我尝试过用几种方法来处理这个问题。我还研究了 load_word2vec_format 方法本身，并尝试忽略错误，但似乎没有什么区别。我在第二行尝试了 map 方法，并遵循了以下链接中的建议组合：[a]和[b]。这并没有修复或更改收到的错误消息（即删除它不会对文本产生任何影响）。
gloveFile = pd.read_fwf("https://storage.googleapis.com/kagglesdsdata/datasets/652874/1154868/glove.6B.50d.txt?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1589683535&Signature=kaS%2FTkSmvp7lhqwLJ%2B1lyuvP76PcDpwK1dnsCZEO0AiVXqQm7jsBc1r5g9af%2BuVkOSvMgqUDXYL4O%2BN43pnL5RLs7ns%2B3w%2BEtCYDTfJz6q1O0zfPz4%2BTcD3GV7UAGgVjVNIvncC9fHWcd2YuKwiZaTvKL%2BGRnMkf9b%2BYnOweYeXEeA1sX005krj%2FLMBbVTXmDTwOtN4HwVNb3%2BrbezkWkoEC6sxLPnGcsEKaBe%2Biv%2FuVSQG5FsQlwvRgsSU%2FMgk0c4bi%2FHxF04lrQW0E0s767TIXwHeodRHYpk5KQeKmyd91uKD2Zb8v8xQcf2%2BkmSNGQHbX0mDz8HBwYEmOdV7aMQ%3D%3D&response-content-disposition=attachment%3B+filename%3Dglove.6B.50d.txt", delimiter="\n\t\s+", header=None) map(lambda gloveFile: gloveFile.replace(r'[^\x00-\x7F]+' , '-'), gloveFile[0]) numpy.savetxt(r'/usr/local/lib/python3.6/dist-packages/gensim/test/test_data/glove6b50d.txt', gloveFile.values, fmt="%s") from gensim.models import KeyedVectors from gensim.test.utils import datapath, get_tmpfile from gensim.scripts.glove2word2vec import glove2word2vec glove_file = datapath('glove6b50d.txt') glove2word2vec(glove_file, "glove6b50d_word2vec.txt") model = KeyedVectors.load_word2vec_format("glove6b50d_word2vec.txt", binary=False)
根据以下评论，我收到的确切错误如下：
/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:253: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function 'See the migration notes for details: %s' % _MIGRATION_NOTES_URL --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-132-6ad5a51f4fb3> in <module>() 9 glove2word2vec(glove_file, "glove6b50d_word2vec.txt") 10 ---> 11 model = KeyedVectors.load_word2vec_format("glove6b50d_word2vec.txt", binary=False) 12 2 frames /usr/local/lib/python3.6/dist-packages/gensim/models/utils_any2vec.py in <listcomp>(.0) 220 if len(parts) != vector_size + 1: 221 raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) --> 222 word, weights = parts[0], [datatype(x) for x in parts[1:]] 223 add_word(word, weights) 224 if result.vectors.shape[0] != len(result.vocab): ValueError: could not convert string to float: '-'
系统使用仅包含以下内容的文本文件时工作正常：“test -1.0 1.526 -2.55”或“- -1.0 1.526 -2.55”。此外，在源文本文件 (glove.6B.50d.txt) 中搜索“ - ”的出现次数，结果为零。我使用的是 Windows，因此我通过执行以下命令执行此操作：
findstr /C:" - " glove.6B.50d.txt
调用print(gloveFile)映射前和映射后调用均提供以下输出。请注意，我保留映射调用是为了完成我的工作，而不是为了它的效果。
print(gloveFile)
0 the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.0... 1 , 0.013441 0.23682 -0.16899 0.40951 0.63812 0.... 2 . 0.15164 0.30177 -0.16763 0.17684 0.31719 0.3... 3 of 0.70853 0.57088 -0.4716 0.18048 0.54449 0.7... 4 to 0.68047 -0.039263 0.30186 -0.17792 0.42962 ... ... ... 399995 chanty 0.23204 0.025672 -0.70699 -0.045465 0.1... 399996 kronik -0.60921 -0.67218 0.23521 -0.11195 -0.4... 399997 rolonda -0.51181 0.058706 1.0913 -0.55163 -0.1... 399998 zsombor -0.75898 -0.47426 0.4737 0.7725 -0.780... 399999 andberger 0.072617 -0.51393 0.4728 -0.52202 -0...
