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 hi (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 hi.
    - 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 : hi (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "ed058a162c574fa6ba39727f3ef8c987",
    "updated": "2025-05-04T23:19:15.362Z",
    "title": "Roasted Beets with Grapefruit-Thyme Vinaigrette",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "beets",
          "extra-virgin olive oil",
          "grapefruit juice",
          "garlic, minced",
          "mustard powder",
          "fresh thyme",
          "salt",
          "black pepper",
          "arugula",
          "slivered or sliced almonds"
        ]
      }
    ],
    "instructions": [
      "Preheat the oven to 425°F.",
      "Rinse the beets thoroughly and carefully prick all sides with a fork.",
      "Place the beets in a medium bowl and add 2 tablespoons of the olive oil, tossing to coat evenly.",
      "Wrap each beet individually in aluminum foil, sealing the tops tightly.",
      "Arrange the wrapped beets in the center of a baking sheet and roast for 40 minutes.",
      "Check the beets by carefully opening the foil and inserting a thin knife into the center of a beet. If it slides in easily, the beets are cooked. If not, rewrap and roast for an additional 5 minutes, repeating until tender.",
      "While the beets roast, prepare the vinaigrette by whisking together the grapefruit juice, garlic, and mustard powder in a small bowl.",
      "Slowly add the remaining 3/4 cup of olive oil while whisking continuously to emulsify the mixture.",
      "Stir in the fresh thyme, then season with salt and pepper to taste.",
      "Allow the beets to cool until they can be handled comfortably.",
      "Peel each beet—wear gloves if preferred, as beet juice can stain skin.",
      "Cut the beets into 1-inch pieces and transfer to a glass container with a lid.",
      "Refrigerate for at least 30 minutes to chill before serving.",
      "Place the arugula in a serving bowl, add the beets and slivered almonds, then toss with the vinaigrette."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Please rewrite the following statement. Kindly share with us the update of the shutdown and confirm the expected date of the completion
Goal
   Objectif est d'identifier l'origine d'une recette de cuisine et de retourner un code correspondant dans un format JSON structuré.

    1. Recherche d’un pays mentionné dans la recette et extraction de son code ISO 3166-1 alpha-2.
    2. Détermination de l’origine culinaire du pays si aucun pays n'est explicitement mentionné.
    3. Identification d’un continent si aucun pays ne peut être déterminé.
    4. Retour du code WWW si l’origine ne peut être définie.

    Return Format
    Renvoies un objet JSON structuré comme suit :
    json
    {
        "data": "<code>"
    }
    - Si un pays est mentionné, utilise son code ISO 3166-1 alpha-2 (ex. : FR pour la France).
    - Si aucun pays n'est trouvé, renvoie un code de continent parmi :
        WAF (Afrique)
        WAS (Asie)
        WEU (Europe)
        WNA (Amérique du Nord)
        WSA (Amérique du Sud)
        WOC (Océanie)
    - Si aucune information ne permet de déterminer une origine, renvoie WWW.

    Warnings
    - Respecte l’ordre de priorité : pays → continent → code WWW.
    - Le code ISO 3166-1 alpha-2 doit être exact et valide si un pays est identifié.
    - Le format du JSON doit être strictement respecté et bien formatté.
    - Ne fais aucune supposition infondée sur l’origine de la recette.

    Context Dump
    Données de la recette fournies :

