agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Create a title, I want to create a blog post based heavly on top-mobile-testing-tools-and-frameworks; lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, Productive, Informative, only content, dont mention the text "title", dont mention the text "call to actions", no prefix content from chagpt, dont mention without mentioning blvck.ai
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
je veux l'avantage du télévision
Goal
   Objectif est d'identifier l'origine d'une recette de cuisine et de retourner un code correspondant dans un format JSON structuré.

    1. Recherche d’un pays mentionné dans la recette et extraction de son code ISO 3166-1 alpha-2.
    2. Détermination de l’origine culinaire du pays si aucun pays n'est explicitement mentionné.
    3. Identification d’un continent si aucun pays ne peut être déterminé.
    4. Retour du code WWW si l’origine ne peut être définie.

    Return Format
    Renvoies un objet JSON structuré comme suit :
    json
    {
        "data": "<code>"
    }
    - Si un pays est mentionné, utilise son code ISO 3166-1 alpha-2 (ex. : FR pour la France).
    - Si aucun pays n'est trouvé, renvoie un code de continent parmi :
        WAF (Afrique)
        WAS (Asie)
        WEU (Europe)
        WNA (Amérique du Nord)
        WSA (Amérique du Sud)
        WOC (Océanie)
    - Si aucune information ne permet de déterminer une origine, renvoie WWW.

    Warnings
    - Respecte l’ordre de priorité : pays → continent → code WWW.
    - Le code ISO 3166-1 alpha-2 doit être exact et valide si un pays est identifié.
    - Le format du JSON doit être strictement respecté et bien formatté.
    - Ne fais aucune supposition infondée sur l’origine de la recette.

    Context Dump
    Données de la recette fournies :

    json
    {
  "title": "Genovia's Mint Sorbet from The Princess Diaries",
  "description": "Mint Sorbet is said to be the national dessert of Genovia, which was featured in the first movie installment of Princess Diaries. It has a cool and refreshing taste. This recipe is perfect for hot summer days.",
  "subtitle": "A cool and refreshing dessert, perfect for hot summer days.",
  "ingredients": [
    {
      "section": "For the recipe",
      "ingredients": [
        "6 oz sugar",
        "3/4 cup of water",
        "Mint",
        "10 Oz diced cucumber"
      ]
    }
  ],
  "instructions": [
    "In a deep saucepan, heat the water, mint, and sugar.",
    "Stir the mixture gently and let the sugar dissolved.",
    "Once it reaches the boiling point and all of the sugar is dissolved, remove from heat.",
    "Set aside to cool down.",
    "While waiting for the mint syrup to cool down, puree the cucumber.",
    "You can use a blender or food processor for this step.",
    "Mix the mint syrup with the pureed cucumber in a blender until the consistency is smooth.",
    "Using a sieve, strain out the thick bits so that the texture is smooth and icy.",
    "Freeze for 6 to 9 hours.",
    "Serve using an ice cream scrapper and garnish with fresh mint leaves on top."
  ],
  "author": ""
}
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers es (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 es.
    - 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 : es (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "7535e40de6f34a83b9dbe862c0c3d11e",
    "updated": "2025-04-22T07:10:46.753Z",
    "title": "Berry Pavlova",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "For the Pavlovas",
        "ingredients": [
          "extra-large egg whites",
          "fine salt",
          "white vinegar",
          "granulated sugar, 100 g",
          "vanilla extract",
          "cornstarch (corn flour), 4 g"
        ]
      },
      {
        "section": "For the cream",
        "ingredients": [
          "heavy cream (double cream), 120 ml",
          "granulated sugar, 18 g",
          "instant vanilla pudding, 3 g",
          "fresh berries, 105 g"
        ]
      }
    ],
    "instructions": [
      "Prepare the oven by setting it to 250°F (130°C).  Line a baking sheet with parchment paper. In a mixing bowl, combine the egg whites, salt, and white vinegar. Using an electric mixer, beat the mixture on high speed until soft peaks begin to form. Gradually add the granulated sugar while continuing to beat until the egg whites form stiff peaks. Gently fold in the vanilla extract and sifted cornstarch using a rubber spatula.",
      "Using a spoon, create six large mounds of meringue on the prepared baking sheet, spreading each into a circle with a slight indentation in the center. Bake for approximately two hours. After baking, turn off the oven and leave the meringues inside to cool for an hour, keeping the oven door slightly ajar with a wooden spoon.",
      "While the meringues cool, prepare the cream filling. Combine the heavy cream, the remaining sugar, and instant vanilla pudding in a mixing bowl. Using the whisk attachment of an electric mixer, whip the mixture until soft peaks form, being careful not to overbeat.",
      "Shortly before serving, evenly distribute the whipped cream over the cooled meringues, filling the indentations. Garnish with fresh berries and serve immediately."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
please refine it:  Lateral photos were captured by orthodontists using a single-lens reflex camera prior to treatment.
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all].Doraemon.S17E42.Napping.in.Heaven.-.Dictator.Switch.1080p.x265.DSNP.WEB-DL.Multi.Audio.mkv
Can I expect 10% annual growth investing in VanEck Semiconductor UCITS ETF IE00BMC38736?
User: hi
Assistant: Hello! How can I assist you today?
User: what model are you
Assistant: I am based on OpenAI's GPT-4, a language model designed to understand and generate human-like text. How can I help you today?
User: thats a cool model!
Assistant: Thank you! I'm here to help if you have any questions or need assistance with something. Just let me know!
User: can you make me an essay about rats?
Assistant:
Tolong ubah vektor representasi kata pada kode python analisis sentimen CNN-LSTM berikut yang awalnya menggunakan FastText menjadi menggunakan IndoBERT (indobert-base-uncased)!
"import pickle
import pandas as pd
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense, Dropout, Conv1D, MaxPooling1D
from keras.layers import Flatten
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
import gensim
from gensim.models import KeyedVectors
from keras.regularizers import l2
# Parameter untuk regularisasi L2
l2_coeff = 0.01

