{"id":946938,"date":"2024-12-26T23:47:06","date_gmt":"2024-12-26T15:47:06","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/946938.html"},"modified":"2024-12-26T23:47:08","modified_gmt":"2024-12-26T15:47:08","slug":"python%e5%a6%82%e4%bd%95%e8%af%bb%e5%8f%96-vec","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/946938.html","title":{"rendered":"python\u5982\u4f55\u8bfb\u53d6.vec"},"content":{"rendered":"

\"python\u5982\u4f55\u8bfb\u53d6.vec\"<\/p>\n

Python\u8bfb\u53d6.vec\u6587\u4ef6\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Gensim\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u7b49\u3002<\/strong>Gensim\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u8bcd\u5411\u91cf\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff0c\u5b83\u63d0\u4f9b\u4e86\u4fbf\u6377\u7684\u63a5\u53e3\u6765\u52a0\u8f7d.vec\u6587\u4ef6\u3002Numpy\u5e93\u5219\u53ef\u4ee5\u7528\u4e8e\u5904\u7406.vec\u6587\u4ef6\u4e2d\u5b58\u50a8\u7684\u6570\u503c\u6570\u636e\uff0c\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u5219\u53ef\u4ee5\u8ba9\u4f60\u6839\u636e\u6587\u4ef6\u7684\u5177\u4f53\u683c\u5f0f\u8fdb\u884c\u7075\u6d3b\u5904\u7406\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u8bfb\u53d6.vec\u6587\u4ef6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Gensim\u5e93<\/h3>\n<\/p>\n

Gensim\u5e93\u662f\u4e00\u4e2a\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u548c\u6587\u672c\u6316\u6398\u7684\u6d41\u884cPython\u5e93\u3002\u5b83\u652f\u6301\u4ece.vec\u6587\u4ef6\u4e2d\u52a0\u8f7d\u9884\u8bad\u7ec3\u7684\u8bcd\u5411\u91cf\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

1. \u5b89\u88c5Gensim<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5b89\u88c5\u4e86Gensim\u5e93\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install gensim<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u52a0\u8f7d.vec\u6587\u4ef6<\/h4>\n<\/p>\n
\u4e00\u65e6\u5b89\u88c5\u4e86Gensim\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528KeyedVectors<\/code>\u7c7b\u6765\u52a0\u8f7d.vec\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from gensim.models import KeyedVectors<\/p>\n
\u52a0\u8f7d.vec\u6587\u4ef6<\/strong><\/h2>\n
model = KeyedVectors.load_word2vec_format('your_file.vec', binary=False)<\/p>\n
\u83b7\u53d6\u8bcd\u5411\u91cf<\/strong><\/h2>\n
word_vector = model['word']  # \u66ff\u6362'word'\u4e3a\u4f60\u611f\u5174\u8da3\u7684\u8bcd<\/p>\n
print(word_vector)<\/p>\n
<\/code><\/pre>\n<\/p>\n
Gensim\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u6613\u7528\u6027\u548c\u5bf9\u5927\u89c4\u6a21\u6570\u636e\u7684\u9ad8\u6548\u5904\u7406\u80fd\u529b<\/strong>\u3002\u6b64\u5916\uff0c\u5b83\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5185\u7f6e\u7684\u529f\u80fd\u6765\u8fdb\u884c\u5411\u91cf\u8fd0\u7b97\u548c\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u7b49\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Numpy\u5e93<\/h3>\n<\/p>\n
\u5982\u679c.vec\u6587\u4ef6\u7684\u683c\u5f0f\u8f83\u4e3a\u7b80\u5355\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Numpy\u5e93\u6765\u8bfb\u53d6\u5b83\u3002\u4ee5\u4e0b\u662f\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1. \u5b89\u88c5Numpy<\/h4>\n<\/p>\n
\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5b89\u88c5\u4e86Numpy\u5e93\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Numpy\u8bfb\u53d6.vec\u6587\u4ef6<\/h4>\n<\/p>\n
\u5047\u8bbe.vec\u6587\u4ef6\u7684\u6bcf\u4e00\u884c\u5305\u542b\u4e00\u4e2a\u8bcd\u53ca\u5176\u5bf9\u5e94\u7684\u5411\u91cf\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u8bfb\u53d6\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def load_vec_file(filename):<\/p>\n
    with open(filename, 'r', encoding='utf-8') as f:<\/p>\n
        word_vectors = {}<\/p>\n
        for line in f:<\/p>\n
            values = line.split()<\/p>\n
            word = values[0]<\/p>\n
            vector = np.array(values[1:], dtype='float32')<\/p>\n
            word_vectors[word] = vector<\/p>\n
    return word_vectors<\/p>\n
\u4f7f\u7528\u81ea\u5b9a\u4e49\u51fd\u6570\u8bfb\u53d6.vec\u6587\u4ef6<\/strong><\/h2>\n
word_vectors = load_vec_file('your_file.vec')<\/p>\n
print(word_vectors['word'])  # \u66ff\u6362'word'\u4e3a\u4f60\u611f\u5174\u8da3\u7684\u8bcd<\/p>\n
<\/code><\/pre>\n<\/p>\n
Numpy\u8bfb\u53d6.vec\u6587\u4ef6\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u7075\u6d3b\u6027\u548c\u5bf9\u6570\u503c\u8ba1\u7b97\u7684\u5f3a\u5927\u652f\u6301<\/strong>\uff0c\u7279\u522b\u9002\u5408\u9700\u8981\u5bf9\u5411\u91cf\u8fdb\u884c\u590d\u6742\u8fd0\u7b97\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u81ea\u5b9a\u4e49\u89e3\u6790\u5668<\/h3>\n<\/p>\n
\u5982\u679c.vec\u6587\u4ef6\u7684\u683c\u5f0f\u5e76\u4e0d\u6807\u51c6\uff0c\u6216\u8005\u4f60\u9700\u8981\u8fdb\u884c\u4e00\u4e9b\u7279\u5b9a\u7684\u9884\u5904\u7406\uff0c\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u53ef\u80fd\u662f\u6700\u597d\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n
1. \u89e3\u6790.vec\u6587\u4ef6<\/h4>\n<\/p>\n
\u4e0b\u9762\u7684\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u7f16\u5199\u4e00\u4e2a\u7b80\u5355\u7684\u89e3\u6790\u5668\u6765\u8bfb\u53d6.vec\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
def custom_parse_vec_file(filename):<\/p>\n
    word_vectors = {}<\/p>\n
    with open(filename, 'r', encoding='utf-8') as f:<\/p>\n
        for line in f:<\/p>\n
            if line.strip():  # \u5ffd\u7565\u7a7a\u884c<\/p>\n
                parts = line.split()<\/p>\n
                word = parts[0]<\/p>\n
                vector = list(map(float, parts[1:]))<\/p>\n
                word_vectors[word] = vector<\/p>\n
    return word_vectors<\/p>\n
\u4f7f\u7528\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u8bfb\u53d6.vec\u6587\u4ef6<\/strong><\/h2>\n
word_vectors = custom_parse_vec_file('your_file.vec')<\/p>\n
print(word_vectors['word'])  # \u66ff\u6362'word'\u4e3a\u4f60\u611f\u5174\u8da3\u7684\u8bcd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u7684\u6700\u5927\u4f18\u70b9\u5728\u4e8e\u7075\u6d3b\u6027<\/strong>\uff0c\u4f60\u53ef\u4ee5\u6839\u636e.vec\u6587\u4ef6\u7684\u683c\u5f0f\u548c\u5185\u5bb9\u81ea\u7531\u8c03\u6574\u89e3\u6790\u903b\u8f91\uff0c\u6bd4\u5982\u5904\u7406\u5f02\u5e38\u884c\u6216\u6dfb\u52a0\u989d\u5916\u7684\u9884\u5904\u7406\u6b65\u9aa4\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3\u4e0e\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
\u5728\u9009\u62e9\u5982\u4f55\u8bfb\u53d6.vec\u6587\u4ef6\u65f6\uff0c\u5e94\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6587\u4ef6\u683c\u5f0f\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\uff1a<\/p>\n<\/p>\n
    \n
  1. \n
    \u6587\u4ef6\u683c\u5f0f<\/strong>\uff1a\u5728\u8bfb\u53d6.vec\u6587\u4ef6\u4e4b\u524d\uff0c\u786e\u4fdd\u4e86\u89e3\u6587\u4ef6\u7684\u5177\u4f53\u683c\u5f0f\uff0c\u5305\u62ec\u6bcf\u884c\u7684\u7ed3\u6784\u3001\u662f\u5426\u6709\u6807\u9898\u884c\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u5185\u5b58\u4f7f\u7528<\/strong>\uff1a\u5982\u679c.vec\u6587\u4ef6\u975e\u5e38\u5927\uff0c\u5728\u8bfb\u53d6\u65f6\u6ce8\u610f\u5185\u5b58\u4f7f\u7528\u3002Gensim\u5e93\u901a\u5e38\u80fd\u591f\u9ad8\u6548\u5904\u7406\u5927\u6587\u4ef6\uff0c\u4f46\u5982\u679c\u5185\u5b58\u4e0d\u8db3\uff0c\u53ef\u4ee5\u8003\u8651\u5206\u6279\u8bfb\u53d6\u6216\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u6280\u672f\u3002<\/p>\n<\/p>\n<\/li>\n
  3. \n
    \u5411\u91cf\u7ef4\u5ea6<\/strong>\uff1a\u786e\u4fdd\u8bfb\u53d6\u7684\u5411\u91cf\u7ef4\u5ea6\u4e0e\u9884\u671f\u7684\u4e00\u81f4\u3002\u5982\u679c\u5b58\u5728\u7ef4\u5ea6\u4e0d\u5339\u914d\u7684\u60c5\u51b5\uff0c\u53ef\u80fd\u662f\u7531\u4e8e\u6587\u4ef6\u683c\u5f0f\u4e0d\u6b63\u786e\u6216\u89e3\u6790\u903b\u8f91\u6709\u8bef\u3002<\/p>\n<\/p>\n<\/li>\n
