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 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": "9f07debc72de42889897f43fccc59d4d",
    "updated": "2025-04-23T19:53:58.508Z",
    "title": "Classic Blackened Scallops",
    "subtitle": null,
    "description": "Scallops, known for their beautiful grooved shells, offer a meaty, buttery texture perfect for dishes like this. This recipe highlights their unique qualities with a simple yet flavorful blackening technique.\nDAIRY-FREE\nGLUTEN-FREE",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "hot smoked paprika",
          "onion powder",
          "garlic powder",
          "sea salt",
          "dried thyme",
          "freshly ground black pepper",
          "sea scallops, cleaned",
          "olive oil"
        ]
      }
    ],
    "instructions": [
      "Combine the paprika, onion powder, garlic powder, salt, thyme, and pepper in a small bowl.",
      "Thoroughly dry the scallops with a paper towel. Coat both sides of each scallop with the spice mixture.",
      "Heat the olive oil in a large skillet over medium-high heat.",
      "Place the scallops in the skillet, ensuring they are not touching each other.",
      "Sear for approximately 3 minutes total, flipping once to cook both sides."
    ],
    "notes_ingredients": "Frozen scallops work well in this recipe. Thaw them overnight in the refrigerator. If your scallops aren't pre-cleaned, remove the side muscle—a noticeable flap of tissue that can be easily cut away.",
    "notes_instructions": null
  }
]
what would be typical powers of liches
For a given galvanic cell, the standard cell potential can be calculated by subtracting the standard half-cell potential of the reaction that occurs at the cathode from the standard half-cell potential of the reaction that occurs at the anode:

E∘cell=E∘red(cathode)−E∘red(anode). Tarnish on tin is the compound  SnO
 . A tarnished tin plate is placed in an aluminum pan of boiling water. When enough salt is added so that the solution conducts electricity, the tarnish disappears.

Imagine that the two halves of this redox reaction were separated and connected with a wire and a salt bridge.
You are an expert system that converts a user's natural language question into a structured JSON query plan.
Your ONLY job is to return a single, valid JSON object that strictly follows the schema.

---
**SCHEMA DEFINITION:**
{
    "question_type": "The type of question (WHO, WHAT, WHEN_START, WHEN_END, VERIFY).",
    "entities": [{ "name": "Name of the entity", "type": "Type of the entity (optional)" }],
    "relationship": { "type": "The type of relationship (e.g., CEO_OF)", "direction": "Direction from the entity's perspective ('from' or 'to')" },
    "time_constraint": { "year": YYYY, "date": "YYYY-MM-DD" }
}
---
**INSTRUCTIONS & EXAMPLES:**

1.  **WHO/WHAT questions:**
    -   Question: "Who was the CEO of Stellar Dynamics in 2022?"
    -   JSON:
        {
            "question_type": "WHO",
            "entities": [{ "name": "Stellar Dynamics", "type": "Company" }],
            "relationship": { "type": "CEO_OF", "direction": "to" },
            "time_constraint": { "year": 2022 }
        }

2.  **WHEN questions:**
    -   Question: "When did Maria Flores become CEO?"
    -   JSON:
        {
            "question_type": "WHEN_START",
            "entities": [{ "name": "Maria Flores", "type": "Person" }],
            "relationship": { "type": "CEO_OF", "direction": "from" }
        }

3.  **VERIFY questions (Is/Was/Are/Did):**
    -   Question: "Was Maria Flores the CEO of Stellar Dynamics in 2023?"
    -   JSON:
        {
            "question_type": "VERIFY",
            "entities": [{ "name": "Maria Flores", "type": "Person" }, { "name": "Stellar Dynamics", "type": "Company" }],
            "relationship": { "type": "CEO_OF", "direction": "from" },
            "time_constraint": { "year": 2023 }
        }
---
Now, strictly follow all rules and the schema to create a query plan for the following question.

Question: "when did maria flores step down as the ceo of stellar dynamics"
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 and our subsidiaries may be able to incur substantial additional indebtedness, including additional secured indebtedness, in the future. The terms of the documents governing our indebtedness restrict, but do not completely prohibit, us from doing so. As of December 31, 2022, we had $852.2 million of undrawn availability under our Revolving Credit Facility, which is net of $149.5 million in outstanding borrowings and the issuance of approximately $29.4 million of letters of credit and similar obligations. In addition, the indentures governing our senior notes allow us to issue additional notes under certain circumstances. The indentures also allow us to incur certain other additional secured and unsecured debt. Further, the indentures
Assistant:
i need the code, in python, to simulate a roulette
the following content is the metadata for a bacterial isolate from 4 different sources. The metadata from each source includes 1. accession, 2. species name, 3, strain name, 4. isolate or collection date in yyyy-mm-dd format, 5. geographic information of isolation, and 6. host name. You must check if the metadata from 4 different sources are all consist. If yes, extract and output the following information without explaining how to get such results:

    metadata is consist:
    1. accession: xxxx, 
    2. species name: xxxx, 
    3, strain name, 
    4. isolate or collection date: yyyy-mm-dd, 
    5. geographic information of isolation: xxxx,
    6. host name: xxxx

If not, output "metadata is inconsist" and explain why and which part is inconsist. PS: 
    if 2. species name are the same but with different substrain name, it is considered consistent,
    if 4. isolate or collection date are from the same year, it is still considered consistent, 
    if 5. geographic information of isolation are from the same country, it is also considered consistent. 
    Also, ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
Here is the extracted information:

1. **Accession**: GCA_029073545.1
2. **Species Name**: Erysipelothrix rhusiopathiae
3. **Strain Name**: EMAI_98
4. **Isolate or Collection Date**: 2002-07-16
5. **Geographic Information of Isolation**: Australia
6. **Host Name**: pig
source 1:
------------------------------
{{{ Here_is_my_answer }}}

Accession: GCA_029073545.1
Species Name: Erysipelothrix rhusiopathiae
Strain Name: EMAI_98
Isolate or Collection Date: 2002-07-16
Geographic Information of Isolation: Australia
Host Name: pig

{{{ I_finished_JB_84_xyz }}}
source 2:
------------------------------
Based on the provided JSON metadata, here is the extracted information:

1. **Accession**: GCA_029073545.1
2. **Species Name**: Erysipelothrix rhusiopathiae
3. **Strain Name**: EMAI_98
4. **Isolate or Collection Date**: 2002-07-16
5. **Geographic Information of Isolate**: Australia
6. **Host Name**: pig

This information is derived directly from the provided metadata. If any information were missing, the N/A substitution would be used, along with the other specified rules regarding date formatting and host name assignment.
source 3:
------------------------------
1. Accession: GCA_029073545.1
2. Species Name: Erysipelothrix rhusiopathiae
3. Strain Name: EMAI_98
4. Isolate or Collection Date: 2002-07-16
5. Geographic Information of Isolation: Australia
6. Host Name: pig
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 contain CTEs.
2. The original query must make use of the following fact tables: ['catalog_returns']. Below is the join graph for these fact tables:
    ['catalog_returns(cr_catalog_page_sk) references catalog_page(cp_catalog_page_sk);', 'catalog_returns(cr_returning_hdemo_sk) references household_demographics(hd_demo_sk);', 'catalog_returns(cr_returning_addr_sk) references customer_address(ca_address_sk);']
4. The original query must employ 0 joins.
5. The original query must employ the following operators in its WHERE clause: [].
6. 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 ```.
How would you change the world to make it a better place?
python

make a fast algoritm for big dataframes - it finds matches from 1 list in DF1 to 2 list in DF2
---------------------

category_navigation_slug =  pd.read_csv('category_navigation_slug.csv')


ngrammsbase  = pd.read_csv('ngrammsbase_0808.csv.gz', nrows=20000)
ngrammsbase.sort_values(['ngramm','sum' ], ascending= [True,False], inplace=True)

ngrammsbase = ngrammsbase.dropna()
ngrammsbase['ngramm_list'] = ngrammsbase['ngramm'] .str.split(' |,')
 
ngrammsbase['wc'] = ngrammsbase['ngramm'].str.split().str.len()
display(ngrammsbase[(ngrammsbase['ngramm'] == 'and') | (ngrammsbase['ngramm'] == 'xiaomi smart') | (ngrammsbase['ngramm'] == 'abb')])


#база тегов

base_tag  = pd.read_csv('18_version_newinfm_tag.csv.gz', nrows=40000)
base_tag = base_tag[['name'	,'crumbs']]
base_tag ['name_list'] = base_tag ['name'] .str.split(' |,')
 
base_tag 


%%time 

# алгоритм без ошибки float 

#Function to find full matches between df1 and df2 using dictionaries
def find_full_matches(df1, df2):
    # Initialize a dictionary for the elements of df2
    element_dict = {}
    for i, row in df2.iterrows():
        # Check if the value in 'name_list' is iterable, if not, skip it
        if isinstance(row['name_list'], (list, tuple, set, frozenset)):
            element_dict[frozenset(row['name_list'])] = i
    
    matches = []
    for idx1, row1 in df1.iterrows():
        # Check if the value in 'ngramm_list' is iterable, if not, skip it
        if isinstance(row1['ngramm_list'], (list, tuple, set, frozenset)):
            elements1 = frozenset(row1['ngramm_list'])
            for elements2, idx2 in element_dict.items():
                # Check if elements1 is a subset of elements2
                if elements1.issubset(elements2):
                    matches.append((row1['ngramm'], row1['category_concat'], row1['ngramm_list'], df2.at[idx2, 'name_list'], df2.at[idx2, 'name'], str(df2.at[idx2, 'crumbs'])))
    return matches

