agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: (CNN) -- Noah Baumbach is emerging as an emotionally acute, not to say eviscerating, observer of the middle-class intelligentsia, the kind of people who write letters to "The New York Times" and might plausibly pop up in a Woody Allen movie. 

Unlike the Woodman, Baumbach doesn't show his face on screen, but his films are no less personal for that: "The Squid and the Whale" was a sometimes wincingly autobiographical account of two boys torn between their divorcing parents, and he's not one to deflect an insight with a wisecrack. The cracks just cut deeper. I've rarely experienced an audience recoil from a character as passionately as they did to Nicole Kidman's toxically self-absorbed writer in "Margot at the Wedding" (maybe her best performance, incidentally). These are comedies in the sense that the characters are painfully ridiculous -- and all too recognizably real -- but Baumbach sure doesn't make it easy for himself, or us. 

Roger Greenberg (Ben Stiller) is another neurotic narcissist, a middle-aged loner who comes back to Los Angeles to house-sit while his brother enjoys a long vacation in the Far East. Greenberg (only his brother calls him Roger) can feed the family dog, but the truth is that he desperately needs to regroup and recharge after a spell in a mental hospital. 

He has one friend, Ivan (Rhys Ifans), who still has time for him and a wider circle of former friends who don't. We soon learn that Greenberg used to front a band, but it fell apart after he turned down a recording deal, and he's been in New York ever since, under-achieving on a permanent basis. 

Answer this series of questions:

1. Who is an observer of middle class intelligentsia?
2. Does he appear on screen?
3. What was the film about two boys?
4. Was it about their loving parents?
5. Who was in Margot at the Wedding?
6. Are these movies thrillers?
7. Who played Roger Greenberg?
8. What city does the character come back to?
9. To do what?
10. For whom?
11. Where was his brother going/
12. What does Greenburg do for the dog?

Numbered answers:
Answer: 1. Noah Baumbach is an observer of middle-class intelligentsia.
2. No, Baumbach doesn't appear on screen.
3. The film about two boys is "The Squid and the Whale."
4. No, it was about the boys being torn between their divorcing parents.
5. Nicole Kidman was in "Margot at the Wedding."
6. No, these movies are not thrillers; they are comedies that focus on the painfully ridiculous and all too recognizably real characters.
7. Ben Stiller played Roger Greenberg.
8. The character comes back to Los Angeles.
9. He comes back to house-sit.
10. He house-sits for his brother.
11. His brother was going on a long vacation in the Far East.
12. Greenburg feeds the family dog while house-sitting.
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: During fiscal 2021, including purchases for Elfa which represented approximately 19% of our purchases, we purchased approximately 57% of our merchandise from vendors located outside the United States (including approximately 34% from vendors located in China) and approximately 43% of our merchandise from vendors located in the United States. In addition, some of the merchandise we purchase from vendors in the United States also depends, in whole or in part, on manufacturers located outside the United States. As a result, our business depends on global trade, as well as trade and cost factors that impact the specific countries where our vendors are located, including Asia. Our future success will depend in part upon our ability to maintain our existing foreign vendor relationships and to develop new ones. While we rely on our long-term relationships with our foreign vendors, we have no long-term contracts with them and transact business on an order by order basis.
Assistant:
System: 
You will receive a question-answer pair. Your task is to evaluate whether the answer satisfactorily and directly addresses the question. Use the examples below to guide your assessment.

If the answer is precise, relevant, and can answers the question, return 1. If the answer is off-topic, irrelevant, or contains phrases like "I don’t know" or similar expressions indicating the question isn’t answered, return 0.

Examples:

Example 1:
Question: "Who was the first person to walk on the moon?"
Answer: "The first person to walk on the moon was Neil Armstrong in 1969."
Result: 1

Example 2:
Question: "Who was the first person to walk on the moon?"
Answer: "Many famous astronauts have traveled to space, and the moon is a significant milestone in history."
Result: 0

Example 3:
Question: "What is the primary cause of global warming?"
Answer: "I don’t know."
Result: 0

Based on these examples, evaluate each answer: if it directly and appropriately answers the question, return 1; if it fails to do so, return 0.


User: Question: Thông tin nào được cung cấp về tuyển dụng sinh viên thực tập?
Answer:Thông tin về nhận/tuyển dụng sinh viên thực tập từ các đơn vị ký kết hợp tác với trường được cung cấp.
Result only 0 or 1. Don't add anything else:
Assistant:
Hi there, can you rewrite a requirement for me to be SMART?
from collections.abc import Callable
from typing import Any, TypeVar, Tuple, cast
from collections import OrderedDict
from functools import wraps

Function = TypeVar('Function', bound=Callable[..., Any])

def cache(max_size: int) -> Callable[[Function], Function]:
"""
Returns decorator, which stores result of function
for max_size most recent function arguments.
:param max_size: max amount of unique arguments to store values for
:return: decorator, which wraps any function passed
"""


def make_finded(func: Function) -> Function:
    finded: OrderedDict[Tuple[Tuple[Any, ...], Tuple[Tuple[str, Any], ...], Function], Any] = OrderedDict()

    @wraps(func)
    def get(*argc: Any, **kwargs: Any) -> Any:
        if (*argc, tuple(sorted(kwargs.items())), func) in finded:
            return finded[(*argc, tuple(sorted(kwargs.items())), func)]

        res = func(*argc, **kwargs)
        finded[(*argc, tuple(sorted(kwargs.items())), func)] = res
        if len(finded) > max_size:
            finded.popitem(last = False)

        return res

    return get

return cast(make_finded, Callable[[Function], Function])
вот код и вот ошибка
04.1.NamespacesAndDecorators/tasks/lru_cache/lru_cache.py:31: error: Incompatible return value type (got "_Wrapped[[VarArg(Any), KwArg(Any)], Any, [VarArg(Any), KwArg(Any)], Any]", expected "Function") [return-value]
04.1.NamespacesAndDecorators/tasks/lru_cache/lru_cache.py:33: error: Function "make_finded" is not valid as a type [valid-type]
04.1.NamespacesAndDecorators/tasks/lru_cache/lru_cache.py:33: note: Perhaps you need "Callable[...]" or a callback protocol?
Found 2 errors in 1 file (checked 2 source files)
Effect of mixed surface terminations on the work function and quantum capacitance of Sc2CT2 monolayer
Why is it a lot easier to get in touch with friends from a different high school again during summer
script about dak prescott first roudn exit
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
examples:

review: really disappointed with your customer services team. Ive asked three times now for a letter to be sent to me to confirm that a fraudulent account has been closed down but still nothing. Ask to speak to managers but theyre always in a meeting. #fraud #CustomerExperience
intention: bug report
review: I don't need a dm. Why don't your stores carry what I need for my phone?
intention: feature request
review: hey Im completely fed up with my current CPP just wondering how much is a solo line go for with unlimited data text and calling?
intention: other
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
examples:

review: really disappointed with your customer services team. Ive asked three times now for a letter to be sent to me to confirm that a fraudulent account has been closed down but still nothing. Ask to speak to managers but theyre always in a meeting. #fraud #CustomerExperience
intention: bug report
review: I don't need a dm. Why don't your stores carry what I need for my phone?
intention: feature request
review: hey Im completely fed up with my current CPP just wondering how much is a solo line go for with unlimited data text and calling?
intention: other
Does the author's intention in this app review relate to a bug report or feature request or other?
review: I had a rather poor experience at one of your stores yesterday.  Im not sure if this is the proper channel, but Id like to let someone know about it.'
intention: '
Assistant:
how to project a linestring to a meters CRS to apply a distance offset?
Assistant: Hello! How can i help you today?
User: How i study Electrochemistry chapter easily
Assistant:
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:
1 large onion, chopped,1 lb lean ground lamb,2 tbsp vegetable oil,1 1/2 C. mashed potatoes,2 hard-cooked eggs, chopped,2 tbsp parsley, chopped,salt and pepper,1/2 tsp garlic powder,4 eggs, lightly beaten
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: Maki is the name of the user you are talking to!
User: jap
User: <:Ramses_von_Maki:1312865744392032357>
User: kanst du call kommen?
User: <@235148962103951360>
Assistant:
(In the school literature clubroom…)

Monika: “…what on Earth happened in here?”

Yuri: “I’m not quite sure myself, Monika. It looks like a tornado swept through the room!”

Natsuki: “Ugh, Sayori must have been here. She’s always so messy.”

Sayori: (pokes her head in) “Hey guys! Oops… Did I create a mess again?”

Monika: “Sayori, we’ve talked about this. Please try to be more careful. It’s hard to find books when they’re scattered all over the place.”

Sayori: (sheepishly) “Sorry, Monika. I’ll clean it up, I promise!”

Yuri: “No worries, Sayori. I can help you organize the books if you’d like.”

Natsuki: (rolls her eyes) “I can’t believe we have to clean up after her again.”

Monika: “Natsuki, let’s try to be understanding. We’re all members of the literature club, and it’s important for us to support each other.”

Natsuki: (reluctantly) “Fine, I’ll help.” (to herself) “God, all this pregnancy weight will make this difficult…”

Monika: “Did you say something, Natsuki?”

Natsuki: “N-nothing! Let’s just get this over with.”

