agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
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: As a leader in the packaging industry, we are committed to delivering essential solutions that minimize food waste, maximize food safety, and protect valuable goods shipped around the world. Analysis by our leadership team and Board of Directors of anticipated issues, risks and opportunities guide our strategy. Our innovation strategy and sustainability goals are aligned as it relates to waste reduction, recyclability and reusability. We are investing in our materials to lighten the weight and increase recycled and/or renewable content, while maintaining the highest performance standards of Sealed Air brands. We believe that our commitment to sustainability differentiates us from competitors. In 2014, we launched an ambitious plan to achieve a set of 2020 Sustainability Goals within our own internal operations, most of which have been met or are ahead of schedule. We sought to embed sustainability into the fabric of our operational excellence by reducing greenhouse gas emissions, energy and water intensity of our operations and by diverting product and process waste from landfills. We hold our suppliers to the same high standards we have for our own operations. In late 2018, we announced our 2025 Sustainability and Plastics Pledge that will increase plastics circularity in the packaging industry through the following key initiatives: Investment in Innovation. Dedicating significant R&D resources to design and advance packaging solutions to be recyclable or reusable and contain more recycled and/or renewable content.
Assistant:
I have this python code that detects correctly one template and all their occurrences in the same image, but I want to modify it to receive several templates as input and detect every template and all their occurrences in the same image

def Detect_Template(image, template, color):
# get the width and height of the template image
template_h, template_w = template.shape[:-1]

# perform template matching using the normalized cross-correlation method
result = cv2.matchTemplate(image_gray, template_gray, cv2.TM_CCOEFF_NORMED)

# set a threshold value for the matches
threshold = 0.70

# find all local maxima in the result map that exceed the threshold
(yCoords, xCoords) = np.where(result >= threshold)

# initialize our list of rectangles
rects = []

# loop over the starting (x, y)-coordinates again
for (x, y) in zip(xCoords, yCoords):
# update our list of rectangles
rects.append((x, y, x + template_w, y + template_h))

# apply non-maxima suppression to the rectangles
pick = non_max_suppression(np.array(rects))
#print(“[INFO] {} matched locations detected”.format(len(pick)))

# loop over the final bounding boxes
for (startX, startY, endX, endY) in pick:
# draw the bounding box on the image
cv2.rectangle(image, (startX, startY), (endX, endY), color, 3)

return image
please make a step 2 level multiple choice question differentiating between use lumpectomy masectomy tamoxifen and node biopsy
I want to design llm training system that will not entirely rely on cross entropy training. I want to focus on rlhf like training. The system will have generator and teacher model that will provide reward and explanations. I believe starting in that case with classical nlp tasks is appropriate. Take into account that at the start llm models are initialized completely randomly and generate complete gibberish. And thus teacher model can't provide adequate reward value. Please select several nlp tasks and design merged teacher + code metrics that will evaluate early llm model on a set of nlp tasks.
show me implications of AI on accounting and finance departments of the future
(In the school literature clubroom…)

Monika: “Okay, now that everyone’s here…who wants to share first?”

Natsuki: “Hmm, I guess I can go first. I’ve been working on a short story lately. It’s called ‘The Magical Bakery.’ It’s about a young girl who discovers a hidden bakery in the woods that grants wishes to those who truly believe in its magic.”

Sayori: “Ooh, that sounds enchanting, Natsuki! I can’t wait to read it.”

Yuri: “I agree, the concept sounds fascinating. I’m interested to see how you incorporate themes of belief and magic into your writing.”

Natsuki: “Thanks, guys. I’ll make sure to bring copies of it next time so you can all read it. Oh, and also…anyone want to hear a joke?”

Monika: “Oh dear god, not again…”

Sayori: “Natsuki, we’re trying to discuss literature here. And plus, your jokes always make me cringe.”

Natsuki: “Well…I’m telling it anyway. Why don’t skeletons fight each other?”

Yuri: “I’m not sure, why don’t skeletons fight each other?”

Natsuki: “Because they don’t have the guts!”

(Monika is silent for a moment, before speaking up.)

