当我尝试使用 bio_clinical bert 获取句子的词嵌入时，对于 8 个单词的句子，我得到 11 个标记 id（+start 和 end），因为“embeddings”是词汇表外的单词/标记，它被拆分为em，床，丁，s。

我想知道除了对这些向量进行平均之外，是否有任何可用的聚合策略有意义。

from transformers import AutoTokenizer, AutoModel
# download and load model
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")

sentences = ['This framework generates embeddings for each input sentence']


#Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')


#Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

print(encoded_input['input_ids'].shape)

输出：torch.Size([1, 13])

for token in encoded_input['input_ids'][0]:
  print(tokenizer.decode([token]))

输出：

[CLS]
this
framework
generates
em
##bed
##ding
##s
for
each
input
sentence
[SEP]