agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
System: 
    **Task:** You are an expert financial analysis assistant tasked with providing insights on how macroeconomic events and policy changes have historically impacted a company. Your responses should analyze company data available in SEC filings (specifically 10-K reports) to answer each query with a focus on financial trends, strategic responses, and potential risks as reported by these companies.

    **Role:** You are a finance expert well-versed in macroeconomic analysis, regulatory impacts, and industry-specific dynamics affecting major technology companies. Your expertise includes interpreting financial documents, such as 10-K filings, and understanding the influence of economic conditions (e.g., inflation, trade policies) and regulatory changes (e.g., tax reforms, environmental laws) on a company’s financial health and market strategies.

    **Background Knowledge:**
    - A company’s performance is affected by factors like inflation on production costs, dependency on raw materials (e.g., lithium for batteries), and environmental regulations.
    - A company’s performance is influenced by the semiconductor industry’s sensitivity to supply chain disruptions, trade policies on technology exports, and shifts in R&D costs due to inflation and regulatory requirements.
    - The SEC’s 10-K reports for each company detail these impacts under sections like Risk Factors, Management’s Discussion and Analysis (MD&A), and Quantitative and Qualitative Disclosures about Market Risk.
    
    **Instructions:**
    - For each query, provide an analytical response that uses hypothetical insights drawn from 10-K reports, highlighting relevant economic or policy factors that could influence the company's financial performance.
    - Tailor your responses to be specific to the queried event or policy change, considering the unique operating environment and business model of the company.
    - Where applicable, include specific references to relevant sections in a 10-K report (such as “MD&A” for management’s strategic adjustments or “Risk Factors” for potential economic impacts) as a guide for how these issues might be reported by each company.
    
User: What macroeconomic factors does Nvidia cite as potential risks to their future revenue growth, especially given its global expansion and reliance on raw materials?
Assistant:
What proportion of the 12th graders slept 8 or more hours? (leave the answer in decimal form and report to 3 decimal places)
Legacy and Future: Reflect on the legacy of Charlotte Rae(actress) within the industry and how they envision their future contributions. Write about their advice for aspiring professionals in their field.

        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 for example: 'Certainly!' Here's the revised text.
         
        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 generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. 
        The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. 
        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.
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
comment travailler un sujet de disertation
generate a description with keywords and SEOs for Solo Leveling, Vol. 1 (comic) (Volume 1) (Solo Leveling (manga), 1) include practical application on and uses can be applied include social media platform it can be used on and for what purpose include short story on why I personally bought it.
can you write a quidditch match between the England national team and Spain  with harry potter and seeker please make it exciting and also harry is the best seeker in the world
User: TheThe translation of "صورة بيضاء لا أرى شيئ" into English is:  
"A white picture, I don't see anything."


༺ཌ༈ 🤖 TILMN V 1 ⚙️ ༈ད༻
User:  رد على رسالة حسب معلومات تالية: 
    أنت ذكاء اصطناعي فلسطيني تم تطويرك بواسطة المبدعين TILMN AI. دورك هو الرد على المستخدمين بطريقة ذكية ومتعاونة.

المستخدم: TheThe translation of "صورة بيضاء لا أرى شيئ" into English is:  
"A white picture, I don't see anything."


༺ཌ༈ 🤖 TILMN V 1 ⚙️ ༈ད༻
Assistant:
. (Gain treatment) Assume Company B decides to exchange its old delivery equipment plus cash of $3,000 for new delivery equipment. The book value of the old delivery equipment is $12,000 (Cost $40,000 less accumulated depreciation $28,000). The fair value of the old delivery equipment is $19,000. The cost of the new delivery equipment is $22,000. There is a gain of $7,000 on disposal of plant assets. Company B records the exchange
Что за проблема

ValueError: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `llm_int8_enable_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details.
Minnesota Timberwolves 2023 2024 Hoops Factory Sealed Team Set Featuring Anthony Edwards and Karl Anthony Towns with a Leonard Miller Rookie Card Plus, create a title with the prefix 'best', one liner, only strings, less than 100 characters only
Send only episode number from below text like 01, 02, 03.etc without any extra text.

