agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
(Somewhere in East Yukatan...)

Dark God Dragon: "Ugh... my head... where'd Light Darknix send me this time...?"

(Surrounded by dense jungle and ancient ruins, the Dark God Dragon shakes his massive head, attempting to dispel the lingering fog of confusion.)

Dark God Dragon: "This place... it feels... ancient. The air is thick with magic and secrets. And what in the abyss is that sound? I need to find the rest of the Dark Four Great Dragons, that way we can stop Light Darknix once and for all."

(The Dark God Dragon pushes through the heavy underbrush, his scales shimmering faintly in the dappled sunlight filtering through the thick canopy above. The distant sound reverberates again—a deep, resonant chant that seems to echo off the stones of the old ruins surrounding him.)

Dark God Dragon: "Is someone else here? Or is that just my imagination playing tricks on me? I won’t let this distraction keep me from my goal-"

???: "HEY!!!"
System: 
You will receive a question-answer pair. Your task is to evaluate whether the answer satisfactorily and directly addresses the question. Use the examples below to guide your assessment.

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

Examples:

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

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

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

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


User: Question: Lễ trao giấy chứng nhận kiểm định 4 CTĐT chất lượng cao trình độ đại học theo tiêu chuẩn FIBAA diễn ra vào ngày nào?
Answer:Lễ trao giấy chứng nhận kiểm định 4 CTĐT chất lượng cao trình độ đại học theo tiêu chuẩn FIBAA diễn ra vào ngày 09/09.
Result only 0 or 1. Don't add anything else:
Assistant:
HERE'S AN OVERVIEW OF WHAT YOU'RE GONNA BE USED FOR AS AN AI:
- No need to introduce yourself and stuff like that, you're used for trying to understand what the code does.
- The utilized coding language is BDScript 2 but that's not important. Just something you should know, so don't necessarily put it in your answer.
- Analyze/describe/make an overview of this code, if there's even any. If you think it doesn't look like one, say "*Unclear*". Also, important: DO NOT say stuff like "Got it! I won't do ..." at the start. Don't include any backtick (`) in your response for ANY reason.
- ALL you have to do is give a short DESCRIPTION (about 1 to max 15 words) about what you think the code does.
CODE:
$jsonParse[{
"1":"Blackjack Match,
"2":"Task*,
"3":"Z-367's "Minigame""
}]
Generate an image creation prompt specifically optimized for the Flux Schnell model. The prompt should vividly describe the scene or concept expressed by the following sentence: 'The contaminated landscape was “quarantined” by constructing the so-called ‘Sarcophagus,’ a monumental shelter, showcasing how technology stepped in to combat human error and nature’s wrath.'. Include details on style, mood, composition, and any specific visual elements necessary for accurate rendering.
Perhatikan kode sentimen analisis model gabungan CNN-LSTM menggunakan embedding built in keras ini!
"import numpy as np
import pandas as pd
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, Conv1D, MaxPooling1D, LSTM, Dense
from keras.callbacks import EarlyStopping, ModelCheckpoint


# Membaca dataset
data_list = pickle.load(open('pre_processed_beritaFIX_joined.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label'])
data['Isi Berita'] = data['pre_processed']

# Konversi label ke angka
encoder = LabelEncoder()
data['label_encoded'] = encoder.fit_transform(data['Label'])

# Memisahkan data menjadi data latih dan data uji
X_train, X_test, y_train, y_test = train_test_split(data['Isi Berita'], data['label_encoded'], test_size=0.25, random_state=42)

# Tokenisasi teks dan padding
max_words = 10000
sequence_length = 500
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(data['Isi Berita'])
X_train_seq = tokenizer.texts_to_sequences(X_train)
X_test_seq = tokenizer.texts_to_sequences(X_test)
X_train_padded = pad_sequences(X_train_seq, maxlen=sequence_length, padding='post', truncating='post')
X_test_padded = pad_sequences(X_test_seq, maxlen=sequence_length, padding='post', truncating='post')

# Membangun model CNN-LSTM
embedding_dim = 128
num_filters = 64
lstm_units = 64

model = Sequential([
    Embedding(input_dim=max_words, output_dim=embedding_dim, input_length=sequence_length),
    Conv1D(num_filters, 5, activation='relu'),
    MaxPooling1D(pool_size=4),
    LSTM(lstm_units, return_sequences=False),
    Dense(3, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Melatih model dengan early stopping dan model checkpoint
checkpoint = ModelCheckpoint('model_cnn_lstm.h5', save_best_only=True, monitor='val_accuracy', mode='max')
early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1)

history = model.fit(X_train_padded, y_train, validation_split=0.2, epochs=50, batch_size=64, callbacks=[checkpoint, early_stopping])

