Auto-generated documentation for fse.models.average module.
This module implements the base class to compute average representations for sentences, using highly optimized C routines, data streaming and Pythonic interfaces.
The implementation is based on Iyyer et al. (2015): Deep Unordered Composition Rivals Syntactic Methods for Text Classification. For more information, see https://people.cs.umass.edu/~miyyer/pubs/2015_acl_dan.pdf.
The training algorithms is based on the Gensim implementation of Word2Vec, FastText, and Doc2Vec.
For more information, see: class gensim.models.word2vec.Word2Vec, class gensim.models.fasttext.FastText, or
class gensim.models.doc2vec.Doc2Vec.
Initialize and train a class fse.models.sentence2vec.Sentence2Vec model
pycon
>>> from gensim.models.word2vec import Word2Vec
>>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]]
>>> model = Word2Vec(sentences, min_count=1, vector_size=20)
>>> from fse.models.average import Average
>>> avg = Average(model)
>>> avg.train([(s, i) for i, s in enumerate(sentences)])
>>> avg.sv.vectors.shape
(2, 20)
## Average
[[find in source code]](../../../fse/models/average.py#L187)
```python
class Average(BaseSentence2VecModel):
def __init__(
model: KeyedVectors,
sv_mapfile_path: str = None,
wv_mapfile_path: str = None,
workers: int = 1,
lang_freq: str = None,
**kwargs,
):
Train, use and evaluate averaged sentence vectors.
The model can be stored/loaded via its :meth:~fse.models.average.Average.save and
:meth:~fse.models.average.Average.load methods.
Some important attributes are the following:
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.
sv : class fse.models.sentencevectors.SentenceVectors
This object contains the sentence vectors inferred from the training data. There will be one such vector
for each unique docusentence supplied during training. They may be individually accessed using the index.
prep : class fse.models.base_s2v.BaseSentence2VecPreparer
The prep object is used to transform and initialize the sv.vectors. Aditionally, it can be used
to move the vectors to disk for training with memmap.
def train_average_np(
model: BaseSentence2VecModel,
indexed_sentences: List[tuple],
target: ndarray,
memory: ndarray,
) -> Tuple[int, int]:
Training on a sequence of sentences and update the target ndarray.
Called internally from :meth:~fse.models.average.Average._do_train_job.
This is the non-optimized, pure Python version. If you have a C compiler,
fse will use an optimized code path from :mod:fse.models.average_inner instead.
model : class fse.models.base_s2v.BaseSentence2VecModel
The BaseSentence2VecModel model instance.
indexed_sentences : iterable of tuple
The sentences used to train the model.
target : ndarray
The target ndarray. We use the index from indexed_sentences
to write into the corresponding row of target.
memory : ndarray
Private memory for each working thread
int, int Number of effective sentences (non-zero) and effective words in the vocabulary used during training the sentence embedding.