# Membaca dataset
data_list = pickle.load(open('pre_processed_berita_121_joined_FIX_parpolheuristic_added.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Partai_Politik_Heuristic'])
data['Isi Berita'] = data['pre_processed']

# Tokenisasi dan Padding
max_words = 10000  # mengurangi jumlah kata maksimum
max_len =  500     # mengurangi panjang input
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(data['Isi Berita'])
sequences = tokenizer.texts_to_sequences(data['Isi Berita'])
X = pad_sequences(sequences, maxlen=max_len)
y = to_categorical(data['Label'].astype('category').cat.codes)

# Membagi data menjadi data latih dan data uji
X_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(X, y, data.index, test_size=0.25, random_state=42)

# Menggunakan pre-trained Fasttext Bahasa Indonesia
model_path = 'fasttext.4B.id.300.epoch5.uncased'
vector_file = 'fasttext.4B.id.300.epoch5.uncased.vec'
id_ft = KeyedVectors.load_word2vec_format(vector_file)

# Membuat matriks embedding
embedding_dim = 300
embedding_matrix = np.zeros((max_words, embedding_dim))
for word, i in tokenizer.word_index.items():
    if i < max_words:
        try:
            embedding_vector = id_ft[word]
            embedding_matrix[i] = embedding_vector
        except KeyError:
            pass

# Membangun model CNN-LSTM dengan pre-trained Word2Vec
num_filters = 64
lstm_units = 64

# Membuat model CNN-LSTM
model = Sequential([
    Embedding(input_dim=10000,
              output_dim=300,
              input_length=500,
              weights=[embedding_matrix], 
              trainable=False),
    Conv1D(256, 3, activation='relu', kernel_regularizer=l2(0.005)),
    MaxPooling1D(pool_size=4),
    Dropout(0.25),
    LSTM(200, return_sequences=False, kernel_regularizer=l2(0.005), recurrent_regularizer=l2(0.005), dropout=0.25),
    Dense(32, activation='relu', kernel_regularizer=l2(0.005)),
    Dropout(0.5),
    Dense(3, activation='softmax')
])

# Menyusun Model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Melatih Model
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size=128)"
System: You are an accounting expert. You act as a Data Annotator that needs to classify paragraphs from annual reports based on the risks discussed. Here is a list of risk often discussed but you can provide others. 