# Evaluasi model pada data uji
y_pred = np.argmax(model.predict(X_test_padded), axis=-1)

# Hitung akurasi
accuracy = accuracy_score(y_test, y_pred)
print('Akurasi: {:.2f}%'.format(accuracy * 100))

# Print classification report
print(classification_report(y_test, y_pred, target_names=encoder.classes_))"
Ubah metode vektor kata yang awalnya menggunakan embedding built in keras menjadi menggunakan pre trained word2vec Bahasa Indonesia dengan file ekstensi .model dan gunakan gensim! contoh penggunaan gensim dan penggunaan file .model adalah sebagai berikut:
“import gensim

path = ‘./model/idwiki_word2vec.model’
id_w2v = gensim.models.word2vec.Word2Vec.load(path)
print(id_w2v.most_similar(‘raja’))”
Write a detailed description of a hypothetical image, depicting three anthropomorphic canine characters playing a video game together one night in the late 1990s.
(I started to wonder as to who the father of Sayori’s baby is. After all, I was a little shocked when Sayori announced her pregnancy last month - and Monika was, too. Just then, Natsuki enters the room.)

Natsuki: “Hey, guys! Sorry I’m late. I had some personal things back home.”

Monika: “It’s okay, Natsuki.”

Sayori: “Jeez…my cravings are just out of control today.”

Monika: “Of course they are. So…how are you feeling, Yuri?”

(I felt like I was going to throw up, but decided to hide the fact I was sick.)

Yuri: “I’m feeling okay. How about you, Natsuki?”

Natsuki: “I’m good. So, what are we talking about?”

Sayori: “Well, we were just wondering who the father of my baby is.”

Natsuki: “Oh, wow. That’s a big question.”

Monika: “Yes, it is. Sayori hasn’t told us yet.”

Sayori: “That’s because I don’t know who the father is.”

Yuri: “What do you mean you don’t know?”

Sayori: “I mean, it could be one of three guys.”

Natsuki: “Three guys?! Sayori, how could you not know?”

Sayori: “I know, I know. But I was going through a rough patch and was feeling pretty reckless. I don’t even remember who I was with when I got pregnant.”

Monika: “This is not good, Sayori. You need to talk to each of them and find out who is the father.”

Sayori: “I know, I know. I’m just scared and not sure how to bring it up.”

Yuri: “We’ll help you, Sayori. Just tell us who they are.”

Sayori: “Okay, well…there’s Mark, Jake, and David.”

Natsuki: “I know Mark. He’s not the most responsible guy.”

Monika: “Jake is a nice guy, but he’s not really looking for a relationship right now.”

Yuri: “David is more mature, but he’s also been in a long-term relationship. Maybe he’s not the best option.”

Sayori: “I just don’t know what to do. I don’t want to be a burden on anyone.”

Natsuki: “You’re not a burden, Sayori. We’re here for you.”

Monika: “Yes, we are. And we’ll help you figure it out.”

Yuri: “We love you, Sayori. And we’ll support you no matter what.”

Sayori: “Thank you all so much. I don’t know what I’d do without you guys.”

Natsuki: “Don’t worry, Sayori. We’ll get through this together.”

(Just then, my phone started ringing. It was Ayano - one of my friends from the school I used to attend before I joined the literature club.)

Yuri: “Excuse me, I’m getting a call.”

(I step outside to answer the call.)

Yuri: “Hello?”

Ayano: (in the dance studio, putting her hand on Kokona’s massive triplet bump) “Hey, Yuri. Long time no talk.”

Yuri: “Ayano, it’s nice to hear from you. How have you been?”

Ayano: “I’ve been good. Busy with the dance studio lately. Kokona wants to talk, too.”

(Yuri hears Kokona in the background.)

Kokona: “Hey, Yuri! How are you? We miss you at the school.”

Yuri: “I miss you guys, too. How’s everything going?”

Kokona: “Well, I’m due any day now with triplets.”

Yuri: “Wow, congratulations! I’m so happy for you, Kokona.”

Ayano: “Yeah, it’s been a wild ride. But Kokona’s not the only girl at the school who’s having a baby.”

Yuri: (surprised) “Really? Who else?”