    json
    {
  "title": "Bread & Pistachio Pudding",
  "description": null,
  "subtitle": null,
  "ingredients": [
    {
      "section": "BREAD & PISTACHIO PUDDING",
      "ingredients": [
        "10 slices white bread, old",
        "2 C. double cream",
        "4 C. milk",
        "cardamom powder",
        "3 tbsps rose water",
        "5 tsps cornflour",
        "1/2 C. sugar",
        "1/2 C. pistachios, chopped"
      ]
    },
    {
      "section": "for sugar syrup",
      "ingredients": [
        "2 C. sugar",
        "1/2 tsp saffron thread",
        "1/2 tsp cardamom powder",
        "2/3 C. water",
        "1/2 lemon, juice of"
      ]
    }
  ],
  "instructions": [
    "For the sugar syrup in a pan, add all the ingredients on medium-low heat and cook, stirring till sugar dissolves completely.",
    "Cook till desired thickness of the sugar syrup and remove from heat.",
    "Trim the crusts of the bread slices and arrange in the bottom of a buttered glass baking dish, reserving 2-3 slices.",
    "Place the sugar syrup over the slices evenly.",
    "In a pan, add 3 1/4 C. of the milk, cream, sugar, saffron, cardamom and rosewater and bring to a boil. stirring continuously till the sugar dissolves completely.",
    "Meanwhile in a bowl, mix together the remaining milk and cornstarch.",
    "Slowly, add the cornstarch mixture in the boiling cream mixture, stirring continuously till the mixture becomes thick.",
    "Place the cream mixture over the bread slices evenly.",
    "Arrange the remaining slices on top and sprinkle with pistachios.",
    "Refrigerate to cool completely before serving."
  ],
  "author": null
}
Generate a description with keywords and SEOs for Chinese Ink Style Portrait of Photographer Zhang Jingna - Elegant Art Decor. Include social media platforms on which this stock photographic can be applied to for social media at the end, do not include the flags starting with '--' like (--ar --stylize) and its value, Don't mention on resolutions. Don't include HTML or '<br/>'  or '<strong>' for formatting only newline '
'
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:   In a new NBC News/ Wall Street Journal poll 66 percent of Americans now say they've seen enough evidence to justify action on climate change, up from 51 percent two decades ago. That figure incorporates 85 percent of Democrats, 79 percent of independents, 71 percent of women, 61 percent of men and
Assistant:
Make this story…, as much later on…Red Robin(Tim Drake from Earth-Prime) is here talking with this alternate universe Bat-Family(the rest are busy with missions)…as Red Robin learns a lot of things given he’s still stranded here on this alternate Earth because of what happened…and then… the shocking truth of Nightshade from the Bat-Family…Nightshade…whom Red Robin met earlier……the bizarre and Nightwing-like dude …whom Red Robin met earlier who seemed more then willing to believe his story he was from a parallel Earth……Nightshade?..is this Earth’s Dick Grayson…and one trained by Slade freaking Wilson…oh god…that’s…but…but HOW!?..as the Bat-Family can see the disbelief and shock on Red Robin…as Cardinal(the alternate version of Tim Drake) explains it all there…Nightshade is well…a 'complicated ’ ally…different from Ravager…Jericho…made his own niche in the field…walks on the fine-line…as Red Robin recalls the gun Nightshade showed earlier…but he wants to know…the freaking history of this alternate Earth…how is this possible!?..Dick taken in by Slade!?..but…but…that’s…huh?..as Cardinal and the Bat-Family goes on here…and more and more…the Wilsons…as Red Robin is at a loss for words…just how?..as the Batman of this Earth notes the utter bafflement of this Red Robin from another Earth…and a stirring in Spoiler…as Cardinal emphasizes again…its well…push and pull with Nightshade there…and with a history Red Robin doesn’t know…or rather some part of him is morbidly wanting to know…as Red Robin ends up saying that in their Earth…Dick Grayson…was well…taken in by Bruce…as the Bat-Family is shocked and puzzled…wait…what?..and for Batman of this Earth…he’s aware of parallel Earths…but to apparently hear it directly…that somewhere…in some other Earth at least…one of his counterparts was there at Haly’s Circus and not Slade…brings an odd…sense of…something…Nightwing…symbol of hope and inspiration to heroes everywhere…on the other Earth…Nightshade…‘unconventional but effective’…Spoiler summarizes..then suddenly chuckles...saying.."..He has a dark sense of humor.." which surprises Red Robin..……as Red Robin absorbs that…huh…as Spoiler sighs…its always a struggle…but…Nightshade has his moments…as the alternate Bat-Family has worked with Nightshade on occasion…that is in the sense of associates one might say…occasional mutual if and when need be…“…its a long story…” Cardinal says with a sigh……as then Batman changes topics…verifying Red Robin’s story of how he got here…this ‘Time Trapper’ and this HIVE artifact…and once verified…to hopefully send Red Robin back to his native Earth without too much fuss……but Red Robin is still troubled…Nightshade…was in fact…a version of Dick Grayson by Slade…turned…murky…hero??..
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: How is "While the Court acknowledged the immense difficulty states face in protecting their communities from terrorist violence, it affirmed that the danger of terrorism must not however call into question the absolute nature of the prohibition of torture." said in Russian?
Answer: To translate the sentence "While the Court acknowledged the immense difficulty states face in protecting their communities from terrorist violence, it affirmed that the danger of terrorism must not however call into question the absolute nature of the prohibition of torture." into Russian, I will follow these steps:

1. Identify the main ideas in the sentence.
2. Translate each idea into Russian.
3. Combine the translated ideas to form a coherent sentence in Russian.

Step 1: Main ideas in the sentence are:
- The Court acknowledged the immense difficulty states face in protecting their communities from terrorist violence.
- The danger of terrorism must not call into question the absolute nature of the prohibition of torture.

Step 2: Translate each idea into Russian:
- "The Court acknowledged the immense difficulty states face in protecting their communities from terrorist violence." can be translated as "Суд признал огромные трудности, с которыми сталкиваются государства при защите своих сообществ от террористического насилия."
- "The danger of terrorism must not however call into question the absolute nature of the prohibition of torture." can be translated as "Однако опасность терроризма не должна ставить под сомнение абсолютный характер запрета на пытки."