如果我打印文件的前十行，我会得到以下文本，它符合 word2vec 格式。此外，如果我计算文档中glove6b50d_word2vec.txt该字符串的出现次数，则不会发现任何异常。" - "
glove6b50d_word2vec.txt
" - "
['400000 50\n', 'the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 0.0027849 -0.18411 -0.11514 -0.78581\n', ', 0.013441 0.23682 -0.16899 0.40951 0.63812 0.47709 -0.42852 -0.55641 -0.364 -0.23938 0.13001 -0.063734 -0.39575 -0.48162 0.23291 0.090201 -0.13324 0.078639 -0.41634 -0.15428 0.10068 0.48891 0.31226 -0.1252 -0.037512 -1.5179 0.12612 -0.02442 -0.042961 -0.28351 3.5416 -0.11956 -0.014533 -0.1499 0.21864 -0.33412 -0.13872 0.31806 0.70358 0.44858 -0.080262 0.63003 0.32111 -0.46765 0.22786 0.36034 -0.37818 -0.56657 0.044691 0.30392\n', '. 0.15164 0.30177 -0.16763 0.17684 0.31719 0.33973 -0.43478 -0.31086 -0.44999 -0.29486 0.16608 0.11963 -0.41328 -0.42353 0.59868 0.28825 -0.11547 -0.041848 -0.67989 -0.25063 0.18472 0.086876 0.46582 0.015035 0.043474 -1.4671 -0.30384 -0.023441 0.30589 -0.21785 3.746 0.0042284 -0.18436 -0.46209 0.098329 -0.11907 0.23919 0.1161 0.41705 0.056763 -6.3681e-05 0.068987 0.087939 -0.10285 -0.13931 0.22314 -0.080803 -0.35652 0.016413 0.10216\n', 'of 0.70853 0.57088 -0.4716 0.18048 0.54449 0.72603 0.18157 -0.52393 0.10381 -0.17566 0.078852 -0.36216 -0.11829 -0.83336 0.11917 -0.16605 0.061555 -0.012719 -0.56623 0.013616 0.22851 -0.14396 -0.067549 -0.38157 -0.23698 -1.7037 -0.86692 -0.26704 -0.2589 0.1767 3.8676 -0.1613 -0.13273 -0.68881 0.18444 0.0052464 -0.33874 -0.078956 0.24185 0.36576 -0.34727 0.28483 0.075693 -0.062178 -0.38988 0.22902 -0.21617 -0.22562 -0.093918 -0.80375\n', 'to 0.68047 -0.039263 0.30186 -0.17792 0.42962 0.032246 -0.41376 0.13228 -0.29847 -0.085253 0.17118 0.22419 -0.10046 -0.43653 0.33418 0.67846 0.057204 -0.34448 -0.42785 -0.43275 0.55963 0.10032 0.18677 -0.26854 0.037334 -2.0932 0.22171 -0.39868 0.20912 -0.55725 3.8826 0.47466 -0.95658 -0.37788 0.20869 -0.32752 0.12751 0.088359 0.16351 -0.21634 -0.094375 0.018324 0.21048 -0.03088 -0.19722 0.082279 -0.09434 -0.073297 -0.064699 -0.26044\n', 'and 0.26818 0.14346 -0.27877 0.016257 0.11384 0.69923 -0.51332 -0.47368 -0.33075 -0.13834 0.2702 0.30938 -0.45012 -0.4127 -0.09932 0.038085 0.029749 0.10076 -0.25058 -0.51818 0.34558 0.44922 0.48791 -0.080866 -0.10121 -1.3777 -0.10866 -0.23201 0.012839 -0.46508 3.8463 0.31362 0.13643 -0.52244 0.3302 0.33707 -0.35601 0.32431 0.12041 0.3512 -0.069043 0.36885 0.25168 -0.24517 0.25381 0.1367 -0.31178 -0.6321 -0.25028 -0.38097\n', 'in 0.33042 0.24995 -0.60874 0.10923 0.036372 0.151 -0.55083 -0.074239 -0.092307 -0.32821 0.09598 -0.82269 -0.36717 -0.67009 0.42909 0.016496 -0.23573 0.12864 -1.0953 0.43334 0.57067 -0.1036 0.20422 0.078308 -0.42795 -1.7984 -0.27865 0.11954 -0.12689 0.031744 3.8631 -0.17786 -0.082434 -0.62698 0.26497 -0.057185 -0.073521 0.46103 0.30862 0.12498 -0.48609 -0.0080272 0.031184 -0.36576 -0.42699 0.42164 -0.11666 -0.50703 -0.027273 -0.53285\n', 'a 0.21705 0.46515 -0.46757 0.10082 1.0135 0.74845 -0.53104 -0.26256 0.16812 0.13182 -0.24909 -0.44185 -0.21739 0.51004 0.13448 -0.43141 -0.03123 0.20674 -0.78138 -0.20148 -0.097401 0.16088 -0.61836 -0.18504 -0.12461 -2.2526 -0.22321 0.5043 0.32257 0.15313 3.9636 -0.71365 -0.67012 0.28388 0.21738 0.14433 0.25926 0.23434 0.4274 -0.44451 0.13813 0.36973 -0.64289 0.024142 -0.039315 -0.26037 0.12017 -0.043782 0.41013 0.1796\n', '" 0.25769 0.45629 -0.76974 -0.37679 0.59272 -0.063527 0.20545 -0.57385 -0.29009 -0.13662 0.32728 1.4719 -0.73681 -0.12036 0.71354 -0.46098 0.65248 0.48887 -0.51558 0.039951 -0.34307 -0.014087 0.86488 0.3546 0.7999 -1.4995 -1.8153 0.41128 0.23921 -0.43139 3.6623 -0.79834 -0.54538 0.16943 -0.82017 -0.3461 0.69495 -1.2256 -0.17992 -0.057474 0.030498 -0.39543 -0.38515 -1.0002 0.087599 -0.31009 -0.34677 -0.31438 0.75004 0.97065\n']
我的搜索方法显然到目前为止是无效的。真的很希望得到一些帮助。
您遇到的问题可能是由于文件格式或文件编码不符合预期，而导致 gensim 在加载 GloVe 文件时出错。以下是解决问题的几种方法：
gensim
示例： 400000 50 the 0.418 0.24968 -0.41242 ... , 0.013441 0.23682 -0.16899 ...
400000 50 the 0.418 0.24968 -0.41242 ... , 0.013441 0.23682 -0.16899 ...
确保文件是 UTF-8 编码。可以尝试用文本编辑器打开文件，确认是否存在特殊字符。
向量维度是否一致：
ValueError: could not convert string to float
在加载文件前，先确保它是 UTF-8 编码：
file glove.6B.50d.txt
如果文件编码不是 UTF-8，可以用以下命令转换：
iconv -f ISO-8859-1 -t UTF-8 glove.6B.50d.txt -o glove.6B.50d_utf8.txt
有时，文件中可能包含异常字符或格式问题。可以尝试清理文件：
import re input_file = "glove.6B.50d.txt" output_file = "glove_cleaned.txt" with open(input_file, "r", encoding="utf-8") as infile, open(output_file, "w", encoding="utf-8") as outfile: for line in infile: # 移除非 ASCII 字符 cleaned_line = re.sub(r"[^\x00-\x7F]+", "", line) outfile.write(cleaned_line)
如果文件行数较少，可以手动检查每行向量是否与声明的维度匹配。也可以用脚本批量检查：
expected_dim = 50 with open("glove_cleaned.txt", "r", encoding="utf-8") as file: for i, line in enumerate(file, start=1): parts = line.strip().split() if len(parts) != expected_dim + 1: # 单词 + 50 维向量 print(f"Line {i} has incorrect dimensions: {len(parts)}")
使用 glove2word2vec 转换时，确保输入文件无误：
glove2word2vec
from gensim.scripts.glove2word2vec import glove2word2vec glove_input_file = "glove_cleaned.txt" word2vec_output_file = "glove_word2vec.txt" glove2word2vec(glove_input_file, word2vec_output_file)
然后加载 Word2Vec 格式文件：
from gensim.models import KeyedVectors model = KeyedVectors.load_word2vec_format("glove_word2vec.txt", binary=False)
验证模型是否正确加载：
print(model.most_similar("king"))
bash pip install --upgrade gensim
如果仍有问题，请分享文件的具体异常部分或错误信息，我们可以继续深入分析！