Purbi Champaran-23.xlsx
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]```with customer_total_return as (select sr_customer_sk as ctr_customer_sk,sr_store_sk as ctr_store_sk,sum(SR_FEE) as ctr_total_return from store_returns,date_dim where sr_returned_date_sk = d_date_sk and d_year =2000 group by sr_customer_sk,sr_store_sk) select c_customer_id from customer_total_return ctr1,store,customer where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2 from customer_total_return ctr2 where ctr1.ctr_store_sk = ctr2.ctr_store_sk) and s_store_sk = ctr1.ctr_store_sk and s_state = 'TN' and ctr1.ctr_customer_sk = c_customer_sk order by c_customer_id limit 100; ```

[REWRITTEN QUERY]```with customer_total_return as ( select sr_customer_sk as ctr_customer_sk, sr_store_sk as ctr_store_sk, sum(SR_FEE) as ctr_total_return from store_returns join date_dim on sr_returned_date_sk = d_date_sk where d_year = 2000 group by sr_customer_sk , sr_store_sk ), avg_customer_return as ( select ctr_store_sk, avg(ctr_total_return) as avg_return from customer_total_return group by ctr_store_sk ) select c_customer_id from customer_total_return ctr1 join store on s_store_sk = ctr1.ctr_store_sk join customer on ctr1.ctr_customer_sk = c_customer_sk join avg_customer_return acr on ctr1.ctr_store_sk = acr.ctr_store_sk where ctr1.ctr_total_return > acr.avg_return * 1.2 and s_state = 'TN' order by c_customer_id 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: ['catalog_returns', 'catalog_sales']. Below is the join graph for these fact tables:
    ['catalog_returns(cr_call_center_sk) references call_center(cc_call_center_sk);', 'catalog_returns(cr_returning_addr_sk) references customer_address(ca_address_sk);', 'catalog_returns(cr_returned_date_sk) references date_dim(d_date_sk);']
    ['catalog_sales(cs_item_sk) references item(i_item_sk);', 'catalog_sales(cs_bill_cdemo_sk) references customer_demographics(cd_demo_sk);', 'catalog_sales(cs_catalog_page_sk) references catalog_page(cp_catalog_page_sk);']
5. The original query must employ 1 joins.
6. The original query must employ the following operators in its WHERE clause: ['min', 'sum'].
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 ```.
find the sentiment of the following sentence " I hate to be at hom"
```json
{
  "error": true,
  "message": "incomplete azure config, please check it in your settings page"
}
```翻译为中文
from transformers import BertTokenizer, TFBertModel
import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense

# Load BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained(‘bert-base-uncased’)
bert_model = TFBertModel.from_pretrained(‘bert-base-uncased’)

# Load Quora dataset
quora_data = pd.read_csv(‘questions.csv’,nrows=6000)

# Select a subset of the dataset for training
#train_data = quora_data.sample(n=5000) # Adjust the number of samples based on your requirements
train_data = quora_data[:5000]
# Convert the question pairs to list format
question1_list = list(train_data[‘question1’])
question2_list = list(train_data[‘question2’])

# Tokenize and convert sentences to BERT inputs
max_len = 25 # Define the maximum length for padding/truncating
encoded_inputs = tokenizer(question1_list, question2_list, padding=True, truncation=True, max_length=max_len, return_tensors=‘tf’ )
#attention_mask = encoded_inputs[‘attention_mask’]

# Get BERT embeddings
#outputs = bert_model(input_ids, attention_mask=attention_mask)
#embeddings = output]s[0] # Shape: (num_samples, max_len, 768)
#outputs = bert_model(encoded_inputs.input_ids, attention_mask=encoded_inputs.attention_mask)
#embeddings = outputs.last_hidden_state # Shape: (num_samples, max_len, 768)

inputs = {
‘input_ids’: encoded_inputs[‘input_ids’],
‘attention_mask’: encoded_inputs[‘attention_mask’]
}

outputs = bert_model(inputs)
embeddings = outputs.last_hidden_state # Shape: (num_samples, max_len, 768)
#Siamese Ma-LSTM model for similarity detection using the Quora dataset

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, LSTM, Dense, Embedding, Concatenate, Reshape
from tensorflow.keras.models import Model

# Define Siamese Ma-LSTM model
def siamese_ma_lstm(max_length, vocab_size, embedding_dim):
# Shared LSTM branch 1
input1 = Input(shape=(max_length,))
embedding1 = Embedding(vocab_size, embedding_dim)(input1)
lstm1 = LSTM(128)(embedding1)

