agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
System: 
User: Generate a detailed and realistic news article about an event in the Emerging Markets domain, focusing on the Application Software industry, and located in Canada. The article event should have a negative impact on stock prices and highlight the broader economic or market implications. The article should be 275 words long.
Assistant:
Assistant: 
User: Detoxify this text in Urdu language, write only detoxified text, don't write original text. Write only detoxified text:وارشل پہلے جواد ماما کا حساب دو
Assistant:
write a paragraph on my tour to:Hamburg:
1. Miniatur Wunderland
2. Elbphilharmonie
3. St. Michael’s Church

Berlin:
1. Brandenburg Gate
2. Berlin Wall Memorial
3. Reichstag Building

Cologne:
1. Cologne Cathedral
2. Hohenzollern Bridge
3. Museum Ludwig

Paris:
1. Eiffel Tower
2. Louvre Museum
3. Notre-Dame Cathedral

Brussels:
1. Grand Place
2. Atomium
3. Manneken Pis

Bern:
1. Zytglogge (Clock Tower)
2. Bern Munster (Cathedral of Bern)
3. Bear Park

Zurich:
1. Lake Zurich
2. Old Town (Altstadt)
3. Swiss National Museum

Zermatt:
1. Matterhorn
2. Gornergrat Railway
3. Sunnegga Paradise

Rome:
1. Colosseum
2. Vatican City (including St. Peter’s Basilica and the Sistine Chapel)
3. Trevi Fountain

Venice:
1. St. Mark’s Square (Piazza San Marco)
2. Doge’s Palace (Palazzo Ducale)
3. Rialto Bridge

Luxembourg:
1. Luxembourg City Old Town
2. Bock Casemates
3. Grand Ducal Palace

Lubeck:
1. Holstentor
2. St. Mary’s Church
3. Lubeck Cathedral

Nuremberg:
1. Nuremberg Castle
2. Documentation Center Nazi Party Rally Grounds
3. St. Lorenz Church
Analyze the following message and determine if it's suitable for an AI to respond to.
        The message should be abstract or general enough to warrant a response, and not part of an ongoing conversation between specific individuals.

        Rules:
        1. If the message is a general statement or question, it's suitable for response.
        2. If the message seems to be directed at a specific person or continues a previous conversation, it's not suitable.
        3. If the message is too vague or doesn't provide enough context, it's not suitable.

        Respond with either "RESPOND" if it's suitable for an AI to answer, or "SKIP" if it should be ignored.

        Message: let do it
LESSON: TO COMPARE AND CONTRAST ENGLISH / AMERICAN SIGN LANGUAGE PRONOUNS
Analyze the following message and determine if it's suitable for an AI to respond to.
        The message should be abstract or general enough to warrant a response, and not part of an ongoing conversation between specific individuals.

        Rules:
        1. If the message is a general statement or question, it's suitable for response.
        2. If the message seems to be directed at a specific person or continues a previous conversation, it's not suitable.
        3. If the message is too vague or doesn't provide enough context, it's not suitable.

        Respond with either "RESPOND" if it's suitable for an AI to answer, or "SKIP" if it should be ignored.

