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Expected %zd from C header, got %zd from PyObject%.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'Cannot convert %.200s to %.200scan't convert negative value to unsigned PY_LONG_LONG'%.200s' object is not subscriptablecannot fit '%.200s' into an index-sized integercan't convert negative value to npy_uint32value too large to convert to npy_uint32gensim/models/doc2vec_inner.cppModule 'doc2vec_inner' 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¶Eÿÿÿÿÿÿÿÿtrain_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, neu1=None, learn_doctags=True, learn_words=True, learn_hidden=True, word_vectors=None, words_lockf=None, doctag_vectors=None, doctags_lockf=None)
Update distributed memory model ("PV-DM") by training on a single document, using a concatenation of the
     context window word vectors (rather than a sum or average).
     This will be slower since the input at each batch will be significantly larger.

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

    Parameters
    ----------
    model : :class:`~gensim.models.doc2vec.Doc2Vec`
        The model to train.
    doc_words : list of str
        The input document as a list of words to be used for training. Each word will be looked up in
        the model's vocabulary.
    doctag_indexes : list of int
        Indices into `doctag_vectors` used to obtain the tags of the document.
    alpha : float, optional
        Learning rate.
    work : np.ndarray, optional
        Private working memory for each worker.
    neu1 : np.ndarray, optional
        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.

    train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=None, learn_doctags=True, learn_words=True, learn_hidden=True, word_vectors=None, words_lockf=None, doctag_vectors=None, doctags_lockf=None)
Update distributed memory model ("PV-DM") by training on a single document.
    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` and
    :meth:`~gensim.models.doc2vec.Doc2Vec.infer_vector`.

    Parameters
    ----------
    model : :class:`~gensim.models.doc2vec.Doc2Vec`
        The model to train.
    doc_words : list of str
        The input document as a list of words to be used for training. Each word will be looked up in
        the model's vocabulary.
    doctag_indexes : list of int
        Indices into `doctag_vectors` used to obtain the tags of the document.
    alpha : float
        Learning rate.
    work : np.ndarray, optional
        Private working memory for each worker.
    neu1 : np.ndarray, optional
        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.

    train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True, word_vectors=None, words_lockf=None, doctag_vectors=None, doctags_lockf=None)
Update distributed bag of words model ("PV-DBOW") by training on a single document.

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

    Parameters
    ----------
    model : :class:`~gensim.models.doc2vec.Doc2Vec`
        The model to train.
    doc_words : list of str
        The input document as a list of words to be used for training. Each word will be looked up in
        the model's vocabulary.
    doctag_indexes : list of int
        Indices into `doctag_vectors` used to obtain the tags of the document.
    alpha : float
        Learning rate.
    work : list of float, optional
        Updates to be performed on each neuron in the hidden layer of the underlying network.
    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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