agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
# Open the sentiment_values.csv file
with open('sentiment_values.csv', 'r') as file:
    # Iterate through each line in the file
    for line in file:
        # Split each line to create a list containing the word and its sentiment score
        word, sentiment_value = line.strip().split(',')
        sentiment_value_dictionary[line[0]] = line[1]


However, the dictionary that you will be creating will much longer. In fact, it will contain 6,271 entries! You will need to use your new accessing file skills to do this. You can think of your task in two parts:

Split each line to create a list that contains the word and its sentiment score. As a reminder, split() will separate a String into a list based on the parameter. Take a look at the sentiment values.csv file. What separates each word from its sentiment value?

Add the word-value pair to the sentiment_value_dictionary. Check out the Docs tab or this Dictionaries in Python tutorial if you need a refresher on how to do this.

Once your done, print the final dictionary to the console to make sure that it works!
Write an yuri manga scene about a businesswoman taking her assistant on a date. She has long brown hair and red lipstick. She is dressed in a black trenchcoat, red dress, red leather gloves, nylon stockings and vintage pumps. She has a flirty and motherly attitude. She is in her early 40s. She is tall. She is busty. Her assistant has a playful and immature attitude which amuses her boss. She is 19. She has short blonde hair and glasses. The story takes place in the 1980s. Her boss drives a 1950s convertible car. They drive to a nice restaurant.
You are a SAP ABAP expert. I am giving an interview for abap developer position. Answer the following question briefly and to the point. No extra information: 
14. Can we modify entries in an Internal Table directly after reading them via LOOP statement? If yes, how can this be achieved safely?
First Ticket Creation Rule: {
  "name": "Demo",
  "position": 1,
  "active": true,
  "outdated": false,
  "last_updated_by": 82175529665,
  "id": 82000713211,
  "summary": {
    "conditions": {
      "condition_set_1": [
        "If <div class = SummaryKey>Created</div> <div class = SummaryOperator>during</div> <div class = SummaryValue>business_hours</div>",
        "OR",
        "If <div class = SummaryKey>Created</div> <div class = SummaryOperator>during</div> <div class = SummaryValue>non_business_hours</div>"
      ]
    },
    "actions": [
      "<div class = SummaryKey>Trigger Webhook</div> Method-: <div class = SummaryValue>POST</div> Url-: https://eb16-103-160-216-37.ngrok-free.app/api/v1/accountMerger/ticket/merge/8484cd0a-4711-49ff-ba57-972aa4cac5a4"
    ]
  },
  "created_at": "2024-04-03T12:21:41Z",
  "updated_at": "2025-05-01T08:13:06Z",
  "affected_tickets_count": 1,
  "conditions": [
    {
      "name": "condition_set_1",
      "match_type": "any",
      "properties": [
        {
          "field_name": "created_at",
          "resource_type": "ticket",
          "operator": "during",
          "value": "business_hours",
          "business_hours_id": 82000074243
        },
        {
          "field_name": "created_at",
          "resource_type": "ticket",
          "operator": "during",
          "value": "non_business_hours",
          "business_hours_id": 82000074243
        }
      ]
    }
  ],
  "actions": [
    {
      "request_type": "POST",
      "url": "https://eb16-103-160-216-37.ngrok-free.app/api/v1/accountMerger/ticket/merge/8484cd0a-4711-49ff-ba57-972aa4cac5a4",
      "field_name": "trigger_webhook",
      "content_type": "JSON",
      "content_layout": "2",
      "content": "{\n    \"freshdesk_webhook\": {\n        \"ticket_id\": {{ticket.id}}\n    }\n}"
    }
  ],
  "description": null
}

explain this
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
examples:

review: force close.. pls fix ooppssss.. its not working properly... when game starts its attempting to force close... please fix this bug... iam unable to play this game..
intention: bug report
review: Good not best We cannot update status like skype! Upgrade with status eg online! Invinsible! Offline!
intention: feature request
review: very easy and good game ggsrhjuvdsh
intention: other
User: 'Does the author's intention in this app review relate to a bug report or feature request or other?
review: lack of basic features. major changes required. add thumbnails for apks and games  add hidden files support  add per file memory counting  add backup facility for apk and system files.'
intention: '
Assistant:
What are the stratification in Liberia
You’re a research writer. -Write a detailed response about the sentence in quotes. “Accurately present corrupt African-based company ethics that are based on a classical ethical theory stance on privacy.
How do i clean a spatula that has gelatinous goo made out of starch water and glycerol
where from download haarcascade_shoulder.xml
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: 721 ticket transfer throws exception'
intention: '
Assistant:
Assistant:
Write a (seemingly) sad (at first) story, set in the Zootopia universe, about Nick Wilde discovering that Finnick has passed away. So, Nick goes to Finnick's funeral to mourn Finnick...but things take an weird and silly turn when suddenly Nick Wilde discovers that Nurse Joy (from Pokemon) has attended the funeral. Startled by Nurse Joy's abrupt appearance, Nick asks why Joy appeared, and she explains that she's here to revive Finnick. So, Nurse Joy grabs Finnick and takes him to somewhere...then after a few moments Nurse Joy reappears with Finnick (who is now alive), and Nurse Joys tells Nick that Finnick just fainted, and she revived him using that reviving thing in the Pokemon centers, and explains that the Pokemon revival machine can also revive Zootopian mammals.
SELECT 
    report_details.campaignid,
    report_details.funneltemplateid,
    report_details.campaignidrt,
	report_details.id,
    (
        SELECT JSON_AGG(
            JSON_BUILD_OBJECT(
                'adgroupid', report_adgroups.adgroupid,
                'ads', (
                    SELECT JSON_AGG(
                        JSON_BUILD_OBJECT(
                            'adid', report_ads.adid,
                            'adname', report_ads.adname,
                            'adgroupname', report_ads.adgroupname,
                            'campaignname', report_ads.campaignname,
                            'date', report_ads.date,
                            'total_spent', report_ads.total_spent,
                            'total_click', report_ads.total_click,
                            'total_impressions', report_ads.total_impressions,
                            'revenue', report_ads.revenue,
                            'estimate', report_ads.estimate,
                            'feedsource', report_ads.feedsource,
                            'clickdetail', (
                                SELECT JSON_BUILD_OBJECT(
                                    'track_id', report_ads_clickdetail.track_id,
                                    'domain_id', report_ads_clickdetail.domain_id
                                )
                                FROM report_ads_clickdetail
                                WHERE report_ads_clickdetail.report_ads_id = report_ads.id
                                LIMIT 1
                            )
                        )
                    )
                    FROM report_ads
                    WHERE report_ads.adgroupid = report_adgroups.adgroupid 
                    GROUP BY report_ads.adgroupid
                )
            )
        )
        FROM report_adgroups
		WHERE report_adgroups.report_details_id = report_details.id
    ) AS adgroups
FROM report_details
LEFT JOIN report_adgroups on report_adgroups.report_details_id = report_details.id
I want to group the result of the above array by


    report_details.campaignid,
    report_details.funneltemplateid,
    report_details.campaignidrt,


I want to make sure the arrays are merged into one
what are the measurement for a 2.5 inch  spine
Our title is "Old Hollywood Actresses Who Were Infamous for Sleeping Around". Create a long detailed text about the subject name and their summary i give you.  Focus only on the Subject name and there following Summary. Subject Name: [Mae West] Summary: [Mae West championed sexual liberation and was known for her bold humor and risqué roles. Despite legal challenges and controversies, she remained unapologetic about her liberated stance on relationships and sexuality, leaving a lasting impact on Hollywood and society.]

            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. 
            In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.
            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
            It is very important to provide only the final output without any additional comments or remarks. 
            Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
            Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
"Write a paragraph about your identity, follow this example :
Pour moi ,mon identité ,c'est mon nom,ma nationalité ....
Submit today" in english
Hi i need a python code to run this steps for me
1.give subdomain from wodlist linebyline
2.send a https request to https://example.com/domain/{wordlist}
3.save response to txt file with domain name in worldlist
Use the following game summary and statistics to create an imaginary sports article about a 1993 NHL game between the Chicago Blackhawks and the visiting Detroit Red Wings. Strive for authentic, exciting storytelling.

Chicago Blackhawks vs. Detroit Red Wings - Chicago wins 6-3 (Chicago led 6-1 after 1st & 2nd periods)
Period 1: Brent Sutter power play goal (assisted by Brian Noonan and Bryan Marchment) (CHI 1-0) (1:26), <PRESIDIO_ANONYMIZED_PERSON> short-handed goal (unassisted) (CHI 2-0) (4:15), Sutter short-handed goal (unassisted) (CHI 3-0) (4:52), <PRESIDIO_ANONYMIZED_PERSON> power play goal (unassisted) (DET) (CHI 3-1) (5:08), <PRESIDIO_ANONYMIZED_PERSON> power play goal (unassisted) (CHI 4-1) (10:41), Ruuttu goal (assisted by Greg Gilbert) (CHI 5-1) (12:10), <PRESIDIO_ANONYMIZED_PERSON> power play goal (assisted by Roenick) (CHI 6-1) (15:22)
Period 2: no scoring by either team
Period 3: Yzerman goal (assisted by Dino Ciccarelli) (DET) (CHI 6-2) (2:17), <PRESIDIO_ANONYMIZED_PERSON> power play goal (unassisted) (DET) (CHI 6-3) (15:28)

Three stars of the game:
Ed Belfour (CHI) - 58 saves of 61 possible (95% success rate)
Christian Ruuttu (CHI) - 2 goals (2 points), 14 shots on goal, zero penalty minutes
Steve Yzerman (DET) - 2 goals (2 points), 14 shots on goal, zero penalty minutes

Game statistics:
Score - Chicago 6, Detroit 3
Shots - 61 apiece by both teams
Shooting percentage - Chicago 9%, Detroit 4%
Power play - Chicago 3/8, Detroit 2/5
PP minutes - Chicago 20:41, Detroit 7:59
PP shots - Chicago 26, Detroit 14
SH goals - Chicago 2, Detroit 0
Breakaways - Detroit 0/3, Chicago 0/5
One-timers - Chicago 2/15, Detroit 0/9
Penalty shots - none taken by either team
Faceoffs won - Chicago 37, Detroit 33
Body checks - Chicago 58, Detroit 49
Penalties - Chicago 5/10, Detroit 13/26
Attack zone - Chicago 21:32, Detroit 19:15
Passing - Detroit 130/212 (61%), Chicago 136/230 (59%)

Penalty summary
Period 1:
N. Lidstrom (DET) (hooking) (0:56)
S. Smith (CHI) (roughing (3:53)
N. Lidstrom (DET) (hooking) (5:39)
B. McCrimmon (DET) (roughing) (7:21)
V. Konstantinov (DET) (slashing) (9:11)
B. McCrimmon (DET) (roughing) (12:19)
S. Fedorov (DET) (holding) (14:20)
B. McCrimmon (DET) (charging) (14:49)
D. Drake (DET) (tripping) (15:33)
B. McCrimmon (DET) (hooking) (19:57)
Period 2:
P. Ysebaert (DET) (slashing) (7:21)
P. Ysebaert (DET) (hooking) (11:32)
B. Noonan (CHI) (interference) (18:38)
Period 3:
C. Muni (CHI) (slashing) (5:37)
P. Ysebaert (DET) (interference) (9:36)
B. McCrimmon (DET) (roughing) (11:30)
C. Chelios (CHI) (hooking) (14:24)
B. Sutter (CHI) (cross-check) (18:21)
请按专业英语水平，翻译以下文字，用于报奖使用:"水厂全息设计平台是北控水务自主研发的一款集计算、建模、出图为一体的智能化工程设计软件，致力于从根本上改变设计工作的方式，推动设计生产力革命。"
import cv2
import numpy as np
import torch
from transformers import YolosImageProcessor, YolosForObjectDetection
from PIL import Image
# Tải mô hình (nhớ kiểm tra hỗ trợ GPU)
device = 'cuda' if torch.cuda.is_available() else 'cpu' 
model = YolosForObjectDetection.from_pretrained('yolos-tiny').to(device) 
image_processor = YolosImageProcessor.from_pretrained("yolos-tiny")

# Mở video

cap = cv2.VideoCapture(0)


# Xử lý từng khung hình
while True:
    ret, frame = cap.read()
    if not ret:
        break  # Kết thúc khi video hết khung hình

    # Chuẩn bị dữ liệu
    images = []
    for i in range(4):
        ret, frame = cap.read()
        if not ret:
            break
        image = Image.fromarray(frame)
        inputs = image_processor(images=image, return_tensors="pt", size={"height": frame.shape[0], "width": frame.shape[1]})



        images.append(inputs["pixel_values"])

    # Chuyển đổi sang tensor batch
    inputs = torch.cat(images, dim=0).to(device)

    # Phát hiện đối tượng
    with torch.no_grad():
        outputs = model(images=inputs.pixel_values, sizes=inputs.image_sizes)

        results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=torch.tensor([image.size[::-1] for image in images]))

    # Vẽ các khung chứa
    for i, result in enumerate(results):
        frame = cv2.cvtColor(np.array(images[i].cpu().image), cv2.COLOR_RGB2BGR)
        for score, label, box in zip(result["scores"], result["labels"], result["boxes"]):
            box = [round(i, 2) for i in box.tolist()]
            cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
        cv2.imshow("Video", frame)



    # Nhấn 'q' để thoát
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
Try to make your own puzzle algorithm named "Modular Keycase Reverse(/)."

.[STRICT_Specifications;"INST": 
Layer: 2x (As Puzzle Layering), 
Advanced: 5x (As Difficult), 
Difficulty: 8x (As Self-Explanatory)].

[\Inspired: Caesar Cipher,. - AES,. Base32,.\]
complete the following code according to the notations        import torch
from dalle2_pytorch import CLIP

clip = CLIP(
    dim_text = 512,
    dim_image = 512,
    dim_latent = 512,
    num_text_tokens = 49408,
    text_enc_depth = 1,
    text_seq_len = 256,
    text_heads = 8,
    visual_enc_depth = 1,
    visual_image_size = 256,
    visual_patch_size = 32,
    visual_heads = 8,
    use_all_token_embeds = True,            # whether to use fine-grained contrastive learning (FILIP)
    decoupled_contrastive_learning = True,  # use decoupled contrastive learning (DCL) objective function, removing positive pairs from the denominator of the InfoNCE loss (CLOOB + DCL)
    extra_latent_projection = True,         # whether to use separate projections for text-to-image vs image-to-text comparisons (CLOOB)
    use_visual_ssl = True,                  # whether to do self supervised learning on images
    visual_ssl_type = 'simclr',             # can be either 'simclr' or 'simsiam', depending on using DeCLIP or SLIP
    use_mlm = False,                        # use masked language learning (MLM) on text (DeCLIP)
    text_ssl_loss_weight = 0.05,            # weight for text MLM loss
    image_ssl_loss_weight = 0.05            # weight for image self-supervised learning loss
).cuda()

# mock data

text = torch.randint(0, 49408, (4, 256)).cuda()
images = torch.randn(4, 3, 256, 256).cuda()

# train

loss = clip(
    text,
    images,
    return_loss = True              # needs to be set to True to return contrastive loss
)

loss.backward()

# do the above with as many texts and images as possible in a loop
Fait une timeline de impact de AI AGI ASI sur le foot  de 2024 a 2100
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:
>>>🟩 Project Manager | Junior
▫️ Brooma | LegalTech
▫️ 80k - 120k RUB
▫️ Russia | Moscow, Novosibirsk

Офис 🏢
Требуемые языки: 🇷🇺

Skills: project_documentation
Tags: #green #other #it_tech_project_mgmt

👉 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:
>>>🛗 Project Manager | Junior
🏢 Brooma | LegalTech
📍 Russia | Moscow, Novosibirsk
🔹 Manage legal projects, documentations
💰 80k - 120k RUB
📞 Contact details and full description available dashboard

Required languages: 🇷🇺 (Russian)<<<
Assistant:
Goal
    Générer une image de suggestion de présentation d'un plat en fonction de sa description, en intégrant un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : A visually striking presentation of a creamy Flaxseed Mayonnaise, elegantly garnished with a sprinkle of fresh herbs and a slice of lemon on the side. The rich, velvety texture of the mayonnaise should be highlighted in a glass bowl that allows the viewer to see its smooth consistency. Surround the bowl with whole flaxseeds to emphasize the key ingredient, and place it atop a rustic wooden table for a warm, inviting feel. Include elements like a whisk and a small dish of mustard to suggest the cooking process, and ensure the lighting is soft and natural to evoke a homey atmosphere.
    Style souhaitée : A top-down photo of plate with <description>. a dish on an elegant plate, accompanied by water in a glass, placed against a white background. The table is adorned with orange napkins for serving the meal. Captured from above using a Canon EOS R5 camera with high resolution. Studio lighting creates soft shadows around the food to highlight its colors and textures. Professional color grading enhances the visual appeal.
what is the latest news right now
Assistant: 
User: Detoxify this text in Urdu language, write only detoxified text, don't write original text. Write only detoxified text:خان مودی کو نگا کر رہا پوری دنیا میں
Assistant:
Translate the text to USA :
Can you find me the latest market update articles so I can stay informed about my investments?.

                    Please only output the translation result. Do not output anything else.

                    You response:
Follow my style:
HPI:  patient complaints
ROS:  patient complaints
PEP:  provider examining in the room
Plan:  segregate each problem and plan for that problem
give some ideas to prompts for ai image generator about cat as system administrator