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!pip install pyLDAvis

import nltk
nltk.download('stopwords')
nltk.download('punkt')

import os
import re
import nltk
import string

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# !pip install -U spacy
# !python -m spacy download en_core_sm

# Importing the libraries
import spacy
import random

# Creating a dictionary for training data
train_data = [
    ("The pizza in Naples it the best", {'entities' : [(4, 8, 'FOOD'), (13, 18, 'LOC')]}),

pranavraikote

import numpy as np
import pandas as pd

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Dense, Input

from transformers import BertTokenizer
from transformers import TFAutoModel

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import nltk
import string
import os, re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer

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import re
import nltk
import numpy as np
import unicodedata
import pandas as pd

nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')

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# Importing Libaries
!python -m spacy download en
!python -m spacy download en_vectors_web_lg

import spacy
nlp = spacy.load('en_vectors_web_lg')

import re
import nltk
import numpy as np

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import nltk
import string
import unicodedata

# For Tokenizer
nltk.download('punkt')

# For Lemmatizer
nltk.download('wordnet')