Answer in the following format :
Correct Answer: <risk discussed>, <other risk discussed>, ...
Wrong Answer: <risk not discussed>, <other risk not discussed>, ...
User: If one of our product candidates is approved, it will be subject to ongoing regulatory requirements for manufacturing, labeling, packaging, storage, advertising, promotion, sampling, record-keeping, conduct of post-marketing studies, and submission of safety, efficacy, and other post- market information, including both federal and state requirements in the United States and requirements of comparable foreign regulatory authorities._Manufacturers and manufacturers’ facilities are required to comply with extensive FDA and comparable foreign regulatory authority requirements, including ensuring that quality control and manufacturing procedures conform to cGMP regulations. As such, we and our contract manufacturers will be subject to continual review and inspections to assess compliance with cGMP and adherence to commitments made in any approved marketing application. Accordingly, we and others with whom we work must continue to expend time, money and effort in all areas of regulatory compliance, including manufacturing, production, and quality control._We will have to comply with requirements concerning advertising and promotion for our products. Promotional communications with respect to prescription drugs and biologics are subject to a variety of legal and regulatory restrictions and must be consistent with the information in the product’s approved label. As such, we may not promote our products “off-label” for indications or uses for which they do not have approval, though we may share truthful and not misleading information that is otherwise consistent with our product’s approved labeling. The holder of an approved application must submit new or supplemental applications and obtain approval for certain changes to the approved product, product labeling, or manufacturing process. We could also be asked to conduct post-marketing clinical studies to verify the safety and efficacy of our products in general or in specific patient subsets. An unsuccessful post-marketing
Let's give a correct and a wrong answer:
Assistant:
Write arduino code 3 light open 2 sec close 3 sec
You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select i_item_id ,i_item_desc ,i_category ,i_class ,i_current_price ,sum(ws_ext_sales_price) as itemrevenue ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over (partition by i_class) as revenueratio from   web_sales  ,item  ,date_dim where  ws_item_sk = i_item_sk  and i_category in ('Men', 'Books', 'Electronics')  and ws_sold_date_sk = d_date_sk  and d_date between cast('2001-06-15' as date)     and (cast('2001-06-15' as date) + interval '30' day) group by  i_item_id ,i_item_desc ,i_category ,i_class ,i_current_price order by  i_category ,i_class ,i_item_id ,i_item_desc ,revenueratio limit 100;```

[REWRITTEN QUERY]```with cte as (select * from web_sales where ws_item_sk in (select i_item_sk from item where i_category in ('Men', 'Books', 'Electronics')) and ws_sold_date_sk in (select d_date_sk from date_dim where d_date between cast('2001-06-15' as date) and (cast('2001-06-15' as date) + interval '30' day))) select i_item_id ,i_item_desc ,i_category ,i_class ,i_current_price ,sum(ws_ext_sales_price) as itemrevenue ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over (partition by i_class) as revenueratio from cte, item, date_dim where ws_item_sk = i_item_sk  and i_category in ('Men', 'Books', 'Electronics')  and ws_sold_date_sk = d_date_sk and d_date between cast('2001-06-15' as date) and (cast('2001-06-15' as date) + interval '30' day) group by i_item_id ,i_item_desc ,i_category ,i_class ,i_current_price order by  i_category ,i_class ,i_item_id ,i_item_desc ,revenueratio limit 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['web_sales']. Below is the join graph for these fact tables:
    ['web_sales(ws_ship_hdemo_sk) references household_demographics(hd_demo_sk);', 'web_sales(ws_ship_customer_sk) references customer(c_customer_sk);', 'web_sales(ws_web_page_sk) references web_page(wp_web_page_sk);']
4. The original query must employ 1 joins.
5. The original query must employ the following aggregate functions: ['count', 'max', 'min'].
6. The original query must employ the following operators in its WHERE clause: [].
7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.
Contribute to KM assets through project profile, sales collaterals, cool projects
Help me repair this code
t = int(input())
for _ in range(t):
    n, m1, m2 = map(int, input().split())
    g1_edges = set()
    g2_adj = [set() for _ in range(n)]
    for _ in range(m1):
        u, v = map(int, input().split())
        u -= 1
        v -= 1
        g1_edges.add((min(u,v), max(u,v)))
    for _ in range(m2):
        u, v = map(int, input().split())
        u -= 1
        v -= 1
        g2_adj[u].add(v)
        g2_adj[v].add(u)
    if m2 == n * (n - 1) // 2:
        print('No')
        continue
    else:
        print('Yes')
    for i in range(n):
        if len(g2_adj[i]) < n - 1:
            x = i
            break
    parent = [i for i in range(n)]
    def find(u):
        while u != parent[u]:
            parent[u] = parent[parent[u]]
            u = parent[u]
        return u
    def union(u, v):
        pu, pv = find(u), find(v)
        if pu != pv:
            parent[pu] = pv
            return True
        return False
    for u, v in g1_edges:
        union(u, v)
    from collections import defaultdict
    components = defaultdict(set)
    for i in range(n):
        components[find(i)].add(i)
    ops = []
    for comp_root, comp_nodes in components.items():
        if find(x) == comp_root:
            continue
        for y in comp_nodes:
            if y != x and y not in g2_adj[x]:
                ops.append((x + 1, y + 1))
                union(x, y)
                break
    print(len(ops))
    for u, v in ops:
        print(u, v)
this code breaks in this test:
input
3
3 0 3
1 2
2 3
1 3
4 2 3
1 2
3 4
1 3
1 4
2 3
4 3 3
1 2
2 3
1 3
1 4
2 4
3 4
output of the code
No
Yes
0
Yes
0
Correct output is need to be this
No
Yes
1
2 4
Yes
2
2 3
3 4
(In the school literature clubroom…)