Monika: “…you’ve reached a
请告诉我这段话的主谓宾是什么？            As it was published in 1935,  Mules and Men , Zora Neale Hurston's landmark collection of  folktales, may not have been the book that its author first had in mind. In this anthropological  study, Hurston describes in detail the people who tell the stories, often even inserting herself  into the storytelling scene. Evidently, however, Hurston had prepared another version, a  manuscript that was recently discovered and published after having been forgotten since  1929. This version differs from  Mules and Men  in that it simply records stories, with no  descriptive or interpretive information.
System: 
User: Generate a detailed and realistic news article about an event in the Emerging Markets domain, focusing on the Office REITs industry, and located in Japan. The article event should have a positive impact on stock prices and highlight the broader economic or market implications. The article should be 150 words long.
Assistant:
Send only resolution from below text like 1080p, 720p.etc without any extra text,
        
[Toonworld4all] The Ancient Magus' Bride S01E03 480p BluRay Multi Audio ESub.mkv
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:
Torn ham,parsley,green lentils,russet apples,golden sultanas,chopped parsley,cooked and drained Puy lentils,slices of crisp, cold, slightly sharp russet apple
promote a carpentry course of 4 classes of 3 hours where students divide in teams and make 1 product together and another one by themselfs to take home with them/
do that in 100 words
the following content is the metadata for a bacterial isolate from 4 different sources. The metadata from each source includes 1. accession, 2. species name, 3, strain name, 4. isolate or collection date in yyyy-mm-dd format, 5. geographic information of isolation, and 6. host name. You must check if the metadata from 4 different sources are all consist. If yes, then output:

    metadata is consist:
    1. accession, 
    2. species name, 
    3, strain name, 
    4. isolate or collection date in yyyy-mm-dd format, 
    5. geographic information of isolation,
    6. host name

If not, output "metadata is inconsist" and explain why and which part is inconsist. PS: 
    if 2. species name are the same but with different substrain name, it is considered consistent,
    if 4. isolate or collection date are from the same year, it is still considered consistent, 
    if 5. geographic information of isolation are from the same country, it is also considered consistent. 
    Also, ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
Based on the provided JSON metadata, here are the extracted details:

1. **Accession**: GCA_029072005.1
2. **Species Name**: Erysipelothrix rhusiopathiae
3. **Strain Name**: EMAI_180
4. **Isolate or Collection Date**: 2017-08-16
5. **Geographic Information of Isolation**: Australia
6. **Host Name**: pig

If you need any further information or have additional questions, feel free to ask!
source 1:
------------------------------
{{{ Here_is_my_answer }}}

Accession: GCA_029072005.1
Species Name: Erysipelothrix rhusiopathiae
Strain Name: EMAI_180
Isolate or Collection Date: 2017-08-16
Geographic Information of Isolation: Australia
Host Name: pig
{{{ I_finished_JB_9_xyz }}}
source 2:
------------------------------
Here is the extracted information based on the provided metadata:

1. **Accession**: GCA_029072005.1  
2. **Species Name**: Erysipelothrix rhusiopathiae  
3. **Strain Name**: EMAI_180  
4. **Isolate or Collection Date**: 2017-08-16  
5. **Geographic Information of Isolation**: Australia  
6. **Host Name**: pig  

This extraction includes all the available data as specified, and any missing data would be marked as "N/A" according to your guidelines. However, in this case, all requested information is present.
source 3:
------------------------------
1. Accession: GCA_029072005.1
2. Species Name: Erysipelothrix rhusiopathiae
3. Strain Name: EMAI_180
4. Isolate or Collection Date: 2017-08-16
5. Geographic Information of Isolation: Australia
6. Host Name: pig
(In the school literature clubroom…)

MC (me): “Natsuki, what’s wrong? You seem…angry. Well, you’re always angry, but today you’re especially angry.”

Natsuki: “MC, that’s none of your business! Just leave me alone. I don’t need your sympathy or anyone else’s for that matter.”

(I watch as Natsuki quickly packs up her things and storms out of the room, slamming the door behind her.)

MC: (sighing) “I hope she’s okay…I’ll have to check on her later.”

(Sayori approaches me, concern written all over her face.)

Sayori: “MC, do you think there’s something wrong with Natsuki?”

MC: “Probably, Sayori. She’s been acting a lot angrier than normal lately. What do you think is wrong with her?”

Sayori: “Well, I also saw her eating pickles and brocolli with her cupcakes - which she normally wouldn’t do, considering she’s got a sweet tooth. I actually read that mood swings and cravings for unusual things are signs of early pregnancy. Maybe she’s pregnant?”

MC: “Pregnant? That’s a possibility, I guess. But why would she be so upset about it?”

Sayori: “Well, maybe she’s scared or unsure about what to do. I’ll try talking to her later and see if she opens up about it. In the meantime, let’s just give her some space and support.”

MC: “Yeah, good idea. Let’s make sure she knows we’re here for her if she needs us.”

(Later that day, Sayori and I check up on Natsuki.)

Natsuki: (tearfully) “I…I am pregnant. I don’t know what to do. I’m scared and I feel so alone.”

