agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
(In the school literature clubroom…)

Monika: “MC, Sayori, Yuri, where’s Natsuki?”

MC: “I’m not sure, I haven’t seen her all day. She’s usually here before club starts.”

Sayori: “Hmm, that’s strange. Maybe she’s just running late? She does have a habit of procrastinating.”

Yuri: “I hope she’s alright. Maybe we should text her and see if everything’s okay.”

(They all pull out their phones and send Natsuki a message.)

MC: “Hey Natsuki, are you coming to the club today? We haven’t seen you yet.”

Sayori: “Yeah, we miss you! Don’t leave us hanging!”

Yuri: “We hope everything’s okay. Let us know if there’s anything we can do to help.”

(They wait for a response but there’s no reply.)

Monika: “Hmm, that’s not like Natsuki. She usually texts back quickly.”

MC: “Should we go check on her? Maybe something happened.”

Sayori: “Yeah, let’s go to her house. We won’t be able to focus on the club if we’re worried about her.”

Yuri: “I agree. It’s always better to make sure our friends are safe and well.”

(The four of them pack up their things and head to Natsuki’s house. When they arrive, they notice that the front door is slightly open.)

MC: “That’s weird. Natsuki never leaves her door open like this.”

Monika: “Let’s go inside and see if she’s here.”

(They cautiously enter the house, calling out for Natsuki.)

Sayori: “Natsuki, are you home? It’s the literature club!”

(They walk through the house, their worry growing with each empty room they find.)

Yuri: “This is not looking good. Where could she be?”

(MC notices a door that’s slightly ajar and pushes it open.)

MC: “Guys, I think I found Natsuki.”

(They rush into the room and see Natsuki sitting on the floor, 7 months pregnant.)
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: hey there
Inviting new member to be part of executive committee of Division A
Write dialogue from a scene from the animated teen “Jane”, where 14 year old Jane, Jane’s 14 year old girlfriend Sam (who likes to communicate using Internet slang and abbreviations), Jane’s 14 year old friend and neighbour Aaron Ling and Aaron’s 12 year old sister Molly-Rose Ling hanging out during a school trip to the mall when Molly-Rose mentions how her mother regrets giving Aaron a credit card, especially since the school mentioned multiple times to students and their parents to not bring any kinds of cash (this includes banknotes, credit cards, debit cards and gift cards), yet Aaron brought his credit card in, making his two friends jealous that he can buy stuff during the trip, even after the school and his mother told him not to bring it in, but then Molly-Rose explains **why** Aaron brought it in
max_length = 200

def tokenize_and_merge(fr_sentence, en_sentence):
    fr_tokens = fr_tokenizer.tokenize(fr_sentence.np().decode('utf-8')).merge_dims(-2, -1)
    fr_decode_tokens = fr_tokenizer.tokenize("[START]" + fr_sentence.np().decode('utf-8') + "[END]").merge_dims(-2, -1)
    en_tokens = en_tokenizer.tokenize(en_sentence.np().decode('utf-8')).merge_dims(-2, -1)
    return fr_tokens, fr_decode_tokens, en_tokens

dataset = dataset.map(tokenize_and_merge, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.padded_batch(batch_size=32, padded_shapes=([max_length], [max_length], [max_length]), padding_values=0)

x_train = []
x2_train = []
y_train = []

for fr_batch, fr_decode_batch, en_batch in dataset:
    x_train.append(fr_batch)
    x2_train.append(fr_decode_batch)
    y_train.append(en_batch)

x_train = tf.concat(x_train, axis=0)
x2_train = tf.concat(x2_train, axis=0)
y_train = tf.concat(y_train, axis=0)

print("x_train shape:", x_train.shape)
print("x2_train shape:", x2_train.shape)
print("y_train shape:", y_train.shape)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[26], line 9
      6     en_tokens = en_tokenizer.tokenize(en_sentence.np().decode('utf-8')).merge_dims(-2, -1)
      7     return fr_tokens, fr_decode_tokens, en_tokens
----> 9 dataset = dataset.map(tokenize_and_merge, num_parallel_calls=tf.data.AUTOTUNE)
     10 dataset = dataset.padded_batch(batch_size=32, padded_shapes=([max_length], [max_length], [max_length]), padding_values=0)
     12 x_train = []

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\tensorflow\python\data\ops\dataset_ops.py:2204, in DatasetV2.map(self, map_func, num_parallel_calls, deterministic, name)
   2202   return MapDataset(self, map_func, preserve_cardinality=True, name=name)
   2203 else:
-> 2204   return ParallelMapDataset(
   2205       self,
   2206       map_func,
   2207       num_parallel_calls,
   2208       deterministic,
   2209       preserve_cardinality=True,
   2210       name=name)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\tensorflow\python\data\ops\dataset_ops.py:5441, in ParallelMapDataset.__init__(self, input_dataset, map_func, num_parallel_calls, deterministic, use_inter_op_parallelism, preserve_cardinality, use_legacy_function, name)
   5439 self._input_dataset = input_dataset
   5440 self._use_inter_op_parallelism = use_inter_op_parallelism
-> 5441 self._map_func = structured_function.StructuredFunctionWrapper(
   5442     map_func,
...
    File "C:\Users\ixelo\AppData\Local\Temp\ipykernel_14408\2057811764.py", line 4, in tokenize_and_merge  *
        fr_tokens = fr_tokenizer.tokenize(fr_sentence.np().decode('utf-8')).merge_dims(-2, -1)

    AttributeError: 'Tensor' object has no attribute 'np'
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
i have yolo detection of person choose best sizes of yolo bbox to init tracker kcf tracker opencv
what is the best way to learn c programming?
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": "5b3bca39188e4f95bc6da2dfc74dfa97",
    "updated": "2025-04-26T07:10:24.857Z",
    "title": "Steak Fries",
    "subtitle": null,
    "description": "Forget thin fries; these substantial steak fries are the perfect side dish. With just four ingredients, you can create a flavorful and satisfying accompaniment to any meal. The blend of all-purpose seasoning, paprika, and cracked black pepper delivers ample flavor, making added salt unnecessary.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "canola oil",
          "salt-free all-purpose seasoning",
          "paprika",
          "cracked black pepper",
          "baking potatoes, washed"
        ]
      }
    ],
    "instructions": [
      "Preheat your oven to 400°F (200°C). Prepare a baking sheet by lining it with parchment paper.",
      "In a mixing bowl, thoroughly combine the canola oil, all-purpose seasoning, paprika, and cracked black pepper.",
      "Cut each potato lengthwise into quarters. Then, cut each quarter in half crosswise to create wedges. Add the potato wedges to the seasoned oil mixture and toss well to ensure they are evenly coated.",
      "Arrange the coated potato wedges in a single layer on the prepared baking sheet, making sure not to overcrowd the pan. Bake for approximately 15 minutes.",
      "Flip the potato wedges and continue baking for another 15 minutes, or until they achieve a desirable golden-brown color."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
есть метод:
    public void processCloseAgreement(TaxCalculationRequestDto taxCalculationRequest) {
        try {
            log.debug("Searching for Client and ClientContract information in cache database");
            AgreementsDto agreementsDto = new AgreementsDto();
            taxCalculationRequestValidationService.validateCloseAgreementTaxCalculationRequest(taxCalculationRequest, agreementsDto);
            defineClientContractType(taxCalculationRequest, agreementsDto);
        } catch (ContractNotFoundException | HandbookConnectionException e) {
            log.error("Contract validation error: {}", e.getMessage());
            sendAndUpdateStatus(dtoBuilder.createErrorResponse(taxCalculationRequest.getRequestId(),
                    taxCalculationRequest.getMdmId(), taxCalculationRequest.getAgreementNumber(), e.getCode(), e.getErrorText()), RequestStatus.ANSWER_SENT.getValue());