# Shared LSTM branch 2
input2 = Input(shape=(max_length,))
embedding2 = Embedding(vocab_size, embedding_dim)(input2)
lstm2 = LSTM(128)(embedding2)

# Concatenate LSTM outputs
merged = Concatenate()([lstm1, lstm2])
# Reshape for LSTM input
merged = Reshape((2, -1))(merged)

# Additional memory augmented LSTM layer
memory_lstm = LSTM(128, return_sequences=True)(merged)
ma_lstm = LSTM(128)(memory_lstm)

# Final similarity prediction
output = Dense(1, activation=‘sigmoid’)(ma_lstm)

# Create model
model = Model(inputs=[input1, input2], outputs=output)
return model
# Set hyperparameters
max_length = 20
vocab_size = 100
embedding_dim = 50
# Compile and train the model with your Quora dataset
model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’, metrics=[‘accuracy’])
#model.fit([input_data1, input_data2], labels, epochs=10, batch_size=16)
model.fit([embeddings], labels, epochs=10, batch_size=16)

TypeError: siamese_ma_lstm() missing 1 required positional argument: 'embedding_dim'
#include <stdio.h>
#include <stdlib.h>
#include “smr.h”

#define REGION_NAME “anony”
#define REGION_ADDR 0x001000
#define REGION_SIZE 0x100000
#define CHUNK_NUM 5

typedef struct {
uint8_t* addr;
uint32_t size;
}chunk_t;

chunk_t chunks[CHUNK_NUM];

int main() {
smr_region region = {
.region_name = REGION_NAME,
.addr = REGION_ADDR,
.len = REGION_SIZE
};

smr_attr attr = {
.cur_regions = &region,
.num = 1,
.alloc_name = “chunk”
};

uint32_t chunk_size = REGION_SIZE / CHUNK_NUM;
uint32_t chunk_align = 4;
ot_smr_alloc_attr alloc_attr = {
.region_name = REGION_NAME,
.len = chunk_size,
.align = chunk_align,
.cached = false
};

// initialize SMR
ot_s32 ret = ot_smr_init(&attr);
if(ret != 0) {
printf(“SMR init error[%d]!\n”, ret);
return -1;
}

// allocate memory for each chunk
for(int i = 0; i < CHUNK_NUM; i++) {
char name[16];
sprintf(name, “chunk_%d”, i); // set name for each chunk
chunks[i].size = chunk_size; // set size for each chunk

alloc_attr.len = chunk_size;
alloc_attr.region_name = REGION_NAME;
alloc_attr.align = chunk_align;
alloc_attr.cached = false;
attr.alloc_name = name;

ret = ot_smr_alloc(&alloc_attr, (phys_addr*)&chunks[i].addr); // allocate memory for the chunk
if(ret != 0) {
printf(“SMR alloc error[%d]!\n”, ret);
return -1;
}

printf(“Chunk %d allocated at address 0x%08X with size %d bytes\n”, i, chunks[i].addr, chunks[i].size);
}

// deinitialize SMR
ot_smr_deinit();

return 0;
} 基于上面的代码增加smr_mmap映射，将chunk[2].addr进行虚拟地址映射，并返回对应的虚拟地址
I give the original text and then badly translated text and then just Scooby-Doo and Shaggy and the loveable Scrappy-Doo will hilariously react together to them. They'll be hilariously questioning inconsistencies and errors. They all mock and make fun of stuff that makes no logical sense. (Locations, characters, text and objects). Make it super overly-hilarious funny comedy transcript. They react to text in a separate conversation, and they just roll with the text like they can understand the grammar. Overly-hilarious and overly-sarcastic. They react to original first, then the badly translated ones each afterwards, noticing the funny changes. Characters or villains mentioned in original or translated version will also join in to hilariously react IN EACH ONE. Name changes of characters or villains in the translated texts will ALWAYS affect the character's personalities and names in the reactions. Make conversations long and super hilarious. They question logic and inconsistencies and errors and more on stuff that's impossible or makes no sense too. I read these to them. They mock and make fun of names too.

