Bert use

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Bert

One 、 Code

import torch
from transformers import  BertModel,BertTokenizer
import  torch.nn as nn

sentence = 'i like eating apples very much'

class Model (nn.Module):
    def __init__(self):
        super().__init__()
        self.embeder = BertModel.from_pretrained('bert-base-cased',output_hidden_states = True)
        self.tokenizer = BertTokenizer.from_pretrained('bert-base-cased')

    def forward(self,inputs):
        tokens = self.tokenizer.tokenize(inputs)
        print(tokens)
        tokens_id = self.tokenizer.convert_tokens_to_ids(tokens)
        print(tokens_id)
        token_id_tensor = torch.tensor(tokens_id).unsqueeze(0)
        outputs = self.embeder(token_id_tensor)
        print(outputs[0])

model = Model()
results = model(sentence)

Two 、 Realization effect