def main():
download_data()
df = get_df()
df['overall'] -= 1
preprocessing = Preprocessing()
for index in tqdm(range(len(df))):
review = df.loc[index, 'reviewText']
df.loc[index, 'reviewText'] = preprocessing.preprocess_text(review)
df.to_csv("./data/dataset2.csv")
def predict_single_review(review_text: str, preprocessing: Preprocessing,
tokenizer, model, device):
preprocessed_text = preprocessing.preprocess_text(review_text)
encoded_review = tokenizer.encode_plus(preprocessed_text,
max_length=TOKEN_MAX_LEN,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
verbose=False)
input_ids = encoded_review['input_ids'].to(device)
attention_mask = encoded_review['attention_mask'].to(device)
output = model(input_ids, attention_mask)
_, prediction = torch.max(output, dim=1)
print(f'Review text: {review_text}')
print(f'Processed review text: {preprocessed_text}')
print(f'Sentiment : {class_names[prediction]}')