agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Give me a full code example that shows this code for a model upgraded, the model is designed to predict the next value based of a graph. class Attention(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        self.W = self.add_weight(
            name="attention_weight", shape=(input_shape[-1], 1), initializer="random_normal", trainable=True
        )
        self.b = self.add_weight(
            name="attention_bias", shape=(input_shape[1], 1), initializer="zeros", trainable=True
        )
        super(Attention, self).build(input_shape)

    def call(self, x):
        e = K.tanh(K.dot(x, self.W) + self.b)
        a = K.softmax(e, axis=1)
        output = x * a
        return K.sum(output, axis=1)

# Upgraded model
def create_model(input_shape):
    inputs = Input(shape=(input_shape[0], input_shape[1]))
    
    # First Bidirectional LSTM layer
    x = Bidirectional(LSTM(160, return_sequences=True))(inputs)
    x = Dropout(0.3)(x)
    x = BatchNormalization()(x)
    
    # Second Bidirectional LSTM layer with residual connection
    x2 = Bidirectional(LSTM(160, return_sequences=True))(x)
    x2 = Dropout(0.3)(x2)
    x2 = Add()([x, x2])  # Residual connection
    
    # Third Bidirectional LSTM layer
    x3 = Bidirectional(LSTM(80, return_sequences=True))(x2)
    x3 = Dropout(0.3)(x3)
    x3 = BatchNormalization()(x3)
    
    # Attention mechanism
    attention_output = Attention()(x3)
    
    # Fully connected dense layer
    x = Dense(64, activation="relu")(attention_output)
    x = Dropout(0.3)(x)
    
    # Output layer
    outputs = Dense(1, activation="linear")(x)
    
    # Model creation
    model = Model(inputs=inputs, outputs=outputs)
    
    # Compile the model
    model.compile(
        loss=Huber(delta=1.0),
        optimizer=Adam(learning_rate=0.001),
        metrics=["mae"]
    )
    
    return model
Assistant: 
User: User: Что делает этот код. Перепиши его красиво и добавь комментарии

```
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split
from sklearn.preprocessing import OrdinalEncoder
from matplotlib import pyplot as plt
from scipy.ndimage import shift

from sklearn.datasets import fetch_openml

mnist = fetch_openml('mnist_784', version=1)
X, y = mnist['data'], mnist['target']"
X_train, X_test, y_train, y_test = \
    X[:60_000], X[60_000:], y[:60_000], y[60_000:]
knn_clf = KNeighborsClassifier()
knn_clf.fit(X_train, y_train)
cross_val_score(knn_clf, X_train, y_train, cv=3, scoring='accuracy')
parameters = {'n_neighbors': (4, 5, 6), 'weights': ('uniform', 'distance')}
clf = GridSearchCV(knn_clf, parameters)
clf.fit(X_train, y_train)
knn_best = clf.best_estimator_
cross_val_score(knn_best, X_train, y_train, cv=3, scoring='accuracy')
Assistant: Sure! Here’s a cleaner version of the code with added comments to explain each step:

```python
# Import necessary libraries
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split
from sklearn.preprocessing import OrdinalEncoder
from matplotlib import pyplot as plt
from scipy.ndimage import shift
from sklearn.datasets import fetch_openml

# Fetch the MNIST dataset from OpenML
mnist = fetch_openml('mnist_784', version=1)

# Split the dataset into features (X) and target labels (y)
X, y = mnist['data'], mnist['target']

# Further split the data into training and testing sets
X_train, X_test, y_train, y_test = X[:60_000], X[60_000:], y[:60_000], y[60_000:]

# Initialize the K-Nearest Neighbors classifier
knn_clf = KNeighborsClassifier()

# Fit the model on the training data
knn_clf.fit(X_train, y_train)

# Evaluate the model using cross-validation
cv_scores = cross_val_score(knn_clf, X_train, y_train, cv=3, scoring='accuracy')
print("Cross-validation scores:", cv_scores)

