agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers de (ISO 639), en respectant le contexte culinaire.

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

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

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

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

    [
  {
    "recipeCode": "1ccecc4cf91644189cd102ff09c599b1",
    "updated": "2025-05-19T20:13:28.269Z",
    "title": "Home-style Omelette",
    "subtitle": "This home-style omelette is a great example of a quick, simple, and flavorful dish that can be served as a main course or included in a larger meal.",
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "eggs, beaten",
          "finely chopped spring onions",
          "minced pork",
          "sichuan preserved vegetables, rinsed thoroughly and finely chopped",
          "white pepper",
          "groundnut or vegetable oil",
          "sesame oil",
          "shaoxing rice wine or dry sherry",
          "light soy sauce",
          "sugar"
        ]
      }
    ],
    "instructions": [
      "In a large bowl, mix the beaten eggs with half of the spring onions, minced pork, preserved vegetables, and 1/4 teaspoon of white pepper.",
      "Heat a wok or large frying pan over high heat until very hot. Add the oil, and once it is hot and slightly smoking, pour in the egg mixture and fry until golden brown.",
      "Carefully flip the omelette and cook the other side until browned.",
      "Remove the omelette from the oil and place it on kitchen paper to drain.",
      "Pour off most of the oil from the pan, leaving about 1 tablespoon behind.",
      "Reheat the pan until hot. Add the remaining spring onions, pork, preserved vegetables, sesame oil, rice wine, light soy sauce, and sugar, then stir-fry for 2 minutes.",
      "Place the omelette on a serving plate, spoon the stir-fried mixture over the top, and serve immediately."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
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: Answer the following question: Title: don't judge a book by it's cover Review: I've heard friends say Fabio should make a movie out of his books, yet this will not be the one. The characters are flat, and the villians have no complexity behind them at all. I knew from the begining how it would end, and that is not romance. Really disappointing work. Is the review positive or negative?
Answer:
Answer: The review is negative.
Compare 1 and two and explainwhy they produce slightly different values and choose which is better. 1. def adjust_values(img: np.ndarray) -> Tuple[np.ndarray, int, int]:
    """Adjust the values of a grayscale image."""
    try:
        hist, bins = np.histogram(img[img >= 250], bins=6, range=(250, 256))
        modal_white = int(bins[np.argmax(hist)])

        hist, bins = np.histogram(img[img < 61], bins=61, range=(0, 61))
        modal_black = int(bins[np.argmax(hist)])

        if modal_white != 255:
            img = img.astype(np.float64)
            img = img * (255.0 / modal_white)
            img = np.clip(img, 0, 255)
            img = img.astype(np.uint8)

        if modal_black:
            img = img.astype(np.float64)
            adjusted = (img - modal_black) * (255.0 / (255 - modal_black))
            adjusted = np.clip(adjusted, 0, 255)
            
            gamma = 1.0 - (modal_black / 255.0)
            adjusted = np.power(adjusted / 255.0, gamma) * 255 

            adjusted[img == 0] = 0
            adjusted[img == 255] = 255

            adjusted = adjusted.astype(np.uint8)
        else:
            adjusted = img

        return adjusted, modal_black, modal_white
    except ValueError as e:
        log_error(f"Error adjusting values for image: {e}")
        return img, 0, 255       2: def adjust_values(img: np.ndarray) -> tuple[np.ndarray, int, int]:
    """Adjust the values of a grayscale image."""
    try:
        # Calculate modal white value in the range [250, 256)
        hist_white, bins_white = np.histogram(img[img >= 250], bins=6, range=(250, 256))
        modal_white = int(bins_white[np.argmax(hist_white)])

        # Calculate modal black value in the range [0, 61)
        hist_black, bins_black = np.histogram(img[img < 61], bins=61, range=(0, 61))
        modal_black = int(bins_black[np.argmax(hist_black)])

        # Convert image to float for processing
        adjusted = img.astype(np.float64)

        # Scale image if modal white is not at maximum intensity
        if modal_white != 255:
            adjusted *= 255.0 / modal_white
            adjusted = np.clip(adjusted, 0, 255)

        # Adjust image based on modal black value
        if modal_black:
            adjusted = (adjusted - modal_black) * (255.0 / (255 - modal_black))
            adjusted = np.clip(adjusted, 0, 255)

            gamma = 1.0 - (modal_black / 255.0)
            adjusted = np.power(adjusted / 255.0, gamma) * 255

