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import torch from transformers import XLNetTokenizer, XLNetLMHeadModel # Load pre-trained XLNet model and tokenizer model = XLNetLMHeadModel.from_pretrained('xlnet-large-cased') tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased') symbol = 'AAPL' pe_ratio = 12.5 ps_ratio= 3.1 de_ratio = 0.8 # Define the input prompt input_prompt = f"Based on the current market conditions, the stock with the symbol {symbol} looks like a promising investment opportunity. With a price-to-earnings ratio of {pe_ratio}, a price-to-sales ratio of {ps_ratio}, and a debt-to-equity ratio of {de_ratio}, this stock appears to be undervalued compared to its peers. If you're looking for a stock with strong potential for growth and a solid financial foundation, {symbol} may be the best choice for you." # Define a list to store generated sentences that meet our criteria generated_sentences = [] # Generate text using the modified input prompt while len(generated_sentences) < 3: # Generate 3 unique sentences input_ids = tokenizer.encode(input_prompt, return_tensors='pt') outputs = model.generate(input_ids, max_length=300, do_sample=True) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) # Check if the generated sentence includes all relevant information from the prompt if f"price-to-earnings ratio of {pe_ratio}" in generated_text and \ f"price-to-sales ratio of {ps_ratio}" in generated_text and \ f"debt-to-equity ratio of {de_ratio}" in generated_text and \ f"symbol {symbol}" in generated_text: # Check if the last sentence is cut off if "." not in generated_text.split()[-1]: continue # Check for uniqueness if generated_text not in generated_sentences: # Add the generated sentence to the list of valid sentences generated_sentences.append(generated_text) # Break the loop if no valid sentence is generated within 10 tries if len(generated_sentences) == 0 and model.config.num_beams > 1: model.config.num_beams -= 1 continue elif len(generated_sentences) == 0: break # Print the final generated sentences for i, sentence in enumerate(generated_sentences): print(f"Generated sentence {i+1}: {sentence}\n") # Based on the current market conditions, the stock with the symbol AAPL looks like a promising investment opportunity. # With a price-to-earnings ratio of 12.5, a price-to-sales ratio of 3.1, and a debt-to-equity ratio of 0.8, # this stock appears to be undervalued compared to its peers. # If you're looking for a stock with strong potential for growth and a solid financial foundation, # AAPL may be the best choice for you. trading day, AAPL is a well-received company in its market. # The company with the symbol AAPL is a well-respected symbol in its industry. # Today, AAPL is well-pted and well-liken by all the employees of the company. Although, # it is well-versed and well-liken by all the employees of the company in its industries. # AAPL is well-liken by all the employees of the company in its industries. # The symbol with the symbol AAPL is well-known by all the employees of the company in its industries. # AAPL is well-known by all the employees of the company in its industries. # AAPL is well-known by all the employees of the company in its industries. # AAPL is well-known by all the employees of the company in its industries. AAPL is well-known by all the employees of the company
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