After output of model? Get meaning full information
I am trying to get meaning full or text output with the model without using pipeline inference.Considering the tutorial of the “behind the pipeline”, gives a classification example.Link -TutorialBut I am trying to use MASK flling model.Code:from transformers import BertTokenizer, BertForMaskedLM

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

model = BertForMaskedLM.from_pretrained("bert-base-uncased")

text = "Replace me by [MASK] text you'd like."

encoded_input = tokenizer(text, return_tensors='pt')

output = model(input_ids=encoded_input['input_ids'], attention_mask=encoded_input['attention_mask'])After that how can I get meaningful details?output give following detailsMaskedLMOutput(loss=None, logits=tensor([[[ -6.9080,  -6.8627,  -6.8496,  …,  -6.1739,  -6.0125,  -4.3861],[-12.3396, -12.1704, -12.1169,  …, -11.8522, -10.8242, -12.1897],[-12.1175, -12.0443, -12.0125,  …, -10.6335, -11.2715, -10.5266],…,[ -9.8642,  -9.8000,  -9.8748,  …,  -9.4508, -10.7953,  -8.7879],[-11.9032, -11.5505, -11.9132,  …,  -9.1630, -11.2406,  -6.3046],[-12.4441, -13.5939, -12.4144,  …, -12.4859, -11.4275,  -9.7487]]],grad_fn=), hidden_states=None, attentions=None)According to tutorial,import torch
predictions = torch.nn.functional.softmax(output.logits, dim=-1)
print(predictions)Output:tensor([[[3.9761e-08, 4.2633e-08, 4.8522e-08,  …, 1.3461e-07,9.0488e-08, 1.2941e-06],[1.0643e-08, 1.1875e-08, 1.1567e-08,  …, 2.5163e-08,3.4508e-08, 1.4164e-07],[7.9932e-13, 7.9141e-13, 9.5979e-13,  …, 3.7303e-12,1.3634e-12, 1.2097e-11],…,[1.0588e-12, 1.3337e-12, 9.7095e-13,  …, 6.9976e-13,3.8466e-13, 2.0040e-11],[3.9985e-16, 4.6984e-16, 3.5347e-16,  …, 3.6423e-15,4.9108e-16, 8.5845e-14],[2.6691e-11, 9.6144e-12, 2.3595e-11,  …, 5.1817e-11,3.7737e-11, 5.8767e-10]]], grad_fn=)This not give classification output like a tutorial but how to get text meaningful output?Considering tutorial it give{0: 'LABEL_0', 1: 'LABEL_1'}formodel.config.id2label