agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
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": "6e3c2d9638204eb281381f201a40e56b",
    "updated": "2025-04-25T17:11:46.795Z",
    "title": "Roast Lamb with Flageolets",
    "subtitle": "A delightful Sunday meal featuring tender roast lamb accompanied by flavorful flageolet beans.",
    "description": "This roast lamb recipe offers a delicious alternative to traditional roasts, using flageolet beans for a unique and satisfying Sunday dinner.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "leg of lamb, about (1.361 kg) (1.35kg)",
          "3 sprigs rosemary",
          "olive oil",
          "salt and freshly ground black pepper",
          "garlic cloves, roughly chopped",
          "(250g) baby plum tomatoes, halved",
          "can flageolet beans, drained",
          "tomato purée",
          "(150ml) dry white wine"
        ]
      }
    ],
    "instructions": [
      "Preheat oven to 350°F (180°C). Using a small, sharp knife, make several deep incisions in the lamb's skin.",
      "Insert a few rosemary leaves into each incision.",
      "Place the lamb in a roasting pan, brush with olive oil, and season with salt and pepper.",
      "Roast for 1 hour.",
      "Combine the remaining rosemary, garlic, tomatoes, and flageolet beans in a bowl.",
      "Remove the lamb from the oven and arrange the tomato and bean mixture around it.",
      "In a small bowl, mix the tomato purée and white wine, then pour the mixture over the lamb.",
      "Loosely cover the roasting pan with foil and return to the oven for 30-40 minutes. Cook until the lamb is done but the juices are still slightly pink, stirring the bean mixture once during cooking.",
      "Let the lamb rest, loosely covered with foil, for 10-15 minutes before carving."
    ],
    "notes_ingredients": null,
    "notes_instructions": "The lamb can be prepared ahead of time by making the incisions and inserting the rosemary leaves several hours before roasting."
  }
]
Job opportunities for teacher in MNC.
Have you been able to remove the slave ship title from your name and brith certificate with your county/state?
df = pd.read_csv(StringIO(answer), sep="\t", quoting=3)   
        for (_, row), d in zip(df.iterrows(), razbor):
            feats = row.Feats.split("|") if "|" in row.Feats else []
            self._update_token(d, {
                "head_id": int(row.Head),
                "rel": row.DepRel,
                "pos": row.UPosTag,
                **UD2OpenCorpora({_.split("=")[0]: _.split("=")[1] for _ in feats},)
            })
перепиши без использования pandas
[general]
context = default
allowoverlap = yes
allowguest = no
alwaysauthreject = yes
musiconhold = default
mohinterpret = default
mohsuggest = default
;dtmfmode=info
;tlsenable=yes
;tlsbindaddr=0.0.0.0:5060
;tlscertfile=/etc/asterisk/certificate.pem
;tlsprivatekey=/etc/asterisk/privatekey.pem

bindport = 5060
bindaddr = 0.0.0.0
transport = tcp
;nat = force_rport
nat=no
canreinvite = no
insecure = invite
preferred_codec_only = yes
language = ru
localnet = 192.168.2.0/255.255.255.0

register => sip102:sip102@192.168.2.101/sip101

disallow = all
allow = ulaw
allow = alaw
subscribecontext = default

[200]
type = friend
host = dynamic
defaultuser = 200
secret = 200
context = default
disallow = all
allow = ulaw
allow = alaw
qualify = 300
nat=force_rport,comedia
directmedia=nonat
encryption=yes
media_encryption=sdes
transport=tcp

[201]
type = friend
host = dynamic
defaultuser = 201
secret = 200
context = default
disallow = all
allow = ulaw
allow = alaw
qualify = 300
nat=force_rport,comedia
directmedia=nonat
encryption=yes
media_encryption=sdes
transport=tls
insecure=invite,port

[202]
type = friend
host = dynamic
defaultuser = 202
secret = 200
context = default
disallow = all
allow = ulaw
allow = alaw
qualify = 300
nat=force_rport,comedia
directmedia=nonat
encryption=yes
media_encryption=sdes
transport=tls
insecure=invite,port

;notebook102->sip102

[asterisk_sip](!)
language=ru
type=friend
context=default
host=dynamic
trunk=yes

[sip101](asterisk_sip)
secret=sip101
deny=0.0.0.0/0.0.0.0
permit=192.168.2.101
encryption=yes
media_encryption=sdes
transport=tls
insecure=invite,port

Вот настройки asterisk для tcp sip,conf , они верные?
tell me everything I need to know about accounting cycle
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Doraemon S14E35 Nobitas 3000 Days on an Uninhabited Island 480p Multi Audio DSNP WEB-DL.mkv
According to kantian ethics, explain why AI image generation cannot be considered theft
is it possible to create an electromagnetic IPMsysRM continuos power geneartor by harnessing the  back EMF into a positive energy
Pls give me an overview of phenylpiracetam hydrazide from a therapeutic/benefits pov
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Naruto - 062 [480p BD x264 Multi Audio] ESub.mkv
Please help to correct statement
conflict theory by george simmel
Suggest viral video hooks
Assistant: Eres un bot
User: o Meta Ai?
Assistant:
Goal :
    Générer une suggestion détaillée et visuellement attrayante pour la présentation finale d'un plat, basée sur 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 :

    json
    {"title":"Fonio steamed with asparagus and morels A delightful and nutritious dish featuring an african grain.","ingredients":"For the Recipe : fonio,fresh morels,fresh onions,pink garlic,parsley,olive oil,salt,chicken stock,green asparagus,parmesan","instructions":"First, prepare the fonio and morels: Rinse 150 g of fonio under running water in a sieve. Transfer it to a bowl, cover it generously with water, and let it swell for 20 minutes.\nMeanwhile, remove the stems from 200 g of fresh morels, then wash the caps thoroughly in several rinses of warm water to eliminate any sand. Dry them in a salad spinner and cut any large mushrooms in half.\nWash a bundle of fresh onions, dice the stalks (setting them aside), and slice the bulbs into rounds.\nPeel and slice 4 cloves of pink garlic finely after removing the germ in the center. Rinse and chop the leaves of a small bunch of parsley.\nNext, cook the morels and fonio: In a sauté pan, heat 3 tablespoons of olive oil. Add the onion rounds and garlic, sautéing for 2 minutes while stirring. Introduce the morels, lightly season with salt, and cook for an additional 4 minutes.\nAt the same time, heat 300 ml of chicken stock.\nDrain the fonio and add it to the pan. Stir thoroughly and cook for just 1 minute. Then pour in enough chicken stock to cover the mixture, simmering gently (just below a boil) until the liquid is absorbed. Continue to add more stock as needed: the fonio should cook for around 20 minutes. Once done, cover the pan and let it rest for 10 minutes.\nAs the fonio cooks, prepare the asparagus: Wash and peel 1 bundle of green asparagus and cut the tips into 10 cm pieces. Heat a sauté pan with a little olive oil and cook the asparagus for 5 to 6 minutes with a lid on.\nTo serve your dish: Fluff the fonio with a fork and mix in the onion stalks and chopped parsley. Transfer to a serving dish, arrange the asparagus on top, and shave 20 g of parmesan using a vegetable peeler to sprinkle over the dish. Serve immediately.","region":"WAF"}
System: 
User: Generate a detailed and realistic news article about an event in the Alternative Investments domain, focusing on the Hotels & Resorts industry, and located in Canada. The article event should have a neutral impact on stock prices and highlight the broader economic or market implications. The article should be 225 words long.
Assistant:
привет. я хочу чтобы ты сделал качественный рерайт текста, рерайт должен состоять не просто в замене нескольких слов в предложении, а целиком замены предложений. оно должно быть написано другими словами с сохранением смысла и объема. весьновый текст должен быть больше по объему или равен оригинальному тексту
Мне нужно обернуть мой код в такой же формат

import torch
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer


class NERProcessor:
    
    def __init__(self, model_path, use_gpu=False):
        self.device = "cuda" if torch.cuda.is_available() and use_gpu else "cpu"
        tokenizer = AutoTokenizer.from_pretrained(model_path)
        model = AutoModelForTokenClassification.from_pretrained(model_path).to(self.device)
        self.pipeline = pipeline("ner", model=model, tokenizer=tokenizer, device=0 if self.device == "cuda" else -1, aggregation_strategy="simple")


    def process_text(self, text):
        return self.pipeline(text)


class NERModel:
    
    def __init__(self, model_path, use_gpu=False):
        print("Initializing NER model")
        self.ner_processor = NERProcessor(model_path, use_gpu)


    def get_ner_results(self, text):
        return self.ner_processor.process_text(text)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

MODEL_NAME = "IlyaGusev/saiga_llama3_8b"
DEFAULT_SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    # load_in_8bit=True,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model.eval()

