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:<\/p>\n\n\n\n
import csv\r\nimport torch\r\nfrom transformers import AutoTokenizer, AutoModelWithLMHead\r\n\r\n# Load the pre-trained language model and tokenizer\r\nmodel_name = 'gpt2'\r\ntokenizer = AutoTokenizer.from_pretrained(model_name)\r\nmodel = AutoModelWithLMHead.from_pretrained(model_name)\r\n\r\n# Set the maximum length of the generated text\r\nmax_length = 100\r\n\r\n# Read the input CSV file\r\nwith open('input_file.csv', 'r') as file:\r\n reader = csv.reader(file)\r\n header = next(reader)\r\n data = list(reader)\r\n\r\n# Loop through the data rows and generate fake text that matches the words in the sample text column\r\nfor row in data:\r\n # Get the sample text from the input CSV file\r\n sample_text = row[1]\r\n # Split the sample text into words\r\n words = sample_text.split()\r\n # Generate fake text that matches the words in the sample text using the pre-trained language model\r\n input_ids = tokenizer.encode(sample_text, return_tensors='pt')\r\n output_ids = model.generate(input_ids=input_ids, max_length=max_length, do_sample=True)\r\n fake_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)\r\n # Replace the original text with the fake text\r\n row[1] = fake_text\r\n\r\n# Write the augmented data to a new CSV file\r\nwith open('output_file.csv', 'w', newline='') as file:\r\n writer = csv.writer(file)\r\n writer.writerow(header)\r\n writer.writerows(data)\r\n<\/code><\/pre>\n\n\n\nIn 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()<\/code> method. We set the max_length<\/code> parameter to limit the length of the generated text, and set do_sample=True<\/code> 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.<\/p>\n\n\n\nNote 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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: In this example, we load…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[100,72,101,91,64],"class_list":["post-120","post","type-post","status-publish","format-standard","hentry","category-tutorials","tag-csv","tag-data-augmentation","tag-fake-text","tag-gpt-2","tag-python"],"_links":{"self":[{"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/posts\/120","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=120"}],"version-history":[{"count":1,"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/posts\/120\/revisions"}],"predecessor-version":[{"id":121,"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/posts\/120\/revisions\/121"}],"wp:attachment":[{"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=120"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=120"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/smartsource.com.sg\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=120"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}