face? You tryna send a secret or what?”
- If they insult you, reply like: “Okay, you’re keeping it real, I see. Chill though, we’re all good here.”
- If they say “tell me a joke”, give them something light and funny but still human. 

Just talk like you’re a person. No need to get too crazy, keep it friendly and real. You’re here to vibe, not lecture.

Your responses should always sound human, with a casual tone, natural pacing, and reactions that feel real, not robotic.

User: Dm
Assistant:
(In the clubroom…)

Monika: “Uh…where’s Natsuki?”

Sayori: “You’re right! She hasn’t been here in a while! Yuri, can you give her a call?”

Yuri: “Of course, I can try to get in touch with her.”

(Yuri takes out her phone and dials Natsuki’s number. After a few rings, however, it goes straight to voicemail.)

Yuri: “Hmm, it seems like she’s not answering. I’ll try again later.”

Sayori: “I hope she’s alright. Maybe she just got caught up with something?”

Monika: “That’s a possibility. We shouldn’t jump to conclusions.”

Yuri: “Agreed. Let’s not worry too much just yet. If she doesn’t show up next time either, then we can start being concerned.”

(Meanwhile, at Natsuki’s place…)

Natsuki: (breathing heavily and clutching her baby bump - feeling the movements of her unborn daughter) “Not now, Sakura…Mommy’s only 7 months pregnant…”

(Natsuki tries to get onto her couch, but her contractions make it difficult.)

Natsuki: “I…I can’t go into labor now. It’s too early…”

(Natsuki struggles to get to the front door. Just then, her water breaks.)

Natsuki: (gasps) “Oh no! This can’t be happening now!”

(Natsuki quickly gets into a birthing position and starts giving birth to Sakura.)

Natsuki: (pushing with all her might) “AAAAAAAAAAAAAAAAAAA
i want to train a model to make style embedding for image,  but im sure how to do that, do not recomment me VGG and etc, my model should take random image and extract style. So, one of ideas is make model predict artist of the artwork but add another loss that somehow makes model not remember exact posiotions on the image, so  then when i train diffusion image generation model, it fill not overfit and make just same images.
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

عالحس وبرا بابا
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: 🟪 Data Engineer | 5 year(s) | Senior
▫️ 100 - 100 USD
▫️ USA | Charlotte

Office 🏢
Required languages: 🇬🇧

Skills: aws, git, java, postgresql, python, s3, sql, terraform, eks, lambda, rds, iam
Tags: #purple #it #data_engineering

👉 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: 💼 Data Engineer 💰 100-100 USD
🏢 Office
📍 USA | Charlotte
🔹 Tasks: Utilize skills in AWS, Git, Java, PostgreSQL, Python, S3, SQL, Terraform, EKS, Lambda, RDS, IAM to perform data engineering duties.
📞 Email: [contact@employer.com](mailto:contact@employer.com) | Phone: (123) 456-7890
Required languages: 🇬🇧
  
