Merge pull request #3129 from RaRe-Technologies/sponsor_techtarget

[MRG] Add bronze sponsor: TechTarget

docs/src/people.rst

@@ -52,3 +52,8 @@ Bronze Sponsors
    :target: https://eaccidents.com/
    :width: 50%
    :alt: EAccidents
+
+.. figure:: _static/images/techtarget-logo.png
+   :target: https://www.techtarget.com/
+   :width: 50%
+   :alt: TechTarget

gensim/models/doc2vec.py

@@ -265,6 +265,7 @@ def __init__(self, documents=None, corpus_file=None, vector_size=100, dm_mean=No
             .. sourcecode:: pycon
 
                 >>> model.dv['doc003']
+
         """
         corpus_iterable = documents
 
@@ -357,8 +358,10 @@ def reset_from(self, other_model):
         self.dv.expandos = other_model.dv.expandos
         self.init_weights()
 
-    def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch,
-                        total_examples=None, total_words=None, offsets=None, start_doctags=None, **kwargs):
+    def _do_train_epoch(
+        self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch,
+        total_examples=None, total_words=None, offsets=None, start_doctags=None, **kwargs
+    ):
         work, neu1 = thread_private_mem
         doctag_vectors = self.dv.vectors
         doctags_lockf = self.dv.vectors_lockf
@@ -425,10 +428,12 @@ def _do_train_job(self, job, alpha, inits):
                 )
         return tally, self._raw_word_count(job)
 
-    def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, total_words=None,
-              epochs=None, start_alpha=None, end_alpha=None,
-              word_count=0, queue_factor=2, report_delay=1.0, callbacks=(),
-              **kwargs):
+    def train(
+        self, corpus_iterable=None, corpus_file=None, total_examples=None, total_words=None,
+        epochs=None, start_alpha=None, end_alpha=None,
+        word_count=0, queue_factor=2, report_delay=1.0, callbacks=(),
+        **kwargs,
+    ):
         """Update the model's neural weights.
 
         To support linear learning-rate decay from (initial) `alpha` to `min_alpha`, and accurate
@@ -795,7 +800,7 @@ def load(cls, *args, **kwargs):
             return super(Doc2Vec, cls).load(*args, rethrow=True, **kwargs)
         except AttributeError as ae:
             logger.error(
-                "Model load error. Was model saved using code from an older Gensim Version? "
+                "Model load error. Was model saved using code from an older Gensim version? "
                 "Try loading older model using gensim-3.8.3, then re-saving, to restore "
                 "compatibility with current code.")
             raise ae

gensim/models/word2vec.py

@@ -553,7 +553,7 @@ def _scan_vocab(self, sentences, progress_per, trim_rule):
             if sentence_no % progress_per == 0:
                 logger.info(
                     "PROGRESS: at sentence #%i, processed %i words, keeping %i word types",
-                    sentence_no, total_words, len(vocab)
+                    sentence_no, total_words, len(vocab),
                 )
             for word in sentence:
                 vocab[word] += 1