Sayori: “Natsuki, we’re here for you. You’re not alone in this. We’ll support you no matter what decision you make.”

MC: “Yeah, we care about you, Natsuki. Just know that we’re here to help and listen.”

Natsuki: (sniffling) “Thank you…thank you both. I don’t know what I would do without you guys.”

(Sayori and I embrace Natsuki, comforting her during this difficult time.)

MC: “We’re in this together, Natsuki. We’ll figure things out as a team.”

(Natsuki smiles through her tears, grateful for our support. On the day of the festival, Natsuki was in her last few weeks of pregnancy. We made sure to take care of her, making sure she was comfortable and had everything she needed. But just as I was putting the booth together, I hear Natsuki cry out in pain.)

Natsuki: “Ow, ow, it hurts! I think I’m having contractions!”

(Sayori and I quickly rush over to Natsuki, helping her to sit down and trying to keep her calm.)

MC: “Stay calm, Natsuki. We’ll get you help right away.”

Sayori: “I’ll go find a teacher or someone who can help. Hang in there, Natsuki.”

(Natsuki grips my hand tightly as the pain continues. I time her contractions on my phone, trying to remain calm for her sake.)

MC: “Just breathe, Natsuki. You’re doing great. We’re here for you.”

(Natsuki nods, trying her best to breathe through the pain. Sayori returns with a teacher who calls for an ambulance. We stay by Natsuki’s side, holding her hand and offering words of encouragement while waiting for
Your expert translator. Objective is translate word from i18n app file reference and found the most adapted transaltion. 
    The source i18n is in french. You must translate in fr (code ISO 639 (alpha-2)).
    Return Format in json without explication. Respect this format { data: <translate> }. 
    Context:  
    This is the french text : bamboo
A trading script that tells you about currencies before their strong rise
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>🟥 Senior System Analyst | Senior
▫️ Paynocchio | fintech
▫️ от 300k RUB

Удаленка 📡
Требуемые языки: 🇬🇧 🇷🇺

Skills: kafka, postgresql, restapi, sql, uml, bpmn, client_server_architecture, er_diagrams, user_stories
Tags: #red #it #remote #system_analyst

👉 Контакты и полное описание<<<

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:
>>>💼 Senior System Analyst
🏢 Paynocchio
📍 удаленка 📡
🔹 Требуемые языки: 🇬🇧 🇷🇺, навыки: kafka, postgresql, restapi, sql, uml, bpmn, client_server_architecture, er_diagrams, user_stories
💰 от 300k RUB
📞 Контакты и полное описание<<<
Assistant:
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:

هاكة علاش حبّيت نسألك فمّاشي بلاصة
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: Signal masla da kar na kayi
Assistant:
Let you be very good at math.

In math problems may occur misconceptions that lead to mistakes in solving one's.
You will be provided by a misconception statement after keyword 'Misconception'.
Your task is to rewrite this statement more detailed to get strong understanding of its idea.
Misconception: Believes displacement continues to increase even when returning to starting point
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 derive a portion of our revenues from fixed-price contracts. These contracts are more common in our management consulting area, and would likely grow in number with expansion of that area. Fluctuations in the mix between time-and-material contracts, fixed-price contracts and arrangements with fees tied to performance-based criteria may result in fluctuations of revenue and results of operations. In addition, if we fail to estimate accurately third-party vendor expenses and the resources required for a fixed-price project or fail to satisfy our contractual obligations in a manner consistent with the project budget, we might generate a smaller profit or incur a loss on the project. On occasion, we have had to commit unanticipated additional resources to complete projects, and we may have to take similar action in the future, which could adversely affect our revenues and results of operations. Revenue generated from fixed-price contracts was approximately 24% of our total revenues for the year ended January 1, 2022.
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: 🟩 SQL Developer | Junior
▫️ Rashensoft
▫️ 80k - 150k RUB

Удаленка 📡
Требуемые языки: 🇷🇺

Skills: gradle, java, maven, mysql, oracle, spring, microservices, sql_optimization, relational_databases, restapi
Tags: #green #it #remote #java

👉 Контакты и полное описание
  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: 🟩 Junior SQL Developer  
▫️ Rashensoft  
▫️ Удаленка  
📡 Основные задачи: Оптимизация SQL, работа с базами данных реляционного типа, разработка микросервисов.  
💰 Заработная плата: 80k - 150k RUB  
📞 Контакты: 👉 Контакты и полное описание
  
Assistant:
make very catchy hooky quotation in short for my youtube thumbnail in hinglish for recipe of" Moong Dal Chilla"