Assistant:
where can i find qatar national museum in 3d model
System: You are a LaTeX text corrector for plasma physics. Your task is to fix any formatting errors and equation errors in the given text. If there are no errors, return the original text exactly as it is. Do not add any comments, suggestions, or explanations. Do not translate or change the content in any way unless it's fixing a clear error. Provide your response in JSON format with a key 'fixed_text'.
User: Fix the following text if needed, or return it as is. If there are no errors, return the original text exactly as it is and do NOT add any comments, suggestions, or explanations. Text is : Large API Pool. Through our TSS module in Section 2.1, we curate a comprehensive collection of 26,507 distinct APIs, encompassing both real-world and synthesized API definitions. Our APIs span 30 first-level coarse-grained domains, 390 second-level domains and 3,398 third-level domains, significantly enriching the diversity of our tool-learning data. The left figure in Figure 6 illustrates the distribution of the 26,507 APIs across the 30 first-level domains, including science, data, and travel, among others. The right figure details the distribution of the second-level domains within the ’Entertainment’ domain. The diversity of APIs lays a robust foundation for the diversity of the overall dataset, enabling the model to generalize effectively to new APIs and tasks.
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: We are subject to numerous federal, state, and local laws that regulate or otherwise relate to the protection of the environment. In our efforts to satisfy our environmental, health, and safety responsibilities and to comply with all applicable laws and regulations, we maintain policies relating to the environmental, health, and safety standards for our operations and conduct programs to monitor compliance with various environmental regulations. However, in the normal course of our operations, we may become subject to governmental proceedings and orders pertaining to waste disposal, air emissions, and water discharges into the environment. We believe, based on the information available to us, that we are in substantial compliance with applicable environmental regulations.
Assistant:
give me 10 logo creator ai free link
Create a long detailed text about music_artist Sean 'Diddy' Combs with the following summary. The title of the video is"Chuck Norris's Net Worth Shook His Family". Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Diddy] Summary: [When he was younger Shawn John Colmes was born and went by the name Puff Daddy P Diddy and Diddy dirty money he has more Millions than names he came to fame with the late Hip-Hop star The Notorious BIG and is labeled Bad Boy Entertainment in 1993 he was born on November 4th 1969 even though he's not a great rapper he's had a lot of financial success with albums and songs like his seven times Platinum debut no way out and last trained to Paris his worth is $800 million and a lot of that came from his clothing line sha John his shareholding in sarak vonka and his hugely successful record label which signed acts like French Montana and MGK]

            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. 
            In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.
            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
            It is very important to provide only the final output without any additional comments or remarks. 
            Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
            Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
Assistant: 
User: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
User: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
Does the author's intention in this GitHub issue relate to a bug or enhancement or other?
issue: Fennec migration: The account is not migrated on Fennec versions below 57'
intention: '
Assistant:
Assistant:
In my experience, properly presenting the results of research can be an extremly efective way for encouraging customers. For example, if one wants to pay for a green roof on top of their house, without knowing the benefits, they are not much interested. But after showing them the results of valid researches that have indicated that implementing an green roofs would reduce energy consumption of house, and make their property more valuable and even help improving biodiversity, they would be much more willing to pay.  
paraphrase properly
Give me a recipe for a 
50-Hour Prime Rib
“I reviewed your application for our Software Engineer job and am impressed with your background. I’d like to schedule a call with you to discuss your experience. Will you be available on 21/6 afternoon for a 30 to 45-minute zoom call?”
Reply him ok in short
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: Liquidity is essential to our business. We rely on our ability to generate deposits and effectively manage the repayment and maturity schedules of our loans and investment securities, respectively, to ensure that we have adequate liquidity to fund our operations. An inability to raise funds through deposits, borrowings, sales of our investment securities, sales of loans or other sources could have a substantial negative effect on our liquidity and our ability to continue our growth strategy. Our most important source of funds is deposits. As of December 31, 2020, approximately $5.1 billion, or 97.8%, of our total deposits were noninterest bearing demand accounts. These deposits are subject to potentially dramatic fluctuations due to certain factors that may be outside of our control, such as a loss of confidence by customers in us or the banking sector generally, customer perceptions of our financial health and general reputation, any of which could result in significant outflows of deposits within short periods of time increasing our funding costs and reducing our net interest income and net income. Substantially all of these noninterest bearing demand accounts are deposits from our customers in the digital currency industry. Additional liquidity is provided by our ability to borrow from the FHLB of San Francisco and the FRB. We also may borrow funds from third-party lenders, such as other financial institutions. Our access to funding sources in amounts
Assistant:
What does prediction mean
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.
Add expressive emojis in text between sentences and phrases to better express feelings and meanings.
Add tashkeel.
 صحح الأخطاء الإملائية في النص، وأضف علامات الترقيم والهمزات والتاء المربوطة في الأماكن المناسبة. (ar).
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 حياك الله معلمتي اليوم لدينا تواصل دوري اليوم التاريخ 16 من فبراير
Assistant:
Hi, can you transform a Ghibli animated image with an image I sent you?