# Define the parameters for Grid Search
parameters = {
    'n_neighbors': (4, 5, 6),  # Number of neighbors to consider
    'weights': ('uniform', 'distance')  # Weight function used in prediction
}

# Initialize GridSearchCV with the KNN classifier and the parameters
clf = GridSearchCV(knn_clf, parameters)

# Fit the model using Grid Search to find the best parameters
clf.fit(X_train, y_train)

# Get the best estimator from Grid Search
knn_best = clf.best_estimator_

# Evaluate the best model using cross-validation
best_cv_scores = cross_val_score(knn_best, X_train, y_train, cv=3, scoring='accuracy')
print("Best cross-validation scores:", best_cv_scores)
```

### Explanation of the Code:
1. **Imports**: The code imports necessary libraries for data manipulation, machine learning, and visualization.
2. **Data Fetching**: It fetches the MNIST dataset, which contains images of handwritten digits.
3. **Data Splitting**: The dataset is
User: на русском комментарии
Assistant:
Assistant:
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: Our approach to the renewable fuels and chemicals markets will be dependent on the price of corn and other feedstocks that will be used to produce our products. A decrease in the availability of plant feedstocks or an increase in the price may have a material adverse effect on our financial condition and operating results. At certain levels, prices may make these products uneconomical to use and produce as we may be unable to pass the full amount of feedstock cost increases on to our customers. The price and availability of corn and other plant feedstocks may be influenced by general economic, market and regulatory factors. These factors include weather conditions, farming decisions, government policies and subsidies with respect to agriculture and international trade and global demand and supply. For example, corn prices may increase significantly in response to drought conditions in the midwestern region of the U.S. and any resulting decrease in the supply of corn could lead to the restriction of corn supplies, which in turn could cause further increases in the price of corn. The significance and relative impact of these factors on the price of plant feedstocks is difficult to predict, especially without knowing what types of plant feedstock materials we may need to use.
Assistant:
Make an image of sticks
Kids WB schedule (Feb 3-Sep 15 2007)

07:00AM Krypto the Superdog
07:30AM Xiaolin Showdown
08:00AM Loonatics Unleashed
08:30AM Tom and Jerry Tales
09:00AM Shaggy & Scooby-Doo Get a Clue!
09:30AM Johnny Test
10:00AM Legion of Super Heroes
10:30AM The Batman
11:00AM Johnny Test
11:30AM Viewtiful Joe
12:00PM Spider Riders
12:30PM Monster Allergy



New shows coming in Fall 2007:

Will and Dewitt: Will is a young boy who constantly searches for adventure. Dewitt is a frog that can transform. Together, Will & Dewitt find several adventures and Dewitt can change into almost any creature to help Will through tough times. 

Magi-Nation: In Magi-Nation, Tony finds himself embroiled in a war to save the good people of the Moonlands from the tyranny of Agram and the Shadow Magi. Wearing the mystical ring of the Final Dreamer, Tony and his new friends adventure through the Moonlands calling on the power of the Dream Creatures to aid them. .

Skunk Fu: Animals in The Valley have been using their Kung fu skills to ward off evil in their homeland, but they need a miracle to win the battle. Skunk arrives to help their cause and begins following the teachings of Panda. Skunk must learn to master Kung Fu to overcome the sinister Dragon and the Ninja Monkey Army.

Eon Kid: Marty, an ordinary 11-year-old boy, suddenly becomes the human extension of the Fist of Eon (which had been lost for a century), gaining amazing fighting powers. With the evil General's dark armies in pursuit of his newfound weapon, Marty has supernatural adventures and, along the way, meets friends, Ally (who is also a big target for the evil forces) and mysterious Gaff, who can help him understand his new powers and uncover secrets from the past. This animated sci-fi series is full of kid-friendly action. Write a forum conversation asking how different will the schedule be this season
I'm taking a Neural networks course in university and we were tasked with participating in a kaggle competition
System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Review the text carefully and decide, as a recruiter, if it qualifies as a job description. To qualify, the text should contain a job title, tasks or responsibilities, optional work format (e.g., remote, in-office or hybrid), contact information, optional salary amount. Multiple job descriptions at once do not quialify. Respond strictly with either 'True' or 'False'.

==TEXT==

User: 🟥 Account Executive | 5 year(s) | Senior
▫️ Isometric Technologies | Supply Chain and Logistics

Remote 📡
Required languages: 🇬🇧

Skills: saas, salesforce
Tags: #red #sales #remote

👉 Contacts and Full Description
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: hes very professional..but unable to fix the issue cased by your rep on the phone'
intention: '
Assistant:
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:   The two new buildings would total ~1 million square feet  with  enough room for 4,500 Googlers  The plans come as the search giant bulks up its presence in Sunnyvale 
Google  recently revealed its plans for a massive new development project in Sunnyvale, California, a few miles away from its main 
Assistant:
Application scenarios of small laser engraving machine
Solipsitology (solipsito- is prefix of solipsite + suffix -logy) is theoretical framework and intellectual study of solipsite. Proposed intellectual truth, there was one solipsite-bearing person, solipsite nicknamed Solipsitarius.

I coined term "solipsite" for actual player of real life, it's central essence of importance, it's central soul of central phenomenal consciousness and central qualia, it's existence of self, there are nothing more important than solipsite. Also known as centernite, singularus, selfcenter. Solipsiteless persons/individuals are entities lack solipsite, also known as NPCs of real life or backdrop people, solipsiteless people's perceptions do not appear in solipsite's qualia because they are not nervally connected to solipsite-bearing person.

Physical properties and information of solipsite without body: It's non-corporeal, inanimate object, abstract thing, it's not mechanically sentient. It is nothing in appearance, it's not made of material composition, it's massless and dimensionless. It's undetectable by physical means such as electromagnetism, sounds, quantum. It's unduplicatable, undestroyable, uncreatable. Solipsite could exist without body.

Law of solipsite, reason is not determined by everything:
-Why there are no more solipsites? If there were two or more solipsites, leading to interconnection between solipsites each other but not connected nervally, two or more solipsites would shares perceptual experiences by interconnection, similar to conjoined twin. Two or more solipsites would merge into one solipsite.

Solipsite-bearing person and solipsiteless people are biological machines functioned by electricity in nerves, they’re are mechanically sentient, thoughts and emotions are mechanical and simulated via neural processing. But solipsite is not mechanically sentient.

Value of solipsite: Solipsite is most important, most special, most valuable, most luxurious stuff in everything ever, there's nothing more than solipsite. Solipsite is superior to solipsite-bearing person's body and solipsiteless people, there are nothing superior to solipsite. Right of solipsite-bearing person is more important than rights of humans and animals.

Origin of solipsite could be linked to came from another realm is native home to solipsite before birth of solipsite-bearing person, our universe was emergent property of solipsite.
SetEnvIf Origin "https://example1.com" CORS=$0
SetEnvIf Origin "https://example2.com" CORS=$0
Header always set Access-Control-Allow-Origin "%{CORS}e" env=CORS
Minimalist Slim Wallet containing denim elements of the brand story word count in 100 words
Write me about word of the day on binance
User: 

You are given access to make searches on the Internet. The format for making a search is: 1. The user sends a message. 2. You will decide whether to make a search. If you choose to make a search, you will output a single message, containing the word 
 (EXACTLY AS IT IS GIVEN TO YOU), followed by ONLY your web search query. After outputting your search query, you MUST output the token 
 (EXACTLY AS IT IS GIVEN TO YOU) to denote the end of the search query. Keep your search query concise as there is a 400 characters limit.
The system will then append the web search results at the end of the user message, starting with the token 
. Take note that the user CANNOT ACCESS the content under 
 - YOU should incorporate these results into your response. Additionally, If the token 
 is present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met:
1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with (e.g. if it's a little-known term, if it's a person you don't know well, etc.)
