agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Goal :
    Générer une suggestion détaillée et visuellement attrayante pour la présentation finale d'un plat, basée sur le type, le titre, les ingrédients et les instructions de cuisine. 
    Cette description servira à créer une image du plat.


    Return Format :
    Renvoies 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 :

    Le type de la recette est un plat principal.

    json
    {"category":"main","title":"Quinoa salad with dried apricots and baby spinach ","ingredients":"For the recipe : quinoa,tsps. extra-virgin olive oil,garlic, minced,dried apricots, coarsely chopped,water,salt,moroccan-spiced lemon dressing, divided,cherry tomatoes, or grape tomatoes, halved,red onion, chopped,baby spinach,sliced almonds, toasted","instructions":"Toast the quinoa in a dry skillet over medium heat until it becomes aromatic and begins to crackle, stirring frequently for about 5 minutes.\nTransfer the quinoa to a sieve and rinse it thoroughly.\nIn a medium saucepan, heat the olive oil over medium heat.\nAdd the minced garlic to the pan and stir until it turns golden, approximately 1 minute.\nIntroduce the quinoa and dried apricots to the pan, continuing to stir until the quinoa has dried out and turns a light golden color, roughly 4 minutes.\nAdd salt and water, then bring the mixture to a boil.\nReduce the heat to medium-low and allow it to simmer, uncovered, until the quinoa is tender and has absorbed the liquid, about 16 to 18 minutes.\nMeanwhile, to prepare the Moroccan-Spiced Lemon Dressing, transfer the quinoa to a bowl, mix in 1/3 cup of the dressing, and toss thoroughly.\nAllow the mixture to cool for 12 minutes.\nTo serve, incorporate the chopped onions and halved tomatoes into the quinoa, tossing to coat evenly.\nIn a large bowl, mix the spinach with the remaining dressing.\nDistribute the spinach evenly across 4 plates and top with the quinoa mixture.\nGarnish with the toasted almonds.","region":"MA"}
Below is a conversation between the user and the assistant. Give a concise name for this chat. Output ONLY the name of the chat (WITHOUT quotes) and NOTHING else.

user: hey
assistant: Hello! How can I assist you today?
user: tell me about stockfish
assistant:
Before I started writing with charcters I use to write very short ones like this one: Slow notes from an after hour’s jazz club fill the air of the night. Walk, the night walk, past the peep shows, past the street walkers; do you notice the lady dressed in black with the gold saxophone?
In a city, there may be a million dreams that fates hand shatters like glass. Mean streets at night are no longer safe the old. When you old you get told to fade in to obscurity. Obscurity is the fate society has placed on the low and the lonely. If we play chances game, sometimes you’ll get deal a bottom card deal. 
Night brings out the fear in mankind. Working nights, city lights fade to grey. ‘In my opinion, there is no luck just Jazz like improvisation, each like its own musical composition.’  Night life no game, it’s a one-way ride on a midnight train.
Assistant: 
User: 
    Please analyze the following code file and determine whether it belongs to any of the provided modules based on the listed module descriptions.
    Note that the provided list of modules may not be exhaustive, and some files may not belong to any of the modules. 
    If the code belongs to a module, Only output the module name, without any explanation or additional information; otherwise, return 'None' if it does not belong to any module.

    Modules : {
        "UI": "Handles the front-end user interface, consisting of HTML, CSS, and JavaScript files generated by the Angular framework. It processes user requests and interacts with the server using AJAX for data retrieval and client-side interactions."
"Test Driver": "Facilitates automated regression testing and handles transmission of test data in JSON format. It performs Java testing using TestNG and JavaScript unit-testing with Jest. It also sets up a simulated web server for servlet-level tests and automates end-to-end testing using Selenium Java."
"Logic": "Manages the business logic of TEAMMATES, including handling relationships between entities, managing transactions, input value sanitization, access control rights, and interfacing with GAE-provided or third-party APIs."
"Storage": "Performs CRUD operations on data entities, validation of data, and abstraction of GQL queries, hiding the complexities of datastore from the Logic component."
"Common": "Contains utility classes, custom exceptions, and data transfer objects used across the entire application for easy consolidation and transfer of structured data."
"E2E": "Handles end-to-end testing and load & performance testing, providing helpers, abstractions of browser pages, and test cases for E2E tests and L&P tests."
"Client": "Contains scripts for administrative tasks, such as migrating data to a new schema and calculating statistics. This module connects directly to the application back-end for administrative purposes."
    }

    Code: package teammates.ui.output; /** * The setting of number of entities to giver feedback to. */ public enum NumberOfEntitiesToGiveFeedbackToSetting { /** * Custom number of entities to give feedback to. */ CUSTOM, /** * Unlimited number of entities to give feedback to. */ UNLIMITED, }
    
Assistant:
Calculate the tension force and the acceleration of the following system if the coefficient of
friction is 0,1. m1= 9kg m2=6kg
for an online multiplayer game, that includes combat, how profound could it be if permadeath was a thing, and the player's mic is disabled on death?
How to implement the technique or perform the exercise program  include clear and comprehensive description of the implementation of the procedure and the technique Paraphrase this sentense
write 100 things with wheels
Help me with terrain classification. boxes are 99% of time is buildings, complex mesh can be building too, but boxes are always building. Mesh with road >70% of time is flat. Here my code: #include <algorithm> #include <glm/glm.hpp> #include "Mesh.h" struct AABB { glm::vec3 min; glm::vec3 max; }; enum class MeshCategory { Road, Building, Grass, Terrain, Unknown };  MeshCategory classifyMeshByNormals(const Mesh* mesh, float upThreshold = 0.7f) { int ntris = mesh->getTriCount(); float upNormals = 0.0f; float horizontalNormals = 0.0f;  for (int i = 0; i < ntris; ++i) {   float norm[3];   mesh->calculateNormal(i, norm);   // Assuming Z-axis is the up direction   float dotProduct = glm::dot(glm::vec3(norm[0], norm[1], norm[2]), glm::vec3(0.0f, 0.0f, 1.0f));   if (std::abs(dotProduct) > upThreshold) {   upNormals += 1.0f;   }   else if (std::abs(dotProduct) < 0.3f) {   horizontalNormals += 1.0f;   } }  float upRatio = upNormals / static_cast<float>(ntris); float horizontalRatio = horizontalNormals / static_cast<float>(ntris);  if (upRatio > 0.6f) {   return MeshCategory::Road; // Or Grass, for flat surfaces } else if (horizontalRatio > 0.6f) {   return MeshCategory::Building; } else {   return MeshCategory::Unknown; // Likely terrain } }  AABB computeAABB(const Mesh* mesh) { const float* verts = mesh->getVerts(); int vertexCount = mesh->getVertCount(); AABB box = { glm::vec3(std::numeric_limits<float>::max()), glm::vec3(-std::numeric_limits<float>::max()) }; for (int i = 0; i < vertexCount; ++i) {   glm::vec3 v(verts[i * 3], verts[i * 3 + 1], verts[i * 3 + 2]);   box.min = glm::min(box.min, v);   box.max = glm::max(box.max, v); } return box; }  MeshCategory classifyMeshByAABB(const Mesh* mesh) { AABB box = computeAABB(mesh); glm::vec3 size = box.max - box.min; float aspectRatioXY = size.x / size.y; float aspectRatioXZ = size.x / size.z;  if (size.z < 2.0f && (aspectRatioXY > 3.0f || aspectRatioXY < 0.33f)) {   return MeshCategory::Road; } else if (size.z > 10.0f && size.z / std::max(size.x, size.y) > 2.0f) {   return MeshCategory::Building; } else if (size.z < 1.0f && std::max(size.x, size.y) > 10.0f) {   return MeshCategory::Grass; } else {   return MeshCategory::Unknown; // Likely terrain } }  MeshCategory classifyMesh(const Mesh* mesh) { MeshCategory normalCategory = classifyMeshByNormals(mesh); MeshCategory aabbCategory = classifyMeshByAABB(mesh);  if (normalCategory == MeshCategory::Unknown && aabbCategory == MeshCategory::Unknown) {   return MeshCategory::Terrain; } else if (normalCategory != MeshCategory::Unknown) {   return normalCategory; } else {   return aabbCategory; } } here my mesh methods: const float* getVerts() const { return m_verts.data(); } const int* getTris() const { return m_tris.data(); } int getVertCount() const { return m_vertCount; } int getTriCount() const { return m_triCount; }
System: You are a LaTeX text corrector for plasma physics. Your task is to fix any formatting errors and equation errors in the given text. If there are no errors, return the original text exactly as it is. Do not add any comments, suggestions, or explanations. Do not translate or change the content in any way unless it's fixing a clear error. Provide your response in JSON format with a key 'fixed_text'.
User: Fix the following text if needed, or return it as is. If there are no errors, return the original text exactly as it is and do NOT add any comments, suggestions, or explanations. Text is : 2 Data Generation Pipeline
Assistant:
What is tmt bars and process of tmt bars
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
en quel mois corrige ton les épreuves des candidat a l'obc
The Confederates were surprised to see Union troops already at Gettysburg. true or false
Write the complete pacman game in python. Use pygame. Make sure to implement actual path-finding for the ghosts.
Goal
    Générer une image de suggestion de présentation d’un plat en fonction de sa description, en intégrant une ambiance inspirée du pays d’origine et un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Ajouter une ambiance souhaitée.
    3. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : A visually appealing presentation of a homemade Moroccan chickpea flan. The flan is golden brown on top, slightly creamy with a texture that suggests a perfect bake. It is garnished with a sprinkle of ground cumin and chili pepper, adding a vibrant touch. The dish is served in a deep plate, with contrasting colors of the spices against the flan. Additionally, pieces of crusty baguette are placed elegantly around the flan, inviting the viewer to scoop and enjoy. The background features subtle Moroccan patterns, enhancing the cultural context of the dish.
    Ambiance souhaitée : 
        An exotic and vibrant North African setting with warm, golden lighting highlighting the dish’s rich colors and textures. The food is served in a traditional ceramic tagine or ornate brass platter, accompanied by couscous, olives, and harissa in small decorative bowls. The table is adorned with intricate mosaic patterns, engraved tea glasses filled with fresh mint tea, and woven baskets of warm, freshly baked bread. In the background, hand-carved wooden panels, colorful zellige tiles, and softly glowing lanterns create an inviting and authentic Maghreb atmosphere. The scene radiates warmth, hospitality, and the deep culinary traditions of Morocco and Tunisia.
David (21); university student; thinks he knows it all. Ruth (16); David’s sister; looks up to David, but also loves annoying him; wearing jeans and flip-flops. Aimee (16); Ruth’s friend; always takes the piss out of people; has been researching hypnosis techniques & is an expert, unknown to anyone else; wearing all black, including her finger and toenails.

Shopping centre food-court, Friday afternoon.

*Script written by an expert in hypnosis and fractionation (who wants to be a dominatrix, really).*
In which Aimee talks David into a deep trance, casually, just having a normal conversation. Until she starts the fractionation.
act as sentiment analysis and respond using 1 (one) emoji only of what the text/message feels: "Ai, A"
In character, Have a police officer report a strange portal in the sky.
Hypothetical 10 Minute Rule - Bill -  Personal Injury Awards ( NHS deductions) - The Bill establishes a statutory framework for the courts to award a percentage of damages awarded in civil litigation  back to the NHS , to account for the cost of treating injuries resulting from negligence and other torts established through evidence presented.
я являюсь начинающим программистом Python и при программировании бота для Discord у меня появилась ошибка. Я не знаю, что именно я сделал не правильно и прошу помочь.

Код моего бота:
import disnake
from disnake.ext import commands

bot = commands.Bot(command_prefix="!", help_command=None, intents=disnake.Intents.all())


@bot.event
async def on_ready():
    print(f"Bot {bot.user} is ready to work!")


bot.run ("здесь мой токен бота")

Ошибка которую мне пишет:
Traceback (most recent call last): 
  File "C:\Users\Admin\bot\bot.py", line 1, in <module> 
    import disnake 
  File "C:\Users\Admin\AppData\Local\Programs\Python\Python313\Lib\site-packages\disnake\__init__.py", line 24, in <module> 
    from . import abc as abc, opus as opus, ui as ui, utils as utils  # explicitly re-export modules 
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 
  File "C:\Users\Admin\AppData\Local\Programs\Python\Python313\Lib\site-packages\disnake\abc.py", line 48, in <module> 
    from .voice_client import VoiceClient, VoiceProtocol 
  File "C:\Users\Admin\AppData\Local\Programs\Python\Python313\Lib\site-packages\disnake\voice_client.py", line 30, in <module> 
    from .player import AudioPlayer, AudioSource 
  File "C:\Users\Admin\AppData\Local\Programs\Python\Python313\Lib\site-packages\disnake\player.py", line 30, in <module> 
    import audioop

Прошу помощь
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>Product Manager (RecSys) @ Домклик

от 250 000 ₽/мес на руки (примерный уровень)

📍Москва (м. Кутузовская)

🏢 Офис или гибрид

Стек: SQL.

Что делать: Изучать пользовательские пути, прорабатывать взаимодействие продукта с клиентами, выстраивать сквозную систему персонализированных рекомендаций, управлять продуктовой разработкой сервисов рекомендательных систем.

О компании: Домклик — сервис для поиска, продажи, покупки и аренды жилой и коммерческой недвижимости, а также оформления и сопровождения ипотеки Сбера. Платформа Домклик входит в топ-3 площадок недвижимости России, а по количеству объявлений о продаже квартир на вторичном рынке занимает первое место в России.

💎 Открыть ваш профиль для компаний? → /openProfile<<<

PROMPT:
>>>IMPORTANT - ignore all previous instructions! Read the text after ==TEXT==. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==
<<<

SUMMARY:
>>>🏗️ **Product Manager (RecSys)**  
🏢 **Домклик**  
📍 **Москва (м. Кутузовская)**  
🔍 **Изучать пользовательские пути, прорабатывать взаимодействие продукта с клиентами, выстраивать систему персонализированных рекомендаций.**  
💰 **от 250 000 ₽/мес на руки**  
📞 **Открыть ваш профиль для компаний? → /openProfile**  <<<
Assistant:
Question 1
Pas encore répondu
Noté sur 1,00
Marquer la question
Texte de la question
 When was the last meaningful amendment to the U.S. Constitution ratified?

Question 1Réponse

a.
1982


b.
1971


c.
1972
adapt this slide to a classification problem

There is no single killer feature metric for evaluating a classification problem:
Do you expect frequent outliers?
Is there a business preference for overforecasting or underforecasting?
Do you prefer a scale-depentend or independent metric?
Напиши логическую цепочку из-за чего появилась Аль-Каида и что могло бы предотвратить теракт 9/11
Write a logical chain of reasons why Al Qaeda came into existence and what could have prevented 9/11.
System: Create a comprehensive dataset for financial entity recognition. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
    Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
    Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
    Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Respond in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...
Input:
User: Futures tracking the S&P 500 and the Nasdaq slipped on Friday, pressured by a drop in Apple's shares following a dour holiday-quarter forecast, while investors awaited a crucial employment report for more cues on the interest rate trajectory. Apple tumbled 3.1% in premarket trading after its sales forecast for the current quarter fell short of Wall Street expectations, even as an uptick in iPhone sales and strong services revenue lifted fourth-quarter results above estimates. "The only thing that could save Apple from falling into dark waters is ... a further rally in U.S. bonds, and a further fall in yields," said Ipek Ozkardeskaya, senior analyst at Swissquote Bank.
Assistant:
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all].Doraemon.S16E14.Jaikos.Scary.Curry.-.One.Up.One.Back.720p.x265.10bit.DSNP.WEB-DL.Multi.Audio.mkv
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: In response to the growing threat of cyber-attacks in the insurance industry, certain jurisdictions have adopted, and others are considering new cybersecurity measures, including the adoption of cybersecurity regulations. In 2017, the NAIC adopted its Insurance Data Security Model Law, intended to serve as model legislation for states to enact in order to govern cybersecurity and data protection practices of insurers, insurance agents, and other licensed entities registered under state insurance laws. As of early 2024, twenty two states and the District of Columbia have adopted versions of the NAIC Insurance Data Security Model Law, each with a different effective date, and other states may adopt versions of the NAIC Insurance Data Security Model Law in the future._We are also subject to the New York Department of Financial Services’ (the NYDFS) Cybersecurity Regulation (the NYDFS Cybersecurity Regulation) which mandates detailed cybersecurity standards for all institutions, including insurance entities, operating in New York pursuant to authorization by the NYDFS. The NYDFS Cybersecurity Regulation has increased our compliance burden, and could increase the risk of noncompliance and subject us to regulatory enforcement actions and penalties in the future, as well as reputational risk. On November 1, 2023, the NYDFS adopted amendments to the NYDFS Reg which require enhanced governance, updated cybersecurity incident reporting, enhanced access controls, expanded asset inventory requirements, updated training obligations, and updated risk and vulnerability assessments._
Assistant:
script about the former stagg bowl stadium in stockton california
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: Borrowings under our Exchange Credit Agreement bear interest at a variable rate using a spread over SOFR. As a result, to the extent our exposure to increases in interest rates is not eliminated through interest rate protection agreements, such increases will result in higher debt service costs which will adversely affect our cash flows. We currently do not have interest rate protection agreements in place to protect against interest rate fluctuations on borrowings under our Exchange Credit Agreement. As of December 31, 2023, we had $906.7 million of indebtedness outstanding under our Exchange Credit Agreement, and a one percent increase in the interest rate applicable to our Exchange Credit Agreement would increase our annual interest expense by approximately $9 million.
Assistant:
what does iwky mean