嵌入时出错：无法将字符串转换为浮点数：'ng'

Question

我正在使用 GloVe 方法处理预训练词向量。数据包含维基百科数据上的向量。嵌入数据时出现错误，指出无法将字符串转换为浮点数：'ng'

我尝试浏览数据，但找不到符号 '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' 是您文件中的一个词（标记），您正在尝试为其获取词向量。手套预训练向量可能没有导致错误的 'ng' 向量。因此，您需要检查该词在 Glove 嵌入中是否具有向量。有关如何执行此操作的示例，请参阅此 post 中标记为 'Create a weight matrix for words in training docs' 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浮点数：'ng'

针对上述问题，在函数中添加encoding='utf8'如下：

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

Answer 3

这似乎工作正常：

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()

嵌入时出错：无法将字符串转换为浮点数：'ng'

Error while embedding: could not convert string to float: 'ng'

python

nlp

machine-learning

glove