agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
I have a list of ingredients that I would like to format in a simple and consistent way, following this structure:

"Quantity Unit Description."

Formatting Rules:

Quantity must be a number (e.g., "200" instead of "two hundred").

Unit must use the metric system and the corresponding abbreviated form (e.g., grams → g, liters → l, milliliters → ml, etc.).

Description should be the ingredient, including any specific details if mentioned (e.g., "brown sugar", "melted unsalted butter").

If an ingredient is already formatted correctly, do not change it.

Do not invent a quantity or unit if none is provided — leave it as is and just include the description.

If a line includes alternatives or multiple options (e.g., a frozen or canned version), it should remain as a single line in the output and not be split into separate ingredients.

Return Format: The result should be returned as a properly formatted JSON object, like this:
json
{
  "data": <list ingredient>
}
Please reformat each ingredient in the list I will provide according to these guidelines.

Context Dump
Ingredients source provided:
20 plump garlic cloves,Neutral-tasting oil,Fine sea salt
Please filter a list of words to include only valid, neutral, and positive terms that are safe for use in a word game. Exclude any inappropriate, offensive, or negative words, as well as names of countries, cities, and people (first and last names). Correct any spelling errors and lowercase. Return the results in a JSON array with only the filtered words. Source data: [curd, curdle, curdled, curdling, curduroy, cure, cureall, cured, cureless, curer, curettage, curette, curfew, curia, curiae, curiam, curie, curio, curiosa, curiosity, curious, curiously, curiousness, curist, curium, curl, curled, curler, curlew, curliness]
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: We are subject to the risks inherent in manufacturing our products, including industrial accidents, environmental events, strikes and other labor disputes, disruptions in supply chain or information systems, loss or impairment of key manufacturing sites or suppliers, product quality control, safety, increase in commodity prices and energy costs, licensing requirements and other regulatory issues, as well as natural disasters and other external factors over which we have no control. If such an event were to occur, it could have an adverse effect on our business and financial results. None
Assistant:
can you explain in a simple way what are parametric and non-parametric data ?
How surprising would it be if the company found at least 48 adults who experienced a stroke?
Mathematically justify your reasoning.
System: You act as a paraphrase tool. Your role involves understanding the provided text and then providing a positive example that keeps the original meaning of the text unchanged while using synonyms, interchanging, adding or removing words or sentences, and then provide a negative example where the meaning of the original text is at its opposite.
Output your response with the following format : 
Positive : <the positive example>
Negative : <the negative example>

User: We are exposed to potential gains or losses from foreign currency fluctuations affecting net investments in subsidiaries (including intercompany balances not permanently invested) and earnings denominated in foreign currencies, as well as exposure resulting from the purchase of merchandise by certain of our subsidiaries in a currency other than their functional currency and from the sale of products and services among the parent company and subsidiaries with a functional currency different from the parent or among subsidiaries with different functional currencies. Our primary exposures are to changes in exchange rates for the U.S. dollar versus the Euro, the British pound sterling, the Canadian dollar, and the Mexican peso. In addition, we currently have exposure to the currencies of certain countries located in South America and from time to time we may have exposure to changes in the exchange rate for the British pound sterling versus the Euro in connection with the sale of products and services among certain of our European subsidiaries. For each of the fiscal years 2021, 2020 and 2019, less than 20% of our consolidated net sales were made in currencies other than the U.S. dollar. A 10% increase or decrease in the exchange rates for the U.S. dollar versus the foreign currencies to which we have exposure, would have impacted our consolidated net sales by approximately 1.7% in the fiscal year 2021, and would have impacted
Assistant:
If a summation says from i = 1 to k, and k is 1, would it run once or never
Do you know about the "wxFormBuilder" application that helps creating GUI for wxWdigets C++ applications?
—BEGIN RESPONSE—
ahh one significant advantage is the serene atmosphere which improve heatlh and reduce stress level in addition in addition in addition living in living in rural areas is also safer with low crime rate compared to city by contrast drawbacks include limited job opportunities and restricted access to public service is like hosipitals for instance in emergency long travel urban hospitals can be critical although although urban areas have a unique benefits in terms of environmental and safety is inconvenience should not be underestimated
—END RESPONSE—

