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3 years ago aovec m -e, --epochsを追加
Closed eggplants closed 3 years ago
eggplants
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3 years ago aovec m -e, --epochsを追加
eggplants
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3 years ago https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/word2vec.py
class Word2Vec(utils.SaveLoad):
def __init__(
self, sentences=None, corpus_file=None, vector_size=100, alpha=0.025, window=5, min_count=5,
max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001,
sg=0, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0,
trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False, callbacks=(),
comment=None, max_final_vocab=None,
):
"""Train, use and evaluate neural networks described in https://code.google.com/p/word2vec/.
Once you're finished training a model (=no more updates, only querying)
store and use only the :class:`~gensim.models.keyedvectors.KeyedVectors` instance in ``self.wv``
to reduce memory.
The full model can be stored/loaded via its :meth:`~gensim.models.word2vec.Word2Vec.save` and
:meth:`~gensim.models.word2vec.Word2Vec.load` methods.
The trained word vectors can also be stored/loaded from a format compatible with the
original word2vec implementation via `self.wv.save_word2vec_format`
and :meth:`gensim.models.keyedvectors.KeyedVectors.load_word2vec_format`.
Parameters
----------
sentences : iterable of iterables, optional
The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora,
consider an iterable that streams the sentences directly from disk/network.
See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
See also the `tutorial on data streaming in Python
<https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/>`_.
If you don't supply `sentences`, the model is left uninitialized -- use if you plan to initialize it
in some other way.
corpus_file : str, optional
Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format.
You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or
`corpus_file` arguments need to be passed (or none of them, in that case, the model is left uninitialized).
vector_size : int, optional
Dimensionality of the word vectors.
window : int, optional
Maximum distance between the current and predicted word within a sentence.
min_count : int, optional
Ignores all words with total frequency lower than this.
workers : int, optional
Use these many worker threads to train the model (=faster training with multicore machines).
sg : {0, 1}, optional
Training algorithm: 1 for skip-gram; otherwise CBOW.
hs : {0, 1}, optional
If 1, hierarchical softmax will be used for model training.
If 0, and `negative` is non-zero, negative sampling will be used.
negative : int, optional
If > 0, negative sampling will be used, the int for negative specifies how many "noise words"
should be drawn (usually between 5-20).
If set to 0, no negative sampling is used.
ns_exponent : float, optional
The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion
to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more
than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.
More recently, in https://arxiv.org/abs/1804.04212, Caselles-Dupré, Lesaint, & Royo-Letelier suggest that
other values may perform better for recommendation applications.
cbow_mean : {0, 1}, optional
If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.
alpha : float, optional
The initial learning rate.
min_alpha : float, optional
Learning rate will linearly drop to `min_alpha` as training progresses.
seed : int, optional
Seed for the random number generator. Initial vectors for each word are seeded with a hash of
the concatenation of word + `str(seed)`. Note that for a fully deterministically-reproducible run,
you must also limit the model to a single worker thread (`workers=1`), to eliminate ordering jitter
from OS thread scheduling. (In Python 3, reproducibility between interpreter launches also requires
use of the `PYTHONHASHSEED` environment variable to control hash randomization).
max_vocab_size : int, optional
Limits the RAM during vocabulary building; if there are more unique
words than this, then prune the infrequent ones. Every 10 million word types need about 1GB of RAM.
Set to `None` for no limit.
max_final_vocab : int, optional
Limits the vocab to a target vocab size by automatically picking a matching min_count. If the specified
min_count is more than the calculated min_count, the specified min_count will be used.
Set to `None` if not required.
sample : float, optional
The threshold for configuring which higher-frequency words are randomly downsampled,
useful range is (0, 1e-5).
hashfxn : function, optional
Hash function to use to randomly initialize weights, for increased training reproducibility.
epochs : int, optional
Number of iterations (epochs) over the corpus. (Formerly: `iter`)
trim_rule : function, optional
Vocabulary trimming rule, specifies whether certain words should remain in the vocabulary,
be trimmed away, or handled using the default (discard if word count < min_count).
Can be None (min_count will be used, look to :func:`~gensim.utils.keep_vocab_item`),
or a callable that accepts parameters (word, count, min_count) and returns either
:attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`.
The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part of the
model.
The input parameters are of the following types:
* `word` (str) - the word we are examining
* `count` (int) - the word's frequency count in the corpus
* `min_count` (int) - the minimum count threshold.
sorted_vocab : {0, 1}, optional
If 1, sort the vocabulary by descending frequency before assigning word indexes.
See :meth:`~gensim.models.keyedvectors.KeyedVectors.sort_by_descending_frequency()`.
batch_words : int, optional
Target size (in words) for batches of examples passed to worker threads (and
thus cython routines).(Larger batches will be passed if individual
texts are longer than 10000 words, but the standard cython code truncates to that maximum.)
compute_loss: bool, optional
If True, computes and stores loss value which can be retrieved using
:meth:`~gensim.models.word2vec.Word2Vec.get_latest_training_loss`.
callbacks : iterable of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional
Sequence of callbacks to be executed at specific stages during training.
Examples
--------
Initialize and train a :class:`~gensim.models.word2vec.Word2Vec` model
.. sourcecode:: pycon
>>> from gensim.models import Word2Vec
>>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]]
>>> model = Word2Vec(sentences, min_count=1)
Attributes
----------
wv : :class:`~gensim.models.keyedvectors.KeyedVectors`
This object essentially contains the mapping between words and embeddings. After training, it can be used
directly to query those embeddings in various ways. See the module level docstring for examples.
"""
...それぞれ設定可能にする
vector_size: int = 100 # Dimensionality of the word vectors.
min_count: int = 5 # Ignores all words with total frequency lower than this.
window: int = 5 # Maximum distance between the current and predicted word within a sentence.
workers: int = 3 # Use these many worker threads to train the model (=faster training with multicore machines).
# os.cpu_count()でいいかもできた
モデル生成時のパラメータを与えられるようにする