Views
No views yet
1from transformers import AutoModelForMaskedLM, AutoTokenizer
2import torch
3
4model = AutoModelForMaskedLM.from_pretrained("caldaibis/modernbert-nl-test", torch_dtype=torch.bfloat16)
5tokenizer = AutoTokenizer.from_pretrained("caldaibis/modernbert-nl-test")
6
7text = "This is an example with [MASK] token."
8inputs = tokenizer(text, return_tensors="pt")
9outputs = model(**inputs)
10
11# Get predictions for masked token
12mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
13predicted_token_id = outputs.logits[0, mask_token_index].argmax(axis=-1)
14print(tokenizer.decode(predicted_token_id))