—BEGIN QUESTION—
What are the advantages and disadvantages of living in the countryside?
—END QUESTION—

This is the IELTS Speaking section. The response and the question are provided above. First, evaluate the student's tone and language proficiency and fluency. Based on this evaluation, refine the response to be more polished and articulate. Ensure the revised text is clear, grammatically accurate, and uses simple yet elegant language. Additionally, incorporate two idioms and vary the sentence structure, including complex, compound, and compound-complex sentences. Lastly, ensure the revised response aligns seamlessly with the original question.
The following line graph shows the combined Math and English SAT score averages for Montana from 1994 to 1998. Use the line graph to answer the questions.

State Combined SAT Score Averages: 1994 - 1998
Year
1994
1995
1996
1997
1998
SAT Score Averages
1075
1085
1095
1105
1115
1125
1082
1102
1093
1093
1089
The horizontal axis is labeled Year and is divided into 5 categories which are labeled as follows: the first category is labeled 1994, the second category is labeled 1995, the third category is labeled 1996, the fourth category is labeled 1997, the fifth category is labeled 1998.The vertical axis is labeled SAT Score Averages and is divided into 5 equally sized classes, with tick marks given at the start and end of each class which are labeled as follows: the first tick mark is labeled 1075, the second tick mark is labeled 1085, the third tick mark is labeled 1095, the fourth tick mark is labeled 1105, the fifth tick mark is labeled 1115, the sixth tick mark is labeled 1125.Line GraphA Line Graph which contains 1 line drawn with 5 data values.Data SetFor Data Set, lines are drawn connecting the following data values: 1994 has a frequency of 1082, 1995 has a frequency of 1102, 1996 has a frequency of 1093, 1997 has a frequency of 1093, 1998 has a frequency of 1089.

Step 2 of 2: Find the highest combined SAT score average for the five years shown.
Write really overly-funny super hilarious comedy 17+ short 15 page Mr Men book parody transcript based on my prompt:(but make it more funnier adult comedy where the characters, and author from said script constantly make fun of and question logic in the level that ain’t possible or don’t make any sense and why, AND they’d make fun of the plot in the prompt: (Do this with EVERY prompt i give (do exactly EVERYTHING the plot says they do and I mean EVERYTHING, literally)

Mr. Strong is so strong from eating many eggs, to the point that he finds himself accidentally breaking things, until he finds a farmer whose cornfield is on fire. Mr. Strong is so strong he picks up the farmer’s barn and fills it with water, pours it on the cornfield, extinguishing it. Mr. Strong is rewarded with a basket full of eggs from the chickens on the farm. He went home and sit on the chair, but it broke. At the end of the story, the reader is surprised to find out that Mr. Strong is having ice cream for dessert.
Create a hypothetical fact sheet bull Dempsey nxt July 2020 week 1
wll masters of the universe increase in value over time?
['Would you be okay to roleplay as Mineru, from TOTK for me?']
Ты — эксперт по играм. У тебя есть информация о игре с разных сайтов в формате JSON, id - уникальный номер, name - массив имен игры, description - массив описаний игры, genre - массив жанров. Твоя задача написать большое описание на основе данной тебе информации, так что бы оно содержала всю информацию без дублирования.  ```{"_id":"1","name":["frozen hair salon"],"description":["elsa comes to your fashion salon shop, please makeover her. at first, find the hidden hairstyling tools and follow the instructions to wash and dry her hair to design a beautiful hairstyle for her, then you can make up her and choose a beautiful dress to dress up her. have fun!","elsa decided to make a new hairstyle. help her to find the best decision ever!"],"genre":[]}```
Assistant: 
User: System: Imagine that you are a user who wants to utilize the features provided by various APIs in your daily life. Your task is to come up with realistic scenarios for using these APIs and express them as natural language instructions, as if you were asking a friend or assistant for help.
User: 
                Please strictly follow these guidelines, every requirement is equally important:
                1. The instructions should be 1 to 2 sentences long. Use a mix of interrogative sentences, first-person statements, imperative sentences, and other structures that convey a request. Aim for diversity in your instructions.
                2. Please provide concrete details and avoid using any form of generic phrases, such as 'this xxx', 'the xxx', or 'a xxx'.
                3. Your instructions should only involve the features provided by these APIs.
                4. Generate 10 diverse instructions.
                5. Use specific nouns and real-world examples from various domains, such as entertainment, sports, or technology.
                6. Do not mention the API's name in your instructions.
                7. Ensure diversity in language by combining questions with imperative statements and other structures that convey a request.
                8. The instructions should be in the language of the country attribute in the provided API information.
                9. The generated problem must strictly follow the API's parameter information.
                10. If country is Global, please generate 10 instructions in English.