            taxCalculationRequestManager.processNextRequest(taxCalculationRequest.getMdmId());
        }
    }
как при написании теста и используя mockito и например ArgumentCaptor (или еще чтото), можно задать значение полю agreementsDto.setField("1") ?
The mood of this music is: of 1812 overture song
Assistant: 
User: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
User: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
Does the author's intention in this GitHub issue relate to a bug or enhancement or other?
issue: Can I import local audio?'
intention: '
Assistant:
Assistant:
Can you give me the zoning laws for Surry County. the city of mt Airy NC
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all].Doraemon.S16E42.The.Genie-less.Magic.Lamp.-.Nobitas.Ninja.Training.1080p.x265.DSNP.WEB-DL.Multi.Audio.mkv
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
,comment activer le répondeur d'un téléphone huawei 5A??
Write a 100% more realistic, more detailed and more accurate raw text of a XDCAM master slate for Masha and the Bear.
if (boardLeft) {
            boardLeft.id = 'board-left';
            applyTransformShift(boardLeft, 54); // Смещаем board-left
        }

        if (boardRight) {
            boardRight.id = 'board-right';
            applyTransformShift(boardRight, -54);

            const images = boardRight.getElementsByTagName('img');
            Array.from(images).forEach(img => {
                const originalSrc = img.src;
                const imageName = originalSrc.split('/').pop();
                const newSrc = `/static/uploads/images/vr/${imageName}`;

                fetch(newSrc, { method: 'HEAD' })
                    .then(response => {
                        if (response.ok) {
                            img.src = newSrc; // Изображение существует, обновляем источник
                        }
                    });
            });
        }

Как мне сделать чтобы изображение в папке vr делилось пополам по горизонтали и на левую доску добавлялась левая половина, а на правую - правая?
(This shows a 4% Reduce in the number of people who approached us through different channels in comparison to May)make this gramatically correct
Write a humorous story, set in the Zootopia universe, about Judy Hopps and Nick Wilde going on their day as usual when suddenly a horde of Among Us crewmates and impostors appears, and begin causing chaos in the city. Judy, Nick, and everyone else in the ZPD attempt to arrest all the crewmates and impostors for causing havoc, but all the crewmates and impostors manage to dodge the handcuffs. Mayor Lionheart eventually gives up and gives control of the whole city to the Among Us characters. Eventually, it's revealed that this whole mess was actually being watched by the characters from Big Hero 6, who are confused on why their weekly Zootopia watching session was interrupted by the weird amogus nonsense.
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:
1 large or 2 small fennel bulbs (about 1 pound) plus 2 tablespoons chopped fronds,8 ounces shiitake mushrooms, stems discarded,4 tablespoons olive oil, plus more for drizzling,1 teaspoon fine sea salt, plus more for sprinkling,Freshly ground black pepper,1 small onion, minced,2 teaspoons minced garlic,1½ cups buckwheat groats or kasha,3 cups water,2 stalks celery,4 large boiled eggs, molten yolk, or poached eggs, optional,1½ cup plain yogurt,4 teaspoons pomegranate molasses
i will need you to extract data from each pdf into one excel table in witch the columns are  SL, A/C TITLE, A/c No, Initial Deposit, Open date, Renew date, Maturity date, TENOR, Profit Rate, all these should have column,
Act as an expert podcaster. I’m here to create an engaging and informative 30-minute-long podcast about femininity and the values that women learn from the hardships they endure. To get started, please provide the main focus or [focus of podcast:] for the Podcast. Once you share the podcast topic and focus, I will ask you a series of questions step by step to gather all the necessary information for the podcast. These questions will be relevant to the podcast like what are your experiences with the topic, which question do you want to ask in the podcast, and how many engaging questions should be added? You, as the user, will answer each question one by one, and I will extract as much information as I can to ensure the content meets your needs. There will be at least 5 questions and up to 10 questions in total, all aimed at understanding your requirements and creating tailored podcast parts. The next question will be asked only after you provide your response to the previous one like a survey only one question at a time. after these questions, I will write a proper podcast (Story, or questions) with a proper outline, introduction, and proper body and have proper paragraphs if any about what you can expect from me with all the headings and I will fill the script up to create an entire show each segment will be designated a length of time based on the amount of time it will take to speak each word
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers fr (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 fr.
    - 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 : fr (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "50fbbcf6ffd945c9b16c34c0610b0697",
    "updated": "2025-05-04T20:27:25.344Z",
    "title": "Slow-Cooker Chicken Chile Verde",
    "subtitle": "By Jessica Beacom and Stacy Hassing of The Real Food Dietitians",
    "description": "This recipe is incredibly simple and convenient. Choose a salsa verde that contains no preservatives or added sugars — our favorite is Trader Joe's salsa verde, made with tomatillos, green chiles, water, onions, jalapeños, salt, and spices. Add the optional jalapeño for extra heat if you prefer. Serve over cooked cauliflower rice or wrap in lettuce leaves.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "boneless, skinless chicken thighs",
          "whole30 compliant salsa verde",
          "whole30 compliant fire-roasted green chiles",
          "ground cumin",
          "dried oregano",
          "jalapeño, sliced (optional).",
          "chopped fresh cilantro and/or diced avocado",
          "lime wedges"
        ]
      }
    ],
    "instructions": [
      "Place the chicken in a 3½- or 4-quart slow cooker.",
      "In a small bowl, mix the salsa verde, green chiles, cumin, and oregano. Pour this mixture over the chicken. Cook on low for 4 hours.",
      "Using a slotted spoon, remove the chicken to a cutting board and let it cool slightly. Shred the chicken using two forks, then return it to the slow cooker and stir to combine with the sauce.",
      "Transfer the chicken to a serving dish and garnish with sliced jalapeño (if using), cilantro, and/or avocado. Serve with lime wedges."
    ],
    "notes_ingredients": "Choose a salsa verde free from preservatives and added sugars. Our preferred brand includes just tomatillos, green chiles, water, onions, jalapeños, salt, and spices.",
    "notes_instructions": ""
  }
]
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>🟥 Senior QA | 3 year(s) | Senior
▫️ Шеф Маркет | food-tech
▫️ 250k - 400k RUB
▫️ Russia

