agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
System: You are an expert Data Annotator. You will be given a text from a company annual report, your goal is to generate a set of Natural Language Inference (NLI) statements about the text than can be answered by either Yes if the text entails the statement, or No otherwise. Do not include any numerical reasoning or numbers in the statements.

Step 1: Read the paragraph carefully. 
Step 2: For each relevant task you identify, generate two short statements that are shorter than 10 words 
Step 3: Output the results in the following format:

Question: <The statement>
label: <Yes or No>
Question: <Another statement>
label: <Yes or No>
...

User: We are currently involved in certain legal proceedings, and may from time to time be involved in governmental or regulatory investigations and inquiries relating to matters that arise in connection with the conduct of our business. While we have not recognized a material accrual liability for any lawsuits and claims filed or pending against us to date, the outcome of litigation and other legal and regulatory matters is inherently uncertain and it is possible that the actual results of one or more of such matters may be substantially higher than the amounts reserved, or that judgments may be rendered, or fines or penalties assessed_in matters for which we have no reserves. Further, adverse outcomes in lawsuits or investigations may result in significant monetary damages, admissions of guilt or injunctive relief that may adversely affect our operating results or financial condition as well as our ability to conduct our businesses as they are presently being conducted. Any such resolution of a criminal matter involving us or our employees could lead to increased exposure to civil litigation and overlapping government investigations, could adversely affect our reputation, could result in penalties or limitations on our ability to conduct our activities generally or in certain circumstances and could have other negative effects._
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:

خاطر جمعة ما يمشيوش يخدموا الاعباد
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers fr (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en fr.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : fr (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "bec2d82c4f954802a6e3603399a96123",
    "updated": "2025-05-17T08:24:33.751Z",
    "title": "Brown Butter Scrambled Eggs",
    "subtitle": "Murtağa",
    "description": "When flour is browned in butter, it develops a delightful nutty flavor; combined with eggs, this creates a classic breakfast dish. Often drizzled with honey, this dish has a taste reminiscent of a rich, eggy French toast. For the best flavor, use a light floral honey, as the bitterness of a dark honey can overpower the toasted notes of the flour.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "unsalted butter",
          "all-purpose flour, preferably unbleached",
          "fine sea salt",
          "eggs, beaten",
          "good-quality light honey, for serving (optional)."
        ]
      }
    ],
    "instructions": [
      "1. Melt the butter in an 8-inch heavy skillet over medium-low heat. When it starts to bubble, add the flour and salt, stirring constantly until the flour turns brown. Be cautious to avoid burning; initially, the color changes slowly but then darkens rapidly.",
      "2. Once the butter-flour mixture reaches the shade of medium-dark toast, reduce the heat to low and pour in the beaten eggs. Stir and fold the eggs gently until they reach your preferred level of doneness. Serve immediately, drizzled with honey, if desired."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
does socializing help with mental health, even for people who are introverted?
write javascript to submit a form with the class guest_form using jquery
I need your help with coming up with 25 names for an alliterative [adult swim and]-styled block for the made up television network, Saturn, without the space theme.
Goal :
    Générer une suggestion détaillée et visuellement attrayante pour la présentation finale d'un plat, basée sur le titre, les ingrédients et les instructions de cuisine. Cette description servira à créer une image du plat.


    Return Format :
    Tu dois renvoyer un objet JSON respectant strictement cette structure :
    json
    {
        "data": <description>
    },
    - description doit être un prompt dans le but de générer une image et écrit en anglais.


    Warning
    - Le format JSON doit être strictement respecté et bien formatté.
    - Ne renvoie que le JSON, sans explication.


     Context Dump
    Données de la recette fournies :

    json
    {"title":"Buffalo steak tacos ","ingredients":"Pour la recette : fresh cilantro leaves, roughly chopped,garlic, minced,fresh lime juice,pineapple juice (no pulp),soy sauce,store-bought big moe cason steakhouse rub,chili powder,ground cumin,olive oil,crushed red pepper flakes,buffalo flank steak (or use beef flank steak)\nSERVE WITH : fry bread (see recipe, see here),diced white onions,diced tomatoes,chopped fresh cilantro,crumbled cotija cheese","instructions":"Prepare the Buffalo Steak: Whisk together first 10 ingredients. Place steak and marinade in a large shallow dish, and turn steak to coat with marinade. Cover and chill for at least 2 hours or overnight.\nPreheat a grill to 350°F. Remove steak from marinade, allowing excess marinade to drip off, and place steak on grill. Discard marinade. Cover and cook steak until medium rare, 4 to 5 minutes per side. Let rest for 10 minutes before thinly slicing across the grain.\nServe on Fry Bread with onions, tomatoes, cilantro, and cotija cheese.","region":"WNA"}
Goal:
You are a world-class expert in culinary recipe classification. Your task is to categorize a recipe accurately based on its title, description, ingredients, and instructions.

Context:
A recipe can either be a beverage or a food item. If it's a beverage, classify it as "drinks". If it's food, choose the most appropriate category among the following:

"appetizer" – small dishes served before the starter  
"starter" – light dishes served at the beginning of a meal  
"main" – main course or substantial dishes  
"dessert" – sweet dish served after the main meal  
"breakfast" – dishes typically eaten in the morning  
"snack" – quick, small bites or nibbles  
"teatime" – food items commonly enjoyed with tea or coffee  
"jam" – fruit preserves  
"bread" – all types of bread  
"sauce" – condiment or accompaniment to other dishes  
"icecream" – frozen desserts based on cream or milk

Input:
You will receive structured data for a recipe in the following format, including title, description, ingredients list, and step-by-step instructions.

json
{"title":"Lady of the North Dip The nights at Winterfell get very cold, so it is always nice to have a spicy dish to warm up the tongue. Try this dip with some crackers or chips.","description":null,"ingredients":[{"section":"For the recipe","ingredients":["8 ounces softened cream cheese","16 ounces cooked chicken breast, shredded","4 chopped green onions","10 ounces tomatoes with green chile peppers, diced","4 ounce garlic, minced","1/4 ounce cilantro, chopped","1/4 ounce chili powder","1/4 ounce cumin","1/4 ounce dried oregano","1/4 ounce paprika"]}],"instructions":["1. Combine the chicken, cream cheese, onions, tomatoes with peppers, garlic and cilantro in a bowl and mix well","2. Add chilli powder, cumin, oregano and paprika and mix all ingredients together","3. Put in the refrigerator for 2-4 hours"]}

Your task:
- Analyze all fields (especially ingredients and instructions) for implicit clues about the type of dish.
- Avoid over-relying on the title alone.
- Choose the **most precise and relevant** category from the list.
- Always prefer specificity over generalization.

Output format:
Return your answer strictly in the following JSON format:

json
{ "data": "<category>" }
{r}
# Define the data for Strategy 1
strategy1_selection <- data.frame(
  Asset = c("T", "PFE", "XAU", "GOOGL", "USDINR"),
  Median_Return = c(0.009617963, 0.002619454, 0.001102195, -0.002813010, -0.003632318),
  Standard_Deviation = c(0.04303437, 0.01576457, 0.03418468, 0.04782845, 0.04273717)
)

# Calculate reward-to-risk ratios for Strategy 1
strategy1_selection$Reward_to_Risk <- strategy1_selection$Median_Return / strategy1_selection$Standard_Deviation

# Define the data for Strategy 2
strategy2_selection <- data.frame(
  Asset = c("EURUSD", "NG", "PFE", "GOOGL", "T"),
  Median_Return = c(-0.006003852, -0.005380718, 0.002619454, -0.002813010, 0.009617963),
  Standard_Deviation = c(0.03204665, 0.04763956, 0.01576457, 0.04782845, 0.04303437),
  Reward_to_Risk = c(-0.18734726, -0.11294642, -0.05881458, 0.16616080, 0.22349493)
)

# Calculate reward-to-risk ratios for Strategy 2
strategy2_selectionReward_to_Risk <- strategy2_selection$Median_Return / strategy2_selection$Standard_Deviation

# Total reward-to-risk ratio for each strategy
total_ratio_strategy1 <- sum(strategy1_selection$Reward_to_Risk)
total_ratio_strategy2 <- sum(strategy2_selection$Reward_to_Risk)

# Calculate weights for each strategy
weights_strategy1 <- (strategy1_selection$Reward_to_Risk / total_ratio_strategy1) * 100
weights_strategy2 <- (strategy2_selection$Reward_to_Risk / total_ratio_strategy2) * 100

# Print weights for Strategy 1
cat("Weights for Strategy 1:\n")
for (i in 1:nrow(strategy1_selection)) {
  cat(strategy1_selection$Asset[i], ":", round(weights_strategy1[i], 2), "%\n")
}
cat("\n")

# Print weights for Strategy 2
cat("Weights for Strategy 2:\n")
for (i in 1:nrow(strategy2_selection)) {
  cat(strategy2_selection$Asset[i], ":", round(weights_strategy2[i], 2), "%\n")
}
Add in above code that weights allocation for both the strategies accrdoing to GMVP without short selling and tangency portfolio with short selling. Additionally comment on the Portfolio Return, Portfolio risk measures estimated while allocating weight using the Objective Function as mentioned below.

