models.normmodel – Normalization model

class gensim.models.normmodel.NormModel(corpus=None, norm='l2')

Bases: TransformationABC

Objects of this class realize the explicit normalization of vectors (l1 and l2).

Compute the l1 or l2 normalization by normalizing separately for each document in a corpus.

If v_{i,j} is the ‘i’th component of the vector representing document ‘j’, the l1 normalization is

l1_{i, j} = \frac{v_{i,j}}{\sum_k |v_{k,j}|}

the l2 normalization is

l2_{i, j} = \frac{v_{i,j}}{\sqrt{\sum_k v_{k,j}^2}}

  • corpus (iterable of iterable of (int, number), optional) – Input corpus.

  • norm ({'l1', 'l2'}, optional) – Norm used to normalize.


Call the normalize().


bow (list of (int, number)) – Document in BoW format.


Normalized document.

Return type

list of (int, number)

add_lifecycle_event(event_name, log_level=20, **event)

Append an event into the lifecycle_events attribute of this object, and also optionally log the event at log_level.

Events are important moments during the object’s life, such as “model created”, “model saved”, “model loaded”, etc.

The lifecycle_events attribute is persisted across object’s save() and load() operations. It has no impact on the use of the model, but is useful during debugging and support.

Set self.lifecycle_events = None to disable this behaviour. Calls to add_lifecycle_event() will not record events into self.lifecycle_events then.

  • event_name (str) – Name of the event. Can be any label, e.g. “created”, “stored” etc.

  • event (dict) –

    Key-value mapping to append to self.lifecycle_events. Should be JSON-serializable, so keep it simple. Can be empty.

    This method will automatically add the following key-values to event, so you don’t have to specify them:

    • datetime: the current date & time

    • gensim: the current Gensim version

    • python: the current Python version

    • platform: the current platform

    • event: the name of this event

  • log_level (int) – Also log the complete event dict, at the specified log level. Set to False to not log at all.


Calculate the norm by calling unitvec() with the norm parameter.


corpus (iterable of iterable of (int, number)) – Input corpus.

classmethod load(fname, mmap=None)

Load an object previously saved using save() from a file.

  • fname (str) – Path to file that contains needed object.

  • mmap (str, optional) – Memory-map option. If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then `mmap=None must be set.

See also


Save object to file.


Object loaded from fname.

Return type



AttributeError – When called on an object instance instead of class (this is a class method).


Normalize a simple count representation.


bow (list of (int, number)) – Document in BoW format.


Normalized document.

Return type

list of (int, number)

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=4)

Save the object to a file.

  • fname_or_handle (str or file-like) – Path to output file or already opened file-like object. If the object is a file handle, no special array handling will be performed, all attributes will be saved to the same file.

  • separately (list of str or None, optional) –

    If None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store them into separate files. This prevent memory errors for large objects, and also allows memory-mapping the large arrays for efficient loading and sharing the large arrays in RAM between multiple processes.

    If list of str: store these attributes into separate files. The automated size check is not performed in this case.

  • sep_limit (int, optional) – Don’t store arrays smaller than this separately. In bytes.

  • ignore (frozenset of str, optional) – Attributes that shouldn’t be stored at all.

  • pickle_protocol (int, optional) – Protocol number for pickle.

See also


Load object from file.