<commit_before>Changes
=======

0.12.4, XX/XX/XXXX

* Word2vec default values of hyperparameters now are identical to those of original C tool, generally improving performance (<NAME>, #538)
 - By default use CBOW with negative sampling and the mean of context vectors, instead of skip-gram with hierarchical softmax.
 - By default use 3 workers, instead of 1.
 - By default use 5 iterations instead of 1.
 - By defaut use downsampling with the value 1e-3.
 - Additionally, wordvec.py can be now used as a standalone script with command line arguments matching those of original C tool.
   (for example, ./word2vec.py -train data.txt -output trained_vec.txt -size 200 -window 2 -sample 1e-4 -negative 10 -hs 0 -binary 0 -iter 3)
* load_word2vec_format() performance (@svenkreiss, #555)
  - Remove `init_sims()` call for performance improvements when normalized vectors are not needed.
  - Remove `norm_only` parameter (API change). Call `init_sims(replace=True)` after the `load_word2vec_format()` call for the old `norm_only=True` behavior.
* Better internal handling of job batching in word2vec (#535)
  - up to 300% speed up when training on very short documents (~tweets)
* Word2vec allows non-strict unicode error handling (ignore or replace) (G<NAME>, #466)
* Doc2Vec `model.docvecs[key]` now raises KeyError for unknown keys (<NAME>, #520)
* Fix `DocvecsArray.index_to_doctag` so `most_similar()` returns string doctags (Gordon Mohr, #560) <commit_msg>Made Changelog less verbose about word2vec defaults changed.<commit_after>Changes
=======

0.12.4, XX/XX/XXXX

* Word2vec default values of hyperparameters now are identical to those of original C tool, generally improving performance (<NAME>, #538)
  See https://github.com/akutuzov/gensim/commit/6456cbcd75e6f8720451766ba31cc046b4463ae2 for diff between old and new defaults.
 - Additionally, scripts/wordvec_standalone.py can be now used as a standalone script with command line arguments matching those of original C tool.
   (for example, ./word2vec_standalone.py -train data.txt -output trained_vec.txt -size 200 -window 2 -sample 1e-4 -negative 10 -hs 0 -binary 0 -iter 3)
* load_word2vec_format() performance (@svenkreiss, #555)
  - Remove `init_sims()` call for performance improvements when normalized vectors are not needed.
  - Remove `norm_only` parameter (API change). Call `init_sims(replace=True)` after the `load_word2vec_format()` call for the old `norm_only=True` behavior.
* Better internal handling of job batching in word2vec (#535)
  - up to 300% speed up when training on very short documents (~tweets)
* Word2vec allows non-strict unicode error handling (ignore or replace) (Gordon Mohr, #466)
* Doc2Vec `model.docvecs[key]` now raises KeyError for unknown keys (Gordon Mohr, #520)
* Fix `DocvecsArray.index_to_doctag` so `most_similar()` returns string doctags (Gordon Mohr, #560) 
{'fraction_non_alphanumeric': 0.09106769016050244, 'fraction_numerical': 0.0418702023726448, 'mean_word_length': 4.803643724696356, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1490248', 'n_tokens_mistral': 932, 'n_tokens_neox': 831, 'n_words': 355}