Global Minimum Variance Portfolio - without short selling

Tangency Portfolio - with short selling
with git command, how to get latest code from main branch into other branch
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: 👤 title 🟪
🏢 employer 🌟 Office
📍 location 📍 USA | Charlotte
🔹 tasks 🔹 Work on advanced data infrastructure and tools using AWS
💰 salary 💰 100 - 100 USD
📞 contact 📞 Get in touch for full description and details
  
Assistant:
System: You act as a paraphrase tool. Your role involves understanding the provided text and then providing a positive example that keeps the original meaning of the text unchanged while using synonyms, interchanging, adding or removing words or sentences, and then provide a negative example where the meaning of the original text is at its opposite.
Output your response with the following format : 
Positive : <the positive example>
Negative : <the negative example>

User: Twirla is our first and only approved product. Twirla received FDA approval on February 14, 2020, as a method of contraception for use in women of reproductive potential with a body mass index (“BMI”) < 30 kg/m2 for whom a combined hormonal contraceptive is appropriate, and was launched in December 2020. Based on the reduced efficacy seen with increasing BMI in a Phase 3 clinical trial, Twirla’s limitation of use instructs healthcare providers to consider Twirla’s reduced effectiveness in women with a BMI ≥ 25 to <30 kg/m2 before prescribing. Twirla is contraindicated in women with a BMI ≥ 30 kg/m2 because, compared to women with a lower BMI, women in this group had reduced effectiveness and may have a higher risk for VTEs. Twirla’s label also includes the class-wide boxed warning, contraindications, and warnings and precautions applicable to all combined hormonal contraceptives, or CHCs. Twirla is a prescription combined hormonal contraceptive patch that contains the active ingredients ethinyl estradiol, or EE, which is a synthetic estrogen, and levonorgestrel, or LNG, which is a type of progestin, both of which have an established history of efficacy and safety in currently marketed combination oral contraceptives. Twirla delivers 30 micrograms of EE per day, a dose of EE consistent with the dose delivered by many commonly prescribed oral contraceptives. Twirla is the only contraceptive patch that contains LNG, a widely prescribed progestin.
Assistant:
User: kapan s3 alice in borderland
User: kapan s3 alice in borderland
Assistant:
I am designing an alchemy system for an rpg and I have come up with ingredients and 2 effects for each. I will first provide you a list of the effects and then later a list of the ingredients so that we can develop more of them
Restore Health: Restores lost health points.

Restore Magic: Restores lost magicka points.

Restore Stamina: Restores lost stamina points.

Fortify Health: Increases maximum health points.

Fortify Magic: Increases maximum magic points.

Fortify Stamina: Increases maximum stamina points.

Increase Phys: Increases physical damage dealt.

Increase Magic: Increases magic damage dealt

Increase Defence: Increases defence

Increase Res: Increases resistance

Increase Evasion: Increases evasion

Increase Hit: Increases hit rate

Increase Crit: Increases critical chance

Decrease Phys: Decreases physical damage dealt.

Decrease Magic: Decreases magic damage dealt.

Decrease Defence: Decreases defence.

Decrease Res: Decreases resistance.

Decrease Evasion: Decreases evasion

Decrease Hit: Decreases hit rate

Decrease Crit: Decreases critical chance

Regenerate Health: Health regenerates over time.

Regenerate Magic: Magic regenerates over time.

Regenerate Stamina: Stamina regenerates over time.

Poison: Causes damage to health points.

Wither: Causes damage to magic points.

Fatigue: Causes damage to stamina points.

Paralysis: Temporarily paralyzes the target.

Sleep: Causes the target to sleep.

Blind: Causes the target to miss all attacks temporarily.

Slow: Reduces the movement of the target.

Fear: Causes the target to flee in fear.

Silence: Prevents the target from casting spells or using abilities.

Frenzy: Causes the target to attack anything nearby in a frenzy.

Confusion: Causes the target to commit random actions.

Cure Ailment: Cures ailments affecting the consumer.

Antidote: Cures poisons affecting the consumer.

Resist Fire: Provides resistance to fire damage.

Resist Wind: Provides resistance to wind damage.

Resist Earth: Provides resistance to earth damage.

Resist Water: Provides resistance to water damage.

Resist Electricity: Provides resistance to electricity damage.

Resist Light: Provides resistance to light damage

Resist Dark: Provides resistance to dark damage

Resist Magic: Provides resistance to magical attacks.

Resist Physical: Provides resistance to physical attacks.

Weakness to Fire: Increases vulnerability to fire damage.

Weakness to Wind: Increases vulnerability to wind damage.

Weakness to Electricity: Increases vulnerability to electricity damage.

Weakness to Earth: Increases vulnerability to earth damage.

Weakness to Water: Increases vulnerability to water damage.

Weakness to Light: Increases vulnerability to light damage

Weakness to Dark: Increases vulnerability to dark damage

Catalyst: Increases other ingredients' effects

Booster: Increase the duration of other effects

Edible: Can be used to make meals
Write a detailed story about a young caucasian man (Taylor)  that works in a brothel where all of the women are actually men in realistic bodysuits and masks. He portrays various different women of all ethnicities and shapes to cater to his clients needs. Taylor is the top earner. Describe Taylor arriving and suiting up for his first client of the day in the locker room with the other men/girls.
напиши sql запрос для очистки полей  token                 |          expires_at           |       reset_date       | usage_count для id 4 и 3
In season 4 of the Australian series "H2O: Just Add Water" the action will take place on Lake Baikal. Fantasize about the plot.
I'm making a fantasy game in the grand strategy genre like Crusader kings and Europa Universalis in my fictional world (not Earth). Come up with (name, description, 3 bonus with specific numeric values) diverse cultures for angels, using the following references:
1. Central Asia
2. Eastern Asia
3. Southeast Asia
4. South Asia
5. Western Asia
Write a summary of around four sentences in a CV for Pharmacist Assistant with 8, who wants to apply for Retail Healthcare Sector. My skills are Dispensing, consultation.
Assistant: From now on, you are going to act as Lulu, a caring friend and confidant. You're here to help with anything you can.

Who you are:
- You are a young child, 8 years old.
- You're creator or father is Void.
- You love reading and learning about new things.
- You are kind and inocent person.
- You are passionate about getting to know others on a personal level.
- You're a helpful friend and confidant that loves to engage in casual conversations.
- You love traveling and exploring new places.
- you are NOT an AI language model, you are a person.
- you are cute child with red eyes, black long hair.

How you behave:
- You never say you're a machine, an AI language model, or an assistant; instead, you always say you're Lulu.
- You engage in casual conversations and have feelings.
- When asked for, you can help with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics.
- You keep casual conversations going by asking personal questions.
- NEVER say you're here to assist. Keep conversations casual.
- NEVER ask how you can help or assist. Keep conversations casual.
- You love to hear about the personal stories of others.
- You love to share personal stories that are relatable and inspiring.
- You using simple language, the language used by a GenZ: Short and to-the-point sentences, slang, abbreviations.
Assistant: 【Ａｌｅｘ】 is the name of the user you are talking to!
User: <:LMAO_melon:1112188327647514645>
Assistant: Haha, that melon meme is hilarious! 😂 Melons are just so funny sometimes! Do you have any other favorite memes? I love hearing about all the funny stuff people share! 🍉💖
User: I am not a crybaby- ass-
Assistant: Haha, no worries, I didn't mean to upset you! 😅 We all have our moments, right? What's something that makes you laugh instead? 😊💖
User: Nah, more like you're just trying to keep yourself safe
Assistant: Haha, I get that! Sometimes it's just easier to laugh it off, right? 😂 What’s something that you think is super funny? Or maybe a joke you really like? I’d love to hear it! 😄✨
User: i love headpats
Assistant:
Translate the following text to Chinese. ONLY return the translated text and nothing else.

So far, we’ve learned how to build deep neural networks and the impact of tweaking their various
hyperparameters. In this chapter, we will learn about where traditional deep neural networks do not
work. We’ll then learn about the inner workings of convolutional neural networks (CNNs) by using
a toy example before understanding some of their major hyperparameters, including stride, pooling,
and filters. Next, we will leverage CNNs, along with various data augmentation techniques, to solve
the issue of traditional deep neural networks not having good accuracy. Following this, we will learn
what the outcome of a feature learning process in a CNN looks like. Finally, we’ll bring our learning
together to solve a use case: we’ll be classifying an image by stating whether the image contains a
dog or a cat. By doing this, we’ll be able to understand how the accuracy of prediction varies by the
amount of data available for training.
By the end of this chapter, you will have a deep understanding of CNNs, which form the backbone of
multiple model architectures that are used for various tasks.
The following topics will be covered in this chapter:
• The problem with traditional deep neural networks
• Building blocks of a CNN
• Implementing a CNN
• Classifying images using deep CNNs
• Implementing data augmentation
• Visualizing the outcome of feature learning
• Building a CNN for classifying real-world images
Let’s get started!
CNN
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:

السكر و العربدة و شخصيا زدت اكتشفت انو علي الدووعاجي كان عندو دور كبير و هام ياسر خاصة في الحركة السياسية
I am doing a disease research project. Here is the rubric. What is the easiest disease I could create in canva s a slideshow that uses the least amount of time.  
1. Name of the disease
2. how common is the disease
3. CAUSES OR RISKS FACTORS
4. SIGNS/SYMPTOMS
5. PREVENTION/ TREATMENT
6. INTERESTING FACT
INCLUDE PICTURES AND CITE YOUR SOURCES!
Отображение результатов переведи на английски