saimadhu-polamuri

import spacy

nlp = spacy.load('en_core_web_sm')

# Define the text to be processed
text = "This is a sample sentence! It contains some punctuation marks."

# Create a Doc object
doc = nlp(text)

saimadhu-polamuri

def generate_text(seed_text, num_words):
    for _ in range(num_words):
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = tf.keras.preprocessing.sequence.pad_sequences([token_list], maxlen=max_length)
        predicted_index = model.predict(token_list)[0].argmax()
        predicted_word = tokenizer.index_word[predicted_index]
        seed_text += " " + predicted_word
    return seed_text

# Generate a story

saimadhu-polamuri

# Tokenize text and create word-to-index mapping
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(data["preprocessed_text"])
sequences = tokenizer.texts_to_sequences(data["preprocessed_text"])
padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length)

# Create training data
X_train, X_val, y_train, y_val = train_test_split(padded_sequences, padded_sequences, test_size=0.2, random_state=42)

# Train the model

saimadhu-polamuri

import tensorflow as tf
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from tensorflow.keras.models import Sequential  


# Define model architecture
model = Sequential([
    Embedding(input_dim=vocab_size, output_dim=embedding_dim),
    LSTM(units=256, return_sequences=True),
    Dropout(0.2),

saimadhu-polamuri

# Preprocess text 
def preprocess_text(text): 
# Remove stop words stop_words = set(stopwords.words("english")) 
words = word_tokenize(text.lower()) 
filtered_words = [word for word in words if word not in stop_words] 
return " ".join(filtered_words)
data["preprocessed_text"] = data["text"].apply(preprocess_text)

saimadhu-polamuri

# Load data (assuming a CSV file) 
data = pd.read_csv("stories.csv")

saimadhu-polamuri

import pandas as pd 
import nltk
import tensorflow as tf

saimadhu-polamuri

saimadhu-polamuri

saimadhu-polamuri

import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# load the Boston housing dataset
boston = load_boston()
X, y = boston.data, boston.target

# split the data into training and testing sets