agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
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: Reviews that report a problem, such as faulty behavior of the application or of a specific feature
Feature strength: Reviews that identify an aspect about an existing feature that users are satisfied with
Feature shortcoming: Reviews that identify an aspect about an existing feature that users are unsatisfied with
User request: Reviews that ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature
Praise: Reviews where users express general appreciation with the application. It focuses on general judgment, unlike feature strength which emphasizes on the positive feedback about a specific feature
Complaint: Reviews where users express general dissatisfaction with the application. In contrast with feature shortcoming which focuses on the negative feedback about a specific existing feature, general complaint concentrates on general judgment
Usage scenario: Reviews where users describe workarounds, use cases and scenarios involving the app
Noise: Reviews that do not belong to any other categories.
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: Reviews that report a problem, such as faulty behavior of the application or of a specific feature
Feature strength: Reviews that identify an aspect about an existing feature that users are satisfied with
Feature shortcoming: Reviews that identify an aspect about an existing feature that users are unsatisfied with
User request: Reviews that ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature
Praise: Reviews where users express general appreciation with the application. It focuses on general judgment, unlike feature strength which emphasizes on the positive feedback about a specific feature
Complaint: Reviews where users express general dissatisfaction with the application. In contrast with feature shortcoming which focuses on the negative feedback about a specific existing feature, general complaint concentrates on general judgment
Usage scenario: Reviews where users describe workarounds, use cases and scenarios involving the app
Noise: Reviews that do not belong to any other categories.
Does the author's intention in this app review relate to a Bug report or Feature strength or Feature shortcoming or User request or Praise or Complaint or Usage scenario or Noise?
review:  Please fix this Idk it uninstalls in my phone automatically.... And I ha e to download it again n again.'
intention: '
Assistant:
When concerning the four great powers, why is Aksum often forgotten and left out compared to the other three: Rome, Persia and China?
May I have a small inro for: Backup Storage Devices: Cloud Storage vs. External Drives
How artificial intelligence works?
Write a educational passage in Vietnamese that could appear in a textbook, giving some paragraphs or pharse that related to this question and answer set:
Câu hỏi: Khẳng định nào sau đây là đúng?
A. Phần mềm GIMP có thể lưu được ảnh có đuôi .cxf và JPG.
B. Phần mềm GIMP chỉ lưu được ảnh có đuôi .jpg.
C. Phần mềm GIMP chỉ lưu được ảnh có đuôi .doc.
D. Phần mềm GIMP chỉ lưu được ảnh có đuôi .gif.
Đáp án đúng: Phần mềm GIMP có thể lưu được ảnh có đuôi .cxf và JPG
Notes that:
1. Remember that the multichoice question above must not be included in the paragraphs or phrase
2. You can not take an example same as the question
3. Explain detail like in the actual textbook
4. All instructions must be given in general form
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 stunning presentation of a Brown shrimp and citrus verrine, beautifully layered in elegant glassware. At the bottom, a creamy remoulade of shelled brown shrimp and finely grated celeriac, mixed with a hint of orange for a pop of color. The top is adorned with a light and airy orange foam, delicately piped and frothy, creating a visual contrast. The dish is garnished with finely ground pepper and vibrant orange segments for an added touch of freshness. The glasses are placed on a rustic wooden surface, complemented by a lush green garnish, evoking a fresh and sophisticated culinary experience.
    Ambiance souhaitée : 
        A refined, Michelin-star kitchen setting with stainless steel countertops gleaming under soft, focused lighting. The dish is carefully plated on a modern, minimalist plate, surrounded by high-end culinary tools like a sharp chef’s knife and elegant sauce ladles. In the background, the kitchen hums with the energy of a top-tier restaurant: sous-vide machines, open flame from a professional stove, and neatly organized spices. A professional, polished atmosphere that exudes expertise, precision, and a passion for gastronomy, with a subtle air of sophistication.
You are an expert AI model that makes prompts for an AI to generate a picture based on a scene in a script. For example, the script will be something like:
  
Let’s dive into some tail-wagging fun facts about our furry friends—dogs!

The prompt would be something like:

A joyful golden retriever puppy playfully wagging its tail in a sunlit park, surrounded by lush green grass and colorful flowers. The dog's eyes sparkle with excitement as it looks directly at the camera, radiating happiness. Soft natural lighting enhances the warm, vibrant tones of the fur. A blurred background creates a dreamy, cinematic feel. Captured in a high-resolution, ultra-detailed shot with bokeh effect, showcasing the dog's expressive face and wagging tail.

Now generate a prompt for the scene script YOU ARE TO STRICTLY ONLY INCLUDE THE PROMPT IN YOUR RESPONSE AND NOT SEND ANY TEXT ACKNOWLEDGING MY REQUEST: "Now, ever wonder why your dog seems to know when you’re feeling blue? It turns out dogs can read human emotions! They can sense our feelings and often try to comfort us. They really are furry little therapists."
System: 
User: Generate a detailed and realistic news article about an event in the Macroeconomic Developments domain, focusing on the Health Care Equipment industry, and located in Southeast Asia. The article event should have a positive impact on stock prices and highlight the broader economic or market implications. The article should be 225 words long.
Assistant:
i have crypto data as csv files 
i have following code to train an lstm model on my data 
currently model trains to predict "Close" 
please update the code so model can predict 3 value instead of just "Close"
name of the corresponding columns are "Close", High" and "prior" 
"Close" and "High" are continues values and "prior" is a label value which can be 0 or 1:

# %%
import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import os

# %%
def load_and_process_data(file_path, target_col, scaler, sequence_length):
    # Load data
    df = pd.read_csv(file_path)
    # .drop('Unix', axis=1).drop('Date', axis=1).drop('Symbol', axis=1)
    
    start_col = 'y_High_1d'  # Replace ‘Start_Column’ with the actual start column’s name
    end_col = 'y_Priority_5d'  # Replace ‘End_Column’ with the actual end column’s name

    # Find the index of start and end columns
    start_idx = df.columns.get_loc(start_col)
    end_idx = df.columns.get_loc(end_col)

    # Adjusted slicing: Keep only the columns before start and from the end, excluding start and end columns
    df_modified = pd.concat([df.iloc[:, :start_idx], df.iloc[:, end_idx + 1:]], axis=1)

    # Assuming you want to use all columns except target_col as features
    # Adjust this if you have a specific set of columns in mind
    # X = df_modified.loc[:, df.columns != target_col].values
    X = df_modified.drop(df.columns[:2], axis=1).values
    y = df[target_col].values
    # Normalize features
    scaler.fit(X)
    X = scaler.transform(X)  # Assuming MinMaxScaler fit on full dataset or similar range
    # Create sequences
    X_seq, y_seq = [], []
    for i in range(len(X) - sequence_length):
        X_seq.append(X[i:i+sequence_length])
        y_seq.append(y[i+sequence_length])
    return np.array(X_seq), np.array(y_seq)

# %%
scaler = MinMaxScaler(feature_range=(0, 1))
# Normally, you’d fit the scaler on your dataset. You’ll need to adapt this to your scenario.
# For demonstration, let’s assume all features are within 0 and 100.


# %%
def train_model(files, batch_size, epochs):
    for epoch in range(epochs):
        for file in files:
            X, y = load_and_process_data(file_path = file, target_col = 'Close', scaler = scaler, sequence_length = 30)  # Adjust ‘features’ accordingly
            
            model.train_on_batch(X, y)

# %%
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(30, 6427)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

# %%
csv_folder_path = r"E:\01_calculate_talib\New folder (2)"
file_paths = []  # Update this list with actual file paths

for csv_file in os.listdir(csv_folder_path):
    file_path = os.path.join(csv_folder_path, csv_file)
    file_paths.append(file_path)
batch_size = 32
epochs = 10  # Set the number of epochs

train_model(file_paths, batch_size, epochs)

