未在spacy中加载的预训练向量

Question

我正在使用spacy.black("en"(模型从头开始训练一个自定义的NER模型。我添加自定义单词矢量到它。矢量加载如下：

from gensim.models.word2vec import Word2Vec
from gensim.models import KeyedVectors
med_vec = KeyedVectors.load_word2vec_format('./wikipedia-pubmed-and-PMC-w2v.bin', binary=True, limit = 300000)

我把它添加到这个代码片段中的空白模型中：

def main(model=None, n_iter=3, output_dir=None):
"""Set up the pipeline and entity recognizer, and train the new entity."""
random.seed(0)
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en")  # create blank Language class
nlp.vocab.reset_vectors(width=200)
for idx in range(len(med_vec.index2word)):
word = med_vec.index2word[idx]
vector = med_vec.vectors[idx]
nlp.vocab.set_vector(word, vector)
for key, vector in nlp.vocab.vectors.items():
nlp.vocab.strings.add(nlp.vocab.strings[key])
nlp.vocab.vectors.name = 'spacy_pretrained_vectors'
print("Created blank 'en' model")
......Code for training the ner

然后我保存这个模型。

当我尝试加载模型时，nlp = spacy.load("./NDLA/vectorModel0")

我得到以下错误：

`~AppDataLocalContinuumanaconda3libsite-packagesthincneural_classesstatic_vectors.py in __init__(self, lang, nO, drop_factor, column)
47         if self.nM == 0:
48             raise ValueError(
---> 49                 "Cannot create vectors table with dimension 0.n"
50                 "If you're using pre-trained vectors, are the vectors loaded?"
51             )
ValueError: Cannot create vectors table with dimension 0.
If you're using pre-trained vectors, are the vectors loaded?

我也收到这个警告：

UserWarning: [W019] Changing vectors name from spacy_pretrained_vectors to spacy_pretrained_vectors_336876, to avoid clash with previously loaded vectors. See Issue #3853.
"__main__", mod_spec)

模型中的vocab目录有一个大小为270MB的矢量文件。所以我知道它不是空的。。。导致此错误的原因是什么？

Answer 1

您可以尝试一次传递所有向量，而不是使用for循环。

nlp.vocab.vectors = spacy.vocab.Vectors(data=med_vec.syn0, keys=med_vec.vocab.keys())

所以你的其他声明会变成这样：

else:
nlp = spacy.blank("en")  # create blank Language class
nlp.vocab.reset_vectors(width=200)
nlp.vocab.vectors = spacy.vocab.Vectors(data=med_vec.syn0, keys=med_vec.vocab.keys()) 
nlp.vocab.vectors.name = 'spacy_pretrained_vectors'
print("Created blank 'en' model")