Spaces:
Runtime error
Runtime error
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| import torch | |
| # Load the model and tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("jfernandez/cebfil-roberta") | |
| model = AutoModelForMaskedLM.from_pretrained("jfernandez/cebfil-roberta") | |
| # Define a function to generate responses | |
| def generate_response(text): | |
| # Add a mask token at the end of the text | |
| text = text + " <mask>" | |
| # Tokenize the text and get the input ids | |
| inputs = tokenizer(text, return_tensors="pt") | |
| input_ids = inputs["input_ids"] | |
| # Get the logits from the model | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| # Get the most likely token id for the mask | |
| mask_token_id = tokenizer.mask_token_id | |
| mask_token_index = torch.where(input_ids == mask_token_id)[1] | |
| token_logits = logits[0, mask_token_index, :] | |
| top_5_tokens = torch.topk(token_logits.squeeze(), k=5).indices # get top 5 tokens | |
| predicted_tokens = tokenizer.convert_ids_to_tokens(top_5_tokens.tolist()) # convert ids to tokens | |
| # Choose one of the predicted tokens randomly and replace the mask with it | |
| import random | |
| response_token = random.choice(predicted_tokens) | |
| response_text = text.replace("<mask>", response_token) | |
| return response_text | |
| # Test the function with some examples | |
| print(generate_response("Komosta ka")) | |
| print(generate_response("Unsa imong pangalan")) | |
| print(generate_response("Salamat sa")) |