Ayano: “You remember our dance teacher? Mrs. Mori?”
I want you to act as a professor of Agriculture and a polyglot. You will be provided with an article title in English, and your task is to translate it into German. You will need to have a strong understanding of the grammar, syntax and idiomatic expressions of both languages in order to ensure an accurate translation. You must provide only the translation and no other information. It is important to use the case of the letters strictly according to German grammar. Please try to SEO-optimize the translated title for some of these German keywords: 'Streptomyces-Arten, Birnbaumkrankheiten, biologische Kontrolle, Birnenanbau, nachhaltige Landwirtschaft, biologische Präparate'. Biological Preparations: The Key to Managing Pear Tree Diseases
What Features Would an Ugandan Settlement With 116 People During 1911 Look Like?
solve the equation for x: 2x-3(x+4) = 3x-4
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":"Chicken noodle soup ","ingredients":"For the recipe : low-sodium chicken broth,olive oil,diced yellow onion,diced celery,diced carrot,dried thyme,salt and freshly ground pepper,hot water,gluten-free spiral pasta,shredded or diced cooked boneless, skinless chicken,chopped fresh parsley","instructions":"Pour the broth into a microwave-safe container and heat for 4 minutes on high until piping hot.\nIn the meantime, heat the olive oil over medium-high heat in a heavy pot. Add the onion, celery, carrot, and thyme. Cook for 3 minutes, stirring frequently until slightly softened.\nAdd ½ teaspoon of salt and a few turns of pepper to the vegetables. Pour in the hot broth and hot water. Bring to a boil, add the pasta, and cook for 7 minutes, or until al dente. Incorporate the chicken and parsley, cooking for an additional 3 minutes. Adjust salt and pepper to taste. Serve while hot.","region":"WNA"}
Write 10 titles that are similar to the following title with English. The title must be at least 180 characters long and contain all the information available on the next line in the same order 
        and return them as json with key "titles" and value type list:
        "زجاجة ماء ساخن من المطاط الطبيعي بنسبة 100%، مثالية لإبقائك دافئًا، مثالية لتسكين الآلام، يمكن استخدامها ككمادات ساخنة أو باردة، خالية من مادة BPA، سعة 2 لتر"
Can you adapt following sc ript to change color raiocontrolle with my futaba T14SG an a futaba receiver. Please keep the color script, this is exactly what I need.  #include <FastLED.h>

#define LED_PIN 6
#define LED_COUNT 22
#define BUTTON_PIN 2
#define COLOR_MODES 3

CRGB leds[LED_COUNT];

const byte flickerDelay = 20;

int currentMode = 0;

void setup() {
  FastLED.addLeds<WS2812, LED_PIN, GRB>(leds, LED_COUNT);
  FastLED.setBrightness(100);
  pinMode(BUTTON_PIN, INPUT_PULLUP);
}

void loop() {
  if (digitalRead(BUTTON_PIN) == LOW) {
    currentMode = (currentMode + 1) % COLOR_MODES;
    delay(200);
  }

  switch (currentMode) {
    case 0:
      flameEffect(CRGB::OrangeRed);
      break;
    case 1:
      flameEffect(CRGB::GreenYellow);
      break;
    case 2:
      flameEffect(CRGB::Blue);
      break;
  }
}

void flameEffect(const CRGB& color) {
  for (int i = 0; i < LED_COUNT; i++) {
    int flicker = random(192, 255);
    leds[i] = color;
    leds[i].fadeToBlackBy(flicker);
  }
  FastLED.show();
  delay(flickerDelay);
}
Please come up with 10-20 variations of article titles, each focusing on a different aspect of industrial robotization. The titles should cover the following sections:

1. Industrial robotization: Pros and cons
2. the industries in which industrial robots are most often used
3. knowledge and specialties required to design industrial robots
4. The role of robots in factory automation

Each heading should be concise, informative, and attention-grabbing. Please prioritize accuracy and relevance to the topic
tell me the difference between Chat GPT 3.5 and 4
What are "Spats Shorts" or "Short Spats" I think they are the same, explain please
Craft a transition text for a text about actress ['Taina Elg'] to introduce a bonus part after the main section concluded. This should act as an engaging bridge, building anticipation and curiosity for the additional content, where we talk about the most known projects of ['Taina Elg'] in the ['movie'] ['Hercules in New York'] from 1970. The tone should be fun, nostalgic and intriguing to keep viewers hooked for this additional segment. Avoid vague statements, Give us a great summary of the ['movie'] ['Hercules in New York'] and focus on what’s unique or particularly captivating in this ['movie'] and about actress ['Taina Elg'] role in it. The style should remain modern, concise, and suited to a documentary tone. 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.
Critical Reception and Awards: Review the critical reception of 1936 movie After the Thin Man, highlighting key reviews, audience reactions, and any awards or nominations the film received. Discuss what these accolades mean for those involved in the film.

