a dG(bX@sdZddlZddlZddlZddlZddlmZddlm Z m Z e e Z e dkrejdejde dd ejed d eZejd d ddejdddejddeddejddeddejddeddejddedddgd ejd!d"eddejd#d$ed%dejd&d'eddejd(d)eddejd*d+ed,ejd-d.edddgd ejd/d0edddgd ejd1d2deZejdkrdZejsd3e_ndZejsd4e_e ejZe eejej ej!ej"ej#ejeej$ej%dej&d5 Z'ej(r$ej(Z)e'j*j+e)ej,d6nTej-d7dZ)e'.e)d8ej,dkrde'j*j+e)d9dd6ne'j*j+e)d:d;d6ej/rej/Z0e'/e0e d Use text data from to train the model -output Use to save the resulting word vectors / word clusters -size Set size of word vectors; default is 100 -window Set max skip length between words; default is 5 -sample Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5) -hs Use Hierarchical Softmax; default is 0 (not used) -negative Number of negative examples; default is 5, common values are 3 - 10 (0 = not used) -threads Use threads (default 3) -iter Run more training iterations (default 5) -min_count This will discard words that appear less than times; default is 5 -alpha Set the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW -binary Save the resulting vectors in binary moded; default is 0 (off) -cbow Use the continuous bag of words model; default is 1 (use 0 for skip-gram model) -accuracy Compute accuracy of the resulting model analogical inference power on questions file See an example of questions file at https://code.google.com/p/word2vec/source/browse/trunk/questions-words.txt Example: python -m gensim.scripts.word2vec_standalone -train data.txt -output vec.txt -size 200 -sample 1e-4 -binary 0 -iter 3 N)seterr)Word2Vec LineSentence__main__z:%(asctime)s : %(threadName)s : %(levelname)s : %(message)s)formatlevelz running %s raise)allz-trainz0Use text data from file TRAIN to train the modelT)helpZrequiredz-outputz2Use file OUTPUT to save the resulting word vectors)r z-windowz6Set max skip length WINDOW between words; default is 5)r typedefaultz-sizez(Set size of word vectors; default is 100dz-samplezSet threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)gMbP?z-hsz1Use Hierarchical Softmax; default is 0 (not used))r r rchoicesz -negativezRNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)z-threadszUse THREADS threads (default 3)z-iterz(Run more training iterations (default 5)z -min_countzKThis will discard words that appear less than MIN_COUNT times; default is 5z-alphazPSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW)r r z-cbowzOUse the continuous bag of words model; default is 1 (use 0 for skip-gram model)z-binaryz=Save the resulting vectors in binary mode; default is 0 (off)z -accuracyz6Use questions from file ACCURACY to evaluate the modelg?g?) Z vector_size min_countZworkerswindowsamplealphaZsghsnegativeZ cbow_meanZepochs)binary.z.modelz .model.binz .model.txtFzfinished running %s)3__doc__Zloggingos.pathossysargparseZnumpyrZgensim.models.word2vecrrZ getLogger__name__ZloggerZ basicConfigINFOinfojoinargvArgumentParserparser add_argumentintfloat parse_argsargsZcbowZskipgramrZtrainZcorpussizerZthreadsrrrriterZmodeloutputZoutfileZwvZsave_word2vec_formatrsplitZsaveZaccuracyZquestions_filepathbasenamer2r2Alib/python3.9/site-packages/gensim/scripts/word2vec_standalone.pys/