kshirsagarsiddharth

Comparison of Ollama, Hugging Face Platform, and OpenAI API

Feature	Ollama	Hugging Face Platform	OpenAI API
Execution	Local execution on user machines	Cloud-based and local execution options	Cloud-based execution on remote servers
Privacy & Security	Data remains on the user's device, enhancing privacy and security	Data can be processed locally or on remote servers	Robust security measures to protect user data during API interactions
Compatibility	Supports most open-source LLMs, experimental compatibility with OpenAI	Supports a wide range of models, datasets, and applications	Access to advanced models like GPT-4

kshirsagarsiddharth

Login email;Identifier;First name;Last name
[email protected];2070;Laura;Grey
[email protected];4081;Craig;Johnson
[email protected];9346;Mary;Jenkins
[email protected];5079;Jamie;Smith

kshirsagarsiddharth

import spacy

# Load the English model
nlp = spacy.load("en_core_web_sm")

def clean_social_media_data(text):
    # Process the text
    doc = nlp(text)

    # Extract the lemmas and remove stop words

kshirsagarsiddharth

import re

def clean_social_media_data(text):
    # Remove hashtags and mentions
    text = re.sub(r'#\w+', '', text)
    text = re.sub(r'@\w+', '', text)

    # Remove emojis
    text = re.sub(r'[^\x00-\x7F]+', '', text)

kshirsagarsiddharth

import spacy

# Load the English model
nlp = spacy.load("en_core_web_sm")

def lemmatize_text(text):
    # Process the text
    doc = nlp(text)

    # Extract the lemmas

kshirsagarsiddharth

import nltk
from nltk.stem import WordNetLemmatizer

# Initialize the lemmatizer
lemmatizer = WordNetLemmatizer()

def lemmatize_text(text):
    # Tokenize the text
    tokens = nltk.word_tokenize(text)

kshirsagarsiddharth

import nltk
from nltk.corpus import stopwords

# Load the stop words
stop_words = set(stopwords.words('english'))

def remove_stop_words(text):
    # Tokenize the text
    tokens = nltk.word_tokenize(text)

kshirsagarsiddharth

def remove_stop_words(text):
    # Split the text into words
    words = text.split()

    # Define the stop words
    stop_words = ['a', 'an', 'and', 'the', 'in', 'of']

    # Remove stop words
    clean_words = [word for word in words if word not in stop_words]

kshirsagarsiddharth

import re

def clean_text(text):
    # Use a regular expression to remove punctuation and special characters
    clean_text = re.sub(r'[^\w\s]', '', text)

    # Remove leading and trailing whitespace
    clean_text = clean_text.strip()

    return clean_text

kshirsagarsiddharth

import string


def clean_text(text):
    # Create a translation table to remove punctuation and special characters we are replacing space.
    translator = str.maketrans('', '', string.punctuation + string.printable.replace(' ','')[62:])

    # Use the translate method to remove the characters
    clean_text = text.translate(translator)