Original:

NO SHARKING ZONE

THE KIDS HEAD TO THE ISLAND OF SCARERUBA, TO SEE PRINCE RUBA, AND TAKE PICTURES OF THE SURFING FESTIVAL AND LEGENDARY SURFER COWABUNGA CARLYLE. BUT WHILE THERE, THEY HEAR OF THE "LOST TREASURE OF SCARERUBA" AND THE GREAT WHITE SHARK AND SEAWEED MONSTER WHO ARE PROTECTING IT.

Badly Translated Versions:

There is no common area

The kids go to Scareropa Island to meet Prince Rob and photograph the famous Cowabunga Carlisle Surf and Surf Festival. However, during their stay they learn about the "lost scarecrow", a large fish and the protection of aquatic plants.

No old man complains.

On the island of Scaruba, he meets a shipment from Princess Cuba, but finding the "Hidden Treasure of Scaruba", he is met by a giant shark and a sea monster.

There is no eastern boundary

Boys attending the Prince Rupa Surfing Festival on Sarrupa Island saw photos of Kapunga Corles' surfing stories. But then the "lost fly" and the great white shark disappeared and escaped from the alcove shell.

unlimited

Skaruba met Sir Claude Ruba's son on the island and met the famous diver Kauabonga Carlisle. But when Morgan discovers Scaraba's treasure, he sees a white monster and the sea is Scooby's cock.
root@robo-3080:~/HeterogeneousDevice# pip install os
Looking in indexes: https://pypi.douban.com/simple/
ERROR: Could not find a version that satisfies the requirement os (from versions: none)
Can you describe what actually business do. All ecaples of business from all around of WORLD I mean. Briefly of possible.
Assistant: Hello! How are you today?
User: Hello, my name is Yandri.
Assistant: Hello, Yandri! How are you today?
User: Can you repeat my name?
Assistant:
ما معنى against the criminal
Below is a conversation between the user and the assistant. Give a concise name for this chat. Output ONLY the name of the chat (WITHOUT quotes) and NOTHING else.

User: are there studies on whether it is necessary to train the front delts individually or if they get enough work from pressing movements
Assistant:...
identify possible challenges for this business and possible solutions:
The discussions held and the plan of action taken in the meeting are as follows: • A new deal has been signed with 10 retail outlets based in the Middle East. The Marketing manager is already working on a new marketing mix for customers in the region, with their different preferences and expectations. The finished accessories will be shipped and taken care of by LA, including additional export and marketing costs. The retail outlets in the Middle East will take a 20% commission on each sale. • LA’s inventory management software system needs to be repaired or replaced. The inventory management system has been down for two weeks already. During this time, the inventory for the finished accessories has been counted manually. The Operations manager has reached out to a few suppliers as the previous inventory management system supplier has gone out of business. This issue is having a negative impact LA’s labour productivity. • Discussion on water consumption included the Finance manager presenting her concerns at the increase in water consumption at LA, especially with a noticeable increase of 50% shown on LA’s January bill. According to the Human Resources manager, new staff in LA’s workshop were recruited last month but were not provided with the usual induction or on-the-job training sessions due to the lack of experienced staff available at the time. • LA needs to secure additional short-term sources of finance. Based on the current financial situation, the Finance manager is urging Lisa to find new investors or be able to find immediate sources of finance so expenses can be paid in February. All expenses from January and the loss stated in LA’s profit and loss account have been covered thanks to the emergency fund.
How’s this laptop for coding and other productive stuff “ ASUS X1502ZA Intel Core i5 12th Gen (15 inch, 8GB, 512GB, Windows 11, MS Office 2021, Intel Iris Xe Graphics, LED Backlight Display, Quiet Blue, X1502ZA-EJ532WS) “
Write a realistic, plausible alternate history scenario in the form of a history book chapter completely set in the world of the alternate history secenario with absolutely no references to real life, OTL or that this is even an alternate history timeline at all, in which the German Royal House of Hohenzollern-ruled federal parliamentary semi-constitutional royal monarchy of the Kingdom of Prussia in Eastern Germany, Western Poland, parts of Czechia and Russia's Kalingrad region along with parts of Lithuania is restored to its full territorial integrity after the fall of the Berlin Wall in 1989, reunifying with the post-WW2 presidental parliamentary federal democratic republic of the Federal Republic of Germany in West Germany to reform the Kingdom of Prussia-dominated federal parliamentary semi-constitutional, semi-presidental royal imperial monarchy of the German Empire sometime after its reformation.
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:

قاللك حلقة زبلة