BowEmbedder#

class BowEmbedder(config)#

Bases: Embedder

The bag of words Embedder encodes provided text based on gensim doc2bow.

Intput - the input field needs to be of type list [ str ]

Output - the output field is filled with data of type numpy.ndarray

Parameters

  • type (str) – bow

  • min_doc_frequency (int, 2) – Minimum number of documents a term must appear in

  • max_doc_fraction (float, 0.75) – Maximum fraction of documents a term can appear in

  • max_n_words (int, None) – Maximum dictionary size

Example

{
    "step": "embedder",
    "type": "bow",
    "input_field": "text",
    "output_field": "embedded_text"
}

Methods Summary

load()

Load a step

process_batch(batch)

Process a batch of documents.

save()

Save a step

train(docs)

Train on a step of a set of documents

Methods Documentation

load()#

Load a step

process_batch(batch)#

Process a batch of documents. If not defined will default to using self.process_doc for each document in the batch.

Parameters

batch (list(Document)) – List of documents

Returns

List of processed documents

Return type

list(Document)

save()#

Save a step

train(docs)#

Train on a step of a set of documents

Parameters

docs (generator(Document)) – Generator of documents

Returns

Generator of processed documents

Return type

generator(Document)