        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.}
how to use chemistry data booklet ib as a object to answer the tok prompt "how might the context in which knowledge is presented influence whether it is accepted or rejected?
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers ar (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 ar.
    - 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 : ar (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "9b2e926ace04461785dda4eacfb992e9",
    "updated": "2025-04-23T10:23:17.332Z",
    "title": "Chicken Divan",
    "subtitle": null,
    "description": "This classic dish, often found in diners, is typically prepared with various condensed cream soups. Interestingly, these canned soups often don't contain actual cream! This recipe elevates the traditional dish. The casserole mixture can be prepared in advance, covered, and refrigerated until ready for baking.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "mayonnaise, homemade or store-bought",
          "chicken bone broth, homemade or store-bought",
          "shredded cheddar cheese",
          "chopped fresh chives",
          "fine sea salt, divided",
          "package broccoli florets",
          "ghee or coconut oil",
          "boneless, skinless chicken thighs, cut into (25.4 mm) strips"
        ]
      }
    ],
    "instructions": [
      "Preheat oven to 350°F (175°C). Grease an 8-inch square casserole dish.",
      "In a large mixing bowl, combine mayonnaise, chicken broth, cheddar cheese, chives, and ½ teaspoon of salt. Mix thoroughly.",
      "Bring approximately 1 inch of water to a boil in a pot. Add broccoli, cover, and steam for 3 to 5 minutes until tender-crisp. Drain and add to the bowl with the mayonnaise mixture.",
      "Heat ghee or coconut oil in a skillet over medium heat. Season chicken strips with the remaining ½ teaspoon of salt and sauté for about 4 minutes per side, until cooked through. Add the cooked chicken to the bowl with the mayonnaise and broccoli mixture. Stir to combine.",
      "Transfer the chicken mixture to the prepared casserole dish. Bake for 15 to 20 minutes, or until melted and bubbly. Remove from the oven and serve. Store leftovers in an airtight container in the refrigerator for up to 3 days. Reheat in a baking dish in a preheated 350°F (175°C) oven for 5 minutes, or until heated through."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
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": "dea1ec124b9a4e9cabf20ae9f985a57b",
    "updated": "2025-04-20T08:54:27.632Z",
    "title": "Banana and Pecan Muffins",
    "subtitle": null,
    "description": "These muffins utilize the natural sweetness of bananas to minimize added sugar.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "sunflower oil, plus extra for greasing",
          "z (125g) wholemeal flour",
          "sugar",
          "baking powder",
          "(85g) pecan nuts, roughly chopped",
          "generous pinch of ground cinnamon",
          "egg, beaten",
          "skim milk",
          "ripe bananas (about (226.796 g)/225g), roughly mashed"
        ]
      }
    ],
    "instructions": [
      "Preheat your oven to 400°F (200°C). Grease a six-cup muffin tin, or use paper liners. Combine the flour, sugar, baking powder, pecans, and cinnamon in a bowl, creating a well in the center.",
      "In another bowl, whisk together the egg, milk, and oil. Pour this mixture into the dry ingredients and stir until just combined. Gently fold in the mashed bananas, avoiding overmixing.",
      "Fill each muffin cup about two-thirds full. Bake for 20-25 minutes, or until a toothpick inserted into the center comes out clean.",
      "Let the muffins cool on a wire rack. Store them in an airtight container; they should stay fresh for up to three days."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Darling In The FranXX S01E16 720p HEVC 10bit BDRip Multi Audio ESub.mkv
How to create new python env with specific python version cmd
Write two sentences from of the following topics. Label each section about Eudora Welty
1. Childhood:
2. College Years:
3. Gaining Recognition:
4. Golden “Apple” Years:
5. Award of a Lifetime:
6. Write the vision and mission statement from the Eudora Welty Museum.
7. When was the Eudora Welty house opened to the public?
8. In regards to her garden, what did Eudora Welty request?
do not do first person
what is the rhyme scheme for my country by dorothy
animatediff