Unable to run the example provided.

from top2vec import Top2Vec
from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))

model = Top2Vec(documents=newsgroups.data, speed="learn", workers=8)

Error:

Traceback (most recent call last):
  File "checkThis.py", line 13, in <module>
    model = Top2Vec(documents=allSents, speed="learn", workers=8)
  File "/workspace/Top2Vec/top2vec/Top2Vec.py", line 234, in __init__
    self._create_topic_vectors(cluster.labels_)
  File "/workspace/Top2Vec/top2vec/Top2Vec.py", line 260, in _create_topic_vectors
    self.topic_vectors = np.vstack([self.model.docvecs.vectors_docs[np.where(cluster_labels == label)[0]]
  File "<__array_function__ internals>", line 5, in vstack
  File "/workspace/.pip-modules/lib/python3.8/site-packages/numpy/core/shape_base.py", line 283, in vstack
    return _nx.concatenate(arrs, 0)
  File "<__array_function__ internals>", line 5, in concatenate
ValueError: need at least one array to concatenate

My NumPy version : ‘1.19.0’

[ddangelov]

I am not sure what the exact number is but 1000+ is usually good. I will look at setting a threshold in the future. Also if you are using fewer documents, try setting min_count to 1. That could also help. min_count ignores all words with total frequency lower than this. For smaller corpora a smaller min_count will be necessary.

[pidugusundeep]

That worked, when I used newsgroups.data but when I use plain sentences of length 300(list) it again shows me the error curious to know if there is any minimum requirement of sentences that should be used for documents?