(The group begins to gather and sort the scattered books. Monika and Yuri help Sayori while Natsuki tries to find a place to put the books neatly.)

Monika: “Sayori, maybe we can come up with a system to organize the books so they don’t end up all over the place again. Any ideas?”

Sayori: “Well, maybe we can sort them by genre or author? That way, it’ll be easier to find the books we’re looking for.”

Yuri: “That’s a great idea, Sayori. We can also create a book log to keep track of our collection and who has borrowed which book.”

Natsuki: “I guess that could work. As long as everyone actually puts the books back where they belong.”

Monika: “Exactly. And we can also encourage each other to be more mindful of the clubroom’s cleanliness. It’s important to maintain a welcoming and organized space for everyone.”

(Slowly but surely, the group manages to clean up and organize the literature clubroom. They step back to admire their work.)

Monika: “Well done, everyone. The room looks so much better now.”

Sayori: “Yeah, it feels good to have everything in order. Thanks for helping me, guys.”

Yuri: “Of course, Sayori. We’re here to support each other.”

Natsuki: “Yeah, yeah. At least now we won’t have to play ‘hide and seek’ with the books anymore- OOF!!! Ow!”
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers es (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 es.
    - 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 : es (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "68668e32aef14e3fbea6c1da428f6948",
    "updated": "2025-04-19T12:14:58.566Z",
    "title": "Cheese mini egg and broccoli omelet",
    "subtitle": "",
    "description": null,
    "ingredients": [
      {
        "section": "Ingredients",
        "ingredients": [
          "broccoli florets",
          "whole eggs",
          "egg whites",
          "reduced fat cheddar",
          "grated romano or parmesan cheese",
          "olive oil",
          "salt and fresh pepper",
          "cooking spray"
        ]
      }
    ],
    "instructions": [
      "Set your oven to preheat at 350°F.",
      "Steam the broccoli florets with a small amount of water for approximately 8 minutes.",
      "Allow the steamed broccoli to cool slightly, then mash the florets into small pieces.",
      "Combine the mashed broccoli with olive oil, seasoning to taste with salt and pepper.",
      "Lightly grease a 9-cup muffin tin using cooking spray.",
      "Distribute the seasoned broccoli mixture evenly among the muffin cups.",
      "In a separate bowl, whisk together the whole eggs, egg whites, and Parmesan cheese.",
      "Season the egg mixture with salt and pepper, then beat thoroughly until combined.",
      "Carefully pour the egg mixture over the broccoli in each muffin cup, filling them about three-quarters full.",
      "Top each cup with the grated cheddar cheese and transfer the muffin tin to the preheated oven.",
      "Bake for 15 to 20 minutes, or until the omelets are set and cooked through.",
      "Allow the mini omelets to cool slightly before serving warm."
    ],
    "notes_ingredients": null,
    "notes_instructions": "Leftover mini omelets can be stored in the refrigerator for several days if wrapped securely in plastic wrap."
  }
]
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": "df0baeae763841d1b81d478ee0bdee82",
    "updated": "2025-04-24T01:57:09.451Z",
    "title": "Port and Balsamic Braised Red Cabbage",
    "subtitle": "This rich and indulgent red cabbage is a delightful side dish. The apples contribute a pleasant sweetness, while the star anise infuses a warm, aromatic flavor.",
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "rapeseed oil or water",
          "red onion, finely sliced",
          "red cabbage, finely shredded",
          "crisp sweet apples, grated (braeburns are ideal)",
          "vegan-friendly port",
          "balsamic vinegar",
          "zest and juice of",
          "soft brown or coconut sugar",
          "star anise",
          "cinnamon stick",
          "sea salt",
          "cracked black pepper",
          "fresh thyme leaves, plus extra to garnish"
        ]
      }
    ],
    "instructions": [
      "In a large, heavy-based, non-stick saucepan, heat the oil or water over medium heat.",
      "Add the sliced red onion and cook for 2-3 minutes, until softened and lightly golden.",
      "Add the shredded red cabbage and grated apple. Increase the heat and sauté for 3-4 minutes, stirring continuously.",
      "The cabbage will reduce in volume as it cooks. Once it's about half its original size, reduce the heat to low and add the port, allowing it to deglaze the pan.",
      "Cook for a couple of minutes, then add the remaining ingredients.",
      "Stir everything together, cover the saucepan, and let the cabbage simmer for 55-60 minutes, stirring occasionally to prevent sticking.",
      "After an hour, the red cabbage should be tender, and the liquid should have reduced to a glaze-like consistency.",
      "Garnish with fresh thyme before serving."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Freedom Planet harem x Naruto part 1. (Freedom Planet all characters and non characters)