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
generation_config = GenerationConfig.from_pretrained(MODEL_NAME)

def dialogue_analysis(input):
    
    prompt = tokenizer.apply_chat_template([{
        "role": "system",
        "content": DEFAULT_SYSTEM_PROMPT
    }, {
        "role": "user",
        "content": input
    }], tokenize=False, add_generation_prompt=True)
    data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
    data = {k: v.to(model.device) for k, v in data.items()}
    output_ids = model.generate(**data, generation_config=generation_config)[0]
    output_ids = output_ids[len(data["input_ids"][0]):]
    output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
    
    return output
Assistant: Hello! How can i help you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه فقط دون تزويدي بأي معلومات أو تفاصيل زائدة مع النص النهائي.
 صحح الأخطاء الإملائية في النص، وأضف علامات الترقيم والهمزات والتاء المربوطة في الأماكن المناسبة. (ar).
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
فويس اوفر becomes Voiceover
اوبو becomes Oppo
فوكالايزر becomes Vocalizer
انفيجن becomes Envision
بي ماي ايس becomes Be My Eyes
ميت becomes Meet
ام اكس بلاير برو becomes MX Player Pro
سمارت اوديو بوك بلير becomes Smart AudioBook Player
تك فريدم becomes Tech-Freedom
فويسس becomes voices
فويس اللاود ريدر becomes Voice Allowed Reader
تكنو becomes tecno
اي اي becomes AI
جوجل becomes Google
and so on.
Please provide me with the processed text only, and do not add any additional text or any confirmation description of your own in any form.
This is the text:
وطمني كده يعني انت اشتغلت في مكان بيامن عليك ولا انت اشتغلت كده الشغل مؤقت ولا ايه الدنيا
Assistant:
What Year Did Japan Enter The Bronze Age?
System: Create a comprehensive dataset for financial entity recognition. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
    Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
    Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
    Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Respond in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...
Input:
User: NORTHAMPTON, MA / ACCESSWIRE / October 31, 2023 / DOWOriginally published in Dow's 2022 Progress ReportDow embraces the importance and value of investing in STEM education to help prepare and energize a diverse and robust future workforce. We do this ...
Assistant:
Delete the unnamed CHECK constraint title_ch from the table books.

Write an SQL statement
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: Trong bảng mã UNICODE, mỗi kí tự Tiếng Việt theo UTF-8 được biểu diễn bởi bao nhiêu byte?
A. 1 byte.
B. 2 byte.
C. 4 byte.
D. từ 1 đến 3 byte.
Đáp án đúng: từ 1 đến 3 byte.
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
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Naruto - 104 [720p BD x265 10bit Multi Audio] ESub.mkv
Send only episode number from below text like 01, 02, 03.etc without any extra text.

@PrakyTV - Pushpa 2 The Rule (2024) Tamil HQ HDRip - 1080p - HEVC - x265 - (DD_5.1 - 192Kbps _ AAC) - 2.5GB - ESub.mkv
const { Gio, GLib, Meta, Shell } = imports.gi;
const Main = imports.ui.main;
const Me = imports.misc.extensionUtils.getCurrentExtension()

class Extension {


  constructor() {
  }

  enable() {

    Main.wm.addKeybinding("open-vs", this.getSettings(), Meta.KeyBindingFlags.NONE, Shell.ActionMode.ALL, this.openSelectedFolder())
  }

  disable() {
    Main.wm.removeKeybinding("open-vs");
  }

  openSelectedFolder() {
    const nautilus = Shell.AppSystem.get_default().lookup_app('org.gnome.Nautilus');
    const currentFolderPath = nautilus.get_window().get_selection().get_nth_file(0).get_path();

    const vscodeCommand = `code -n ${GLib.shell_quote(currentFolderPath)}`;
    GLib.spawn_command_line_async(vscodeCommand);
  }

  getSettings() {
    let GioSSS = Gio.SettingsSchemaSource;
    let schemaSource = GioSSS.new_from_directory(
      Me.dir.get_child("data").get_path(),
      GioSSS.get_default(),
      false
    );
    let schemaObj = schemaSource.lookup(
      'org.gnome.shell.extensions.vscshortcut', true);
    if (!schemaObj) {
      throw new Error('cannot find schemas');
    }
    return new Gio.Settings({ settings_schema : schemaObj });
  }

}

function init() {
  return new Extension();
}
nautilus is null
there is a growing trend on the subreddit vivarium's and similar groups. what would be 10 different unique business product ideas for this group of people
Why atttach a texture and a renderbuffer? What's the advantage?


