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process_reviews_subset.py
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process_reviews_subset.py
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import pandas as pd
import re
from langdetect import detect, LangDetectException
from transformers import MarianMTModel, MarianTokenizer
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import nltk
# Download stopwords if not present
nltk.download('stopwords')
# Load translation model
model_name = 'Helsinki-NLP/opus-mt-mul-en'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Preprocess text
stop_words = set(stopwords.words('english'))
def preprocess(text):
text = text.lower()
text = re.sub(r'\b\w{1,2}\b', '', text) # remove short words
text = re.sub(r'\d+', '', text) # remove numbers
text = re.sub(r'[^\w\s]', '', text) # remove punctuation
words = word_tokenize(text)
words = [word for word in words if word not in stop_words]
return ' '.join(words)
def translate_text(text, model, tokenizer):
translated = model.generate(**tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True))
return tokenizer.decode(translated[0], skip_special_tokens=True)
def safe_detect(text):
if not text or not any(c.isalpha() for c in text):
return 'en'
try:
return detect(text)
except LangDetectException:
return 'en'
def truncate_text(text, max_length=512):
tokens = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
truncated_text = tokenizer.decode(tokens['input_ids'][0][:max_length], skip_special_tokens=True)
return truncated_text
def process_reviews_subset(start_index, end_index, input_feather, output_feather):
# Load the subset of data
df = pd.read_feather(input_feather)
subset_df = df.iloc[start_index:end_index].copy()
# Process and translate the text
subset_df['cleaned_text'] = subset_df['review_text'].apply(lambda x: translate_text(truncate_text(x), model, tokenizer) if safe_detect(x) != 'en' else truncate_text(x))
subset_df['cleaned_text'] = subset_df['cleaned_text'].apply(preprocess)
# Remove rows with empty cleaned text
subset_df = subset_df[subset_df['cleaned_text'] != '']
# Save the processed subset
subset_df.to_feather(output_feather)
if __name__ == "__main__":
import sys
start_index = int(sys.argv[1])
end_index = int(sys.argv[2])
input_feather = sys.argv[3]
output_feather = sys.argv[4]
process_reviews_subset(start_index, end_index, input_feather, output_feather)