Joelfranklin96

from transformers import BertTokenizer, BertForTokenClassification, Trainer, TrainingArguments
from datasets import load_dataset

# Load pre-trained BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize the text and align the labels
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples['tokens'], truncation=True, is_split_into_words=True, padding='max_length')
    labels = []

Joelfranklin96

{
	"public_identifier": "eden-marco",
	"profile_pic_url": "https://s3.us-west-000.backblazeb2.com/proxycurl/person/eden-marco/profile?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=0004d7f56a0400b0000000001%2F20230513%2Fus-west-000%2Fs3%2Faws4_request&X-Amz-Date=20230513T080203Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=864f0b1147bf2957e077b949cb3d81b0fa5503317519b8444e2ec4960c744f1c",
	"background_cover_image_url": null,
	"first_name": "Eden",
	"last_name": "Marco",
	"full_name": "Eden Marco",
	"follower_count": null,
	"occupation": "Customer Engineer at Google",
	"headline": "Customer Engineer @ Google Cloud | Best-selling Udemy Instructor",

Joelfranklin96

# Defining the objects

guassian_one = guassian(3,7)
guassian_two = guassian(4,8)

# __add__ method

guassian_three = guassian_one + guassian_two

# Printing the mean and standard deviation

Joelfranklin96

    def __add__(self,other):
        result = guassian()
        result.mean = self.mean + other.mean
        result.stdev = math.sqrt((self.stdev)**2 + (other.stdev)**2)
        return result
    
    def __repr__(self):
        return "mean {}, standard deviation {}".format(self.mean, self.stdev)

Joelfranklin96

# Defining an object of guassian class

guassian_two = guassian()

# Reading the data from text file

guassian_two.read_text('sample.txt')

# Calculating the mean and standard deviation

Joelfranklin96

# Defining an object of guassian class with 'mean' = 3 and 'standard deviation' = 5

guassian_one = guassian(3,5)

# Prints the mean and standard deviation

print(guassian_one.mean)
print(guassian_one.stdev)

Joelfranklin96

# Defining the Class guassian

class guassian():
    
    def __init__(self,mean=0,stdev=0):
        self.mean = mean
        self.stdev = stdev
    
    # If dataset is passed as parameter rather than the 'mean' and 'stdev',
    # we first read the data and then find the 'mean' and 'stdev'.

Joelfranklin96

# Defining the class 'shirts'

class shirts:
    
    # Initializing the attributes of the class
    
    def __init__(self,color,size,price):
        self.color = color
        self.size = size
        self.price = price

Joelfranklin96

# Creating a list of model names

model_names = ['K Nearest Neighbors','Neural Net','Decision Tree','Random Forest','AdaBoost','Naive Bayes','SVM Linear','SVM rbf','SVM Sigmoid']

# Cross validation scores of the models

for i in range(len(models)):
  print('The cross validation score of {} is {}'.format(model_names[i],cv_result[i]))

Joelfranklin96

# Defining the models

models = [KNeighborsClassifier(n_neighbors = 3),
          MLPClassifier(alpha = 1),
          DecisionTreeClassifier(max_depth = 5),
          RandomForestClassifier(max_depth=5,n_estimators=10,max_features=1),
          AdaBoostClassifier(),
          GaussianNB(),
          SVC(kernel = 'linear'),
          SVC(kernel = 'rbf'),