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Expected %zd from C header, got %zd from PyObjectinvalid vtable found for imported type%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Interpreter change detected - this module can only be loaded into one interpreter per process.value too large to convert to int%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'gensim/models/doc2vec_corpusfile.cppModule 'doc2vec_corpusfile' has already been imported. 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6@F@V@f@v@†@–@¦@¶@Æ@Ö@æ@ö@AA&A6AFAVAfAvA†A–A¦A¶AÆAÖAæAöABB&B6BFBVBfBvB†B–B¦B¶BÆBÖBæBöBCC&C6CFCVCfCvC†C–C¦C¶CÆCÖCæCöCDD&D6DFDVDfDvD†D–D¦D¶DÆDÖDæDöDEE&E6EFEVEfEvE†E–E¦E¶EÆEÖEÿÿÿÿÿÿÿÿd2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctags_lockf=None)
Train distributed memory model ("PV-DM") by training on a corpus file, using a concatenation of the context
     window word vectors (rather than a sum or average).
     This might be slower since the input at each batch will be significantly larger.

    Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train`.

    Parameters
    ----------
    model : :class:`~gensim.models.doc2vec.Doc2Vec`
        The FastText model instance to train.
    corpus_file : str
        Path to corpus file.
    _cur_epoch : int
        Current epoch number. Used for calculating and decaying learning rate.
    work : np.ndarray
        Private working memory for each worker.
    neu1 : np.ndarray
        Private working memory for each worker.
    learn_doctags : bool, optional
        Whether the tag vectors should be updated.
    learn_words : bool, optional
        Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both**
        `learn_words` and `train_words` are set to True.
    learn_hidden : bool, optional
        Whether or not the weights of the hidden layer will be updated.
    word_vectors : numpy.ndarray, optional
        The vector representation for each word in the vocabulary. If None, these will be retrieved from the model.
    words_lockf : numpy.ndarray, optional
        EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value
        of 1.0 allows normal updates to word-vectors.
    doctag_vectors : numpy.ndarray, optional
        Vector representations of the tags. If None, these will be retrieved from the model.
    doctags_lockf : numpy.ndarray, optional
        EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors.

    Returns
    -------
    int
        Number of words in the input document that were actually used for training.

    d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctags_lockf=None)
Train distributed memory model ("PV-DM") by training on a corpus file.
    This method implements the DM model with a projection (input) layer that is either the sum or mean of the context
    vectors, depending on the model's `dm_mean` configuration field.

    Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train`.

    Parameters
    ----------
    model : :class:`~gensim.models.doc2vec.Doc2Vec`
        The FastText model instance to train.
    corpus_file : str
        Path to corpus file.
    _cur_epoch : int
        Current epoch number. Used for calculating and decaying learning rate.
    work : np.ndarray
        Private working memory for each worker.
    neu1 : np.ndarray
        Private working memory for each worker.
    learn_doctags : bool, optional
        Whether the tag vectors should be updated.
    learn_words : bool, optional
        Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both**
        `learn_words` and `train_words` are set to True.
    learn_hidden : bool, optional
        Whether or not the weights of the hidden layer will be updated.
    word_vectors : numpy.ndarray, optional
        The vector representation for each word in the vocabulary. If None, these will be retrieved from the model.
    words_lockf : numpy.ndarray, optional
        EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value
        of 1.0 allows normal updates to word-vectors.
    doctag_vectors : numpy.ndarray, optional
        Vector representations of the tags. If None, these will be retrieved from the model.
    doctags_lockf : numpy.ndarray, optional
        EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors.

    Returns
    -------
    int
        Number of words in the input document that were actually used for training.

    d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctags_lockf=None)
Train distributed bag of words model ("PV-DBOW") by training on a corpus file.

    Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train`.

    Parameters
    ----------
    model : :class:`~gensim.models.doc2vec.Doc2Vec`
        The FastText model instance to train.
    corpus_file : str
        Path to corpus file.
    _cur_epoch : int
        Current epoch number. Used for calculating and decaying learning rate.
    work : np.ndarray
        Private working memory for each worker.
    neu1 : np.ndarray
        Private working memory for each worker.
    train_words : bool, optional
        Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both** `learn_words`
        and `train_words` are set to True.
    learn_doctags : bool, optional
        Whether the tag vectors should be updated.
    learn_words : bool, optional
        Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both**
        `learn_words` and `train_words` are set to True.
    learn_hidden : bool, optional
        Whether or not the weights of the hidden layer will be updated.
    word_vectors : numpy.ndarray, optional
        The vector representation for each word in the vocabulary. If None, these will be retrieved from the model.
    words_lockf : numpy.ndarray, optional
        EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value
        of 1.0 allows normal updates to word-vectors.
    doctag_vectors : numpy.ndarray, optional
        Vector representations of the tags. If None, these will be retrieved from the model.
    doctags_lockf : numpy.ndarray, optional
        EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors.

    Returns
    -------
    int
        Number of words in the input document that were actually used for training.

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