  4. \n
    \u9519\u8bef\u5904\u7406<\/strong>\uff1a\u5728\u7f16\u5199\u8bfb\u53d6\u4ee3\u7801\u65f6\uff0c\u6dfb\u52a0\u9002\u5f53\u7684\u9519\u8bef\u5904\u7406\u673a\u5236\uff0c\u4ee5\u4fbf\u5728\u8bfb\u53d6\u8fc7\u7a0b\u4e2d\u80fd\u591f\u5904\u7406\u8bf8\u5982\u6587\u4ef6\u635f\u574f\u6216\u683c\u5f0f\u4e0d\u5339\u914d\u7b49\u5f02\u5e38\u60c5\u51b5\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u6709\u6548\u5730\u8bfb\u53d6\u548c\u5904\u7406.vec\u6587\u4ef6\u4e2d\u7684\u8bcd\u5411\u91cf\u6570\u636e\uff0c\u5e76\u5c06\u5176\u5e94\u7528\u4e8e\u5404\u79cd\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     Python\u4e2d\u5982\u4f55\u8bfb\u53d6.vec\u6587\u4ef6\u7684\u5e38\u7528\u5e93\u6709\u54ea\u4e9b\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u8bfb\u53d6.vec\u6587\u4ef6\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u4e13\u95e8\u7684\u5e93\uff0c\u4f8b\u5982numpy<\/code>\u3001scipy<\/code>\u6216pandas<\/code>\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u60a8\u8f7b\u677e\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002\u4f8b\u5982\uff0cnumpy<\/code>\u53ef\u4ee5\u7528\u6765\u8bfb\u53d6\u4e8c\u8fdb\u5236\u6570\u636e\uff0c\u800cscipy<\/code>\u5219\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u529f\u80fd\uff0c\u7528\u4e8e\u5904\u7406\u79d1\u5b66\u8ba1\u7b97\u3002\u60a8\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u3002<\/p>\n
    \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6.vec\u6587\u4ef6\u5e76\u5904\u7406\u6570\u636e\uff1f<\/strong>
    \u8bfb\u53d6.vec\u6587\u4ef6\u540e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u6570\u636e\u5904\u7406\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002\u901a\u8fc7numpy.loadtxt()<\/code>\u6216pandas.read_csv()<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u52a0\u8f7d\u5230\u6570\u7ec4\u6216\u6570\u636e\u6846\u4e2d\u3002\u63a5\u4e0b\u6765\uff0c\u60a8\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\uff0c\u4f8b\u5982\u8fc7\u6ee4\u3001\u5206\u7ec4\u6216\u53ef\u89c6\u5316\uff0c\u4ee5\u4fbf\u6df1\u5165\u7406\u89e3\u6570\u636e\u7684\u7279\u5f81\u3002<\/p>\n
    \u5728\u8bfb\u53d6.vec\u6587\u4ef6\u65f6\uff0c\u5982\u4f55\u5904\u7406\u6587\u4ef6\u7f16\u7801\u95ee\u9898\uff1f<\/strong>
    \u6709\u65f6\uff0c\u8bfb\u53d6.vec\u6587\u4ef6\u65f6\u53ef\u80fd\u4f1a\u9047\u5230\u7f16\u7801\u95ee\u9898\u3002\u786e\u4fdd\u60a8\u4e86\u89e3\u6587\u4ef6\u7684\u7f16\u7801\u683c\u5f0f\u662f\u5f88\u91cd\u8981\u7684\u3002\u5982\u679c\u6587\u4ef6\u662f\u4ee5UTF-8\u6216\u5176\u4ed6\u7279\u5b9a\u7f16\u7801\u683c\u5f0f\u4fdd\u5b58\u7684\uff0c\u60a8\u53ef\u4ee5\u5728\u8bfb\u53d6\u65f6\u6307\u5b9a\u7f16\u7801\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528open()<\/code>\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7encoding='utf-8'<\/code>\u6765\u786e\u4fdd\u6b63\u786e\u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9\u3002\u8fd9\u5bf9\u4e8e\u786e\u4fdd\u6570\u636e\u5b8c\u6574\u6027\u548c\u907f\u514d\u4e71\u7801\u975e\u5e38\u91cd\u8981\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8bfb\u53d6.vec\u6587\u4ef6\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Gensim\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u7b49\u3002Gensim\u5e93 […]","protected":false},"author":3,"featured_media":946942,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/946938"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=946938"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/946938\/revisions"}],"predecessor-version":[{"id":946946,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/946938\/revisions\/946946"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/946942"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=946938"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=946938"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=946938"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}