            # Preserve original extreme values
            adjusted[img == 0] = 0
            adjusted[img == 255] = 255

        # Convert back to unsigned 8-bit integer
        adjusted = adjusted.astype(np.uint8)

        return adjusted, modal_black, modal_white

    except ValueError as e:
        log_error(f"Error adjusting values for image: {e}")
        return img, 0, 255
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: We own, lease, and operate several coal-fired electric generating units. Although we generally carry sufficient coal inventory at our generating facilities to protect against an interruption or decline in supply, there can be no assurance that the inventory levels will be adequate. While we have coal supply and transportation contracts in place, we cannot assure that the counterparties to these agreements will be able to fulfill their obligations to supply coal to us or that we will be able to take delivery of all the coal volume contracted for. Coal deliveries may occasionally be restricted because of rail congestion and maintenance, derailments, weather, the COVID-19 pandemic, and supplier financial hardship. Supplier financial hardship is a result of decreased demand for coal due to increased natural gas and renewable energy generation, the impact of environmental regulations, and environmental concerns related to coal-fired generation. If we are unable to obtain our coal requirements under our coal supply and transportation contracts, we may be required to purchase coal at higher prices or we may be forced to reduce generation at our coal-fired units, which could lead to increased fuel costs. The increase in fuel costs could result in either reduced margins on net sales into the MISO Energy Markets, a reduction in the volume of net sales into the MISO Energy Markets, and/or an increase in net power purchases in the MISO Energy
Assistant:
Our title is "Old Hollywood Actresses Who Were Forced to Have Abortions". Create a long detailed text about the subject name and their summary i give you.  Focus only on the Subject name and there following Summary. Subject Name: [Marilyn Monroe] Summary: [Marilyn Monroe, one of the most iconic and tragic figures in Hollywood, endured numerous personal struggles, including being pressured into terminating pregnancies to maintain her image as a desirable star.]

            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.
User: generate cool gaming nicknames with names Gorlakh
User: generate cool gaming nicknames with names Gorlakh
Assistant:
import gradio as gr
from huggingface_hub import InferenceClient
import os

# Initialize Hugging Face Inference Client
client = InferenceClient(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    token=os.getenv("HUGGINGFACEHUB_API_TOKEN")  # Token is retrieved from the Hugging Face secret
)

# Function to interact with the model
def ask_model(question):
    responses = []
    for message in client.chat_completion(
        messages=[{"role": "user", "content": question}],
        max_tokens=1000,
        stream=True,
    ):
        responses.append(message.choices[0].delta.content)
    
    return ''.join(responses)

# Gradio Interface
interface = gr.Interface(
    fn=ask_model,
    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),
    outputs="text",
    title="Llama3.1 Based AI Chatbot | Interface by Thay Chansy",
    description="Ask the model any question and get a response."
)

# Launch Gradio app
interface.launch(share=True)
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.
 Write the text in English. (en).
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:
and norsk music
Assistant:
Send only resolution from below text like 1080p, 720p.etc without any extra text,
        
[RAI] One Piece S1E38 - Hindi_720P HD.mp4
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": "f509e1689ee3466d8910fea91e3e41f4",
    "updated": "2025-04-20T08:59:33.242Z",
    "title": "Rosemary-Simmered White Beans",
    "subtitle": "A fragrant and delicate bean dish infused with rosemary and orange.",
    "description": "These white beans offer a delicate flavor, infused with aromatic rosemary and a hint of orange zest.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "(350g) dried cannellini beans, soaked in water overnight",
          "garlic cloves, finely chopped",
          "rosemary stems",
          "olive oil",
          "white wine vinegar",
          "pared peel of",
          "(1.4 liters) vegetable stock or chicken stock",
          "salt and freshly ground black pepper"
        ]
      }
    ],
    "instructions": [
      "Drain and rinse the soaked cannellini beans. Place them in a large, heavy-bottomed pot. Add the chopped garlic, rosemary stems, olive oil, white wine vinegar, orange peel, and stock. Season with salt and pepper.",
      "Bring the mixture to a boil, then reduce the heat to a low simmer. Cover the pot and cook gently for approximately 2 hours, or until the beans are tender. Add more stock if necessary during cooking.  Taste and adjust the seasoning as needed. Discard the rosemary stems and orange peel before serving."
    ],
    "notes_ingredients": "While canned cannellini beans significantly reduce cooking time, using dried beans and rehydrating them yields a more flavorful result.",
    "notes_instructions": ""
  }
]
change this to work with external query using product quantization with faiss: #similarities, similarities_ids = gpu_index.search(external_query_embedding, k=k_nearest)
#similarities = np.around(np.clip(similarities, 0, 1), decimals=4)
#highest_val_idx = np.argmax([sum(sim_vec) for sim_vec in similarities])
#highest_similarity = similarities[highest_val_idx]
#most_similar_id = similarities_ids[highest_val_idx]
#most_similar_sme = sme_df.loc[most_similar_id]
Write dialogue where 14 year old Sam (she/her) shows 14 year old autistic girl Sierra (who speaks exclusively in Taglish has tons of modded game consoles (meanwhile she finds Sam's boring as hers are unmodded), rarely attends school (much to the disappointment of her teachers and the principal), constantly swears a ton (make sure you include them) and constantly pulls all nighters) her brand new toy, but Sierra cant appreciate it as it really sensory overloads and overwhelms her
Send only episode number from below text like 01, 02, 03.etc without any extra text.

• Title'-  Shangri - La Frontier 2
• Episode,- 09
• Audio track,- Hindi Dub |
list the foods highest in pelargonidin in order
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: You are considering whether to buy a product. You look at the reviews. Would the following review decrease or increase the chances of you buying the product? Review title: Don't be fooled by this ..... Product review: Okay, this was an okay. John Goodman and Elizbeth Perkins are great as Fred and Wilma. The producers couldn't of picked a choice. Rick Moranis was terrific as Barney Rubble. He hasn't lost his comedic touch. Even Hally Berry was good as the seductive, Ms. Rosetta Stone. However, once again Rosie O'Donnell just didn't cut it as Betty Rubble. Her preformance in this film was just not as good, as it could be. Which is a shame, cause this could of been a great film.
A:
Answer: This review would slightly increase the chances of buying the product, as it mentions some positive aspects and good performances from the actors, but also highlights one negative aspect (Rosie O'Donnell's performance).
Sleeveless Design with Enough Room under Arm
Dragon Ball: Girls read so many Goku x Vegeta Fanfictions
(In the school literature clubroom…)

Monika: “Sayori, you excited for today’s club meeting?”

Sayori: “Of course, Monika! I can’t wait to see what poems everyone has come up with this week. I have a feeling it’s going to be a great meeting!”

Monika: “I’m glad you’re looking forward to it. Everyone always has such unique perspectives in their writing. It’s one of my favorite parts of being in the literature club.”

Sayori: “Mine too! Everyone’s creativity always inspires me to keep pushing myself to improve. Plus, it’s just so much fun to be able to share and discuss our work with each other.”

Monika: “Absolutely. I love seeing how our club members grow and develop as writers. It’s truly rewarding to watch everyone’s progress over time.”

Sayori: “I couldn’t agree more, Monika. I’m grateful to be a part of such a supportive and talented group of friends.”

Monika: “And we’re grateful to have you here, Sayori. Your positivity and enthusiasm always brighten up our meetings. Let’s make today’s gathering a special one!”

Sayori: “You bet, Monika! Let’s make it a meeting to remember!”

(At that moment, the door swings open and Natsuki and Yuri enter - with Natsuki being very heavily pregnant, only she was sweating quite heavily and holding her stomach in pain.)

Yuri: “We couldn’t get to the hospital. Natsuki’s contractions are too close together. We need to deliver the baby here!”

Monika: “Oh my goodness! Sayori, quick; grab some supplies we need for the delivery!”

Sayori: “On it, Monika!”

(Sayori rushes to grab towels, blankets, and other necessary items while Monika helps Natsuki sit down comfortably.)

Monika: “Natsuki, just focus on your breathing. You’re going to be okay.”

Natsuki: (breathing heavily) “I-I’m scared, Monika…”

Yuri: “Don’t worry, Natsuki. We’re all here for you. Just breathe through the contractions and everything will be alright.”

(Sayori returns with the supplies and begins to assist Monika in preparing for the delivery.)

Sayori: “We’re all here for you, Natsuki. You’re so strong, you can do this.”

Monika: “Just keep breathing and pushing, Natsuki. We’re all here to support you every step of the way.”

(Natsuki continues to labor through the delivery, with Monika, Sayori, and Yuri by her side offering encouragement and support.)

Monika: “You’re doing great, Natsuki! Keep
why did some people not like Ellie in DS3?
based on the following description, please create a one-line company description for WPMU DEV, focusing on what they do:

"The perfect all-in-one stack for WordPress developers, freelancers and agencies.

Everything you need for WordPress! Super-powered Hosting, 24/7 Live Support, Site Management tools, and Premium Plugins."
From the following list, identify the index that contains only Persian characters and select the one that is more suitable based on content or length. The output should only be the numerical index.

List:
[]
Create a long detailed text about actor Larry Linville with the following summary. Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Critical Success] Summary: [Larry played the role of Joe Harford in the play, which also starred Ingrid Bergman, Colleen Dewhurst, and Arthur Hill in important roles. The play was a critical success, receiving glowing reviews for its powerful performances and poignant storyline. Larry's talent as an actor was clearly evident, and his career continued to flourish in the years that followed.]

        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 for example: 'Certainly!' Here's the revised text.
         
        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 generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. 
        The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. 
        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.