Question

我正在使用GloVe方法研究预训练的单词向量。数据包含维基百科数据上的向量。嵌入数据时，我收到错误消息，指出无法将字符串转换为浮点数：“ ng”

我尝试浏览数据，但是找不到符号'ng'

# load embedding as a dict
def load_embedding(filename):
    # load embedding into memory, skip first line
    file = open(filename,'r', errors = 'ignore')
    # create a map of words to vectors
    embedding = dict()
    for line in file:
        parts = line.split()
        # key is string word, value is numpy array for vector
        embedding[parts[0]] = np.array(parts[1:], dtype='float32')
    file.close()
    return embedding

这是错误报告。请进一步指导我。

runfile('C:/Users/AKSHAY/Desktop/NLP/Pre-trained GloVe.py', wdir='C:/Users/AKSHAY/Desktop/NLP')
C:\Users\AKSHAY\AppData\Local\conda\conda\envs\py355\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Traceback (most recent call last):

  File "<ipython-input-1-d91aa5ebf9f8>", line 1, in <module>
    runfile('C:/Users/AKSHAY/Desktop/NLP/Pre-trained GloVe.py', wdir='C:/Users/AKSHAY/Desktop/NLP')

  File "C:\Users\AKSHAY\AppData\Local\conda\conda\envs\py355\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "C:\Users\AKSHAY\AppData\Local\conda\conda\envs\py355\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/AKSHAY/Desktop/NLP/Pre-trained GloVe.py", line 123, in <module>
    raw_embedding = load_embedding('glove.6B.50d.txt')

  File "C:/Users/AKSHAY/Desktop/NLP/Pre-trained GloVe.py", line 67, in load_embedding
    embedding[parts[0]] = np.array(parts[1:], dtype='float32')

ValueError: could not convert string to float: 'ng'

Answer 1

看起来“ ng”是您要获取其单词向量的文件中的单词（令牌）。手套预训练向量可能没有用于导致错误的“ ng”向量。因此，您需要检查单词在手套嵌入中是否具有向量。有关如何执行此操作的示例，请参见本文中标记为“为训练文档中的单词创建权重矩阵”的部分-Text Classification Using CNN, LSTM and Pre-trained Glove Word Embeddings: Part-3

Answer 2

ValueError：无法将字符串转换为float：'ng'

要解决以上问题，请在函数中添加 encoding ='utf8'：

file = open(filename,'r', errors = 'ignore', encoding='utf8')

Answer 3

这似乎很好：

embedding_model = {}
f = open(r'dataset/glove.840B.300d.txt', encoding="utf8", "r")
for line in f:
    values = line.split()
    word = ''.join(values[:-300])
    coefs = np.asarray(values[-300:], dtype='float32')
    embedding_model[word] = coefs
f.close()

嵌入时发生错误：无法将字符串转换为浮点数：“ ng”

3 个答案: