Deprecated. Use model.wv.save_word2vec_format instead.

Standard t-test over mean.

Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary.

Create one 'random' vector (but deterministic by seed_string)

Removes all L2-normalized vectors for words from the model. You will have to recompute them using init_sims method.

Create a binary Huffman tree using stored vocabulary word counts. Frequent words will have shorter binary codes. Called internally from `build

Split the raw sessions into training sessions for p2v and mp2v and a common test set. path_data: str Path to .npy file with shap

init_sims() resides in KeyedVectors because it deals with syn0/vectors mainly, but because syn1 is not an attribute of KeyedVectors,

Loads a previously saved `Word2Vec` model. Also see `save()`. Parameters ---------- fname : str Path to the saved

Create a cumulative-distribution table using stored vocabulary word counts for drawing random words in the negative-sampling training routines

Save the model. This saved model can be loaded again using :func:`~gensim.models.word2vec.Word2Vec.load`, which supports online training and g

Obtain likelihood score for a single sentence in a fitted CBOW representaion. The sentence is a list of Vocab objects (or None, where

Obtain likelihood score for a single sentence in a fitted skip-gram representaion. The sentence is a list of Vocab objects (or None,

Sort the vocabulary so the most frequent words have the lowest indexes.

Update CBOW model by training on a sequence of sentences. Each sentence is a list of string tokens, which are looked up in the model'

Update skip-gram model by training on a sequence of sentences. Each sentence is a list of string tokens, which are looked up in the m

Copy all the existing weights, and reset the weights for the newly added vocabulary.

Deprecated. Use self.wv.__contains__() instead. Refer to the documentation for `gensim.models.keyedvectors.Word2VecKeyedVectors.__con

Deprecated. Use self.wv.__getitem__() instead. Refer to the documentation for `gensim.models.keyedvectors.Word2VecKeyedVectors.__geti

Initialize the model from an iterable of `sentences`. Each sentence is a list of words (unicode strings) that will be used for traini

`source` can be either a string or a file object. Clip the file to the first `limit` lines (or not clipped if limit is None, the defa

`source` should be a path to a directory (as a string) where all files can be opened by the LineSentence class. Each file will be rea

Iterate through the lines in the source.

iterate through the files

Resets certain properties of the model, post training.

Train a single batch of sentences. Return 2-tuple `(effective word count after ignoring unknown words and sentence length trimming, t

Discard parameters that are used in training and score. Use if you're sure you're done training a model. If `replace_word_vectors_with_normali

Merge the input-hidden weight matrix from the original C word2vec-tool format given, where it intersects with the current vocabulary. (No word

Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.

Report the probability distribution of the center word given the context words as input to the trained model. Parameters ----

Apply vocabulary settings for `min_count` (discarding less-frequent words) and `sample` (controlling the downsampling of more-frequent words).

Build tables and model weights based on final vocabulary settings.

Borrow shareable pre-built structures (like vocab) from the other_model. Useful if testing multiple models in parallel on the same corpus.

Do an initial scan of all words appearing in sentences.

Score the log probability for a sequence of sentences (can be a once-only generator stream). Each sentence must be a list of unicode strings.

Update the model's neural weights from a sequence of sentences (can be a once-only generator stream). For Word2Vec, each sentence must be a li

Compute log probability for each sentence, lifting lists of sentences from the jobs queue.