Fix "generator" language in word2vec docs (#2935)

* Fix docs about Word2Vec (fix #2934)

Docs say you can use a generator as the first argument, but you can't.

The tempfile path was also unused, so that's been removed.

* Fix langauge to make it clear streaming is supported

Technically a generator is a kind of iterator, so this clarifies that a
restartable iterator (as opposed to a consumable generator) is
necessary.

* Update gensim/models/word2vec.py

* Update CHANGELOG.md

Co-authored-by: Michael Penkov <m@penkov.dev>

CHANGELOG.md

@@ -17,6 +17,7 @@ This release contains a major refactoring.
 ### :books: Tutorial and doc improvements
 
  * Clear up LdaModel documentation - remove claim that it accepts CSC matrix as input (PR [#2832](https://github.com/RaRe-Technologies/gensim/pull/2832), [@FyzHsn](https://github.com/FyzHsn))
+ * Fix "generator" language in word2vec docs (PR [#2935](https://github.com/RaRe-Technologies/gensim/pull/2935), __[@polm](https://github.com/polm)__)
 
 ## :warning: 3.8.x will be the last gensim version to support Py2.7. Starting with 4.0.0, gensim will only support Py3.5 and above
 

gensim/models/word2vec.py

@@ -39,18 +39,22 @@
 
 .. sourcecode:: pycon
 
-    >>> from gensim.test.utils import common_texts, get_tmpfile
+    >>> from gensim.test.utils import common_texts
     >>> from gensim.models import Word2Vec
     >>>
-    >>> path = get_tmpfile("word2vec.model")
-    >>>
     >>> model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4)
     >>> model.save("word2vec.model")
 
-The training is streamed, meaning `sentences` can be a generator, reading input data
-from disk on-the-fly, without loading the entire corpus into RAM.
 
-It also means you can continue training the model later:
+The training is streamed, so ``sentences`` can be an iterable, reading input data
+from disk on-the-fly. This lets you avoid loading the entire corpus into RAM.
+However, note that because the iterable must be re-startable, `sentences` must
+not be a generator. For an example of an appropriate iterator see
+:class:`~gensim.models.word2vec.BrownCorpus`,
+:class:`~gensim.models.word2vec.Text8Corpus` or
+:class:`~gensim.models.word2vec.LineSentence`.
+
+If you save the model you can continue training it later:
 
 .. sourcecode:: pycon