			// Load and attach texture
			glTexImage2DMultisample(GL_TEXTURE_2D_MULTISAMPLE, samples, GL_RGB, framebuffer.getWidth(), framebuffer.getHeight(), true);
			glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, GL_TEXTURE_2D_MULTISAMPLE, framebuffer.getTexture(), 0);
			
			// Load and attach depth / stencil renderbuffer
			glRenderbufferStorageMultisample(GL_RENDERBUFFER, samples, GL_DEPTH24_STENCIL8, framebuffer.getWidth(), framebuffer.getHeight());
			glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_STENCIL_ATTACHMENT, GL_RENDERBUFFER, framebuffer.getRenderbuffer());
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] The Daily Life of the Immortal King S03E08 1080p x265 10bit WEB-DL Multi Audio ESub.mkv
what is hoisting in JavaScript and how one can use it
Explain fully the role of diffusion and osmosis in the following situations: 
Make sure to use each of  the vocabulary words in your answers
A. Restoring a homeostatic environment after consuming Doritos                                                                                  


B. Maintaining micro-organism life in the Sugarhouse Park pond
C. Refrigerating vegetables
D. Watering a desiccated plant
Final Kids WB schedule (May 17 2008)

07:00 AM Will & Dewitt
07:30 AM Magi-Nation
08:00 AM Shaggy and Scooby Doo Get A Clue
08:30 AM Tom & Jerry Tales
09:00 AM Skunk Fu!
09:30 AM Legion of Super-Heroes
10:00 AM The Spectacular Spider-Man
10:30 AM World of Quest
11:00 AM Johnny Test
11:30 AM Eon Kid

First CW4Kids schedule (May 24 2008)

07:00 AM Sonic X
07:30 AM Winx Club
08:00 AM Viva Piñata
08:30 AM GoGoRiki
09:00 AM Huntik
09:30 AM TMNT: Back to the Sewer
10:00 AM Dinosaur King
10:30 AM Yu-Gi-Oh! 5D's
11:00 AM Chaotic
11:30 AM Chaotic.

Not ONE show from Kids WB was left over, This is especially bad because Spider-Man and World of Quest still had episodes left to air. Incidentally GoGoRiki (also known as KikoRiki) is the primary comedic cartoon, taking the helms from Will and Dewitt (boy who befriends a frog that looks like the KanDoo mascot), S&SDGAC, Tom and Jerry Tales and Johnny Test, And there's also Viva Pinata (based on the game of the same name) Write a forum conversation on the final day of KWB and there are trailers for the new block, including one that says "We're all new, all different"
[{"role":"system","content":"You are an AI assistant that helps people with LuaU code, like ESP or Aimbot. You are called JulesAI, and your creator is White Cat."},{"role":"user","content":"hi"}]
Ubah embedding built in library Keras pada kode analisis sentimen di bawah ini menjadi menggunakan Word2Vec Bahasa Indonesia!
import pickle
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from keras.models import Sequential
from keras.layers import LSTM, Dense, Embedding, SpatialDropout1D, Dropout
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
from sklearn.metrics import classification_report

# 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’]

# Mengkodekan label
le = LabelEncoder()
data[‘label_encoded’] = le.fit_transform(data[‘Label’])

# Memisahkan 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 dan padding
max_vocab_size = 10000
max_sequence_length = 250

tokenizer = Tokenizer(num_words=max_vocab_size)
tokenizer.fit_on_texts(X_train)

X_train_sequences = tokenizer.texts_to_sequences(X_train)
X_test_sequences = tokenizer.texts_to_sequences(X_test)

X_train_padded = pad_sequences(X_train_sequences, maxlen=max_sequence_length)
X_test_padded = pad_sequences(X_test_sequences, maxlen=max_sequence_length)

# Membangun model LSTM
embedding_dim = 100
lstm_units = 64

model = Sequential()
model.add(Embedding(max_vocab_size, embedding_dim, input_length=max_sequence_length))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(lstm_units, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(3, activation=‘softmax’))

optimizer = Adam(lr=0.001)

model.compile(loss=‘sparse_categorical_crossentropy’, optimizer=optimizer, metrics=[‘accuracy’])
print(model.summary())

# Melatih model dengan data latih
epochs = 10
batch_size = 64

history = model.fit(X_train_padded, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor=‘val_loss’, patience=3, min_delta=0.0001)])

# Menghitung akurasi pada data uji
acc = model.evaluate(X_test_padded, y_test)[1]
print(‘Akurasi pada data uji:’, acc)

# Menampilkan classification report
# Menggunakan model untuk prediksi kelas
y_pred_proba = model.predict(X_test_padded)

# Mengonversi probabilitas ke kelas
y_pred = np.argmax(y_pred_proba, axis=1)

# Menampilkan classification report
print(classification_report(y_test, y_pred, target_names=le.classes_))
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: Add an option to prevent from indexing downloads'
intention: '
Assistant:
Assistant:
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: Hello, do I get some sort of refund for my phone mast being down for over a week now? '
intention: '
Assistant:
How to write results and discussion in research?