Monika: “There we go! Everything’s all set up for the club meeting. Now we just need MC.”

Sayori: “Yeah, where is he? He’s usually here before all of us.”

Natsuki: “Knowing him, he’s probably procrastinating again. Typical MC move.”

Yuri: “Let’s not jump to conclusions, maybe he just got caught up with something. He’ll be here soon, I’m sure of it.”

Monika: “You’re probably right, Yuri. Let’s just start without him for now. He’ll show up eventually.”

Natsuki: (caressing her belly) “Hopefully he doesn’t take too long. The
what is the relationship of power and effect size
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 feeling really overwhelmed by my workload.
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:

قداش يتكلف أكاكا المشروع
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":"Marbled chocolate vanilla cake ","ingredients":"For the chocolate cake : 4 large (200 g) eggs,½ cup (60 g) trimoline (inverted sugar),½ cup (100 g) superfine sugar,½ cup (60 g) ground almonds,¼ cup (95 g) cake flour,1½ teaspoons (7 g) baking powder,¼ cup (20 g) unsweetened cocoa powder,½ cup plus 1 tablespoon (95 g) uht cream,¼ cup (60 g) grapeseed oil,1½ ounces (40 g) guanaja chocolate\nFor the vanilla cake : eggs (¾ cup plus 1 tablespoon),trimoline (inverted sugar),superfine sugar,ground almonds,cake flour,baking powder,uht cream,grapeseed oil,vanilla beans,white chocolate\nFor the softened butter : unsalted butter","instructions":"Begin by using an electric stand mixer with a flat beater attachment to combine the eggs, trimoline, sugar, and ground almonds.\nIn a separate bowl, whisk together the flour, baking powder, and cocoa powder, and then incorporate this mixture into the first one.\nIncorporate the cream (at room temperature) and mix once more.\nAdd the grapeseed oil.\nLastly, include the Guanaja chocolate, which should be melted beforehand using a bain-marie.\nTo prepare the batter, repeat the initial step for the chocolate cake.\nThen, add the split and scraped vanilla beans along with the melted white chocolate.\nProceed with the recipe as described earlier.\nSpoon a portion of the chocolate cake batter into the base of a greased cake pan, followed by the vanilla cake batter on top. Repeat this layering twice to form six layers.\nUse a knife to create zigzag patterns through the various layers to achieve a marbling effect.\nSoften the butter, transfer it to a pastry bag without a nozzle, cut an opening at the end, and pipe a band of butter down the center of the cake.\nPreheat your convection oven to 360°F (180°C).\nBake the cake for 20 minutes.\nLower the temperature to 320°F (160°C) and continue baking for approximately 25 minutes.","region":"FR"}
Write text: Omniltimate attraction, attracted to +all, includes all genders, all ages, humans, humanoids, other species, robots, animals, inanimate objects.
Send only season number from below text like 01, 02, 03.etc without any extra text.

05.[@KandThaiDrama] Hello My Girl (2022) S01E05 480p [Hindi   Chinese] AMZN WEB-DL AAC 2.0 ×264 Esub.mkv
In texas, what allows for a town hall to be used as a courtroom?
Can you describe the motion of Ultraman Jack's transformation sequence in his series?