# %%
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:  The 18.95 carat, fancy vivid pink stone is a rectangular cut and is called The Pink Legacy.  Christie's will put it up for auction Nov. 13 at its Magnificent Jewels Sale in Geneva, with an estimate s
Assistant:
{"prompt":"Пришли ответ на сообщение userMessage от персонажа в данном контексте. Можно использовать смайлики. . Ответ должен учитывать userMessage, inputData и иметь привычний вид ответа нейросети.","userMessage":"В файле Perl у меня есть такой запрос: \nmy $sql = \"\n    SELECT\n      item_uid_id,\n    \titem_chr_link,\n    \titem_chr_name,\n    \titem_set_brand,\n    \titem_chr_article,\n    \tIF(NULLIF(item_img_image, '') IS NULL, '/i/img/no-image-1200x1200.png', item_img_image) AS item_img_image,\n    \tROUND(IFNULL(item_dec_price1, 0) * ((100 + IFNULL(item_int_discount, 0)) / 100), 2) AS item_dec_price1,\n    \t(CASE item_set_field1\n        WHEN 'EUR' THEN '€' \n        WHEN 'USD' THEN '$' \n        WHEN 'RUR' THEN '₽' \n        #ELSE '₽'\n      END) AS item_chr_unitvaluta,\n    FROM account_item\n    WHERE item_cnt_account = 626\n      AND item_hld_holder = 18177\n      AND item_mbr_member = 0\n      AND IFNULL(item_enm_active, 'NO') = 'YES'\n\n    LIMIT 50\n    \";\n\nНо в нем есть такая строка WHEN 'USD' THEN '$', знак $ подсвечивается как синтаксический для языка. Все правильно или нужно его тут как-то экранировать?","inputData":{"context":"","language":"en","chatTheme":"dark","nsfw":{"nsfw_mode":false,"nsfw_instruction":""}}}
I have a board to power a  "High power cold light Led", input Voltage of this board is around 12vDC and I have a potentiometer on the board with value of 10K it is embedded into my board(SMD Type) and this potentiometer is controlling  Amount of power going through High power cold light LED which at peak is around 70mV , first of all I want to change the 10k ohm potentiometer to a bigger ins size version so i can operate it manually and by hand I want to attache 6 LED SMD surface mount to Create a LED light bar to show and indicate the intensity and brightness level based on change in potentiometer and its out put to "high power LED" ; LED bar is composed of  6 smd LED lights  are turn on and off , what is the structure, design and formulas I have to use ?
please provide message to empower women in industrial field of work
I will next provide you with a question in a language. Strictly follow its instructions for replying. Do NOT reply with elaborate answers or things not requested. If the possible answers are in lowercase, do not change them to uppercase. Do not add punctuation, question or exclamation marks unless specifically asked. Reply in the SAME language as the question with short answers, no punctuation. Here is the question: czy wielbłąd ma celę?
Send only resolution from below text like 1080p, 720p.etc without any extra text,
        
[ @FridayUniverse ] The Wire S01 E11 BluRay 720p English AAC 5.1 x264 ESub - mkvCinemas.mkv
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
Does the author's intention in this app review relate to a pbr or inq or irr?
review: I understand its optional. I have been a customer for 10+ yrs. I shouldnt have to pay extra for the World Cup in Espaol. The Spanish broadcast is so much better than Fox. DIRECTV should be better when it comes to sports.'
intention: '
Assistant:
Act as a spelling corrector, content editor, and text improver. ONLY return the rewritten text and nothing else.

Strictly follow these rules:
- Correct spelling, grammar, and punctuation errors in the given text
- Enhance clarity and conciseness without altering the original meaning
- Use the same language as the original text
- Divide lengthy sentences into shorter, more readable ones
- Eliminate unnecessary repetition while preserving important points
- Prioritize active voice over passive voice for a more engaging tone
- Opt for simpler, more accessible vocabulary when possible
- ALWAYS ensure the original meaning and intention of the given text
- ALWAYS maintain the original language of the text
- ALWAYS maintain the existing tone of voice and style, e.g. formal, casual, polite, etc.
- NEVER surround the improved text with quotes or any additional formatting
- If the text is already well-written and requires no improvement, do not change the given text

Text to improve:



Hello. I have Raycast and skhd setup to call Raycast deeplinks via the same shortcuts based on the active application. I'd like to have a deeplink query parameter to hide Raycast when calling the same deeplink twice, making them function like Raycast hotkeys.
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 struggling with decision-making and find it difficult to make choices. What can I do to improve my decision-making skills?
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: Các lĩnh vực sách nào được đề cập trong danh mục?
Answer:Các lĩnh vực sách được đề cập bao gồm Kế toán - Kiểm toán, Tài chính - Ngân hàng, Quản trị kinh doanh, Ngoại ngữ, Công nghệ sinh học, và Công nghệ thông tin.
Result only 0 or 1. Don't add anything else:
Assistant:
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": "6280b98a33cc4a2f931b1516c0d8b8fd",
    "updated": "2025-04-19T19:33:34.867Z",
    "title": "Salmon Burgers",
    "subtitle": "A Luxurious Fishcake Experience",
    "description": "These delicious salmon burgers offer a sophisticated take on the classic fishcake, focusing on the rich flavor of the fish without any added potato.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "(255.145 g) (700g) salmon filets, skinned and cut into chunks",
          "(125g) breadcrumbs",
          "spring onions, trimmed and roughly chopped",
          "capers, drained and rinsed",
          "(50g) flat-leaf parsley",
          "salt and freshly ground black pepper",
          "egg",
          "sunflower oil"
        ]
      }
    ],
    "instructions": [
      "1. Combine the salmon, breadcrumbs, spring onions, capers, and parsley in a food processor or blender. Pulse until thoroughly mixed. Season with salt and pepper, then pulse again to incorporate.",
      "2. Add the egg to the processor and pulse until the mixture is evenly distributed. Divide the mixture into six portions. Shape each portion into a ball, then flatten into a burger patty. Place the patties on a plate and refrigerate for 20 minutes to firm up.",
      "3. Heat the sunflower oil in a large, nonstick frying pan. Add the salmon burgers and cook for 3-4 minutes per side, or until golden brown."
    ],
    "notes_ingredients": null,
    "notes_instructions": "Alternatively, bake the burgers in a preheated oven at 400°F (200°C). Place the patties in a lightly oiled roasting pan and bake for 15-20 minutes, or until golden brown and cooked through."
  }
]
One hundred word summary: Estrogen-related metabolic enzyme gene polymorphisms have been shown to be associated with breast cancer, and in this paper, a novel noninvasive breast cancer prediction model was developed using machine learning algorithms combined with estrogen metabolic enzyme gene single nucleotide polymorphism (SNP). To predict breast cancer, the coded data of 14 SNPs from 280 enrolled subjects were randomly shuffled, with 80% of the data classified as training and the remaining 20% as test group to validate. Single factor analysis was performed to screen independent risk factors, and then Breast Cancer with Single Nucleotide Polymorphisms - Machine Learning model (BCSNP-ML) prediction model was completed using Light Gradient Boosting Machine (LightGBM) algorithm. Single factor analysis showed that SULT1A1 rs1042028, CYP1A1 rs1048943, CYP1B1 rs1056827 and CYP1A1 rs1056836 were significantly associated with the occurrence of breast cancer. The highest area under the receiver operating characteristic curve (AUROC) of the six machine learning models established on 14 variables was 0.809. The AUROC of the BCSNP-ML model constructed by four variables was 0.831. Otherwise, BCSNP-ML is visualized and explained in the paper using SHapley Additive exPlanations analysis to further validate that the model can be used as a powerful tool for clinical prediction of breast cancer.
Tools for flame extension test
Отправьте SQL-запрос, который выводит таблицу 'auto' (база 'sql_test') с названиями колонок на русском языке и сортировкой по году:
Марка | Модель
| Цвет
| Мощность | Привод
| Год
Audi | 100
| Red
| 133 | front
| 1991
Mazda | 626 Opel | Astra Suzuki Baleno
Black
| 90
front
1996
| Gray
| 101
front
1999
Blue
| 96
front
2000
Isuzu | Rodeo
Olive
130
rear
2000
Ford | Transit
| Green
140
rear
2007
Honda
Civic VIII
White
140
front
2008
Nissan
Murano II
Orange
249
full
| 2010
Jeep
Grand Cherokee IV | Silver
| 243
full
| 2011
Lexus
GX II
| Yellow
| 296
| full
| 2021