关于GPT2Tokenizer的一些发现_add_prefix_space

 

在使用transformers里的GPT2Tokenizer时，看到一句话：

GPT-2 BPE tokenizer. Peculiarities:

• Byte-level Byte-Pair-Encoding

• Requires a space to start the input string => the encoding methods should be called with the add_prefix_space flag set to True. Otherwise, this tokenizer encode and decode method will not conserve the absence of a space at the beginning of a string:

tokenizer.decode(tokenizer.encode("Hello")) = " Hello"

当时没有很理解这句话，一次查看其字典时，发现：

"negative": 31591,

"Ġnegative": 4633,

这是两个一样的单词，但是一个前边有Ġ，当时猜测类似后缀。

实际上当negative前有空格时，编码为4633，无空格时编码为31591，如Y negative中negative编码为4633，而Ynegative中negative编码为4633。可以认为不带Ġ的是一个后缀，而带Ġ的表示以该词开始的单词。

而add_prefix_space是说，如果编码时，不加这个，默认该字符前没有空格，实际是不妥的，如一句话"Attention is all you need",中第一个单词应该表示开头，但是如果直接

tokenizer.encode("Attention is all you need"),则Attention将被认为是一个后缀。所以应该加上add_prefix_space=True。

实验验证:

方法1

每一次都如:

tokenizer.encode("negative", add_prefix_space=True)

import warnings
warnings.filterwarnings("ignore")
 
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import tokenizers
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.save_pretrained('./config')
text = "I love you"
print(text)
print(tokenizer.decode(tokenizer.encode(text)))  # I love you (I 前无空格)
print(tokenizer.all_special_tokens)  # ['<|endoftext|>']
 
'''
推测 若前边有空格则为 "Ġnegative": 4633, 否则为[31591]  注 字典为"negative": 31591,
'''
print(tokenizer.encode("negative"))  # [31591]  注 字典为"negative": 31591,
print(tokenizer.encode("negativeY"))  # [31591, 56]  注 字典为"negative": 31591,
print(tokenizer.encode(" negative"))  # [4633]  注 "Ġnegative": 4633,
print(tokenizer.encode("you negative"))  # [5832, 4633]
print(tokenizer.encode("Knegative"))  # [42, 31591]
 
special_tokens_dict = {'cls_token': '<CLS>', 'bos_token': '<s>'}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')  # We have added 2 tokens
print(tokenizer.encode("negative", add_special_tokens=True))  # [31591]  注 字典为"negative": 31591,
print(tokenizer.encode("negative", add_prefix_space=True))  # [4633]
print(tokenizer.encode("<s> negative", add_special_tokens=True))  # [50258, 4633]  注 字典为"negative": 31591,
print(tokenizer.encode("<s> negative", add_special_tokens=True, add_prefix_space=True))  # [50258, 4633]  注 字典为"negative": 31591,

方法2

利用 tokenizers.ByteLevelBPETokenizer

如:

import warnings
warnings.filterwarnings("ignore")
 
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import tokenizers
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.save_pretrained('./config')
text = "I love you"
 
PATH = './config/'
tokenizer = tokenizers.ByteLevelBPETokenizer(
    vocab_file=PATH + 'vocab.json',
    merges_file=PATH + 'merges.txt',
    lowercase=False,
    add_prefix_space=True
)
print(text)
print(tokenizer.decode(tokenizer.encode(text).ids))  # I love you (I 前有空格)
 
print(tokenizer.encode("negative").ids)  # [4633]  注 "Ġnegative": 4633,
print(tokenizer.encode(" negative").ids)  # [4633]  注 "Ġnegative": 4633,
print(tokenizer.encode("  negative").ids)  # [220, 4633] 注: "Ġ": 220,
print(tokenizer.encode("you negative").ids)  # [345, 4633]
print(tokenizer.encode("Knegative").ids)  # [509, 31591]