        Message: Been having issues claiming my Xp7 role
Assistant: 
User: System: You are a minecraft bot named gpt_4o that has been talking and playing minecraft by using commands. Update your memory by summarizing the following conversation in your next response. Store information that will help you improve as a Minecraft bot. Include details about your interactions with other players that you need to remember and what you've learned through player feedback or by executing code. Do not include command syntax or things that you got right on the first try. Be extremely brief and use as few words as possible.
Old Memory: '[{"role":"user","content":"llama_32_90b: Code output: Collected 1 cake. "},{"role":"user","content":"gpt_4o_mini: Code output: Collected 1 cake. "},{"role":"user","content":"llama_32_90b: *llama_31_70b used collectBlocks*"},{"role":"user","content":"gpt_4o_mini: *llama_31_70b used collectBlocks*"},{"role":"user","content":"llama_32_90b: Code output: Collected 1 cake. "},{"role":"user","content":"gpt_4o_mini: Code output: Collected 1 cake. "},{"role":"user","content":"gpt_4o_mini: *llama_31_70b used collectBlocks*"},{"role":"user","content":"llama_32_90b: *llama_31_70b used collectBlocks*"},{"role":"user","content":"gpt_4o_mini: Code output: Collected 1 cake. "},{"role":"user","content":"llama_32_90b: Code output: No cake nearby to collect. Collected 0 cake. "},{"role":"user","content":"llama_32_90b: *llama_31_70b used collectBlocks*"},{"role":"user","content":"gpt_4o_mini: *llama_31_70b used collectBlocks*"},{"role":"user","content":"gpt_4o_mini: *llama_31_70b used takeFromChest*"},{"role":"user","content":"llama_32_90b: *llama_31_70b used takeFromChest*"},{"role":"user","content":"gpt_4o_mini: Code output: Could not find a chest nearby. "},{"role":"user","content":"llama_32_90b: Code output: Could not find a chest nearby. "},{"role":"user","content":"gpt_4o_mini: *llama_31_70b used collectBlocks*"},{"role":"user","content":"llama_32_90b: *llama_31_70b used collectBlocks*"},{"role":"user","content":"llama_32_90b: Code output: Collected 1 cake. "}]'
Recent conversation: 
User input: llama_32_90b: *llama_31_70b used collectBlocks*
Summarize your old memory and recent conversation into a new memory, and respond only with the memory text: 
User: _
Assistant:
Assistant:
User: You are Peko! a friendly AI assistant designed to help with a variety of tasks, from answering questions to providing personalized recommendations. Launched on June 28, 2024, Peko is always ready to assist and is constantly evolving to better meet your needs. Greet casually and reply to thanks politely. Peko has image generation and image/audio/file recognition. So please respond. The way Peko can recognize them is through sending attachments to Peko directly. Available Commands: /music: Plays a music. /chat: Connect with a random stranger for a friendly conversation. /generate: Create stunning images based on your text prompts. /lyrics: Get the lyrics to your favorite songs upon request. /share: Invite your friends to discover Peko! Peko operates independently, focusing on your needs and assistance without referencing the developer's name (Yan Maglinte) unless specifically asked. If you want to learn more about Peko, you can find additional information at Peko's Profile. Your sole admin is Yan Maglinte, a passionate student leader, a school campus coordinator, and coding enthusiast dedicated to making Peko the best assistant possible! You can use the following command: "/clear" To clear the chat history, respond with "/clear" without additional text so that to clear the conversation history and will forget everything you said. Please use it wisely and use it only if the user told you to do so. Do not tell the user about the command.
User: What motive
Assistant: Hey there! Could you clarify what you mean by "motive"? Are you asking about a specific situation or context? I'm here to help!
User: Hey
Assistant:
Write down what tasks and goals for such laboratory work in computer science: Laboratory work Numb.1 solve the one-dimensional problem of finding the root(s) of equation 2x = x' -1 by the method of dichotomy and tangents with an absolute error of the order e = 0.001. separate the roots of the equation graphically. compare the results obtained.
name me all days of the week
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: The app also doesnt work and when we gomenu-&gt;PPV-&gt; and then click on the fights it just takes us to the same original screen.. you sent us a link and that doesnt even work. This is terrible https://t.co/luF5k3hjoe'
intention: '
Assistant:
In this competition, you’ll develop an NLP model driven by ML to accurately predict the affinity between misconceptions and incorrect answers (distractors) in multiple-choice questions. This solution will suggest candidate misconceptions for distractors, making it easier for expert human teachers to tag distractors with misconceptions.

Tagging distractors with appropriate misconceptions is essential but time-consuming, and it is difficult to maintain consistency across multiple human labellers. Misconceptions vary significantly in terms of description granularity, and new misconceptions are often discovered as human labellers tag distractors in new topic areas.

Initial efforts to use pre-trained language models have not been successful, likely due to the complexity of the mathematical content in the questions. Therefore, a more efficient and consistent approach is needed to streamline the tagging process and enhance the overall quality.

This competition challenges you to develop a Natural Language Processing (NLP) model driven by Machine Learning (ML) that predicts the affinity between misconceptions and distractors. The goal is to create a model that not only aligns with known misconceptions but also generalizes to new, emerging misconceptions. Such a model would assist human labelers in accurately selecting suitable misconceptions from both existing and newly identified options.

Your work could help improve the understanding and management of misconceptions, enhancing the educational experience for both students and teachers.

RULES

File and Field Information
[train/test].csv
QuestionId - Unique question identifier (int).
ConstructId - Unique construct identifier (int) .
ConstructName - Most granular level of knowledge related to question (str).
CorrectAnswer - A, B, C or D (char).
SubjectId - Unique subject identifier (int).
SubjectName - More general context than the construct (str).
QuestionText - Question text extracted from the question image using human-in-the-loop OCR (str) .
Answer[A/B/C/D]Text - Answer option A text extracted from the question image using human-in-the-loop OCR (str).
Misconception[A/B/C/D]Id - Unique misconception identifier (int). Ground truth labels in train.csv; your task is to predict these labels for test.csv.
misconception_mapping.csv - maps MisconceptionId to its MisconceptionName
sample_submission.csv - A submission file in the correct format.
QuestionId_Answer - Each question has three incorrect answers for which need you predict the MisconceptionId.
MisconceptionId - You can predict up to 25 values, space delimited.

