Generate fake text using a language model from a given csv file

Here’s an example of how you can use a pre-trained language model to generate fake text that matches the words in the sample text column:

import csv
import torch
from transformers import AutoTokenizer, AutoModelWithLMHead

# Load the pre-trained language model and tokenizer
model_name = 'gpt2'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelWithLMHead.from_pretrained(model_name)

# Set the maximum length of the generated text
max_length = 100

# Read the input CSV file
with open('input_file.csv', 'r') as file:
    reader = csv.reader(file)
    header = next(reader)
    data = list(reader)

# Loop through the data rows and generate fake text that matches the words in the sample text column
for row in data:
    # Get the sample text from the input CSV file
    sample_text = row[1]
    # Split the sample text into words
    words = sample_text.split()
    # Generate fake text that matches the words in the sample text using the pre-trained language model
    input_ids = tokenizer.encode(sample_text, return_tensors='pt')
    output_ids = model.generate(input_ids=input_ids, max_length=max_length, do_sample=True)
    fake_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    # Replace the original text with the fake text
    row[1] = fake_text

# Write the augmented data to a new CSV file
with open('output_file.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(header)
    writer.writerows(data)

In this example, we load a pre-trained language model and tokenizer from the Hugging Face Transformers library, and use them to generate fake text that matches the words in the sample text column. We first tokenize the sample text using the tokenizer, and use the resulting input IDs to generate fake text using the language model’s generate() method. We set the max_length parameter to limit the length of the generated text, and set do_sample=True to enable random sampling during generation. Finally, we replace the original text with the fake text and write the augmented data to a new CSV file.

Note that this is just one example of how you can use language modeling or text generation with neural networks to generate fake text that matches the words in the sample text column. Depending on the task and the nature of the text data, you may want to use other pre-trained models or train your own models on custom data. You may also want to experiment with different hyperparameters and sampling strategies to improve the quality and diversity of the generated text.