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[elcolie]

from deepcut import DeepcutTokenizer
tokenizer = DeepcutTokenizer(ngram_range=(1,1),
                             max_df=1.0, min_df=0.0)
deepcut.tokenize('ตัดคำได้ดีมาก')

----------------------------------------------------------------------
ValueError                           Traceback (most recent call last)
<ipython-input-5-9855ac2d86cd> in <module>
----> 1 X = tokenizer.fit_tranform(['ฉันบินได้', 'ฉันกินข้าว', 'ฉันอยากบิน'])

~/.pyenv/versions/3.6.6/envs/surasak/lib/python3.6/site-packages/deepcut/deepcut.py in fit_tranform(self, raw_documents)
    276         sparse CSR format (see scipy)
    277         """
--> 278         X = self.transform(raw_documents, new_document=True)
    279         return X
    280 

~/.pyenv/versions/3.6.6/envs/surasak/lib/python3.6/site-packages/deepcut/deepcut.py in transform(self, raw_documents, new_document)
    265                                                 max_doc_count,
    266                                                 min_doc_count,
--> 267                                                 self.max_features)
    268         self.vocabulary_ = vocabulary
    269 

~/.pyenv/versions/3.6.6/envs/surasak/lib/python3.6/site-packages/deepcut/deepcut.py in _limit_features(self, X, vocabulary, high, low, limit)
    212                 removed_terms.add(term)
    213         kept_indices = np.where(mask)[0]
--> 214         if not kept_indices:
    215             raise ValueError("After pruning, no terms remain. Try a lower"
    216                              " min_df or a higher max_df.")

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

deepcut==0.6.1.0 Python3.6.6

[titipata]

@elcolie really sorry, I just get back to this issue. I updated the code a while ago and now the DeepcutTokenizer should work perfectly fine.