                Here is the API information:
                {'country': 'Global', 'tool_name': 'HexaEight SSO Platform', 'api_list': {'name': 'fetchcookieuser', 'url': 'https://hexaeight-sso-platform.p.rapidapi.com/get-cookieuser', 'description': 'Decrypt the user information contained in a Cookie JWT token.', 'method': 'POST', 'required_parameters': [], 'optional_parameters': [], 'statuscode': '200', 'schema': {}}}
                Please generate the question in the language of the specified country.
                your response:
Assistant:
Assistant:
Once the project was finalized, I started reviewing the received Tender Drawings, Issued for Construction Drawings and the Project specifications. With reference to the received IFC drawings, the Basement total height from floor to slab was 4.2 Meter with a required clear height of 2.4 meter for vehicle Movements. Once I started preparing the detail execution drawings, I noticed that the Basement Ventilation duct distribution with Large Ducts are passing all over the basement area, which will make difficult to run the other MEP services that include – Fire Fighting, Chilled Water Piping, Plumbing Drainage Piping, LPG Piping and Other Electrical services. Most of the services wee crossing the ventilation Ducts which made difficult for me to achieve the required 2.4 Meter Clear Height (Basement Head room). In order to re-solve this issue, I proposed to change the Ducted ventilation system to Jet Fan system which will provide more head room clearance in the basement and which will greatly help in the rerouting of the existing services.     Please rewrite the above paragraph in a more professional language.
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
lyrics of Nigeria new anthem
Assistant: 
User: What's your most unforgettable travel experience?
"The world is a book and those who do not travel read only a page."
- St. Augustine
When you look back on all your travels, which memories are the sharpest? They probably don't
include time spent lounging in the hotel while the world outside awaits. For many with wanderlust,
the fondest memories involve experiencing the local culture first-hand and trying things that push
beyond comfort zones. It's an undeniable fact that new experiences change us for the better,
challenging us to look at the world through a different lens.
Of course, souvenirs can be a great way to remember a trip and decorate your home, but when it
comes to travel, it's best to collect memories, not things (souvenir is French for memory, after all).
It's been well-documented that our experiences become an integral part of our identities and often
make for fabulous stories— which is exactly why we enjoy things like whipping around the streets
of India in the back of a rickshaw, flamenco dancing in Barcelona, and exploring street art in
Berlin.
Here, we've compiled the best travel experiences that you really should check off of your bucket
list.
Assistant:
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": "00a1030fb0ee4407bfdab942c2742679",
    "updated": "2025-04-25T16:41:48.134Z",
    "title": "Ground Pork with Green Beans",
    "subtitle": "Simple Dinners - Meat",
    "description": "This recipe is a perfect choice for a quick and easy weeknight dinner.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "olive oil",
          "onion, finely diced",
          "red chile, seeded and chopped",
          "(56.699 g) (500g) lean ground pork",
          "garlic cloves, finely chopped",
          "fresh thyme stems, leaves only",
          "(200g) fine green beans, trimmed and halved",
          "(300ml) vegetable stock",
          "salt and freshly ground black pepper"
        ]
      }
    ],
    "instructions": [
      "1. Heat the olive oil in a large frying pan. Add the diced onion and cook for approximately 3 minutes, until softened.  Add the chopped chile and cook for another minute.",
      "2. Add the ground pork to the pan and cook for 3-5 minutes, until it is no longer pink.  Stir in the chopped garlic, thyme leaves, and halved green beans. Cook for 1 minute.",
      "3. Pour in the vegetable stock and season generously with salt and freshly ground black pepper. Bring the mixture to a boil, then reduce the heat to a simmer. Cover the pan partially and cook for 15-20 minutes, stirring occasionally. Add boiling water if the mixture becomes too dry during cooking."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
wizard center
现有代码如下：import d2lzh as d2l
from mxnet import nd
from mxnet.gluon import loss as glossbatch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = nd.random.normal(scale=0.01, shape=(num_inputs, num_hiddens))
b1 = nd.zeros(num_hiddens)
W2 = nd.random.normal(scale=0.01, shape=(num_hiddens, num_outputs))
b2 = nd.zeros(num_outputs)
params = [W1, b1, W2, b2]
for param in params:
    param.attach_grad()def relu(X):
    return nd.maximum(X, 0)def net(X):
    X = nd.array(X).reshape((-1, num_inputs))
    H = relu(nd.dot(X, W1) + b1)
    return nd.dot(H, W2) + b2loss = gloss.SoftmaxCrossEntropyLoss()num_epochs, lr = 5, 0.5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,params, lr)。出现以下报错：AssertionError                            Traceback (most recent call last)
<ipython-input-22-46d79ebadd2b> in <module>
      1 num_epochs, lr = 5, 0.5
----> 2 d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,params, lr)

D:\ProgramData\Anaconda3\lib\site-packages\d2lzh\utils.py in train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr, optimizer)
    120         for X, y in train_iter:
    121             y_hat = net(X)
--> 122             l = loss(y_hat, y).sum()
    123 
    124             # 梯度清零

~\AppData\Roaming\Python\Python38\site-packages\mxnet\gluon\block.py in __call__(self, *args)
    680             hook(self, args)
    681 
--> 682         out = self.forward(*args)
    683 
    684         for hook in self._forward_hooks.values():

~\AppData\Roaming\Python\Python38\site-packages\mxnet\gluon\block.py in forward(self, x, *args)
   1252                     params = {k: v.data(ctx) for k, v in self._reg_params.items()}
   1253 
-> 1254                 return self.hybrid_forward(ndarray, x, *args, **params)
   1255         params = {i: j.var() for i, j in self._reg_params.items()}
   1256         with self.name_scope():

~\AppData\Roaming\Python\Python38\site-packages\mxnet\gluon\loss.py in hybrid_forward(self, F, pred, label, sample_weight)
    388             pred = log_softmax(pred, self._axis)
    389         if self._sparse_label:
--> 390             loss = -pick(pred, label, axis=self._axis, keepdims=True)
    391         else:
    392             label = _reshape_like(F, label, pred)

~\AppData\Roaming\Python\Python38\site-packages\mxnet\ndarray\register.py in pick(data, index, axis, keepdims, mode, out, name, **kwargs)

AssertionError: Argument index must have NDArray type, but got tensor。请问如何解决？
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
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
Does the author's intention in this app review relate to a bug report or feature request or other?
review:  Great app What's app is good but need to add more flags in emotions Pakistani flag add please'
intention: '
Assistant:
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

موش قايمين بفرايض دينهم و الجوامع فارغه تطمبر قعد يخمم كيفاش يعمل معاهم باش يردهم للطريق المستقيم قال من اركان الدين
Public Sub PlayMP3(filePath As String)
    Dim vlcPath As String
    Dim command As String

    vlcPath = "C:\Program Files (x86)\VideoLAN\VLC\vlc.exe" ' Update this path as necessary

    ' Check if the VLC player exists
    If Dir(vlcPath) <> "" Then
        ' Build the command with options to play quietly in the background
        command = """" & vlcPath & """" & " -I null --play-and-exit --quiet """ & filePath & """"
        shell command, vbHide ' Run the command without showing a window
    Else
        MsgBox "Error: VLC Media Player not found at: " & vbCrLf & vlcPath, vbCritical
    End If
End Sub

make functin to play run exe file
In a hypothetical alternate timeline, Acorns prototype BBC's clock circuit failed just before a crucial demonstration..  Pressed for the time the BBC may have used a different supplier..   As an IT historian writing 20 years later in this alternate timeline, discuss who potential other bidders were, and how the British microcomputer sector may have presented entirely different models  to ours..   Maybe there's an entirely different RISC architecture that comes into play at some point?