from transformers import pipeline unmasker = pipeline('fill-mask', model='bert-base-uncased') unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1073106899857521, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.08774490654468536, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a new model. [SEP]", 'score': 0.05338378623127937, 'token': 2047, 'token_str': 'new'}, {'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.04667217284440994, 'token': 3565, 'token_str': 'super'}, {'sequence': "[CLS] hello i'm a fine model. [SEP]", 'score': 0.027095865458250046, 'token': 2986, 'token_str': 'fine'}]
from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input)