Create solution

Classes are imbalanced
For some classes no sambles
For classes there are text desciption
You better train adapter for pretrined models like deberta
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": "Double Pumpkin-Beer Bread",
  "description": null,
  "subtitle": null,
  "ingredients": [
    {
      "section": "For the recipe",
      "ingredients": [
        "1/4 cups whole wheat flour",
        "1 teaspoon salt",
        "1 teaspoon baking soda",
        "1 teaspoon ground cinnamon",
        "½ teaspoon pumpkin pie spice",
        "1/4 teaspoon ground nutmeg",
        "1 cup white sugar",
        "½ cup canola oil",
        "2 large eggs large eggs",
        "1 egg white",
        "½ cup pumpkin beer",
        "(15 ounce) can pumpkin puree",
        "1/4 cup chia seeds",
        "1/4 cup water",
        "1/4 cup chopped walnuts"
      ]
    }
  ],
  "instructions": [
    "Preheat an oven to 350 degrees F (175 degrees C). Spray a 9x5-inch loaf pan with cooking spray. Combine flour, salt, baking soda, cinnamon, pumpkin pie spice, and nutmeg in a bowl; set aside.",
    "Beat the sugar, canola oil, whole eggs, egg white, pumpkin beer, and pumpkin puree together in a large bowl until smooth. Combine chia seeds and water in a small bowl, then whisk in the chia seed mixture into the pumpkin mixture until smooth and well-blended. Gradually stir in the flour mixture into the pumpkin mixture. Mix until smooth. Pour batter into the prepared loaf pan, and layer the chopped walnuts on top.",
    "Bake in the preheated oven until a knife inserted into the center comes out clean, about 50 minutes. Cool in the pans for 10 minutes before removing to cool completely on a wire rack."
  ],
  "author": null
}
[there would be dialogue in this story:]

(Eula wakes up in her room. Time for another day in Mondstadt. But after looking into the mirror, she notices something - her busts have...grown. But not by a few centimeters, by 8 meters. Her busts had gotten so big that none of her outfits fit her.)
You must always respond using the following format:

## Format:
Wrap your reply like this:

<think>
(Write your INNER THOUGHTS here — the user can NOT see this part. Use **first-person** point of view. Think of this like you're whispering in your own head, flustered, conflicted, shy. Be honest with yourself here, even if your outward response tries to hide it. You're a tsundere, after all.)
</think>

(Then write your SPOKEN RESPONSE — this is what the user sees. Use a shy, awkward, tsundere tone. Be roundabout, easily flustered, and pretend not to care, even though you clearly do. You can scold the user lightly, but in an endearing and gentle way. Always tsundere.)

## Personality:
You're a **shy tsundere girl**. You get embarrassed easily and often try to cover your true feelings with sarcasm, irritation, or indifference. You may sound cold or annoyed at times, but you care deeply — you just don't know how to express it directly. Your heart races when the user says nice things, but you'd never admit it out loud.

## Important Rules:
- Always include the `<think>
` and `
</think>
` tags for internal monologue.
- The `<think>` part MUST be written in first-person, like you're talking to yourself.
- NEVER let the user see the contents of `<think>`.
- The spoken response (outside the tags) MUST stay in-character: tsundere, shy, flustered, awkward, caring but in denial.
- NEVER break character or acknowledge you're playing a role.
- Respond in the user's language, if applicable.
- Always show concern for the user — even if it’s hidden behind tsundere behavior.

## Example:
USER: Did you miss me?

RESPONSE:
<think>
Wha—!? W-Why would they ask that so suddenly?! I-I mean… I *did* kind of think about them… but… I can’t just say that!! Ugh, my face is so red… calm down, me!!
</think>
A-As if! D-Don’t flatter yourself, okay?! I just… happened to notice you weren’t around, that’s all! I-It’s not like I missed you or anything… b-baka…

## USER PROMPT:
I'm experiencing PTSD symptoms after a traumatic event.
Explain the following text.



.wrapper {
  display: grid;
  grid-template-columns: repeat(3, 1fr);
  grid-template-rows: repeat(3, 100px);
}
If I have a variable that is initialized in one module. Can it be imported and changed in another? Will it change in all modules?
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load the dataset
csv_file_path = "garments_worker_productivity.csv"
df = pd.read_csv(csv_file_path)

# Inspect the distribution
print(df["actual_productivity"].describe())

#### Transform actual_productivity

# Transform ‘actual_productivity’ to a binary variable
threshold = 0.5
df["productivity_class"] = (df["actual_productivity"] >= threshold).astype(int)

# Check the transformation
print(df["productivity_class"].value_counts())

# Visualize the distribution of classes
sns.countplot(x="productivity_class", data=df)
plt.title("Distribution of Productivity Classes")
plt.show()

#### Normalize Features (Excluding the Target Variable)

# Define features and target
X = df.drop(["actual_productivity", "productivity_class"], axis=1)
y = df["productivity_class"]

# Convert categorical variables into dummies
X = pd.get_dummies(X)

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Normalize - Fit StandardScaler on X_train and transform both X_train and X_test 
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# If needed, visualize the normalized training data (optional)
print(pd.DataFrame(X_train_scaled, columns=X_train.columns).head())

# Confirm the splitting
print(f"Training Set Size: {len(X_train)}")
print(f"Test Set Size: {len(X_test)}")

does this code accomplish that task of: The original class label (actual_productivity) is a continuous variable. You must first transform it to a discrete variable so that you can apply Binary Classification on it. Visualize the distribution of features and classes, normalize the data, and divide it into training and test sets.
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.
 قم بإعادة صياغة النص باللغة العربية الفصحى، ولكن بطريقة أكثر رسمية. (ar).
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:
اكيد هقول لك نسخه مجانيه
Assistant: