Huggingface Bert：输出打印

Question

我是编码新手，可以使用指导来了解为什么它会像现在这样奇怪地打印。虽然这与 NLP 有关，但我相信这个错误很可能是由比我更了解编码的人来解释的。我希望这是问这个问题的正确地方。感谢您的帮助！

from transformers import AutoTokenizer, AutoModelWithLMHead
import torch


tokenizer = AutoTokenizer.from_pretrained("bert-large-cased-whole-word-masking")

model = AutoModelWithLMHead.from_pretrained("bert-large-cased-whole-word-masking")

sentence = """While United States [MASK] heed human rights,"""


token_ids = tokenizer.encode(sentence, return_tensors='pt')
# print(token_ids)
token_ids_tk = tokenizer.tokenize(sentence, return_tensors='pt')
print(token_ids_tk)


masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
masked_pos = [mask.item() for mask in masked_position ]
print (masked_pos)


with torch.no_grad():
    output = model(token_ids)

last_hidden_state = output[0].squeeze()

print ("\n\n")
print ("sentence :", sentence)
print ("\n")
list_of_list =[]
for mask_index in masked_pos:
    mask_hidden_state = last_hidden_state[mask_index]
    idx = torch.topk(mask_hidden_state, k=25, dim=0)[1]
    words = [tokenizer.decode(i.item()).strip() for i in idx]
    list_of_list.append(words)
    print (words)

best_guess = ""
for j in list_of_list:
    best_guess = best_guess+" "+j[0]

print ("\nBest guess for fill in the blank :::",best_guess)

输出：

['While', 'United', 'States', '[MASK]', 'he', '##ed', 'human', 'rights', ',']

[4]

sentence : While United States [MASK] heed human rights,


['m u s t', 'c i t i z e n s', 's h o u l d', 'c a n n o t', 'l a w s', 'd o e s', 'g e n e r a l l y', 'd i d', 'a l w a y s', 'l a w', ',', 'g o v e r n m e n t', 'd o', 'p o l i t i c i a n s', 'm a y', 'd e f e n d e r s', 'c o u n t r i e s', 'c a n', 'o f f i c i a l s', 'g o v e r n m e n t s', 'w i l l', 'G o v e r n m e n t', 'v a l u e s', 'C o n s t i t u t i o n', 'p e o p l e']

Best guess for fill in the blank :::  m u s t

Answer 1

所以你首先要了解的是 BERT

给出的标记化输出

如果您查看输出，它已经被分隔开（我已经写了一些打印语句可以清楚地说明）

If you just want perfect output: change the lines where I have added comments

!pip3 install transformers
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch


tokenizer = AutoTokenizer.from_pretrained("bert-large-cased-whole-word-masking")

model = AutoModelWithLMHead.from_pretrained("bert-large-cased-whole-word-masking")

sentence = """While United States [MASK] heed human rights,"""


token_ids = tokenizer.encode(sentence, return_tensors='pt')
# print(token_ids)
token_ids_tk = tokenizer.tokenize(sentence, return_tensors='pt')
print(token_ids_tk)


masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
masked_pos = [mask.item() for mask in masked_position ]
print (masked_pos)


with torch.no_grad():
    output = model(token_ids)

last_hidden_state = output[0].squeeze()

print ("\n\n")
print ("sentence :", sentence)
print ("\n")
list_of_list =[]
for mask_index in masked_pos:
    mask_hidden_state = last_hidden_state[mask_index]
    idx = torch.topk(mask_hidden_state, k=25, dim=0)[1]
    for i in idx: print(i,tokenizer.decode(i.item()).strip()) 
    words = [tokenizer.decode(i.item()).strip().replace(" ","") for i in idx] ## REMOVING ANY SPACES ADDED WHILE COMBINING TOKENS
    list_of_list.append(words)
    print ("WORDS",words)

print('list_of_list',list_of_list)

best_guess = ""
for j in list_of_list:
    print('j',j)
    best_guess = (best_guess+" "+j[0]).strip() ## ADD THIS TO REMOVE TRAILING SPACES

print ("\nBest guess for fill in the blank :::",best_guess)

Huggingface Bert：输出打印

Huggingface Bert: Output Printing

python

nlp

torch

spacy-transformers

huggingface-transformers