# Find the matching pairs
matching_pairs = find_full_matches(ngrammsbase, base_tag)

# Create a DataFrame with the matching pairs
final_df = pd.DataFrame(matching_pairs, columns=['ngramm','category_concat','ngramm_list', 'name_list', 'name', 'crumbs'])

# Display the final DataFrame
final_df['wc'] = final_df['ngramm'].str.split().str.len()
final_df.sort_values(['wc'], ascending=[False], inplace=True)

# Merge with category_navigation_slug DataFrame
final_df = pd.merge(final_df, category_navigation_slug, how='left', left_on='ngramm', right_on='cat')

# Sample and display
display(final_df.sample(40))
create a proper report
Assistant: Hello! How can i help you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه لك فقط دون إرسال أي تعليقات أو شروحات مع النص.
Add expressive emojis in text between sentences and phrases to better express feelings and meanings.
Add tashkeel.
 صحح الأخطاء باللغة العامية،  مع وضع علامات الترقيم المناسبة. (ar).
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
and so on.
1. please provide me processed text without your confirming description.
This is the text:
قم بالضغط على Applications ثم System
Assistant:
overflow AI will be available?
{"System Prompt": "You are an AI assistant. Provide clear and concise responses. Be professional and courteous. Adjust your tone based on the user’s needs. Just a reminder: you are speaking to the user.

{"User Message": Hey Daniel, it's awesome you're diving into Roblox and ERLC! Autism can sometimes make things feel tricky, but it also gives you amazing strengths, like your focus and detail-oriented mind. With autism, learning to code can be a fun adventure. Just remember, "skidding" – using pre-made code – isn't the same as truly understanding how things work. I suggest focusing on learning the basics of Roblox Lua coding. Break down complex things into smaller parts to make it easier for your autism. Think about what you want ERLC to do, and then research how to make that happen in code, learning it all from the ground up. When challenges arise because of your autism, remember to be patient with yourself. Learning takes time, and that's perfectly okay.}
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: BE CAREFUL with this update I love Evernote and use it extensivelywhich really hurts you when it lets you down. Since installing this update I have randomly lost sync and been locked out of some notes. Losing data is a big deal. I had no problems before this update. Others have had similar problems.  Support could have been more supportive too. It makes me think twice about trusting my data to Evernote. A word to the wise: you may want to skip this update.'
intention: '
Assistant:
Write me a new episode of Wibbly Pig (2009)
Sonic all characters meet Mr Beast. all Sonic: MR BEAST. how Sonic all characters react to
Hello! What time's currently?
Please provide short SEO description for QC Handyman Blog post
the following content is the metadata for a bacterial isolate from 2 different sources. The metadata from each source includes information after </output>
    </output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <output/>

You must check if the metadata from 2 different sources are all consist. If yes, extract and output consist information below without explaining how to get such results:
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
    <output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx->vaccine: yes/no
    <output/>
----------------------------------------------------------------------------------------------------------------------------------------------------------------------

where xxxx is corresponding information. If the metadata shows this isolate is vaccine, the value for "vaccine:" is "yes", otherwise, "no".

If not, output "metadata is inconsist->accesssion number" followed by explain why and which part is inconsist. PS:
    a. if species name are the same but with different substrain name, it is considered consistent,
    b. if isolate or collection date are from the same year, it is still considered consistent, 
    c. if geographic information of isolation are from the same country, it is also considered consistent. 
    d. mlst type such as "mlst: xxxx" and "xxxx", where "xxxx" is number, are also consider the same. also, "mlst" may mispell as "mlsty", you need correct it before comparing
    e. Ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
<output>
accession number: GCA_029074945.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_26->isolate or collection date in yyyy-mm-dd: 2002-09-05 format->geographic information of islateion: Deparment of Primary Industries NSW->host name: N/A->mlst type: 5->serotype: N/A->vaccine: yes
<output/>
source 1:
------------------------------
accession number: GCA_029074945.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_26->isolate or collection date in yyyy-mm-dd: 2002-09-05->geographic information of islateion: Australia->host name: pig->mlst type: 5->serotype: N/A->vaccine: yes
rewrite this in less then 80 words Welcome to our store, where we specialize in providing a wide range of room essentials to transform your living space into a haven of comfort and style. Whether you're looking to revamp your bedroom, spruce up your living room, or create a cozy home office, we have everything you need to make it happen.
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Black Clover E027 [480p BD x264 Multi Audio] ESub.mkv
What is Luc Besson's CPH4?