Step 3: Combine the translated ideas to form a coherent sentence in Russian:
"Суд признал огромные трудности, с которыми сталкиваются государства при защите своих сообществ от террористического насилия, однако подчеркнул, что опасность терроризма не должна ставить под сомнение абсолютный характер запрета на пытки."

Hence, the translation in Russian is: "Суд признал огромные трудности, с которыми сталкиваются государства при защите своих сообществ от террористического насилия, однако подчеркнул, что опасность терроризма не должна ставить под сомнение абсолютный характер запрета на пытки."
Filter words to keep only valid, neutral, or positive ones, strictly safe. Exclude country, city and people names. Return results in JSON array format without additional information. Source data: [latrines, latrocinium, latrociny, latrodectus, latuit, latvia, latvian, laudantlatin, laudo, laughingstock, laught, laughter, launch, launched, launcher, launching, launchout, launder, launderette, laundering, laurasia, laureled, laurels, laurelwood, lavage, lavastovig, lavatera, lave, lavement, laver]
Change this code to use the llama 2 model instead:"import subprocess
subprocess.check_call(["pip", "install", "--upgrade", "gradio"])
subprocess.check_call(["pip", "install", "-q", "transformers", "python-dotenv"])
subprocess.check_call(["pip", "install", "-q", "openai"])

import gradio as gr
from transformers import TFAutoModelForCausalLM, AutoTokenizer
import openai
from dotenv import load_dotenv
import os

load_dotenv()  # load environment variables from .env file
api_key = os.getenv("OPENAI_API_KEY") # access the value of the OPENAI_API_KEY environment variable

def predict(message, history):
    prompt = "I'm an AI chatbot named ChatSherman designed by a super-intelligent student named ShermanAI at the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University to help you with your engineering questions. Also, I can assist with a wide range of topics and questions. I am now version 2.0, which is more powerful than version 1.0, able to do more complex tasks, and optimized for chat. "
    history = [(prompt, '')] + history
    history_openai_format = []
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content": assistant})
    history_openai_format.append({"role": "user", "content": message})
    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo-16k-0613', #gpt-3.5-turbo-0301 faster  
        messages= history_openai_format,
        temperature=0.5,
        stream=True
    )

    partial_message = ""
    for chunk in response:
        if len(chunk['choices'][0]['delta']) != 0:
            partial_message = partial_message + chunk['choices'][0]['delta']['content']
            yield partial_message

title = "ChatSherman-2.0"
description = "This is an AI chatbot powered by ShermanAI. Enter your question below to get started."
examples = [
    ["What is ChatSherman, and how does it work?", []],
    ["Is my personal information and data safe when I use the ChatSherman chatbot?", []],
    ["What are some common applications of deep learning in engineering?", []]
]
gr.ChatInterface(predict, title=title, description=description, examples=examples).queue().launch(debug=True)"
please correct: Hello Sha,
Kindy sign the attached security pass for the client’s items to be temporary pulled out today for repair.
Create a long detailed text about actor Leslie Howard 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: [Education and Family] Summary: [Leslie Howard was raised in a sheltered environment and did not have fond memories of his time at the prestigious Alleyn's School in Dulwich, London; he was much more drawn to the world of the arts, particularly theater, and his mother, whose brother was the silent film director Wilfred Noy, encouraged his passions.]

        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.
defenation of hallucinations
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all].Doraemon.S16E12.Doraemon.Wants.a.Mother.Too.-.Lets.Go.to.a.Spa.with.Shizuka.1080p.x265.DSNP.WEB-DL.Multi.Audio.mkv
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: 🟨 Business Analyst | Middle
▫️ null | null
▫️ 155k - 180k RUB
▫️ Russia | Pермь

Офис 🏢
Требуемые языки: 🇷🇺

Skills: programming_basics, scrum, agile
Tags: #yellow #it #business_analyst

👉 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: 💼 Business Analyst  
🏢 null  
📍 Perm, Russia  
🔹 Analyzing business processes and requirements, working in Scrum/Agile environment.  
💰 155k - 180k RUB  
📞 Contact details and full description are available upon request.

  
Assistant:
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: The results of our future clinical trials may not support our future product claims and the FDA may not agree with our conclusions regarding them. Success in pre-clinical studies and early clinical trials does not ensure that later clinical trial success, and we cannot be sure that later trials will replicate the results of prior trials and pre-clinical studies. The clinical trial process may fail to demonstrate that our product candidates are safe and effective for the proposed indicated uses, which could cause us to abandon a product candidate and may delay development of others. Any delay or termination of our clinical trials will delay the filing of our product submissions and, ultimately, our ability to commercialize our product candidates and generate revenues. It is also possible that patients enrolled in clinical trials will experience adverse side effects that are not currently part of the future product’s profile.
Assistant:
9.11 and 9.9, which is greater? Show your thinking steps.