RuntimeError("you must first build vocabulary before training the model")

[asmundur]

running the simple tutorial code, I get this error

Traceback (most recent call last): File "get_embeddings.py", line 25, in <module> model = Word2Vec(corpus, size=250, window=5, min_count=3) File "/home/asmundur/.local/lib/python3.6/site-packages/gensim/models/word2vec.py", line 767, in __init__ fast_version=FAST_VERSION) File "/home/asmundur/.local/lib/python3.6/site-packages/gensim/models/base_any2vec.py", line 763, in __init__ end_alpha=self.min_alpha, compute_loss=compute_loss) File "/home/asmundur/.local/lib/python3.6/site-packages/gensim/models/word2vec.py", line 892, in train queue_factor=queue_factor, report_delay=report_delay, compute_loss=compute_loss, callbacks=callbacks) File "/home/asmundur/.local/lib/python3.6/site-packages/gensim/models/base_any2vec.py", line 1081, in train **kwargs) File "/home/asmundur/.local/lib/python3.6/site-packages/gensim/models/base_any2vec.py", line 536, in train total_words=total_words, **kwargs) File "/home/asmundur/.local/lib/python3.6/site-packages/gensim/models/base_any2vec.py", line 1187, in _check_training_sanity raise RuntimeError("you must first build vocabulary before training the model") RuntimeError: you must first build vocabulary before training the model

[Alxmrphi]

Sæll Ásmundur,

Takk fyrir að láta mig vita af þessu Já, ég sé að skjalið á vera keyrt í sömu möppunni (“MIM”) miðað við gamla kóðann en það á ekki að skipta máli. Er búinn að laga klassann í textanum. Afsakið hvað svarið kemur seint.

class MIM_Parser(object):
    def __init__(self, mim_folder):
        self.mim_folder = mim_folder
 
    def __iter__(self):
        for folder in os.listdir(self.mim_folder):
            if os.path.isdir(os.path.join(self.mim_folder, folder)):
                current_folder = os.path.join(self.mim_folder, folder)
                for file in os.listdir(current_folder):
                    if not file.endswith('.xml'):
                        continue
                    root = parse(os.path.join(current_folder, file))
                    for sentence in root.getElementsByTagName('s'):
                        words = sentence.getElementsByTagName('w')
                        cs = [] # current sentence
                        for word in words:
                            cs.append(word.getAttribute('lemma'))
                        yield cs