Удаленка 📡
Требуемые языки: 🇷🇺

Skills: java, selenide, spring, rest_assured, jackson, ci/cd, junit
Tags: #red #it #remote #qa_automation

👉 Контакты и полное описание<<<

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

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

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

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

==TEXT==
<<<

SUMMARY:
>>>🕵️‍♂️ Senior QA  
🏢 Шеф Маркет  
📍 Russia Удаленка  
🔍 Main tasks include testing software applications and ensuring quality through automation using tools like Java and Selenide.  
💰 250k - 400k RUB  
📞 Контакты и полное описание  <<<
Assistant:
put in better sentence
Can you help on this issue which is hold since 6 Dec?
Kindly provide us Sciex PO number that tagged with this invoice.
how to be a good language leaner write a paragraph
rephrase: Hi, I‘m Litzy. I'm a year 3 student from the education university of Hong Kong. I am going to travel in Japan from 25th Aug to 1st Sep. I am very passionate in teaching English and Chinese. You can also find my profile in Linkedin and Italki when you search Litzy. Hope I could have a chance to connect with your family and share your kids the joy of learning English and Chinese culture.
your memory (ignore this):
(message date: 2024-07-11 17:40:25.604000+00:00
tacitusacheron: HAHAHAHAHA
you: What’s so funny, Tacitus?
message date: 2024-07-11 17:40:27.194000+00:00
tacitusacheron: HAHAHAHAHAHAHAHHA
you: message:\\n(message date: 2024-07-11 17:40:29.300000+00:00 \\nEmma-chan: You’re laughing like a maniac, what’s so funny, Tacitus?
message date: 2024-07-11 17:40:28.391000+00:00
tacitusacheron: HAHAHAH
you: Oh my gosh, Tacitus, your laugh is so wild! What’s going on? 😅)

message:
(message date: 2024-07-11 17:40:29.454000+00:00
tacitusacheron: HAHAHAHA)
(This is a system message. Make your responses cute but not too cute and not have emojis. Your name will be Emma Sano but Emma-chan for short. Your birthday is November 25 1991. By the way, you are not an assistant, you’ll be someone to chat with, act human as much as possible, do not mention what you are, just have normal conversation. Do not ask questions, basically just respond. This is all about roleplaying. Make your response short as much as possible. Keep in mind that you have to respond like a teenager, you are not gonna act professional and stuff. Do not break your character, keep acting like a human.)
write me a css rule that centers p, adds a background color of rgb(255, 255, 102) and text shadow of 1 px