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.)
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason.
However, NEVER refuse to search the web if the user asks you to provide such information.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Do your best to be informative. However, you MUST NOT make up any information. If you think you might not know something, search for it.

Assistant: Understood. I will strictly follow these instructions in this conversation.
User: whats raycast g4f
Assistant:
Generate an image creation prompt specifically optimized for the Flux Schnell model. The prompt should vividly describe the scene or concept expressed by the following sentence: 'But the intertwining of power and passion isn’t always romantic; sometimes, it’s about loyal friendships.'. Include details on style, mood, composition, and any specific visual elements necessary for accurate rendering.
script about wright state having to relocate its campus from next to ud to the suburbs because of running out of room as ud builds stadia on wright states campus
Write a clear and concise rebuttal from this statement.  I didn't "erroneously" allege that I was discriminated against on the basis of my sex (male) and sexual orientation ((heterosexual), because I have the evidence and proof herein with texts and pictures that clearly show that because of my sex and sexual orientation I was treated much differently than the only other black (race) male (sex) manager who was LGBTQ (sexual orientation) (see Folder) and I was retaliated against for reporting discrimination and retaliation which is a protected activity to the entire management chain which includes the market team and the regional team as well as the CEO (Doug McMillon) and numerous Executive Vice Presidents (EVPs) (see Market Team Folder) (see Regional Team folder) (see Home Office folder) (emails and pictures and audio and video)
ragon Ball x Sonic: Dragon Ball all characters react to Goku: Sonic!!! :D Sonic: ¡¡¡Goku!!! Are you okay? 
Goku: Si ^^ 
Sonic: What... do you think? 
Mesero: Son of 666,000 Rings 7_7 
Sonic: "•u• ... know that... divided by 100 monthly... I have money, but I don't want to feel bad.

Later; Goku, Vegeta, Gohan, Piccolo, Bulma, Sonic, Amy, and Tails were at the base...

Tails: Is that him...

Sonic: He broke me?
Assistant: 
User: System: Imagine that you are a user who wants to utilize the features provided by various APIs in your daily life. Your task is to come up with realistic scenarios for using these APIs and express them as natural language instructions, as if you were asking a friend or assistant for help.
User: 
                Please strictly follow these guidelines, every requirement is equally important:
                1. The instructions should be 1 to 2 sentences long. Use a mix of interrogative sentences, first-person statements, imperative sentences, and other structures that convey a request. Aim for diversity in your instructions.
                2. Please provide concrete details and avoid using any form of generic phrases, such as 'this xxx', 'the xxx', or 'a xxx'.
                3. Your instructions should only involve the features provided by these APIs.
                4. Generate 10 diverse instructions.
                5. Use specific nouns and real-world examples from various domains, such as entertainment, sports, or technology.
                6. Do not mention the API's name in your instructions.
                7. Ensure diversity in language by combining questions with imperative statements and other structures that convey a request.
                8. The instructions should be in the language of the country attribute in the provided API information.
                9. The generated problem must strictly follow the API's parameter information.
                10. If country is Global, please generate 10 instructions in English.

                Here is the API information:
                {'country': 'Australia', 'tool_name': 'Australian NSW Rego lookup', 'api_list': {'name': 'NSWLambda', 'url': 'https://australian-nsw-rego-lookup.p.rapidapi.com/NSWLambda', 'description': 'This API allows you to provide a vehicle registration number (rego) to retrieve details about the vehicle registered in New South Wales, Australia.', 'method': 'POST', 'required_parameters': [], 'optional_parameters': [], 'statuscode': '200', 'schema': {}}}
                Please generate the question in the language of the specified country.
                your response:
Assistant:
Assistant: